diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
index 74cf6a91c2d4be..0e3da7f417d751 100644
--- a/.ci/azure/linux.yml
+++ b/.ci/azure/linux.yml
@@ -1,3 +1,12 @@
+trigger:
+  branches:
+    include:
+    - master
+    - releases/*
+  paths:
+    exclude:
+    - docs/*
+
 resources:
   repositories:
   - repository: openvino_contrib
@@ -12,6 +21,16 @@ resources:
 
 jobs:
 - job: Lin
+  strategy:
+    matrix:
+      Dynamic:
+        CMAKE_BUILD_SHARED_LIBS: 'ON'
+        PYTHON_STATIC_ARGS:
+      Static:
+        CMAKE_BUILD_SHARED_LIBS: 'OFF'
+        PYTHON_STATIC_ARGS: -m "not dynamic_library and not template_plugin"
+    maxParallel: 2
+
   # About 150% of total time
   timeoutInMinutes: 90
 
@@ -117,6 +136,9 @@ jobs:
         -DVERBOSE_BUILD=ON
         -DCMAKE_BUILD_TYPE=$(BUILD_TYPE)
         -DENABLE_PYTHON=ON
+        -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS)
+        -DENABLE_GNA=$(CMAKE_BUILD_SHARED_LIBS)
+        -DENABLE_ONEDNN_FOR_GPU=$(CMAKE_BUILD_SHARED_LIBS)
         -DPYTHON_EXECUTABLE=/usr/bin/python3.8
         -DENABLE_WHEEL=ON
         -DENABLE_TESTS=ON
@@ -174,10 +196,12 @@ jobs:
   - script: $(INSTALL_DIR)/samples/cpp/build_samples.sh
     workingDirectory: $(BUILD_SAMPLES_DIR)
     displayName: 'Build cpp samples'
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: $(INSTALL_DIR)/samples/c/build_samples.sh
     workingDirectory: $(BUILD_SAMPLES_DIR)
     displayName: 'Build c samples'
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: rm -fr $(BUILD_DIR)
     displayName: 'Clean build dir'
@@ -187,13 +211,13 @@ jobs:
   - script: |
       export DATA_PATH=$(MODELS_PATH)
       export MODELS_PATH=$(MODELS_PATH)
-      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_TEST_DIR)/pyngraph --junitxml=TEST-Pyngraph.xml --ignore=$(INSTALL_TEST_DIR)/pyngraph/tests/test_onnx/test_zoo_models.py --ignore=$(INSTALL_TEST_DIR)/pyngraph/tests/test_onnx/test_backend.py
+      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_TEST_DIR)/pyngraph $(PYTHON_STATIC_ARGS) --junitxml=TEST-Pyngraph.xml --ignore=$(INSTALL_TEST_DIR)/pyngraph/tests/test_onnx/test_zoo_models.py --ignore=$(INSTALL_TEST_DIR)/pyngraph/tests/test_onnx/test_backend.py
     displayName: 'nGraph Python Bindings Tests'
     continueOnError: false
 
   - script: |
       export MO_ROOT=$(INSTALL_DIR)/tools/model_optimizer
-      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_DIR)/tools/model_optimizer/unit_tests --junitxml=TEST-ModelOptimizer.xml
+      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest -s $(INSTALL_DIR)/tests/model_optimizer/unit_tests --junitxml=TEST-ModelOptimizer.xml
     displayName: 'Model Optimizer UT'
     continueOnError: false
 
@@ -226,6 +250,7 @@ jobs:
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/gnaUnitTests --gtest_output=xml:TEST-gnaUnitTests.xml
     displayName: 'GNA UT'
     continueOnError: false
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/vpuUnitTests --gtest_output=xml:TEST-vpuUnitTests.xml
     displayName: 'VPU UT'
@@ -246,6 +271,7 @@ jobs:
   - script: . $(SETUPVARS) && $(INSTALL_TEST_DIR)/cpuFuncTests --gtest_filter=*smoke* --gtest_print_time=1 --gtest_output=xml:TEST-cpuFuncTests.xml
     displayName: 'CPU FuncTests'
     continueOnError: false
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'OFF')
 
   - script: |
       export DATA_PATH=$(MODELS_PATH)
@@ -258,7 +284,7 @@ jobs:
       export DATA_PATH=$(MODELS_PATH)
       export MODELS_PATH=$(MODELS_PATH)
       cd $(REPO_DIR)/inference-engine/ie_bridges/python/tests
-      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest --junitxml=TEST-PythonAPI.xml
+      . $(SETUPVARS) -pyver 3.8 && python3 -m pytest --junitxml=TEST-PythonAPI.xml $(PYTHON_STATIC_ARGS)
     displayName: 'Python API Tests'
     continueOnError: false
 
diff --git a/.ci/azure/linux_conditional_compilation.yml b/.ci/azure/linux_conditional_compilation.yml
index 1a69b7c3dcd9a2..d60bc412cc9900 100644
--- a/.ci/azure/linux_conditional_compilation.yml
+++ b/.ci/azure/linux_conditional_compilation.yml
@@ -1,3 +1,12 @@
+trigger:
+  branches:
+    include:
+    - master
+    - releases/*
+  paths:
+    exclude:
+    - docs/*
+
 jobs:
 - job: LinCC
   # About 150% of total time
diff --git a/.ci/azure/linux_ngraph_onnx.yml b/.ci/azure/linux_ngraph_onnx.yml
index 8218a0874cdd30..6858024e877250 100644
--- a/.ci/azure/linux_ngraph_onnx.yml
+++ b/.ci/azure/linux_ngraph_onnx.yml
@@ -1,14 +1,21 @@
+trigger:
+  branches:
+    include:
+    - master
+    - releases/*
+  paths:
+    exclude:
+    - docs/*
+
 jobs:
 - job: OpenVINO_ONNX_CI
   strategy:
     matrix:
       Release:
         BUILD_TYPE: 'Release'
-        PROTOBUF_LITE: 'ON'
         TOX_COMMAND: 'tox && tox -e zoo_models'
       Debug:
         BUILD_TYPE: 'Debug'
-        PROTOBUF_LITE: 'ON'
         TOX_COMMAND: 'tox'
     maxParallel: 2
 
@@ -77,10 +84,10 @@ jobs:
     condition: ne(variables['BUILD_TYPE'], 'Debug')
 
   - script: sudo docker build --tag=openvino-onnx-ci-image --file=.ci/openvino-onnx/Dockerfile --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg PROTOBUF_LITE=$(PROTOBUF_LITE) .
-    displayName: 'Docker build $(BUILD_TYPE) protobuf-lite: $(PROTOBUF_LITE)'
+    displayName: 'Docker build $(BUILD_TYPE)'
 
   - script: sudo fallocate -l 64G /swapfile ; sudo mkswap /swapfile ; sudo swapon /swapfile ; df ; free -h
     displayName: 'Create swap'
 
   - script: sudo docker run --name openvino-onnx-ci-container --volume $(MODELS_DIR)/models_data/model_zoo/onnx_model_zoo_$(ONNX_MODEL_ZOO_SHA):/root/.onnx/model_zoo/onnx_model_zoo --volume $(MODELS_DIR)/msft:/root/.onnx/model_zoo/MSFT openvino-onnx-ci-image /bin/bash -c "$(TOX_COMMAND)"
-    displayName: 'Docker run $(BUILD_TYPE) protobuf-lite: $(PROTOBUF_LITE)'
+    displayName: 'Docker run $(BUILD_TYPE)'
diff --git a/.ci/azure/linux_onnxruntime.yml b/.ci/azure/linux_onnxruntime.yml
index 4fd14f2c1b1e39..7a7c4754610bf8 100644
--- a/.ci/azure/linux_onnxruntime.yml
+++ b/.ci/azure/linux_onnxruntime.yml
@@ -1,3 +1,11 @@
+trigger:
+  branches:
+    include:
+    - releases/*
+  paths:
+    exclude:
+    - docs/*
+
 jobs:
 - job: onnxruntime
   timeoutInMinutes: 90
diff --git a/.ci/azure/mac.yml b/.ci/azure/mac.yml
index 4534c08b0651d5..0df0c05a2e226a 100644
--- a/.ci/azure/mac.yml
+++ b/.ci/azure/mac.yml
@@ -1,3 +1,12 @@
+trigger:
+  branches:
+    include:
+    - master
+    - releases/*
+  paths:
+    exclude:
+    - docs/*
+
 resources:
   repositories:
   - repository: openvino_contrib
diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index e234836ce1bc80..72642660043fdc 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -1,3 +1,12 @@
+trigger:
+  branches:
+    include:
+    - master
+    - releases/*
+  paths:
+    exclude:
+    - docs/*
+
 resources:
   repositories:
   - repository: openvino_contrib
@@ -12,6 +21,14 @@ resources:
 
 jobs:
 - job: Win
+  strategy:
+    matrix:
+      Dynamic:
+        CMAKE_BUILD_SHARED_LIBS: 'ON'
+      Static:
+        CMAKE_BUILD_SHARED_LIBS: 'OFF'
+    maxParallel: 2
+
   # About 150% of total time
   timeoutInMinutes: 120
 
@@ -102,7 +119,7 @@ jobs:
 
   - script: |
       set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && cmake -GNinja -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DENABLE_TEMPLATE_PLUGIN=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
+      call "$(MSVS_VARS_PATH)" && cmake -GNinja -DENABLE_CLDNN=$(CMAKE_BUILD_SHARED_LIBS) -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DENABLE_TEMPLATE_PLUGIN=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
     workingDirectory: $(BUILD_DIR)
     displayName: 'CMake'
 
@@ -135,10 +152,12 @@ jobs:
   - script: $(INSTALL_DIR)\samples\cpp\build_samples_msvc.bat
     workingDirectory: $(BUILD_SAMPLES_DIR)
     displayName: 'Build cpp samples'
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: $(INSTALL_DIR)\samples\c\build_samples_msvc.bat
     workingDirectory: $(BUILD_SAMPLES_DIR)
     displayName: 'Build c samples'
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: rd /Q /S $(BUILD_DIR)
     displayName: 'Clean build dir'
@@ -152,10 +171,12 @@ jobs:
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\paddlepaddle_tests --gtest_print_time=1 --gtest_filter=-backend_api.config_unsupported:*IE_GPU* --gtest_output=xml:TEST-PaddlePaddle.xml
     displayName: 'PaddlePaddle Frontend UT'
     continueOnError: false
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\tensorflow_tests --gtest_print_time=1 --gtest_output=xml:TEST-Tensorflow.xml
     displayName: 'Tensorflow Frontend UT'
     continueOnError: false
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: |
       set PATH=$(IB_DIR);%PATH%
@@ -197,6 +218,7 @@ jobs:
       call $(SETUPVARS) && "$(IB_TESTCONSOLE)" $(INSTALL_TEST_DIR)\cpuFuncTests.exe --gtest_filter=*smoke*:-*CompareWithRefs/base_size=16_pre_nms_topn=100_post_nms_topn=100_nms_thresh=0.7_feat_stride=1_min_size=1_ratio*:*smoke_GRUSequenceCommonZeroClip/GRUSequenceTest.CompareWithRefs/mode=CONVERT_TO_TI_MAX_SEQ_LEN_CONST_seq_lengths* --gtest_output=xml:TEST-cpuFuncTests-IB.xml /testlevel=24
     displayName: 'CPU FuncTests - IB'
     continueOnError: false
+    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'OFF')
 
   - script: |
       set DATA_PATH=$(MODELS_PATH)
diff --git a/.ci/azure/windows_conditional_compilation.yml b/.ci/azure/windows_conditional_compilation.yml
index 80c89e8d20f28e..2d0fdc63d7ceb7 100644
--- a/.ci/azure/windows_conditional_compilation.yml
+++ b/.ci/azure/windows_conditional_compilation.yml
@@ -1,3 +1,12 @@
+trigger:
+  branches:
+    include:
+    - master
+    - releases/*
+  paths:
+    exclude:
+    - docs/*
+
 jobs:
 - job: WinCC
   # About 150% of total time
diff --git a/.ci/openvino-onnx/Dockerfile b/.ci/openvino-onnx/Dockerfile
index ada3c36adb126c..0992ea21220e04 100644
--- a/.ci/openvino-onnx/Dockerfile
+++ b/.ci/openvino-onnx/Dockerfile
@@ -4,7 +4,6 @@ LABEL version=2021.03.30.1
 
 # Build configuration arguments
 ARG BUILD_TYPE=Release
-ARG PROTOBUF_LITE=ON
 
 ARG http_proxy
 ARG https_proxy
@@ -69,8 +68,7 @@ RUN cmake .. \
     -DPYTHON_EXECUTABLE=/usr/bin/python3 \
     -DNGRAPH_ONNX_FRONTEND_ENABLE=ON \
     -DOPENVINO_DEBUG_ENABLE=OFF \
-    -DCMAKE_INSTALL_PREFIX=/openvino/dist \
-    -DNGRAPH_USE_PROTOBUF_LITE=${PROTOBUF_LITE}
+    -DCMAKE_INSTALL_PREFIX=/openvino/dist
 RUN make -j $(nproc) install
 
 # Run tests via tox
diff --git a/.ci/openvino-onnx/Jenkinsfile b/.ci/openvino-onnx/Jenkinsfile
index 5581c7c2ea8ad0..cfe866c23ebbfe 100644
--- a/.ci/openvino-onnx/Jenkinsfile
+++ b/.ci/openvino-onnx/Jenkinsfile
@@ -6,9 +6,8 @@ DOCKER_IMAGE_TAG = "openvino-onnx-ci-image"
 ONNX_MODEL_ZOO_SHA = "d58213534f2a4d1c4b19ba62b3bb5f544353256e"
 
 BACKEND_CONFIGURATIONS = [
-    [ name: "Release", build_type: "Release", protobuf_lite : "OFF" ],
-    [ name: "Debug", build_type: "Debug", protobuf_lite : "OFF" ],
-    [ name: "Rel_Lite", build_type: "Release", protobuf_lite : "ON" ],
+    [ name: "Release", build_type: "Release" ],
+    [ name: "Debug", build_type: "Debug" ],
 ]
 
 // workaround for aborting previous builds on PR update
@@ -111,7 +110,6 @@ def buildDockerImage(Map configuration, String workdir) {
     sh """
         docker build --tag=${docker_image_tag} \
         --build-arg BUILD_TYPE=${configuration.build_type} \
-        --build-arg PROTOBUF_LITE=${configuration.protobuf_lite} \
         --file=.ci/openvino-onnx/Dockerfile \
         --build-arg http_proxy=${HTTP_PROXY}  \
         --build-arg https_proxy=${HTTPS_PROXY} .
diff --git a/CMakeLists.txt b/CMakeLists.txt
index b7b7f1fb86fa48..b0c6d805e6d504 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -85,13 +85,9 @@ include(cmake/test_model_zoo.cmake)
 add_subdirectory(thirdparty)
 add_subdirectory(openvino)
 add_subdirectory(ngraph)
-add_subdirectory(runtime)
 add_subdirectory(inference-engine)
-
-# for Template plugin
-openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph_backend interpreter_backend)
+add_subdirectory(runtime)
 include(cmake/extra_modules.cmake)
-
 if(ENABLE_SAMPLES)
     add_subdirectory(samples)
 endif()
diff --git a/README.md b/README.md
index 97f2ed60b37e8b..b4b3f49f81041e 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 # OpenVINO™ Toolkit
-[![Stable release](https://img.shields.io/badge/version-2021.4.1-green.svg)](https://github.com/openvinotoolkit/openvino/releases/tag/2021.4.1)
+[![Stable release](https://img.shields.io/badge/version-2021.4.2-green.svg)](https://github.com/openvinotoolkit/openvino/releases/tag/2021.4.2)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
 ![GitHub branch checks state](https://img.shields.io/github/checks-status/openvinotoolkit/openvino/master?label=GitHub%20checks)
 ![Azure DevOps builds (branch)](https://img.shields.io/azure-devops/build/openvinoci/b2bab62f-ab2f-4871-a538-86ea1be7d20f/13?label=Public%20CI)
diff --git a/cmake/dependencies.cmake b/cmake/dependencies.cmake
index 711495b904527b..662b829478c059 100644
--- a/cmake/dependencies.cmake
+++ b/cmake/dependencies.cmake
@@ -103,17 +103,22 @@ if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO")
     endif()
 
     if(WIN32 AND X86_64)
-        #TODO: add target_path to be platform specific as well, to avoid following if
+        # TODO: add target_path to be platform specific as well, to avoid following if
         RESOLVE_DEPENDENCY(TBB
                 ARCHIVE_WIN "tbb2020_20200415_win.zip"
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
                 SHA256 "f1c9b9e2861efdaa01552bd25312ccbc5feeb45551e5f91ae61e29221c5c1479")
-        RESOLVE_DEPENDENCY(TBBBIND_2_5
-                ARCHIVE_WIN "tbbbind_2_5_static_win_v1.zip"
-                TARGET_PATH "${TEMP}/tbbbind_2_5"
-                ENVIRONMENT "TBBBIND_2_5_ROOT"
-                SHA256 "a67afeea8cf194f97968c800dab5b5459972908295242e282045d6b8953573c1")
+        if(ENABLE_TBBBIND_2_5)
+            RESOLVE_DEPENDENCY(TBBBIND_2_5
+                    ARCHIVE_WIN "tbbbind_2_5_static_win_v1.zip"
+                    TARGET_PATH "${TEMP}/tbbbind_2_5"
+                    ENVIRONMENT "TBBBIND_2_5_ROOT"
+                    SHA256 "a67afeea8cf194f97968c800dab5b5459972908295242e282045d6b8953573c1")
+        else()
+            message(WARNING "prebuilt TBBBIND_2_5 is not available.
+    Build oneTBB from sources and set TBBROOT environment var before OpenVINO cmake configure")
+        endif()
     elseif(ANDROID)  # Should be before LINUX due LINUX is detected as well
         RESOLVE_DEPENDENCY(TBB
                 ARCHIVE_ANDROID "tbb2020_20200404_android.tgz"
@@ -126,11 +131,16 @@ if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO")
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
                 SHA256 "95b2f3b0b70c7376a0c7de351a355c2c514b42c4966e77e3e34271a599501008")
-        RESOLVE_DEPENDENCY(TBBBIND_2_5
-                ARCHIVE_LIN "tbbbind_2_5_static_lin_v2.tgz"
-                TARGET_PATH "${TEMP}/tbbbind_2_5"
-                ENVIRONMENT "TBBBIND_2_5_ROOT"
-                SHA256 "865e7894c58402233caf0d1b288056e0e6ab2bf7c9d00c9dc60561c484bc90f4")
+        if(ENABLE_TBBBIND_2_5)
+            RESOLVE_DEPENDENCY(TBBBIND_2_5
+                    ARCHIVE_LIN "tbbbind_2_5_static_lin_v2.tgz"
+                    TARGET_PATH "${TEMP}/tbbbind_2_5"
+                    ENVIRONMENT "TBBBIND_2_5_ROOT"
+                    SHA256 "865e7894c58402233caf0d1b288056e0e6ab2bf7c9d00c9dc60561c484bc90f4")
+        else()
+            message(WARNING "prebuilt TBBBIND_2_5 is not available.
+    Build oneTBB from sources and set TBBROOT environment var before OpenVINO cmake configure")
+        endif()
     elseif(LINUX AND AARCH64)
         RESOLVE_DEPENDENCY(TBB
                 ARCHIVE_LIN "keembay/tbb2020_38404_kmb_lic.tgz"
diff --git a/cmake/developer_package/IEDevScriptsConfig.cmake b/cmake/developer_package/IEDevScriptsConfig.cmake
index 2bdb7e6123656c..990628c24998d8 100644
--- a/cmake/developer_package/IEDevScriptsConfig.cmake
+++ b/cmake/developer_package/IEDevScriptsConfig.cmake
@@ -208,6 +208,13 @@ endif()
 
 # General flags
 
+macro(ov_install_static_lib target comp)
+    if(NOT BUILD_SHARED_LIBS)
+        install(TARGETS ${target} EXPORT OpenVINOTargets
+                ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ${comp})
+    endif()
+endmacro()
+
 set(THREADS_PREFER_PTHREAD_FLAG ON)
 find_package(Threads REQUIRED)
 
@@ -235,7 +242,11 @@ endif()
 # macro to mark target as conditionally compiled
 
 function(ie_mark_target_as_cc TARGET_NAME)
-    target_link_libraries(${TARGET_NAME} PRIVATE openvino::conditional_compilation)
+    set(cc_library openvino::conditional_compilation)
+    if(TARGET IE::conditional_compilation)
+        set(cc_library IE::conditional_compilation)
+    endif()
+    target_link_libraries(${TARGET_NAME} PRIVATE ${cc_library})
 
     if(NOT (SELECTIVE_BUILD STREQUAL "ON"))
         return()
diff --git a/cmake/developer_package/plugins/create_plugins_hpp.cmake b/cmake/developer_package/plugins/create_plugins_hpp.cmake
index c63f964660fb6b..4d73b0bca3bbc2 100644
--- a/cmake/developer_package/plugins/create_plugins_hpp.cmake
+++ b/cmake/developer_package/plugins/create_plugins_hpp.cmake
@@ -54,8 +54,4 @@ endforeach()
 set(IE_PLUGINS_MAP_DEFINITION "${IE_PLUGINS_MAP_DEFINITION}
     };\n")
 
-
-message("${IE_PLUGINS_DECLARATIONS}")
-message("${IE_PLUGINS_MAP_DEFINITION}")
-
 configure_file("${IE_PLUGINS_HPP_HEADER_IN}" "${IE_PLUGINS_HPP_HEADER}" @ONLY)
diff --git a/cmake/developer_package/plugins/plugins.cmake b/cmake/developer_package/plugins/plugins.cmake
index 1d9277fff2d572..f386a0db54caf0 100644
--- a/cmake/developer_package/plugins/plugins.cmake
+++ b/cmake/developer_package/plugins/plugins.cmake
@@ -75,6 +75,8 @@ function(ie_add_plugin)
                 target_compile_definitions(${IE_PLUGIN_NAME} PRIVATE
                     IE_CREATE_EXTENSION=CreateExtensionShared${IE_PLUGIN_DEVICE_NAME})
             endif()
+            # install static plugins
+            ov_install_static_lib(${IE_PLUGIN_NAME} core)
         endif()
 
         ie_add_vs_version_file(NAME ${IE_PLUGIN_NAME}
@@ -173,8 +175,9 @@ macro(ie_register_plugins_dynamic)
         message(FATAL_ERROR "Please, define MAIN_TARGET")
     endif()
 
+    # Unregister <device_name>.xml files for plugins from current build tree
+
     set(plugins_to_remove ${IE_REGISTER_POSSIBLE_PLUGINS})
-    set(plugin_files_local)
     set(config_output_file "$<TARGET_FILE_DIR:${IE_REGISTER_MAIN_TARGET}>/plugins.xml")
 
     foreach(plugin IN LISTS plugins_to_remove)
@@ -190,6 +193,9 @@ macro(ie_register_plugins_dynamic)
                   VERBATIM)
     endforeach()
 
+    # Generate <device_name>.xml files
+
+    set(plugin_files_local)
     foreach(name IN LISTS PLUGIN_FILES)
         string(REPLACE ":" ";" name "${name}")
         list(LENGTH name length)
@@ -217,6 +223,8 @@ macro(ie_register_plugins_dynamic)
         list(APPEND plugin_files_local "${config_file_name}")
     endforeach()
 
+    # Combine all <device_name>.xml files into plugins.xml
+
     add_custom_command(TARGET ${IE_REGISTER_MAIN_TARGET} POST_BUILD
                       COMMAND
                         "${CMAKE_COMMAND}"
@@ -229,15 +237,37 @@ macro(ie_register_plugins_dynamic)
                       VERBATIM)
 endmacro()
 
+macro(ie_register_plugins)
+    if(BUILD_SHARED_LIBS)
+        ie_register_plugins_dynamic(${ARGN})
+    endif()
+endmacro()
+
+function(ie_target_link_plugins TARGET_NAME)
+    if(BUILD_SHARED_LIBS)
+        return()
+    endif()
+
+    foreach(name IN LISTS PLUGIN_FILES)
+        string(REPLACE ":" ";" name "${name}")
+        list(LENGTH name length)
+        if(NOT ${length} EQUAL 2)
+            message(FATAL_ERROR "Unexpected error, please, contact developer of this script")
+        endif()
+
+        # link plugin to ${TARGET_NAME} static version
+        list(GET name 1 plugin_name)
+        target_link_libraries(${TARGET_NAME} PRIVATE ${plugin_name})
+    endforeach()
+endfunction()
+
 #
-# ie_register_plugins_static(MAIN_TARGET <main target name>
-#                            POSSIBLE_PLUGINS <list of plugins which can be build by this repo>)
+# ie_generate_plugins_hpp()
 #
-macro(ie_register_plugins_static)
-    set(options)
-    set(oneValueArgs MAIN_TARGET)
-    set(multiValueArgs POSSIBLE_PLUGINS)
-    cmake_parse_arguments(IE_REGISTER "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+function(ie_generate_plugins_hpp)
+    if(BUILD_SHARED_LIBS)
+        return()
+    endif()
 
     set(device_mapping)
     set(device_configs)
@@ -266,13 +296,15 @@ macro(ie_register_plugins_static)
         if(${device_name}_CONFIG)
             list(APPEND device_configs -D "${device_name}_CONFIG=${${device_name}_CONFIG}")
         endif()
-
-        # link plugin to inference_engine static version
-        list(GET name 1 plugin_name)
-        target_link_libraries(${IE_REGISTER_MAIN_TARGET} PRIVATE ${plugin_name})
     endforeach()
 
-    set(ie_plugins_hpp "${CMAKE_CURRENT_BINARY_DIR}/ie_plugins.hpp")
+    # add plugins to libraries including ie_plugins.hpp
+    ie_target_link_plugins(inference_engine)
+    if(TARGET inference_engine_s)
+        ie_target_link_plugins(inference_engine_s)
+    endif()
+
+    set(ie_plugins_hpp "${CMAKE_BINARY_DIR}/inference-engine/src/inference_engine/ie_plugins.hpp")
     set(plugins_hpp_in "${IEDevScripts_DIR}/plugins/plugins.hpp.in")
 
     add_custom_command(OUTPUT "${ie_plugins_hpp}"
@@ -291,30 +323,25 @@ macro(ie_register_plugins_static)
                          "Generate ie_plugins.hpp for static build"
                        VERBATIM)
 
+    # for some reason dependency on source files does not work
+    # so, we have to use explicit target and make it dependency for inference_engine
+    add_custom_target(ie_generate_hpp DEPENDS ${ie_plugins_hpp})
+    add_dependencies(inference_engine ie_generate_hpp)
+
     # add dependency for object files
-    get_target_property(sources ${IE_REGISTER_MAIN_TARGET} SOURCES)
+    get_target_property(sources inference_engine SOURCES)
     foreach(source IN LISTS sources)
         if("${source}" MATCHES "\\$\\<TARGET_OBJECTS\\:([A-Za-z0-9_]*)\\>")
             # object library
             set(obj_library ${CMAKE_MATCH_1})
             get_target_property(obj_sources ${obj_library} SOURCES)
-            list(APPEND patched_sources ${obj_sources})
+            list(APPEND all_sources ${obj_sources})
         else()
             # usual source
-            list(APPEND patched_sources ${source})
+            list(APPEND all_sources ${source})
         endif()
     endforeach()
-    set_source_files_properties(${patched_sources} PROPERTIES OBJECT_DEPENDS ${ie_plugins_hpp})
-endmacro()
 
-#
-# ie_register_plugins(MAIN_TARGET <main target name>
-#                     POSSIBLE_PLUGINS <list of plugins which can be build by this repo>)
-#
-macro(ie_register_plugins)
-    if(BUILD_SHARED_LIBS)
-        ie_register_plugins_dynamic(${ARGN})
-    else()
-        ie_register_plugins_static(${ARGN})
-    endif()
-endmacro()
+    # add dependency on header file generation for all inference_engine source files
+    set_source_files_properties(${all_sources} PROPERTIES OBJECT_DEPENDS ${ie_plugins_hpp})
+endfunction()
diff --git a/cmake/extra_modules.cmake b/cmake/extra_modules.cmake
index e9027e21f06502..d72497749ac869 100644
--- a/cmake/extra_modules.cmake
+++ b/cmake/extra_modules.cmake
@@ -1,88 +1,112 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
 
 function(ie_generate_dev_package_config)
-# dummy check that OpenCV is here
-find_package(OpenCV QUIET)
-
-set(all_dev_targets gflags ie_libraries)
-foreach(component IN LISTS openvino_export_components)
-    export(TARGETS ${${component}} NAMESPACE IE::
-        APPEND FILE "${CMAKE_BINARY_DIR}/${component}_dev_targets.cmake")
-    list(APPEND all_dev_targets ${${component}})
-endforeach()
-add_custom_target(ie_dev_targets ALL DEPENDS ${all_dev_targets})
-
-configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in"
-                              "${CMAKE_BINARY_DIR}/InferenceEngineDeveloperPackageConfig.cmake"
-                              INSTALL_DESTINATION share # not used
-                              PATH_VARS "OpenVINO_SOURCE_DIR;gflags_BINARY_DIR"
-                              NO_CHECK_REQUIRED_COMPONENTS_MACRO)
-
-configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig-version.cmake.in"
-               "${CMAKE_BINARY_DIR}/InferenceEngineDeveloperPackageConfig-version.cmake"
-               @ONLY)
-endfunction()
+    # dummy check that OpenCV is here
+    find_package(OpenCV QUIET)
+
+    set(all_dev_targets gflags ie_libraries)
+    foreach(component IN LISTS openvino_export_components)
+        # export all targets with prefix and use them during extra modules build
+        export(TARGETS ${${component}} NAMESPACE IE::
+            APPEND FILE "${CMAKE_BINARY_DIR}/${component}_dev_targets.cmake")
+        list(APPEND all_dev_targets ${${component}})
+    endforeach()
+    add_custom_target(ie_dev_targets ALL DEPENDS ${all_dev_targets})
 
-ie_generate_dev_package_config()
+    configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in"
+                                  "${CMAKE_BINARY_DIR}/InferenceEngineDeveloperPackageConfig.cmake"
+                                  INSTALL_DESTINATION share # not used
+                                  PATH_VARS "OpenVINO_SOURCE_DIR;gflags_BINARY_DIR"
+                                  NO_CHECK_REQUIRED_COMPONENTS_MACRO)
+
+    configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig-version.cmake.in"
+                   "${CMAKE_BINARY_DIR}/InferenceEngineDeveloperPackageConfig-version.cmake"
+                   @ONLY)
+endfunction()
 
 #
 # Add extra modules
 #
 
 function(register_extra_modules)
-# post export
-openvino_developer_export_targets(COMPONENT inference_engine TARGETS inference_engine)
-openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph)
+    # post export
+    openvino_developer_export_targets(COMPONENT inference_engine TARGETS inference_engine)
+    openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph)
 
-set(InferenceEngineDeveloperPackage_DIR "${CMAKE_CURRENT_BINARY_DIR}/runtime")
+    set(InferenceEngineDeveloperPackage_DIR "${CMAKE_CURRENT_BINARY_DIR}/runtime")
 
-function(generate_fake_dev_package)
-    set(iedevconfig_file "${InferenceEngineDeveloperPackage_DIR}/InferenceEngineDeveloperPackageConfig.cmake")
-    file(REMOVE "${iedevconfig_file}")
+    function(generate_fake_dev_package)
+        set(iedevconfig_file "${InferenceEngineDeveloperPackage_DIR}/InferenceEngineDeveloperPackageConfig.cmake")
+        file(REMOVE "${iedevconfig_file}")
 
-    file(WRITE "${iedevconfig_file}" "\# !! AUTOGENERATED: DON'T EDIT !!\n\n")
-    file(APPEND "${iedevconfig_file}" "ie_deprecated_no_errors()\n")
+        file(WRITE "${iedevconfig_file}" "\# !! AUTOGENERATED: DON'T EDIT !!\n\n")
+        file(APPEND "${iedevconfig_file}" "ie_deprecated_no_errors()\n")
 
-    foreach(target IN LISTS ${openvino_export_components})
-        if(target)
-            file(APPEND "${iedevconfig_file}" "add_library(IE::${target} ALIAS ${target})\n")
-        endif()
-    endforeach()
-endfunction()
+        foreach(target IN LISTS ${openvino_export_components})
+            if(target)
+                file(APPEND "${iedevconfig_file}" "add_library(IE::${target} ALIAS ${target})\n")
+            endif()
+        endforeach()
+    endfunction()
 
-generate_fake_dev_package()
+    generate_fake_dev_package()
 
-# automatically import plugins from the 'plugins' folder
-file(GLOB local_extra_modules "runtime/plugins/*")
-# add template plugin
-list(APPEND local_extra_modules "${OpenVINO_SOURCE_DIR}/docs/template_plugin")
-
-# detect where IE_EXTRA_MODULES contains folders with CMakeLists.txt
-# other folders are supposed to have sub-folders with CMakeLists.txt
-foreach(module_path IN LISTS IE_EXTRA_MODULES)
-    if(EXISTS "${module_path}/CMakeLists.txt")
-        list(APPEND extra_modules "${module_path}")
-    elseif(module_path)
-        file(GLOB extra_modules ${extra_modules} "${module_path}/*")
+    # automatically import plugins from the 'runtime/plugins' folder
+    file(GLOB local_extra_modules "runtime/plugins/*")
+    # add template plugin
+    if(ENABLE_TEMPLATE)
+        list(APPEND local_extra_modules "${OpenVINO_SOURCE_DIR}/docs/template_plugin")
     endif()
-endforeach()
-
-# add each extra module
-foreach(module_path IN LISTS extra_modules local_extra_modules)
-    if(module_path)
-        get_filename_component(module_name "${module_path}" NAME)
-        set(build_module ON)
-        if(NOT EXISTS "${module_path}/CMakeLists.txt") # if module is built not using cmake
-            set(build_module OFF)
-        endif()
-        if(NOT DEFINED BUILD_${module_name})
-            set(BUILD_${module_name} ${build_module} CACHE BOOL "Build ${module_name} extra module" FORCE)
+
+    # detect where IE_EXTRA_MODULES contains folders with CMakeLists.txt
+    # other folders are supposed to have sub-folders with CMakeLists.txt
+    foreach(module_path IN LISTS IE_EXTRA_MODULES)
+        if(EXISTS "${module_path}/CMakeLists.txt")
+            list(APPEND extra_modules "${module_path}")
+        elseif(module_path)
+            file(GLOB extra_modules ${extra_modules} "${module_path}/*")
         endif()
-        if(BUILD_${module_name})
-            message(STATUS "Register ${module_name} to be built in build-modules/${module_name}")
-            add_subdirectory("${module_path}" "build-modules/${module_name}")
+    endforeach()
+
+    # add each extra module
+    foreach(module_path IN LISTS extra_modules local_extra_modules)
+        if(module_path)
+            get_filename_component(module_name "${module_path}" NAME)
+            set(build_module ON)
+            if(NOT EXISTS "${module_path}/CMakeLists.txt") # if module is built not using cmake
+                set(build_module OFF)
+            endif()
+            if(NOT DEFINED BUILD_${module_name})
+                set(BUILD_${module_name} ${build_module} CACHE BOOL "Build ${module_name} extra module" FORCE)
+            endif()
+            if(BUILD_${module_name})
+                message(STATUS "Register ${module_name} to be built in build-modules/${module_name}")
+                add_subdirectory("${module_path}" "build-modules/${module_name}")
+            endif()
         endif()
-    endif()
-endforeach()
+    endforeach()
 endfunction()
 
-register_extra_modules()
\ No newline at end of file
+#
+# Extra modules support
+#
+
+# for Template plugin
+openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph_backend interpreter_backend)
+
+# this InferenceEngineDeveloperPackageConfig.cmake is not used
+# during extra modules build since it's generated after modules
+# are configured
+ie_generate_dev_package_config()
+
+# extra modules must be registered after inference_engine library
+# and all other IE common libraries (ie_libraries) are creared
+# because 'register_extra_modules' creates fake InferenceEngineDeveloperPackageConfig.cmake
+# with all imported developer targets
+register_extra_modules()
+
+# for static libraries case we need to generate final ie_plugins.hpp
+# with all the information about plugins
+ie_generate_plugins_hpp()
diff --git a/cmake/features.cmake b/cmake/features.cmake
index c50f6f4da9a998..a62b6f2f9c40fe 100644
--- a/cmake/features.cmake
+++ b/cmake/features.cmake
@@ -14,6 +14,17 @@ ie_option (ENABLE_STRICT_DEPENDENCIES "Skip configuring \"convinient\" dependenc
 
 ie_dependent_option (ENABLE_CLDNN "clDnn based plugin for inference engine" ON "X86_64;NOT APPLE;NOT MINGW;NOT WINDOWS_STORE;NOT WINDOWS_PHONE" OFF)
 
+if (NOT ENABLE_CLDNN OR ANDROID OR
+    (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 7.0))
+    # oneDNN doesn't support old compilers and android builds for now, so we'll
+    # build GPU plugin without oneDNN
+    set(ENABLE_ONEDNN_FOR_GPU_DEFAULT OFF)
+else()
+    set(ENABLE_ONEDNN_FOR_GPU_DEFAULT ON)
+endif()
+
+ie_dependent_option (ENABLE_ONEDNN_FOR_GPU "Enable oneDNN with GPU support" ON "ENABLE_ONEDNN_FOR_GPU_DEFAULT" OFF)
+
 ie_option (ENABLE_PROFILING_ITT "Build with ITT tracing. Optionally configure pre-built ittnotify library though INTEL_VTUNE_DIR variable." OFF)
 
 ie_option_enum(ENABLE_PROFILING_FILTER "Enable or disable ITT counter groups.\
@@ -62,6 +73,15 @@ if (NOT THREADING STREQUAL "TBB" AND
     message(FATAL_ERROR "THREADING should be set to TBB, TBB_AUTO, OMP or SEQ. Default option is ${THREADING_DEFAULT}")
 endif()
 
+if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND
+    (BUILD_SHARED_LIBS OR (LINUX AND X86_64)))
+    set(ENABLE_TBBBIND_2_5_DEFAULT ON)
+else()
+    set(ENABLE_TBBBIND_2_5_DEFAULT OFF)
+endif()
+
+ie_dependent_option (ENABLE_TBBBIND_2_5 "Enable TBBBind_2_5 static usage in OpenVINO runtime" ON "ENABLE_TBBBIND_2_5_DEFAULT" OFF)
+
 if (ENABLE_GNA)
     if (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.4)
         set (DEFAULT_GNA_LIB GNA1)
@@ -90,6 +110,8 @@ ie_option (ENABLE_MULTI "Enables Multi Device Plugin" ON)
 
 ie_option (ENABLE_HETERO "Enables Hetero Device Plugin" ON)
 
+ie_option (ENABLE_TEMPLATE "Enable template plugin" ON)
+
 ie_dependent_option (ENABLE_VPU "vpu targeted plugins for inference engine" ON "NOT WINDOWS_PHONE;NOT WINDOWS_STORE" OFF)
 
 ie_dependent_option (ENABLE_MYRIAD "myriad targeted plugin for inference engine" ON "ENABLE_VPU" OFF)
@@ -118,7 +140,7 @@ set(IE_EXTRA_MODULES "" CACHE STRING "Extra paths for extra modules to include i
 
 ie_dependent_option(ENABLE_TBB_RELEASE_ONLY "Only Release TBB libraries are linked to the Inference Engine binaries" ON "THREADING MATCHES TBB;LINUX" OFF)
 
-ie_option (ENABLE_SYSTEM_PUGIXML "use the system copy of pugixml" OFF)
+ie_dependent_option (ENABLE_SYSTEM_PUGIXML "use the system copy of pugixml" OFF "BUILD_SHARED_LIBS" OFF)
 
 ie_option (ENABLE_DEBUG_CAPS "enable OpenVINO debug capabilities at runtime" OFF)
 
@@ -136,8 +158,6 @@ ie_dependent_option(NGRAPH_ONNX_FRONTEND_ENABLE "Enable ONNX FrontEnd" ON "proto
 ie_dependent_option(NGRAPH_PDPD_FRONTEND_ENABLE "Enable PaddlePaddle FrontEnd" ON "protoc_available" OFF)
 ie_option(NGRAPH_IR_FRONTEND_ENABLE "Enable IR FrontEnd" ON)
 ie_dependent_option(NGRAPH_TF_FRONTEND_ENABLE "Enable TensorFlow FrontEnd" ON "protoc_available" OFF)
-ie_dependent_option(NGRAPH_USE_PROTOBUF_LITE "Compiles and links with protobuf-lite" ON
-    "NGRAPH_ONNX_FRONTEND_ENABLE" OFF)
 ie_dependent_option(NGRAPH_USE_SYSTEM_PROTOBUF "Use system protobuf" OFF
     "NGRAPH_ONNX_FRONTEND_ENABLE OR NGRAPH_PDPD_FRONTEND_ENABLE OR NGRAPH_TF_FRONTEND_ENABLE" OFF)
 ie_dependent_option(NGRAPH_UNIT_TEST_ENABLE "Enables ngraph unit tests" ON "ENABLE_TESTS;NOT ANDROID" OFF)
diff --git a/cmake/templates/OpenVINOConfig.cmake.in b/cmake/templates/OpenVINOConfig.cmake.in
index 818080a0ac8581..bb21d19b400d48 100644
--- a/cmake/templates/OpenVINOConfig.cmake.in
+++ b/cmake/templates/OpenVINOConfig.cmake.in
@@ -160,8 +160,20 @@ if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO" AND NOT TBB_FOUND)
                               ${_tbb_dir}
                         NO_CMAKE_FIND_ROOT_PATH
                         NO_DEFAULT_PATH)
+
+    set(install_tbbbind "@install_tbbbind@")
+    if(install_tbbbind)
+        set_and_check(_tbb_bind_dir "@PACKAGE_IE_TBBBIND_DIR@")
+        _ov_find_dependency(TBBBIND_2_5
+                            PATHS ${_tbb_bind_dir}
+                            NO_CMAKE_FIND_ROOT_PATH
+                            NO_DEFAULT_PATH)
+        set_target_properties(${TBBBIND_2_5_IMPORTED_TARGETS} PROPERTIES IMPORTED_GLOBAL ON)
+    endif()
 endif()
 
+_ov_find_dependency(Threads)
+
 if(NOT TARGET inference_engine)
     set(_ov_as_external_package ON)
     include("${CMAKE_CURRENT_LIST_DIR}/OpenVINOTargets.cmake")
diff --git a/cmake/test_model_zoo.cmake b/cmake/test_model_zoo.cmake
index 64ec7a6909d117..c8ec6691ca97cc 100644
--- a/cmake/test_model_zoo.cmake
+++ b/cmake/test_model_zoo.cmake
@@ -86,6 +86,11 @@ ov_model_convert("${OpenVINO_SOURCE_DIR}/${rel_path}"
                  "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/onnx_import"
                  ie_onnx_import_out_files)
 
+set(rel_path "docs/onnx_custom_op")
+ov_model_convert("${OpenVINO_SOURCE_DIR}/${rel_path}"
+                 "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/docs/models"
+                 docs_onnx_out_files)
+
 if(ENABLE_TESTS)
     if(NGRAPH_ONNX_FRONTEND_ENABLE AND ENABLE_REQUIREMENTS_INSTALL)
         find_package(PythonInterp 3 REQUIRED)
@@ -124,13 +129,14 @@ if(ENABLE_TESTS)
                                              ${ft_out_files}
                                              ${ie_onnx_out_files}
                                              ${ie_serialize_out_files}
-                                             ${ie_onnx_import_out_files})
+                                             ${ie_onnx_import_out_files}
+                                             ${docs_onnx_out_files})
 
     if(TARGET test_pip_prerequsites)
         add_dependencies(test_model_zoo test_pip_prerequsites)
     endif()
 
-    if (NGRAPH_PDPD_FRONTEND_ENABLE)
+    if (NGRAPH_PDPD_FRONTEND_ENABLE AND NGRAPH_UNIT_TEST_ENABLE)
         add_dependencies(test_model_zoo paddlepaddle_test_models)
     endif()
 
diff --git a/cmake/toolchains/ia32.linux.toolchain.cmake b/cmake/toolchains/ia32.linux.toolchain.cmake
index 4526bc3ab9de81..f5c24fffb89fe2 100644
--- a/cmake/toolchains/ia32.linux.toolchain.cmake
+++ b/cmake/toolchains/ia32.linux.toolchain.cmake
@@ -17,5 +17,8 @@ macro(_set_if_not_defined var val)
     endif()
 endmacro()
 
+# for ittapi
+_set_if_not_defined(FORCE_32 ON)
+
 # need libusb 32-bits version
 _set_if_not_defined(ENABLE_VPU OFF)
diff --git a/cmake/toolchains/mt.runtime.win32.toolchain.cmake b/cmake/toolchains/mt.runtime.win32.toolchain.cmake
index 899fecb679bce9..716d958fcd1f74 100644
--- a/cmake/toolchains/mt.runtime.win32.toolchain.cmake
+++ b/cmake/toolchains/mt.runtime.win32.toolchain.cmake
@@ -29,6 +29,8 @@ onecoreuap_set_runtime(STATIC_CRT)
 onecoreuap_set_runtime(protobuf_MSVC_STATIC_RUNTIME)
 # clDNN
 onecoreuap_set_runtime(CLDNN__COMPILE_LINK_USE_STATIC_RUNTIME)
+# OpenCL
+onecoreuap_set_runtime(USE_DYNAMIC_VCXX_RUNTIME)
 # google-test
 if(use_static_runtime)
     set(gtest_force_shared_crt OFF CACHE BOOL "" FORCE)
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 93d1feed4050d1..b2ccee1dcdd1fc 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -23,8 +23,8 @@ if(NOT ENABLE_DOCKER)
     add_subdirectory(template_extension)
 
     set(all_docs_targets
-        ie_docs_snippets
-        template_extension templatePlugin templateFuncTests)
+        ie_docs_snippets templateFuncTests
+        template_extension template_ov_extension templatePlugin)
     foreach(target_name IN LISTS all_docs_targets)
         if(TARGET ${target_name})
             set_target_properties(${target_name} PROPERTIES FOLDER docs)
@@ -36,8 +36,14 @@ if(NOT ENABLE_DOCKER)
 
     # install
 
-    install(TARGETS templatePlugin template_extension template_ov_extension
-            LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+    foreach(target templatePlugin template_extension template_ov_extension)
+        if(TARGET ${target})
+            install(TARGETS ${target}
+                    LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH}
+                    COMPONENT tests
+                    EXCLUDE_FROM_ALL)
+        endif()
+    endforeach()
 endif()
 
 set(OMZ_DOCS_DIR "" CACHE PATH "Path to open_model_zoo documentation")
diff --git a/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md b/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
index dd5543202418fe..5875995e21e462 100644
--- a/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
+++ b/docs/IE_DG/Extensibility_DG/Custom_ONNX_Ops.md
@@ -26,7 +26,18 @@ New operator registration must happen before an ONNX model is read. For example,
 Reregistering ONNX operators within the same process is supported. If you register an existing operator, you get a warning.
 
 The example below demonstrates an exemplary model that requires a previously created `CustomRelu` operator:
-@snippet onnx_custom_op/onnx_custom_op.cpp onnx_custom_op:model
+@include onnx_custom_op/custom_relu_model.prototxt
+This model is in text format, so before it can be passed to Inference Engine, it has to be converted to binary using:
+```py
+from google.protobuf import text_format
+import onnx
+
+with open("custom_relu_model.prototxt") as in_file:
+    proto = onnx.ModelProto()
+    text_format.Parse(in_file.read(), proto, allow_field_number=True)
+    s = onnx._serialize(proto)
+    onnx._save_bytes(s, "custom_relu_model.onnx")
+```
 
 
 To create a graph with nGraph operations, visit [Custom nGraph Operations](AddingNGraphOps.md).
diff --git a/docs/IE_DG/supported_plugins/GPU.md b/docs/IE_DG/supported_plugins/GPU.md
index 8a30f5ce98ebe3..28bee7d5316039 100644
--- a/docs/IE_DG/supported_plugins/GPU.md
+++ b/docs/IE_DG/supported_plugins/GPU.md
@@ -105,7 +105,7 @@ When specifying key values as raw strings (that is, when using Python API), omit
 | `KEY_CACHE_DIR`      | `"<cache_dir>"`                    | `""`              | Specifies a directory where compiled OCL binaries can be cached. First model loading generates the cache, and all subsequent LoadNetwork calls use precompiled kernels which significantly improves load time. If empty - caching is disabled             |
 | `KEY_PERF_COUNT`      | `YES` / `NO`                    | `NO`              | Collect performance counters during inference             |
 | `KEY_CONFIG_FILE`     | `"<file1> [<file2> ...]"`         | `""`              | Load custom layer configuration files                     |
-| `KEY_GPU_MODEL_`<br>`PRIORITY` | `GPU_MODEL_PRIORITY_<HIGH\|LOW>`  <br/> `GPU_QUEUE_PRIORITY_<LOW\|HIGH\|MED\|DISABLED>`   <br/> `GPU_HOST_TASK_PRIORITY_<HIGH\|LOW\|ANY>`  | `GPU_QUEUE_PRIORITY_DEFAULT` <br/>  `\|GPU_HOST_TASK_PRIORITY_ANY`             | Specifies two types of priority: host task priority and OpenCL queue priority.<br/><br/>Host task priority is specified by `GPU_HOST_TASK_PRIORITY_[level]` and there are three types of task levels: `HIGH`, `LOW`, and `ANY`. Note that `HIGH` and `LOW` are effective only when tbb is used for multithreading the LoadNetwork workload and the host processor is hybrid type. For hybrid processors, if the task priority type is set as `HIGH` the task will have higher priority for core type selection, and vice versa. If the host processor is not hybrid core or the multi threading is not using tbb, it is set as `ANY`, which is the default type.<br/><br/>OpenCL queue priority is specified by `GPU_QUEUE_PRIORITY_[level]` and there are four types of levels: `HIGH`, `MED`, `LOW`, and `DEFAULT`, where the default value is `DEFAULT`. Before usage, make sure your OpenCL driver supports appropriate extension.<br/><br/>Basically `GPU_MODEL_PRIORITY` can be set as combination of the two priority types, such as<br/>-`GPU_QUEUE_PRIORITY_HIGH\|GPU_HOST_TASK_PRIORITY_HIGH` or<br/>-`GPU_QUEUE_PRIORITY_LOW\|GPU_HOST_TASK_PRIORITY_HIGH`.<br/><br/>Also it can be set as a more abstract level of priority PLUGIN_PRIORIY_[level], which represents combination of the two priorities as follows:<br/>-`GPU_MODEL_PRIORITY_HIGH` : `GPU_QUEUE_PRIORITY_HIGH\|GPU_HOST_TASK_PRIORITY_HIGH`<br/>-`GPU_MODEL_PRIORITY_LOW` : `GPU_QUEUE_PRIORITY_LOW\|GPU_HOST_TASK_PRIORITY_LOW`<br/><br/>The default of `KEY_GPU_MODEL_PRIORITY` is `GPU_QUEUE_PRIORITY_DEFAULT\|GPU_HOST_TASK_PRIORITY_ANY`.<br>  |
+| `KEY_GPU_MODEL_`<br>`PRIORITY` | `GPU_MODEL_PRIORITY_<HIGH\|LOW>`  <br/> `GPU_QUEUE_PRIORITY_<LOW\|HIGH\|MED\|DEFAULT>`   <br/> `GPU_HOST_TASK_PRIORITY_<HIGH\|LOW\|ANY>`  | `GPU_QUEUE_PRIORITY_DEFAULT` <br/>  `\|GPU_HOST_TASK_PRIORITY_ANY`             | Specifies two types of priority: host task priority and OpenCL queue priority.<br/><br/>Host task priority is specified by `GPU_HOST_TASK_PRIORITY_[level]` and there are three types of task levels: `HIGH`, `LOW`, and `ANY`. Note that `HIGH` and `LOW` are effective only when tbb is used for multithreading the LoadNetwork workload and the host processor is hybrid type. For hybrid processors, if the task priority type is set as `HIGH` the task will have higher priority for core type selection, and vice versa. If the host processor is not hybrid core or the multi threading is not using tbb, it is set as `ANY`, which is the default type.<br/><br/>OpenCL queue priority is specified by `GPU_QUEUE_PRIORITY_[level]` and there are four types of levels: `HIGH`, `MED`, `LOW`, and `DEFAULT`, where the default value is `DEFAULT`. Before usage, make sure your OpenCL driver supports appropriate extension.<br/><br/>Basically `GPU_MODEL_PRIORITY` can be set as combination of the two priority types, such as<br/>-`GPU_QUEUE_PRIORITY_HIGH\|GPU_HOST_TASK_PRIORITY_HIGH` or<br/>-`GPU_QUEUE_PRIORITY_LOW\|GPU_HOST_TASK_PRIORITY_HIGH`.<br/><br/>Also it can be set as a more abstract level of priority PLUGIN_PRIORIY_[level], which represents combination of the two priorities as follows:<br/>-`GPU_MODEL_PRIORITY_HIGH` : `GPU_QUEUE_PRIORITY_HIGH\|GPU_HOST_TASK_PRIORITY_HIGH`<br/>-`GPU_MODEL_PRIORITY_LOW` : `GPU_QUEUE_PRIORITY_LOW\|GPU_HOST_TASK_PRIORITY_LOW`<br/><br/>The default of `KEY_GPU_MODEL_PRIORITY` is `GPU_QUEUE_PRIORITY_DEFAULT\|GPU_HOST_TASK_PRIORITY_ANY`.<br>  |
 | `KEY_GPU_PLUGIN_`<br>`PRIORITY` | `<0-3>`                       | `0`               | OpenCL queue priority (before usage, make sure your OpenCL driver supports appropriate extension)<br> Higher value means higher priority for OpenCL queue. 0 disables the setting. **Deprecated**. Please use KEY_GPU_MODEL_PRIORITY |
 | `KEY_GPU_PLUGIN_`<br>`THROTTLE` | `<0-3>`                       | `0`               | OpenCL queue throttling (before usage, make sure your OpenCL driver supports appropriate extension)<br> Lower value means lower driver thread priority and longer sleep time for it. 0 disables the setting. |
 | `KEY_CLDNN_ENABLE_`<br>`FP16_FOR_QUANTIZED_`<br>`MODELS` | `YES` / `NO`                       | `YES`               | Allows using FP16+INT8 mixed precision mode, so non-quantized parts of a model will be executed in FP16 precision for FP16 IR. Does not affect quantized FP32 IRs |
diff --git a/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md b/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md
index 1a8070e4dc4d6e..8d132abe429163 100644
--- a/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md
+++ b/docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md
@@ -525,7 +525,7 @@ Some TensorFlow\* operations do not match to any Inference Engine layer, but are
 | GRU |  |
 | Gather |  |
 | GatherElements | Doesn't work with negative indices |
-| GatherND |  |
+| GatherND | Doesn't work with negative indices |
 | GatherTree |  |
 | Gemm |  |
 | GlobalAveragePool |  |
diff --git a/docs/migration_ov_2_0/docs/common_inference_pipeline.md b/docs/migration_ov_2_0/docs/common_inference_pipeline.md
index af2dbf25304116..2b842e2e9e125a 100644
--- a/docs/migration_ov_2_0/docs/common_inference_pipeline.md
+++ b/docs/migration_ov_2_0/docs/common_inference_pipeline.md
@@ -32,6 +32,8 @@ OpenVINO™ 2.0 API:
 
 @snippet snippets/ov_common.cpp ov_api_2_0:read_model
 
+Read model has the same structure as in the example from [OpenVINO™ Graph Construction](@ref ov_graph_construction) guide.
+
 ### 2.1 Configure Input and Output of the Model
 
 Inference Engine API:
@@ -52,4 +54,108 @@ OpenVINO™ 2.0 API:
 
 @snippet snippets/ov_common.cpp ov_api_2_0:compile_model
 
-## 5. TBD
+## 4. Create an Inference Request
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:create_infer_request
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:create_infer_request
+
+## 5. Prepare input
+
+### IR v10
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_input_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_input_tensor_v10
+
+### IR v11
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_input_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_input_tensor_aligned
+
+### ONNX
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_input_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_input_tensor_aligned
+
+### From Function
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_input_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_input_tensor_aligned
+
+## 6. Start Inference
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:inference
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:inference
+
+
+## 7. Process the Inference Results
+
+### IR v10
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_output_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_output_tensor_v10
+
+### IR v11
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_output_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_output_tensor_aligned
+
+### ONNX
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_output_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_output_tensor_aligned
+
+### From Function
+
+Inference Engine API:
+
+@snippet snippets/ie_common.cpp ie:get_output_tensor
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_common.cpp ov_api_2_0:get_output_tensor_aligned
+
diff --git a/docs/migration_ov_2_0/docs/graph_construction.md b/docs/migration_ov_2_0/docs/graph_construction.md
new file mode 100644
index 00000000000000..f3fe2c7a1dce34
--- /dev/null
+++ b/docs/migration_ov_2_0/docs/graph_construction.md
@@ -0,0 +1,12 @@
+# OpenVINO™ graph construction {#ov_graph_construction}
+
+OpenVINO™ 2.0 includes nGraph engine in a common part. The `ngraph` namespace was changed to `ov`.
+Code snippets below show how application code should be changed for migration to OpenVINO™ 2.0.
+
+nGraph API:
+
+@snippet snippets/ngraph.cpp ngraph:graph
+
+OpenVINO™ 2.0 API:
+
+@snippet snippets/ov_graph.cpp ov:graph
diff --git a/docs/migration_ov_2_0/docs/intro.md b/docs/migration_ov_2_0/docs/intro.md
index 5d89b7aff3d809..5afc4a11304ffb 100644
--- a/docs/migration_ov_2_0/docs/intro.md
+++ b/docs/migration_ov_2_0/docs/intro.md
@@ -9,4 +9,5 @@ The list with differences between APIs below:
  - Namespaces were aligned between components.
 
 Please look at next transition guides to understand how transit own application to OpenVINO™ API 2.0.
+ - [OpenVINO™ Graph Construction](@ref ov_graph_construction)
  - [OpenVINO™ Common Inference pipeline](@ref ov_inference_pipeline)
diff --git a/docs/onnx_custom_op/custom_relu_model.prototxt b/docs/onnx_custom_op/custom_relu_model.prototxt
new file mode 100644
index 00000000000000..3845cc00ce2e8a
--- /dev/null
+++ b/docs/onnx_custom_op/custom_relu_model.prototxt
@@ -0,0 +1,52 @@
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "in"
+    output: "out"
+    name: "customrelu"
+    op_type: "CustomRelu"
+    domain: "com.example"
+    attribute {
+        name: "alpha"
+        type: FLOAT
+        f: 2
+    }
+    attribute {
+        name: "beta"
+        type: FLOAT
+        f: 3
+    }
+  }
+  name: "custom relu graph"
+  input {
+    name: "in"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "out"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 8
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  domain: "com.example"
+  version: 1
+}
diff --git a/docs/onnx_custom_op/onnx_custom_op.cpp b/docs/onnx_custom_op/onnx_custom_op.cpp
index 2d8c6198e64f73..09ea58cc2b6b89 100644
--- a/docs/onnx_custom_op/onnx_custom_op.cpp
+++ b/docs/onnx_custom_op/onnx_custom_op.cpp
@@ -9,68 +9,6 @@
 #include <ngraph/opsets/opset5.hpp>
 //! [onnx_custom_op:headers]
 
-
-std::string custom_relu_model() {
-    return
-//! [onnx_custom_op:model]
-R"ONNX(
-ir_version: 3
-producer_name: "nGraph ONNX Importer"
-graph {
-  node {
-    input: "in"
-    output: "out"
-    name: "customrelu"
-    op_type: "CustomRelu"
-    domain: "com.example"
-    attribute {
-        name: "alpha"
-        type: FLOAT
-        f: 2
-    }
-    attribute {
-        name: "beta"
-        type: FLOAT
-        f: 3
-    }
-  }
-  name: "custom relu graph"
-  input {
-    name: "in"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 8
-          }
-        }
-      }
-    }
-  }
-  output {
-    name: "out"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 8
-          }
-        }
-      }
-    }
-  }
-}
-opset_import {
-  domain: "com.example"
-  version: 1
-}
-)ONNX";
-//! [onnx_custom_op:model]
-}
-
-
 void register_custom_relu_operator() {
     // CustomRelu is defined as follows:
     // x >= 0 => f(x) = x * alpha
diff --git a/docs/onnx_custom_op/onnx_custom_op.hpp b/docs/onnx_custom_op/onnx_custom_op.hpp
index fae687097045a4..e1670a5f82bf5b 100644
--- a/docs/onnx_custom_op/onnx_custom_op.hpp
+++ b/docs/onnx_custom_op/onnx_custom_op.hpp
@@ -4,8 +4,5 @@
 
 #pragma once
 
-#include <string>
-
-std::string custom_relu_model();
 void register_custom_relu_operator();
 void unregister_custom_relu_operator();
diff --git a/docs/ops/opset8.md b/docs/ops/opset8.md
index c1a13c15b3fdaf..f9c55e8ce094c8 100644
--- a/docs/ops/opset8.md
+++ b/docs/ops/opset8.md
@@ -65,7 +65,7 @@ declared in `namespace opset8`.
 * [FloorMod](arithmetic/FloorMod_1.md)
 * [Gather](movement/Gather_8.md)
 * [GatherElements](movement/GatherElements_6.md)
-* [GatherND_8](movement/GatherND_8.md)
+* [GatherND](movement/GatherND_8.md)
 * [GatherTree](movement/GatherTree_1.md)
 * [Gelu](activation/GELU_7.md)
 * [Greater](comparison/Greater_1.md)
@@ -94,12 +94,14 @@ declared in `namespace opset8`.
 * [LSTMCell](sequence/LSTMCell_1.md)
 * [LSTMSequence](sequence/LSTMSequence_1.md)
 * [MatMul](matrix/MatMul_1.md)
-* [MaxPool](pooling/MaxPool_1.md)
+* [MatrixNMS](sort/MatrixNMS_8.md)
+* [MaxPool](pooling/MaxPool_8.md)
 * [Maximum](arithmetic/Maximum_1.md)
 * [Minimum](arithmetic/Minimum_1.md)
 * [Mish](activation/Mish_4.md)
 * [Mod](arithmetic/Mod_1.md)
 * [MVN](normalization/MVN_6.md)
+* [MulticlassNMS](sort/MulticlassNMS_8.md)
 * [Multiply](arithmetic/Multiply_1.md)
 * [Negative](arithmetic/Negative_1.md)
 * [NonMaxSuppression](sort/NonMaxSuppression_5.md)
@@ -152,6 +154,7 @@ declared in `namespace opset8`.
 * [Sign](arithmetic/Sign_1.md)
 * [Sin](arithmetic/Sin_1.md)
 * [Sinh](arithmetic/Sinh_1.md)
+* [Slice](movement/Slice_8.md)
 * [SoftMax](activation/SoftMax_8.md)
 * [SoftPlus](activation/SoftPlus_4.md)
 * [SpaceToBatch](movement/SpaceToBatch_2.md)
diff --git a/docs/ops/sequence/LSTMCell_1.md b/docs/ops/sequence/LSTMCell_1.md
index eb979a94126b56..dd73b1b7dd9824 100644
--- a/docs/ops/sequence/LSTMCell_1.md
+++ b/docs/ops/sequence/LSTMCell_1.md
@@ -6,21 +6,20 @@
 
 **Short description**: *LSTMCell* operation represents a single LSTM cell. It computes the output using the formula described in the original paper [Long Short-Term Memory](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.676.4320&rep=rep1&type=pdf).
 
-**Detailed description**
+**Detailed description**: *LSTMCell* computes the output *Ht* and *ot* for current time step based on the following formula:
 
 ```
 Formula:
-  *  - matrix mult
- (.) - eltwise mult
+  *  - matrix multiplication
+ (.) - Hadamard product (element-wise)
  [,] - concatenation
-sigm - 1/(1 + e^{-x})
-tanh - (e^{2x} - 1)/(e^{2x} + 1)
-   f = sigm(Wf*[Hi, X] + Bf)
-   i = sigm(Wi*[Hi, X] + Bi)
-   c = tanh(Wc*[Hi, X] + Bc)
-   o = sigm(Wo*[Hi, X] + Bo)
-  Co = f (.) Ci + i (.) c
-  Ho = o (.) tanh(Co)
+ f, g, h - are activation functions.
+     it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+     ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Wbf + Rbf)
+     ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
+     Ct = ft (.) Ct-1 + it (.) ct
+     ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Wbo + Rbo)
+     Ht = ot (.) h(Ct)
 ```
 
 **Attributes**
@@ -37,7 +36,7 @@ tanh - (e^{2x} - 1)/(e^{2x} + 1)
   * **Description**: *activations* specifies activation functions for gates, there are three gates, so three activation functions should be specified as a value for this attributes
   * **Range of values**: any combination of *relu*, *sigmoid*, *tanh*
   * **Type**: a list of strings
-  * **Default value**: *sigmoid,tanh,tanh*
+  * **Default value**: *sigmoid* for f, *tanh* for g, *tanh* for h
   * **Required**: *no*
 
 * *activations_alpha, activations_beta*
@@ -68,7 +67,7 @@ tanh - (e^{2x} - 1)/(e^{2x} + 1)
 
 * **5**: `R` - 2D tensor of type *T* `[4 * hidden_size, hidden_size]`, the recurrence weights for matrix multiplication, gate order: fico. **Required.**
 
-* **6**: `B` 1D tensor of type *T* `[4 * hidden_size]`, the sum of biases (weights and recurrence weights). **Required.**
+* **6**: `B` 1D tensor of type *T* `[4 * hidden_size]`, the sum of biases (weights and recurrence weights), if not specified - assumed to be 0. **optional.**
 
 
 **Outputs**
diff --git a/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md b/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md
index 7bd1540e91dbad..e74b9f78e4d8d0 100644
--- a/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md
+++ b/docs/ops/sparse/EmbeddingBagOffsetsSum_3.md
@@ -8,6 +8,8 @@
 
 **Detailed description**: This is the second case of the PyTorch [EmbeddingBag](https://pytorch.org/docs/stable/nn.html#embeddingbag), it has indices in two 1D tensors provided as 2nd and 3rd inputs. For each index in `indices` this operator gets values from `data` embedding table and sums all values belonging to each bag. Values in `offsets` define starting index in `indices` tensor of each "bag", e.g. `offsets` with value `[0,3,4,4,6]` define 5 "bags" containing `[3,1,0,2,n-6]` elements.
 
+**Attributes**: EmbeddingBagOffsetsSum operation has no attributes.
+
 **Inputs**:
 
 *   **1**: `emb_table` tensor containing the embedding lookup table of the module of shape `[num_emb, emb_dim1, emb_dim2, ...]` and  of type *T*. **Required.**
@@ -42,7 +44,7 @@
         <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
             <dim>4</dim>
         </port>
-        <port id="2">     <!-- offsets value is: [0, 2, 2] - second "bag" is empty -->
+        <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
             <dim>3</dim>
         </port>
         <port id="3"/>    <!-- default_index value is: 0 -->
diff --git a/docs/ops/sparse/EmbeddingBagPackedSum_3.md b/docs/ops/sparse/EmbeddingBagPackedSum_3.md
index ceaebe3862326c..41b66668440628 100644
--- a/docs/ops/sparse/EmbeddingBagPackedSum_3.md
+++ b/docs/ops/sparse/EmbeddingBagPackedSum_3.md
@@ -8,6 +8,8 @@
 
 **Detailed description**: This is the first case of the PyTorch [EmbeddingBag](https://pytorch.org/docs/stable/nn.html#embeddingbag), it has indices in the tensor of format `[batch, indices_per_bag]`. If 3rd input is not provided, this operation is equivalent to *Gather* followed by *ReduceSum(axis=0)*. However, *EmbeddingBagPackedSum* is much more time and memory efficient than using a chain of these operations.
 
+**Attributes**: EmbeddingBagPackedSum operation has no attributes.
+
 **Inputs**:
 
 *   **1**: `emb_table` tensor containing the embedding lookup table of the module of shape `[num_emb, emb_dim1, emb_dim2, ...]` and of type *T*. **Required.**
diff --git a/docs/ops/sparse/EmbeddingSegmentsSum_3.md b/docs/ops/sparse/EmbeddingSegmentsSum_3.md
index 71dc4897251a1f..b1c8404aacad73 100644
--- a/docs/ops/sparse/EmbeddingSegmentsSum_3.md
+++ b/docs/ops/sparse/EmbeddingSegmentsSum_3.md
@@ -8,6 +8,8 @@
 
 **Detailed description**: This is [sparse.segment_sum](https://www.tensorflow.org/api_docs/python/tf/sparse/segment_sum) operation from Tensorflow. For each index in `indices` this operator gets values from `data` embedding table and sums all values belonging to each segment. Values in `segment_ids` define which segment index in `indices` tensor belong to, e.g. `segments_ids` with value `[0,0,0,1,1,3,5,5]` define 4 non empty segments other segments are empty, the number of segments is defined by `num_segments` input.
 
+**Attributes**: EmbeddingSegmentsSum operation has no attributes.
+
 **Inputs**:
 
 *   **1**: `emb_table` tensor containing the embedding lookup table of the module of shape `[num_emb, emb_dim1, emb_dim2, ...]` and of type *T*. **Required.**
diff --git a/docs/snippets/ie_common.cpp b/docs/snippets/ie_common.cpp
index 6a558129243082..25cf78a0c43582 100644
--- a/docs/snippets/ie_common.cpp
+++ b/docs/snippets/ie_common.cpp
@@ -27,8 +27,29 @@ int main() {
     //! [ie:create_infer_request]
 
     //! [ie:get_input_tensor]
-    InferenceEngine::Blob::Ptr input_blob = infer_request.GetBlob(inputs.begin()->first);
-    // fill input blob
+    InferenceEngine::Blob::Ptr input_blob1 = infer_request.GetBlob(inputs.begin()->first);
+    // fill first blob
+    InferenceEngine::SizeVector dims1 = input_blob1->getTensorDesc().getDims();
+    InferenceEngine::MemoryBlob::Ptr minput1 = InferenceEngine::as<InferenceEngine::MemoryBlob>(input_blob1);
+    if (minput1) {
+        // locked memory holder should be alive all time while access to its
+        // buffer happens
+        auto minputHolder = minput1->wmap();
+        // Original I64 precision was converted to I32
+        auto data = minputHolder.as<InferenceEngine::PrecisionTrait<InferenceEngine::Precision::I32>::value_type*>();
+        // Fill data ...
+    }
+    InferenceEngine::Blob::Ptr input_blob2 = infer_request.GetBlob("data2");
+    // fill first blob
+    InferenceEngine::MemoryBlob::Ptr minput2 = InferenceEngine::as<InferenceEngine::MemoryBlob>(input_blob2);
+    if (minput2) {
+        // locked memory holder should be alive all time while access to its
+        // buffer happens
+        auto minputHolder = minput2->wmap();
+        // Original I64 precision was converted to I32
+        auto data = minputHolder.as<InferenceEngine::PrecisionTrait<InferenceEngine::Precision::I32>::value_type*>();
+        // Fill data ...
+    }
     //! [ie:get_input_tensor]
 
     //! [ie:inference]
@@ -37,7 +58,16 @@ int main() {
 
     //! [ie:get_output_tensor]
     InferenceEngine::Blob::Ptr output_blob = infer_request.GetBlob(outputs.begin()->first);
-    // process output data
+    InferenceEngine::MemoryBlob::Ptr moutput = InferenceEngine::as<InferenceEngine::MemoryBlob>(output_blob);
+    if (moutput) {
+        // locked memory holder should be alive all time while access to its
+        // buffer happens
+        auto minputHolder = moutput->rmap();
+        // Original I64 precision was converted to I32
+        auto data =
+            minputHolder.as<const InferenceEngine::PrecisionTrait<InferenceEngine::Precision::I32>::value_type*>();
+        // process output data
+    }
     //! [ie:get_output_tensor]
     return 0;
 }
diff --git a/docs/snippets/ngraph.cpp b/docs/snippets/ngraph.cpp
new file mode 100644
index 00000000000000..931140f99d9519
--- /dev/null
+++ b/docs/snippets/ngraph.cpp
@@ -0,0 +1,48 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <ngraph/ngraph.hpp>
+#include <ngraph/opsets/opset8.hpp>
+
+int main() {
+    //! [ngraph:graph]
+    // _____________    _____________
+    // | Parameter |    | Parameter |
+    // |   data1   |    |   data2   |
+    // |___________|    |___________|
+    //         |            |
+    // data1_t |            | data2_t
+    //          \          /
+    //           \        /
+    //            \      /
+    //         ____\____/____
+    //         |   Concat   |
+    //         |   concat   |
+    //         |____________|
+    //               |
+    //               | concat_t
+    //               |
+    //        _______|_______
+    //        |    Result   |
+    //        |    result   |
+    //        |_____________|
+    auto data1 = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1, 3, 2, 2});
+    data1->set_friendly_name("data1");        // operation name
+    data1->output(0).set_names({"data1_t"});  // tensor names
+    auto data2 = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1, 2, 2, 2});
+    data2->set_friendly_name("data2");        // operation name
+    data2->output(0).set_names({"data2_t"});  // tensor names
+
+    auto concat = std::make_shared<ngraph::opset8::Concat>(ngraph::OutputVector{data1, data2}, 1);
+    concat->set_friendly_name("concat");        // operation name
+    concat->output(0).set_names({"concat_t"});  // tensor name
+
+    auto result = std::make_shared<ngraph::opset8::Result>(concat);
+    result->set_friendly_name("result");  // operation name
+
+    auto f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result},
+                                                ngraph::ParameterVector{data1, data2},
+                                                "function_name");
+    //! [ngraph:graph]
+    return 0;
+}
diff --git a/docs/snippets/ov_common.cpp b/docs/snippets/ov_common.cpp
index 7cb9e344f7cbaa..1392e3a509850f 100644
--- a/docs/snippets/ov_common.cpp
+++ b/docs/snippets/ov_common.cpp
@@ -1,9 +1,61 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-#include <openvino/core/function.hpp>
+#include <openvino/core/core.hpp>
 #include <openvino/runtime/runtime.hpp>
 
+void inputs_v10(ov::runtime::InferRequest& infer_request) {
+    //! [ov_api_2_0:get_input_tensor_v10]
+    // Get input tensor by index
+    ov::runtime::Tensor input_tensor1 = infer_request.get_input_tensor(0);
+    // IR v10 works with converted precisions (i64 -> i32)
+    auto data1 = input_tensor1.data<int32_t>();
+    // Fill first data ...
+
+    // Get input tensor by tensor name
+    ov::runtime::Tensor input_tensor2 = infer_request.get_tensor("data2_t");
+    // IR v10 works with converted precisions (i64 -> i32)
+    auto data2 = input_tensor1.data<int32_t>();
+    // Fill first data ...
+    //! [ov_api_2_0:get_input_tensor_v10]
+}
+
+void inputs_aligned(ov::runtime::InferRequest& infer_request) {
+    //! [ov_api_2_0:get_input_tensor_aligned]
+    // Get input tensor by index
+    ov::runtime::Tensor input_tensor1 = infer_request.get_input_tensor(0);
+    // Element types, names and layouts are aligned with framework
+    auto data1 = input_tensor1.data<int64_t>();
+    // Fill first data ...
+
+    // Get input tensor by tensor name
+    ov::runtime::Tensor input_tensor2 = infer_request.get_tensor("data2_t");
+    // Element types, names and layouts are aligned with framework
+    auto data2 = input_tensor1.data<int64_t>();
+    // Fill first data ...
+    //! [ov_api_2_0:get_input_tensor_aligned]
+}
+
+void outputs_v10(ov::runtime::InferRequest& infer_request) {
+    //! [ov_api_2_0:get_output_tensor_v10]
+    // model has only one output
+    ov::runtime::Tensor output_tensor = infer_request.get_output_tensor();
+    // IR v10 works with converted precisions (i64 -> i32)
+    auto out_data = output_tensor.data<int32_t>();
+    // process output data
+    //! [ov_api_2_0:get_output_tensor_v10]
+}
+
+void outputs_aligned(ov::runtime::InferRequest& infer_request) {
+    //! [ov_api_2_0:get_output_tensor_aligned]
+    // model has only one output
+    ov::runtime::Tensor output_tensor = infer_request.get_output_tensor();
+    // Element types, names and layouts are aligned with framework
+    auto out_data = output_tensor.data<int64_t>();
+    // process output data
+    //! [ov_api_2_0:get_output_tensor_aligned]
+}
+
 int main() {
     //! [ov_api_2_0:create_core]
     ov::runtime::Core core;
@@ -14,21 +66,24 @@ int main() {
     //! [ov_api_2_0:read_model]
 
     //! [ov_api_2_0:get_inputs_outputs]
-    ov::ParameterVector inputs = network->get_parameters();
-    ov::ResultVector outputs = network->get_results();
+    std::vector<ov::Output<ov::Node>> inputs = network->inputs();
+    std::vector<ov::Output<ov::Node>> outputs = network->outputs();
     //! [ov_api_2_0:get_inputs_outputs]
 
     //! [ov_api_2_0:compile_model]
     ov::runtime::ExecutableNetwork exec_network = core.compile_model(network, "CPU");
     //! [ov_api_2_0:compile_model]
 
+    //! [ov_api_2_0:create_infer_request]
     ov::runtime::InferRequest infer_request = exec_network.create_infer_request();
-    //
-    // InferenceEngine::Blob::Ptr input_blob = infer_request.GetBlob(inputs.begin()->first);
-    // // fill input blob
-    // infer_request.Infer();
-    //
-    // InferenceEngine::Blob::Ptr output_blob = infer_request.GetBlob(outputs.begin()->first);
-    // process output data
+    //! [ov_api_2_0:create_infer_request]
+
+    inputs_aligned(infer_request);
+    //! [ov_api_2_0:inference]
+    infer_request.infer();
+    //! [ov_api_2_0:inference]
+
+    outputs_aligned(infer_request);
+
     return 0;
 }
diff --git a/docs/snippets/ov_graph.cpp b/docs/snippets/ov_graph.cpp
new file mode 100644
index 00000000000000..b47abce44514cb
--- /dev/null
+++ b/docs/snippets/ov_graph.cpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <openvino/core/core.hpp>
+#include <openvino/opsets/opset8.hpp>
+
+int main() {
+    //! [ov:graph]
+    // _____________    _____________
+    // | Parameter |    | Parameter |
+    // |   data1   |    |   data2   |
+    // |___________|    |___________|
+    //         |            |
+    // data1_t |            | data2_t
+    //          \          /
+    //           \        /
+    //            \      /
+    //         ____\____/____
+    //         |   Concat   |
+    //         |   concat   |
+    //         |____________|
+    //               |
+    //               | concat_t
+    //               |
+    //        _______|_______
+    //        |    Result   |
+    //        |    result   |
+    //        |_____________|
+    auto data1 = std::make_shared<ov::opset8::Parameter>(ov::element::i64, ov::Shape{1, 3, 2, 2});
+    data1->set_friendly_name("data1");        // operation name
+    data1->output(0).set_names({"data1_t"});  // tensor names
+    auto data2 = std::make_shared<ov::opset8::Parameter>(ov::element::i64, ov::Shape{1, 2, 2, 2});
+    data2->set_friendly_name("data2");        // operation name
+    data2->output(0).set_names({"data2_t"});  // tensor names
+
+    auto concat = std::make_shared<ov::opset8::Concat>(ov::OutputVector{data1, data2}, 1);
+    concat->set_friendly_name("concat");        // operation name
+    concat->output(0).set_names({"concat_t"});  // tensor name
+
+    auto result = std::make_shared<ov::opset8::Result>(concat);
+    result->set_friendly_name("result");  // operation name
+
+    auto f =
+        std::make_shared<ov::Function>(ov::ResultVector{result}, ov::ParameterVector{data1, data2}, "function_name");
+    //! [ov:graph]
+    return 0;
+}
diff --git a/docs/template_plugin/src/CMakeLists.txt b/docs/template_plugin/src/CMakeLists.txt
index a6411523780629..e22bb0a6dedfa3 100644
--- a/docs/template_plugin/src/CMakeLists.txt
+++ b/docs/template_plugin/src/CMakeLists.txt
@@ -40,13 +40,5 @@ set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_REL
 #                     POSSIBLE_PLUGINS ${TARGET_NAME})
 # [cmake:plugin]
 
-# ATTENTION: uncomment to install component
-# install
-
-# set(component_name template)
-# ie_cpack_add_component(${component_name} REQUIRED)
-
-# install(TARGETS ${TARGET_NAME}
-#         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH}
-#         LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH}
-#         COMPONENT ${component_name})
+ov_install_static_lib(ngraph_backend ngraph)
+ov_install_static_lib(interpreter_backend ngraph)
diff --git a/docs/template_plugin/src/template_executable_network.cpp b/docs/template_plugin/src/template_executable_network.cpp
index 8479a027550b26..cff492f5c4acd7 100644
--- a/docs/template_plugin/src/template_executable_network.cpp
+++ b/docs/template_plugin/src/template_executable_network.cpp
@@ -218,7 +218,9 @@ void TemplatePlugin::ExecutableNetwork::Export(std::ostream& modelStream) {
     // Note: custom ngraph extensions are not supported
     std::map<std::string, ngraph::OpSet> custom_opsets;
     std::stringstream xmlFile, binFile;
+    OPENVINO_SUPPRESS_DEPRECATED_START
     ov::pass::Serialize serializer(xmlFile, binFile, custom_opsets);
+    OPENVINO_SUPPRESS_DEPRECATED_END
     serializer.run_on_function(_function);
 
     auto m_constants = binFile.str();
diff --git a/docs/template_plugin/tests/functional/op_reference/batch_to_space.cpp b/docs/template_plugin/tests/functional/op_reference/batch_to_space.cpp
new file mode 100644
index 00000000000000..600a5448d5804a
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/batch_to_space.cpp
@@ -0,0 +1,222 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/opsets/opset1.hpp"
+#include "openvino/opsets/opset2.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct BatchToSpaceParams {
+    BatchToSpaceParams(const Tensor& dataTensor, const Tensor& blockShapeTensor,
+                       const Tensor& cropsBeginTensor, const Tensor& cropsEndTensor,
+                       const Tensor& expectedTensor, const std::string& testcaseName = "") :
+                  dataTensor(dataTensor), blockShapeTensor(blockShapeTensor),
+                  cropsBeginTensor(cropsBeginTensor), cropsEndTensor(cropsEndTensor),
+                  expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor blockShapeTensor;
+    Tensor cropsBeginTensor;
+    Tensor cropsEndTensor;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceBatchToSpaceLayerTest : public testing::TestWithParam<BatchToSpaceParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<BatchToSpaceParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_bsType=" << param.blockShapeTensor.type;
+        result << "_bsShape=" << param.blockShapeTensor.shape;
+        result << "_cbType=" << param.cropsBeginTensor.type;
+        result << "_cbShape=" << param.cropsBeginTensor.shape;
+        result << "_ceType=" << param.cropsEndTensor.type;
+        result << "_ceShape=" << param.cropsEndTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const BatchToSpaceParams& params) {
+        const auto data = std::make_shared<opset1::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto blockShape = std::make_shared<opset1::Constant>(element::i64, params.blockShapeTensor.shape, params.blockShapeTensor.data.data());
+        const auto cropsBegin = std::make_shared<opset1::Constant>(element::i64, params.cropsBeginTensor.shape, params.cropsBeginTensor.data.data());
+        const auto cropsEnd = std::make_shared<opset1::Constant>(element::i64, params.cropsEndTensor.shape, params.cropsEndTensor.data.data());
+        const auto batchToSpace = std::make_shared<opset2::BatchToSpace>(data, blockShape, cropsBegin, cropsEnd);
+        return std::make_shared<Function>(NodeVector {batchToSpace}, ParameterVector {data});
+    }
+};
+
+TEST_P(ReferenceBatchToSpaceLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<BatchToSpaceParams> generateBatchToSpaceParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<BatchToSpaceParams> batchToSpaceParams {
+        // input_with_shape_4x3
+        BatchToSpaceParams(
+            Tensor({4, 3}, IN_ET, std::vector<T>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}),
+            Tensor({2}, element::i64, std::vector<int64_t>{1, 2}),
+            Tensor({2}, element::i64, std::vector<int64_t>{0, 0}),
+            Tensor({2}, element::i64, std::vector<int64_t>{0, 0}),
+            Tensor({2, 6}, IN_ET, std::vector<T>{1, 7, 2, 8, 3, 9, 4, 10, 5, 11, 6, 12}),
+            "input_with_shape_4x3"),
+
+        // input_with_shape_4x1x1x3
+        BatchToSpaceParams(
+            Tensor({4, 1, 1, 3}, IN_ET, std::vector<T>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 1, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({2, 1, 1, 6}, IN_ET, std::vector<T>{1, 7, 2, 8, 3, 9, 4, 10, 5, 11, 6, 12}),
+            "input_with_shape_4x1x1x3"),
+        // input_with_shape_4x1x1x3_1
+        BatchToSpaceParams(
+            Tensor({4, 1, 1, 3}, IN_ET, std::vector<T>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 2, 1}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({2, 1, 2, 3}, IN_ET, std::vector<T>{1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}),
+            "input_with_shape_4x1x1x3_1"),
+        // input_with_shape_4x1x1x3_2
+        BatchToSpaceParams(
+            Tensor({4, 1, 1, 3}, IN_ET, std::vector<T>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 2, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({2, 1, 1, 6}, IN_ET, std::vector<T>{1, 4, 2, 5, 3, 6, 7, 10, 8, 11, 9, 12}),
+            "input_with_shape_4x1x1x3_2"),
+
+        // input_with_shape_4x1x2x3
+        BatchToSpaceParams(
+            Tensor({4, 1, 2, 3}, IN_ET, std::vector<T>{1,  2,  3,  4,  5,  6,
+                                                       7,  8,  9,  10, 11, 12,
+                                                       13, 14, 15, 16, 17, 18,
+                                                       19, 20, 21, 22, 23, 24}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 1, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({2, 1, 2, 6}, IN_ET, std::vector<T>{1, 13, 2, 14, 3, 15,
+                                                       4, 16, 5, 17, 6, 18,
+                                                       7, 19, 8, 20, 9, 21,
+                                                       10, 22, 11, 23, 12, 24}),
+            "input_with_shape_4x1x2x3"),
+        // input_with_shape_4x1x2x3_1
+        BatchToSpaceParams(
+            Tensor({4, 1, 2, 3}, IN_ET, std::vector<T>{1,  2,  3,  4,  5,  6,
+                                                       7,  8,  9,  10, 11, 12,
+                                                       13, 14, 15, 16, 17, 18,
+                                                       19, 20, 21, 22, 23, 24}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 2, 1}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({2, 1, 4, 3}, IN_ET, std::vector<T>{1,  2,  3,  13, 14, 15,
+                                                       4,  5,  6,  16, 17, 18,
+                                                       7,  8,  9,  19, 20, 21,
+                                                       10, 11, 12, 22, 23, 24}),
+            "input_with_shape_4x1x2x3_1"),
+        // input_with_shape_4x1x2x3_2
+        BatchToSpaceParams(
+            Tensor({4, 1, 2, 3}, IN_ET, std::vector<T>{1,  2,  3,  4,  5,  6,
+                                                       7,  8,  9,  10, 11, 12,
+                                                       13, 14, 15, 16, 17, 18,
+                                                       19, 20, 21, 22, 23, 24}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 2, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({1, 1, 4, 6}, IN_ET, std::vector<T>{1,  7,  2,  8,  3,  9,
+                                                       13, 19, 14, 20, 15, 21,
+                                                       4,  10, 5,  11, 6,  12,
+                                                       16, 22, 17, 23, 18, 24}),
+            "input_with_shape_4x1x2x3_2"),
+
+        // input_with_shape_with_crop_4x1x2x3
+        BatchToSpaceParams(
+            Tensor({4, 1, 2, 3}, IN_ET, std::vector<T>{1,  2,  3,  4,  5,  6,
+                                                       7,  8,  9,  10, 11, 12,
+                                                       13, 14, 15, 16, 17, 18,
+                                                       19, 20, 21, 22, 23, 24}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 2, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 2}),
+            Tensor({1, 1, 4, 4}, IN_ET, std::vector<T>{1, 7,  2, 8,  13, 19, 14, 20,
+                                                       4, 10, 5, 11, 16, 22, 17, 23}),
+            "input_with_shape_with_crop_4x1x2x3"),
+        // input_with_shape_with_crop_4x1x2x3_1
+        BatchToSpaceParams(
+            Tensor({4, 1, 2, 3}, IN_ET, std::vector<T>{1,  2,  3,  4,  5,  6,
+                                                       7,  8,  9,  10, 11, 12,
+                                                       13, 14, 15, 16, 17, 18,
+                                                       19, 20, 21, 22, 23, 24}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 2, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({1, 1, 4, 4}, IN_ET, std::vector<T>{2, 8,  3, 9,  14, 20, 15, 21,
+                                                       5, 11, 6, 12, 17, 23, 18, 24}),
+            "input_with_shape_with_crop_4x1x2x3_1"),
+        // input_with_shape_with_crop_4x1x2x3_2
+        BatchToSpaceParams(
+            Tensor({4, 1, 2, 3}, IN_ET, std::vector<T>{1,  2,  3,  4,  5,  6,
+                                                       7,  8,  9,  10, 11, 12,
+                                                       13, 14, 15, 16, 17, 18,
+                                                       19, 20, 21, 22, 23, 24}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 2, 2}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 1, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 1, 0}),
+            Tensor({1, 1, 2, 6}, IN_ET, std::vector<T>{13, 19, 14, 20, 15, 21,
+                                                       4,  10, 5,  11, 6,  12}),
+            "input_with_shape_with_crop_4x1x2x3_2"),
+    };
+    return batchToSpaceParams;
+}
+
+std::vector<BatchToSpaceParams> generateBatchToSpaceCombinedParams() {
+    const std::vector<std::vector<BatchToSpaceParams>> batchToSpaceTypeParams {
+        generateBatchToSpaceParams<element::Type_t::i8>(),
+        generateBatchToSpaceParams<element::Type_t::i16>(),
+        generateBatchToSpaceParams<element::Type_t::i32>(),
+        generateBatchToSpaceParams<element::Type_t::i64>(),
+        generateBatchToSpaceParams<element::Type_t::u8>(),
+        generateBatchToSpaceParams<element::Type_t::u16>(),
+        generateBatchToSpaceParams<element::Type_t::u32>(),
+        generateBatchToSpaceParams<element::Type_t::u64>(),
+        generateBatchToSpaceParams<element::Type_t::bf16>(),
+        generateBatchToSpaceParams<element::Type_t::f16>(),
+        generateBatchToSpaceParams<element::Type_t::f32>(),
+        generateBatchToSpaceParams<element::Type_t::f64>(),
+    };
+    std::vector<BatchToSpaceParams> combinedParams;
+
+    for (const auto& params : batchToSpaceTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_BatchToSpace_With_Hardcoded_Refs, ReferenceBatchToSpaceLayerTest,
+    testing::ValuesIn(generateBatchToSpaceCombinedParams()), ReferenceBatchToSpaceLayerTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/ctc_greedy_decoder.cpp b/docs/template_plugin/tests/functional/op_reference/ctc_greedy_decoder.cpp
new file mode 100644
index 00000000000000..c602d0d8ed9bb6
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/ctc_greedy_decoder.cpp
@@ -0,0 +1,132 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/ctc_greedy_decoder.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct CTCGreedyDecoderParams {
+    CTCGreedyDecoderParams(
+        const Tensor& dataTensor, const Tensor& masksTensor, int64_t ctcMergedRepeat,
+        const Tensor& expectedTensor, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), masksTensor(masksTensor), ctcMergedRepeat(ctcMergedRepeat),
+        expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor masksTensor;
+    bool ctcMergedRepeat;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceCTCGreedyDecoderTest : public testing::TestWithParam<CTCGreedyDecoderParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data, params.masksTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<CTCGreedyDecoderParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_aType=" << param.masksTensor.type;
+        result << "_aShape=" << param.masksTensor.shape;
+        result << "_bDims=" << param.ctcMergedRepeat;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const CTCGreedyDecoderParams& params) {
+        std::shared_ptr<Function> function;
+        const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto indices = std::make_shared<op::v0::Parameter>(params.masksTensor.type, params.masksTensor.shape);
+        const auto decoder = std::make_shared<op::v0::CTCGreedyDecoder>(data, indices, params.ctcMergedRepeat);
+        function = std::make_shared<ov::Function>(NodeVector{decoder}, ParameterVector{data, indices});
+        return function;
+    }
+};
+
+TEST_P(ReferenceCTCGreedyDecoderTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<CTCGreedyDecoderParams> generateParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<CTCGreedyDecoderParams> params {
+        CTCGreedyDecoderParams(
+            Tensor(IN_ET, {3, 1, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f}),
+            Tensor(IN_ET, {3, 1}, std::vector<T>{1.0f, 1.0f, 1.0f}),
+            false,
+            Tensor(IN_ET, {1, 3, 1, 1}, std::vector<T>{1.0f, 0.0f, 1.0f}),
+            "ctc_greedy_decoder"),
+        CTCGreedyDecoderParams(
+            Tensor(IN_ET, {3, 2, 3}, std::vector<T>{
+                0.1f, 0.2f, 0.f, 0.15f, 0.25f, 0.f, 0.4f, 0.3f, 0.f, 0.45f, 0.35f, 0.f, 0.5f, 0.6f, 0.f, 0.55f, 0.65f, 0.f}),
+            Tensor(IN_ET, {3, 2}, std::vector<T>{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
+            false,
+            Tensor(IN_ET, {2, 3, 1, 1}, std::vector<T>{1.0f, 0.0f, 1.0f, 1.0f, 0.0f, 1.0f}),
+            "ctc_greedy_decoder_multiple_batches"),
+        CTCGreedyDecoderParams(
+            Tensor(IN_ET, {3, 1, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f}),
+            Tensor(IN_ET, {3, 1}, std::vector<T>{1.0f, 1.0f, 0.0f}),
+            false,
+            Tensor(IN_ET, {1, 3, 1, 1}, std::vector<T>{1.0f, 0.0f, -1.0f}),
+            "ctc_greedy_decoder_single_batch_short_sequence"),
+        CTCGreedyDecoderParams(
+            Tensor(IN_ET, {3, 1, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.3f, 0.4f, 0.f, 0.6f, 0.5f, 0.f}),
+            Tensor(IN_ET, {3, 1}, std::vector<T>{1.0f, 1.0f, 1.0f}),
+            true,
+            Tensor(IN_ET, {1, 3, 1, 1}, std::vector<T>{1.0f, 0.0f, -1.0f}),
+            "ctc_greedy_decoder_merge"),
+        CTCGreedyDecoderParams(
+            Tensor(IN_ET, {3, 1, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.3f, 0.4f, 0.f, 0.6f, 0.5f, 0.f}),
+            Tensor(IN_ET, {3, 1}, std::vector<T>{1.0f, 1.0f, 1.0f}),
+            false,
+            Tensor(IN_ET, {1, 3, 1, 1}, std::vector<T>{1.0f, 1.0f, 0.0f}),
+            "ctc_greedy_decoder_single_no_merge"),
+        CTCGreedyDecoderParams(
+            Tensor(IN_ET, {2, 2, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f, 0.7f, 0.8f, 0.f}),
+            Tensor(IN_ET, {2, 2}, std::vector<T>{1.0f, 1.0f, 1.0f, 0.0f}),
+            false,
+            Tensor(IN_ET, {2, 2, 1, 1}, std::vector<T>{1.0f, 1.0f, 0.0f, -1.0f}),
+            "ctc_greedy_decoder_multiple_sequences"),
+    };
+    return params;
+}
+
+std::vector<CTCGreedyDecoderParams> generateCombinedParams() {
+    const std::vector<std::vector<CTCGreedyDecoderParams>> generatedParams {
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<CTCGreedyDecoderParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_CTCGreedyDecoder_With_Hardcoded_Refs, ReferenceCTCGreedyDecoderTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceCTCGreedyDecoderTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/ctc_greedy_decoder_seq_len.cpp b/docs/template_plugin/tests/functional/op_reference/ctc_greedy_decoder_seq_len.cpp
new file mode 100644
index 00000000000000..36b37523d3a2e8
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/ctc_greedy_decoder_seq_len.cpp
@@ -0,0 +1,228 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/ctc_greedy_decoder_seq_len.hpp"
+#include "openvino/op/constant.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct CTCGreedyDecoderSeqLenParams {
+    CTCGreedyDecoderSeqLenParams(
+        const Tensor& dataTensor, const Tensor& seqLenTensor, const Tensor& blankIndexTensor, int64_t mergeRepeated,
+        const Tensor& expectedTensor, const Tensor& expectedTensor2, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), seqLenTensor(seqLenTensor), blankIndexTensor(blankIndexTensor), mergeRepeated(mergeRepeated),
+        expectedTensor(expectedTensor), expectedTensor2(expectedTensor2), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor seqLenTensor;
+    Tensor blankIndexTensor;
+    bool mergeRepeated;
+    Tensor expectedTensor;
+    Tensor expectedTensor2;
+    std::string testcaseName;
+};
+
+struct CTCGreedyDecoderSeqLenParamsNoOptionalInput {
+    CTCGreedyDecoderSeqLenParamsNoOptionalInput(
+        const Tensor& dataTensor, const Tensor& seqLenTensor, int64_t mergeRepeated,
+        const Tensor& expectedTensor, const Tensor& expectedTensor2, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), seqLenTensor(seqLenTensor), mergeRepeated(mergeRepeated),
+        expectedTensor(expectedTensor), expectedTensor2(expectedTensor2), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor seqLenTensor;
+    bool mergeRepeated;
+    Tensor expectedTensor;
+    Tensor expectedTensor2;
+    std::string testcaseName;
+};
+
+class ReferenceCTCGreedyDecoderSeqLenTest : public testing::TestWithParam<CTCGreedyDecoderSeqLenParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data, params.seqLenTensor.data};
+        refOutData = {params.expectedTensor.data, params.expectedTensor2.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<CTCGreedyDecoderSeqLenParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_sType=" << param.seqLenTensor.type;
+        result << "_sShape=" << param.seqLenTensor.shape;
+        result << "_bType=" << param.blankIndexTensor.type;
+        result << "_bShape=" << param.blankIndexTensor.shape;
+        result << "_mRepeated=" << param.mergeRepeated;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const CTCGreedyDecoderSeqLenParams& params) {
+        std::shared_ptr<Function> function;
+        const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto seq_len = std::make_shared<op::v0::Parameter>(params.seqLenTensor.type, params.seqLenTensor.shape);
+        auto blank_index = std::make_shared<op::v0::Constant>(params.blankIndexTensor.type, params.blankIndexTensor.shape,
+                                                              params.blankIndexTensor.data.data());
+        const auto decoder = std::make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, blank_index, params.mergeRepeated);
+        function = std::make_shared<ov::Function>(decoder->outputs(), ParameterVector{data, seq_len});
+        return function;
+    }
+};
+
+class ReferenceCTCGreedyDecoderSeqLenTestNoOptionalInput :
+    public testing::TestWithParam<CTCGreedyDecoderSeqLenParamsNoOptionalInput>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data, params.seqLenTensor.data};
+        refOutData = {params.expectedTensor.data, params.expectedTensor2.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<CTCGreedyDecoderSeqLenParamsNoOptionalInput>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_sType=" << param.seqLenTensor.type;
+        result << "_sShape=" << param.seqLenTensor.shape;
+        result << "_mRepeated=" << param.mergeRepeated;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const CTCGreedyDecoderSeqLenParamsNoOptionalInput& params) {
+        std::shared_ptr<Function> function;
+        const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto seq_len = std::make_shared<op::v0::Parameter>(params.seqLenTensor.type, params.seqLenTensor.shape);
+        const auto decoder = std::make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, params.mergeRepeated);
+        function = std::make_shared<ov::Function>(decoder->outputs(), ParameterVector{data, seq_len});
+        return function;
+    }
+};
+
+TEST_P(ReferenceCTCGreedyDecoderSeqLenTest, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceCTCGreedyDecoderSeqLenTestNoOptionalInput, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET>
+std::vector<CTCGreedyDecoderSeqLenParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<CTCGreedyDecoderSeqLenParams> params {
+        CTCGreedyDecoderSeqLenParams(
+            Tensor(ET, {1, 3, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f}),
+            Tensor(element::i32, {1}, std::vector<int32_t>{2}),
+            Tensor(element::i32, {}, std::vector<int32_t>{2}),
+            false,
+            Tensor(element::i32, {1, 3}, std::vector<int32_t>{1, 0, -1}),
+            Tensor(element::i32, {1}, std::vector<int32_t>{2}),
+            "evaluate_ctc_greedy_decoder_seq_len"),
+        CTCGreedyDecoderSeqLenParams(
+            Tensor(ET, {1, 3, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f}),
+            Tensor(element::i32, {1}, std::vector<int32_t>{2}),
+            Tensor(element::i32, {}, std::vector<int32_t>{2}),
+            true,
+            Tensor(element::i32, {1, 3}, std::vector<int32_t>{1, 0, -1}),
+            Tensor(element::i32, {1}, std::vector<int32_t>{2}),
+            "evaluate_ctc_greedy_decoder_seq_len_merge"),
+        CTCGreedyDecoderSeqLenParams(
+            Tensor(ET, {2, 3, 3}, std::vector<T>{0.1f,  0.2f,  0.f, 0.15f, 0.25f, 0.f, 0.4f,  0.3f,  0.f,
+                                                 0.45f, 0.35f, 0.f, 0.5f,  0.6f,  0.f, 0.55f, 0.65f, 0.f}),
+            Tensor(element::i32, {2}, std::vector<int32_t>{1, 1}),
+            Tensor(element::i32, {}, std::vector<int32_t>{2}),
+            false,
+            Tensor(element::i32, {2, 3}, std::vector<int32_t>{1, -1, -1, 0, -1, -1}),
+            Tensor(element::i32, {2}, std::vector<int32_t>{1, 1}),
+            "evaluate_ctc_greedy_decoder_seq_len_multiple_batches"),
+        CTCGreedyDecoderSeqLenParams(
+            Tensor(ET, {3, 3, 3}, std::vector<T>{0.1f,  0.2f,  0.f, 0.15f, 0.25f, 0.f, 0.4f,  0.3f,  0.f,
+                                                 0.45f, 0.35f, 0.f, 0.5f,  0.6f,  0.f, 0.55f, 0.65f, 0.f,
+                                                 0.1f,  0.2f,  0.f, 0.15f, 0.25f, 0.f, 0.4f,  0.3f,  0.f}),
+            Tensor(element::i32, {3}, std::vector<int32_t>{2, 3, 1}),
+            Tensor(element::i32, {}, std::vector<int32_t>{2}),
+            false,
+            Tensor(element::i32, {3, 3}, std::vector<int32_t>{1, 1, -1, 0, 1, 1, 1, -1, -1}),
+            Tensor(element::i32, {3}, std::vector<int32_t>{2, 3, 1}),
+            "evaluate_ctc_greedy_decoder_seq_len_multiple_batches2"),
+    };
+    return params;
+}
+
+std::vector<CTCGreedyDecoderSeqLenParams> generateCombinedParams() {
+    const std::vector<std::vector<CTCGreedyDecoderSeqLenParams>> generatedParams {
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<CTCGreedyDecoderSeqLenParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<CTCGreedyDecoderSeqLenParamsNoOptionalInput> generateParamsNoOptionalInput() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<CTCGreedyDecoderSeqLenParamsNoOptionalInput> params {
+        CTCGreedyDecoderSeqLenParamsNoOptionalInput(
+            Tensor(ET, {1, 3, 3}, std::vector<T>{0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f}),
+            Tensor(element::i32, {1}, std::vector<int32_t>{2}),
+            false,
+            Tensor(element::i32, {1, 3}, std::vector<int32_t>{1, 0, -1}),
+            Tensor(element::i32, {1}, std::vector<int32_t>{2}),
+            "evaluate_ctc_greedy_decoder_seq_len_no_optional_input"),
+    };
+    return params;
+}
+
+std::vector<CTCGreedyDecoderSeqLenParamsNoOptionalInput> generateCombinedParamsNoOptionalInput() {
+    const std::vector<std::vector<CTCGreedyDecoderSeqLenParamsNoOptionalInput>> generatedParams {
+        generateParamsNoOptionalInput<element::Type_t::bf16>(),
+        generateParamsNoOptionalInput<element::Type_t::f16>(),
+        generateParamsNoOptionalInput<element::Type_t::f32>(),
+        generateParamsNoOptionalInput<element::Type_t::f64>(),
+    };
+    std::vector<CTCGreedyDecoderSeqLenParamsNoOptionalInput> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_CTCGreedyDecoderSeqLen_With_Hardcoded_Refs, ReferenceCTCGreedyDecoderSeqLenTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceCTCGreedyDecoderSeqLenTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CTCGreedyDecoderSeqLen_With_Hardcoded_Refs, ReferenceCTCGreedyDecoderSeqLenTestNoOptionalInput,
+    testing::ValuesIn(generateCombinedParamsNoOptionalInput()), ReferenceCTCGreedyDecoderSeqLenTestNoOptionalInput::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/depth_to_space.cpp b/docs/template_plugin/tests/functional/op_reference/depth_to_space.cpp
new file mode 100644
index 00000000000000..b317ece708ffdb
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/depth_to_space.cpp
@@ -0,0 +1,157 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/opsets/opset1.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct DepthToSpaceParams {
+    DepthToSpaceParams(const Tensor& dataTensor, const std::string mode, const int32_t blockSize,
+                       const Tensor& expectedTensor, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), mode(mode), blockSize(blockSize), expectedTensor(expectedTensor),
+        testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    std::string mode;
+    int32_t blockSize;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceDepthToSpaceLayerTest : public testing::TestWithParam<DepthToSpaceParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<DepthToSpaceParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const DepthToSpaceParams& params) {
+        opset1::DepthToSpace::DepthToSpaceMode mode = params.mode == "DEPTH_FIRST" ?
+            opset1::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST : opset1::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST;
+        const auto data = std::make_shared<opset1::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto depthToSpace = std::make_shared<opset1::DepthToSpace>(data, mode, params.blockSize);
+        return std::make_shared<Function>(NodeVector {depthToSpace}, ParameterVector {data});
+    }
+};
+
+TEST_P(ReferenceDepthToSpaceLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<DepthToSpaceParams> generateDepthToSpaceParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<DepthToSpaceParams> depthToSpaceParams {
+        // depth_to_space_block_first_K1_BS2
+        DepthToSpaceParams(
+            Tensor({1, 8, 2}, IN_ET, std::vector<T>{0, 2, 8, 10, 16, 18, 24, 26, 1, 3, 9, 11, 17, 19, 25, 27}),
+            "BLOCKS_FIRST",
+            2,
+            Tensor({1, 4, 4}, IN_ET, std::vector<T>{0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27}),
+            "depth_to_space_block_first_K1_BS2"),
+
+        // depth_to_space_block_first_K2_BS2
+        DepthToSpaceParams(
+            Tensor({1, 8, 2, 2}, IN_ET, std::vector<T>{0,  2,  8,  10, 16, 18, 24, 26, 1,  3,  9,
+                                                       11, 17, 19, 25, 27, 4,  6,  12, 14, 20, 22,
+                                                       28, 30, 5,  7,  13, 15, 21, 23, 29, 31}),
+            "BLOCKS_FIRST",
+            2,
+            Tensor({1, 2, 4, 4}, IN_ET, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
+                                                       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31}),
+            "depth_to_space_block_first_K2_BS2"),
+
+        // depth_to_space_block_first_K2_BS4
+        DepthToSpaceParams(
+            Tensor({1, 16, 2, 1}, IN_ET, std::vector<T>{0,  16, 1,  17, 2,  18, 3,  19, 4,  20, 5,
+                                                        21, 6,  22, 7,  23, 8,  24, 9,  25, 10, 26,
+                                                        11, 27, 12, 28, 13, 29, 14, 30, 15, 31}),
+            "BLOCKS_FIRST",
+            4,
+            Tensor({1, 1, 8, 4}, IN_ET, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
+                                                       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31}),
+            "depth_to_space_block_first_K2_BS4"),
+
+        // depth_to_space_depth_first_1K_BS2
+        DepthToSpaceParams(
+            Tensor({1, 8, 2}, IN_ET, std::vector<T>{0, 2, 1, 3, 4, 6, 5, 7, 8, 10, 9, 11, 12, 14, 13, 15}),
+            "DEPTH_FIRST",
+            2,
+            Tensor({1, 4, 4}, IN_ET, std::vector<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}),
+            "depth_to_space_depth_first_1K_BS2"),
+
+        // depth_to_space_depth_first_2K_BS2
+        DepthToSpaceParams(
+            Tensor({1, 8, 2, 2}, IN_ET, std::vector<T>{0,  2,  8,  10, 16, 18, 24, 26, 1,  3,  9,
+                                                       11, 17, 19, 25, 27, 4,  6,  12, 14, 20, 22,
+                                                       28, 30, 5,  7,  13, 15, 21, 23, 29, 31}),
+            "DEPTH_FIRST",
+            2,
+            Tensor({1, 2, 4, 4}, IN_ET, std::vector<T>{0, 16, 2, 18, 1, 17, 3, 19, 8,  24, 10, 26, 9,  25, 11, 27,
+                                                       4, 20, 6, 22, 5, 21, 7, 23, 12, 28, 14, 30, 13, 29, 15, 31}),
+            "depth_to_space_depth_first_2K_BS2"),
+
+        // depth_to_space_depth_first_2K_BS4
+        DepthToSpaceParams(
+            Tensor({1, 16, 2, 1}, IN_ET, std::vector<T>{0,  16, 1,  17, 2,  18, 3,  19, 4,  20, 5,
+                                                        21, 6,  22, 7,  23, 8,  24, 9,  25, 10, 26,
+                                                        11, 27, 12, 28, 13, 29, 14, 30, 15, 31}),
+            "DEPTH_FIRST",
+            4,
+            Tensor({1, 1, 8, 4}, IN_ET, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
+                                                       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31}),
+            "depth_to_space_depth_first_2K_BS4"),
+    };
+    return depthToSpaceParams;
+}
+
+std::vector<DepthToSpaceParams> generateDepthToSpaceCombinedParams() {
+    const std::vector<std::vector<DepthToSpaceParams>> depthToSpaceTypeParams {
+        generateDepthToSpaceParams<element::Type_t::i8>(),
+        generateDepthToSpaceParams<element::Type_t::i16>(),
+        generateDepthToSpaceParams<element::Type_t::i32>(),
+        generateDepthToSpaceParams<element::Type_t::i64>(),
+        generateDepthToSpaceParams<element::Type_t::u8>(),
+        generateDepthToSpaceParams<element::Type_t::u16>(),
+        generateDepthToSpaceParams<element::Type_t::u32>(),
+        generateDepthToSpaceParams<element::Type_t::u64>(),
+        generateDepthToSpaceParams<element::Type_t::bf16>(),
+        generateDepthToSpaceParams<element::Type_t::f16>(),
+        generateDepthToSpaceParams<element::Type_t::f32>(),
+        generateDepthToSpaceParams<element::Type_t::f64>(),
+    };
+    std::vector<DepthToSpaceParams> combinedParams;
+
+    for (const auto& params : depthToSpaceTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_DepthToSpace_With_Hardcoded_Refs, ReferenceDepthToSpaceLayerTest,
+    testing::ValuesIn(generateDepthToSpaceCombinedParams()), ReferenceDepthToSpaceLayerTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/embedding_segments_sum.cpp b/docs/template_plugin/tests/functional/op_reference/embedding_segments_sum.cpp
new file mode 100644
index 00000000000000..5a0924b929ea48
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/embedding_segments_sum.cpp
@@ -0,0 +1,214 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <shared_test_classes/base/layer_test_utils.hpp>
+
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ngraph;
+using namespace InferenceEngine;
+
+struct EmbeddingSegmentsSumParams {
+    template <class IT>
+    EmbeddingSegmentsSumParams(const ov::PartialShape& iShape,
+                               const ov::element::Type& iType,
+                               const std::vector<IT>& iValues,
+                               const ov::PartialShape& oShape,
+                               const ov::element::Type& oType,
+                               const std::vector<IT>& oValues,
+                               const std::shared_ptr<ngraph::opset1::Constant>& indices,
+                               const std::shared_ptr<ngraph::opset1::Constant>& segment_ids,
+                               const std::shared_ptr<ngraph::opset1::Constant>& num_segments,
+                               const std::shared_ptr<ngraph::opset1::Constant>& default_index = nullptr,
+                               const std::shared_ptr<ngraph::opset1::Constant>& per_sample_weights = nullptr)
+        : _iShape(iShape),
+          _iType(iType),
+          _iData(CreateTensor(iType, iValues)),
+          _refShape(oShape),
+          _refType(oType),
+          _refData(CreateTensor(oType, oValues)) {
+        _segmentIds = segment_ids;
+        _indices = indices;
+        _numSegments = num_segments;
+        _defaultIndex = default_index;
+        _perSampleWeights = per_sample_weights;
+    }
+
+    ov::PartialShape _iShape;
+    ov::element::Type _iType;
+    ov::runtime::Tensor _iData;
+
+    ov::PartialShape _refShape;
+    ov::element::Type _refType;
+    ov::runtime::Tensor _refData;
+
+    std::shared_ptr<ngraph::opset1::Constant> _indices;
+    std::shared_ptr<ngraph::opset1::Constant> _segmentIds;
+    std::shared_ptr<ngraph::opset1::Constant> _numSegments;
+    std::shared_ptr<ngraph::opset1::Constant> _defaultIndex;      // Optional, default filled zero.
+    std::shared_ptr<ngraph::opset1::Constant> _perSampleWeights;  // Optional, default is tensor of ones.
+};
+
+class ReferenceEmbeddingSegmentsSumLayerTest : public testing::TestWithParam<EmbeddingSegmentsSumParams>,
+                                               public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params._iShape,
+                                  params._iType,
+                                  params._indices,
+                                  params._segmentIds,
+                                  params._numSegments,
+                                  params._defaultIndex,
+                                  params._perSampleWeights);
+        inputData = {params._iData};
+        refOutData = {params._refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<EmbeddingSegmentsSumParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "_iShape=" << param._iShape << "_";
+        result << "_iType=" << param._iType << "_";
+        result << "_refShape=" << param._refShape << "_";
+        result << "_refType=" << param._refType;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(
+        const PartialShape& input_shape,
+        const element::Type& input_type,
+        const std::shared_ptr<ngraph::opset1::Constant> indices,
+        const std::shared_ptr<ngraph::opset1::Constant> segment_ids,
+        const std::shared_ptr<ngraph::opset1::Constant> num_segments,
+        const std::shared_ptr<ngraph::opset1::Constant> default_index,
+        const std::shared_ptr<ngraph::opset1::Constant> per_sample_weights) {
+        const auto in = std::make_shared<op::Parameter>(input_type, input_shape);
+
+        if (default_index) {
+            if (per_sample_weights) {
+                const auto ess = std::make_shared<op::v3::EmbeddingSegmentsSum>(in,
+                                                                                indices,
+                                                                                segment_ids,
+                                                                                num_segments,
+                                                                                default_index,
+                                                                                per_sample_weights);
+                return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+            } else {
+                const auto ess = std::make_shared<op::v3::EmbeddingSegmentsSum>(in,
+                                                                                indices,
+                                                                                segment_ids,
+                                                                                num_segments,
+                                                                                default_index);
+                return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+            }
+        } else {
+            const auto ess = std::make_shared<op::v3::EmbeddingSegmentsSum>(in, indices, segment_ids, num_segments);
+            return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+        }
+    }
+};
+
+TEST_P(ReferenceEmbeddingSegmentsSumLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+INSTANTIATE_TEST_SUITE_P(
+    smoke_EmbeddingSegmentsSum_With_Hardcoded_Refs,
+    ReferenceEmbeddingSegmentsSumLayerTest,
+    ::testing::Values(
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::f32,
+            std::vector<float>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+            ov::PartialShape{3, 2},
+            ov::element::f32,
+            {-1.05f, -1.2f, -0.2f, -0.6f, -0.1f, 0.4f},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{3}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0}),
+            std::make_shared<ngraph::opset1::Constant>(element::f32,
+                                                       ov::Shape({4}),
+                                                       std::vector<float>{0.5, 0.5, 0.5, 0.5})),
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::f64,
+            std::vector<double>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+            ov::PartialShape{3, 2},
+            ov::element::f64,
+            std::vector<double>{-2.1, -2.4, -0.2, -0.6, -0.2, 0.8},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{3}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0})),
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::i32,
+            std::vector<int32_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::i32,
+            std::vector<int32_t>{-6, -4, 0, 0, 2, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{3})),
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::u32,
+            std::vector<uint32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::u32,
+            std::vector<uint32_t>{6, 8, 3, 4, 16, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{3}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{1})),
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::f16,
+            std::vector<float16>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+            ov::PartialShape{3, 2},
+            ov::element::f16,
+            std::vector<float16>{-2.1, -2.4, 0, 0, -0.2, 0.8},
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape(), std::vector<int64_t>{3})),
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::i64,
+            std::vector<int64_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::i64,
+            std::vector<int64_t>{-6, -4, -1, 2, 2, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape(), std::vector<int64_t>{3}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape(), std::vector<int64_t>{0})),
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::i8,
+            std::vector<int8_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::i8,
+            std::vector<int8_t>{-12, -8, -1, 2, 4, 36},
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape(), std::vector<int64_t>{3}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape(), std::vector<int64_t>{0}),
+            std::make_shared<ngraph::opset1::Constant>(element::i8, ov::Shape({4}), std::vector<int8_t>{2, 2, 2, 2})),
+        EmbeddingSegmentsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::u8,
+            std::vector<uint8_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::u8,
+            std::vector<uint8_t>{6, 8, 1, 2, 16, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{3}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0}))),
+    ReferenceEmbeddingSegmentsSumLayerTest::getTestCaseName);
diff --git a/docs/template_plugin/tests/functional/op_reference/embeddingbag_offsetssum.cpp b/docs/template_plugin/tests/functional/op_reference/embeddingbag_offsetssum.cpp
new file mode 100644
index 00000000000000..4c137d5bf643a2
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/embeddingbag_offsetssum.cpp
@@ -0,0 +1,195 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <shared_test_classes/base/layer_test_utils.hpp>
+
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ngraph;
+using namespace InferenceEngine;
+
+struct EmbeddingBagOffsetsSumParams {
+    template <class IT>
+    EmbeddingBagOffsetsSumParams(const ov::PartialShape& iShape,
+                                 const ov::element::Type& iType,
+                                 const std::vector<IT>& iValues,
+                                 const ov::PartialShape& oShape,
+                                 const ov::element::Type& oType,
+                                 const std::vector<IT>& oValues,
+                                 const std::shared_ptr<ngraph::opset1::Constant>& indices,
+                                 const std::shared_ptr<ngraph::opset1::Constant>& offsets,
+                                 const std::shared_ptr<ngraph::opset1::Constant>& default_index = nullptr,
+                                 const std::shared_ptr<ngraph::opset1::Constant>& per_sample_weights = nullptr)
+        : _iShape(iShape),
+          _iType(iType),
+          _iData(CreateTensor(iType, iValues)),
+          _refShape(oShape),
+          _refType(oType),
+          _refData(CreateTensor(oType, oValues)) {
+        _indices = indices;
+        _offsets = offsets;
+        _defaultIndex = default_index;
+        _perSampleWeights = per_sample_weights;
+    }
+    ov::PartialShape _iShape;
+    ov::element::Type _iType;
+    ov::runtime::Tensor _iData;
+
+    ov::PartialShape _refShape;
+    ov::element::Type _refType;
+    ov::runtime::Tensor _refData;
+
+    std::shared_ptr<ngraph::opset1::Constant> _indices;
+    std::shared_ptr<ngraph::opset1::Constant> _offsets;
+    std::shared_ptr<ngraph::opset1::Constant> _defaultIndex;      // Optional, default filled zero.
+    std::shared_ptr<ngraph::opset1::Constant> _perSampleWeights;  // Optional, default is tensor of ones.
+};
+
+class ReferenceEmbeddingBagOffsetsSumLayerTest : public testing::TestWithParam<EmbeddingBagOffsetsSumParams>,
+                                                 public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params._iShape,
+                                  params._iType,
+                                  params._indices,
+                                  params._offsets,
+                                  params._defaultIndex,
+                                  params._perSampleWeights);
+        inputData = {params._iData};
+        refOutData = {params._refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<EmbeddingBagOffsetsSumParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "_iShape=" << param._iShape << "_";
+        result << "_iType=" << param._iType << "_";
+        result << "_refShape=" << param._refShape << "_";
+        result << "_refType=" << param._refType;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(
+        const PartialShape& input_shape,
+        const element::Type& input_type,
+        const std::shared_ptr<ngraph::opset1::Constant> indices,
+        const std::shared_ptr<ngraph::opset1::Constant> offsets,
+        const std::shared_ptr<ngraph::opset1::Constant> default_index,
+        const std::shared_ptr<ngraph::opset1::Constant> per_sample_weights) {
+        const auto in = std::make_shared<op::Parameter>(input_type, input_shape);
+
+        if (default_index) {
+            if (per_sample_weights) {
+                const auto ess = std::make_shared<op::v3::EmbeddingBagOffsetsSum>(in,
+                                                                                  indices,
+                                                                                  offsets,
+                                                                                  default_index,
+                                                                                  per_sample_weights);
+                return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+            } else {
+                const auto ess = std::make_shared<op::v3::EmbeddingBagOffsetsSum>(in, indices, offsets, default_index);
+                return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+            }
+        } else {
+            const auto ess = std::make_shared<op::v3::EmbeddingBagOffsetsSum>(in, indices, offsets);
+            return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+        }
+    }
+};
+
+TEST_P(ReferenceEmbeddingBagOffsetsSumLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+INSTANTIATE_TEST_SUITE_P(
+    smoke_EmbeddingBagOffsetsSum_With_Hardcoded_Refs,
+    ReferenceEmbeddingBagOffsetsSumLayerTest,
+    ::testing::Values(
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::f32,
+            std::vector<float>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+            ov::PartialShape{3, 2},
+            ov::element::f32,
+            {-1.05f, -1.2f, -0.2f, -0.6f, -0.1f, 0.4f},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0}),
+            std::make_shared<ngraph::opset1::Constant>(element::f32,
+                                                       ov::Shape({4}),
+                                                       std::vector<float>{0.5, 0.5, 0.5, 0.5})),
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::f64,
+            std::vector<double>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+            ov::PartialShape{3, 2},
+            ov::element::f64,
+            std::vector<double>{-2.1, -2.4, -0.2, -0.6, -0.2, 0.8},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0})),
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::i32,
+            std::vector<int32_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::i32,
+            std::vector<int32_t>{-6, -4, 0, 0, 2, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2})),
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::u32,
+            std::vector<uint32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::u32,
+            std::vector<uint32_t>{6, 8, 3, 4, 16, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{1})),
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::f16,
+            std::vector<float16>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+            ov::PartialShape{3, 2},
+            ov::element::f16,
+            std::vector<float16>{-2.1, -2.4, 0, 0, -0.2, 0.8},
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({3}), std::vector<int64_t>{0, 2, 2})),
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::i64,
+            std::vector<int64_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::i64,
+            std::vector<int64_t>{-6, -4, -1, 2, 2, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({3}), std::vector<int64_t>{0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape(), std::vector<int64_t>{0})),
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::i8,
+            std::vector<int8_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::i8,
+            std::vector<int8_t>{-12, -8, -1, 2, 4, 36},
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape({3}), std::vector<int64_t>{0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i64, ov::Shape(), std::vector<int64_t>{0}),
+            std::make_shared<ngraph::opset1::Constant>(element::i8, ov::Shape({4}), std::vector<int8_t>{2, 2, 2, 2})),
+        EmbeddingBagOffsetsSumParams(
+            ov::PartialShape{5, 2},
+            ov::element::u8,
+            std::vector<uint8_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+            ov::PartialShape{3, 2},
+            ov::element::u8,
+            std::vector<uint8_t>{6, 8, 1, 2, 16, 18},
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
+            std::make_shared<ngraph::opset1::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0}))),
+    ReferenceEmbeddingBagOffsetsSumLayerTest::getTestCaseName);
diff --git a/docs/template_plugin/tests/functional/op_reference/embeddingbag_packedsum.cpp b/docs/template_plugin/tests/functional/op_reference/embeddingbag_packedsum.cpp
new file mode 100644
index 00000000000000..0ae1bce6d9f9d3
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/embeddingbag_packedsum.cpp
@@ -0,0 +1,170 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <shared_test_classes/base/layer_test_utils.hpp>
+
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ngraph;
+using namespace InferenceEngine;
+
+struct EmbeddingBagPackedSumParams {
+    template <class IT>
+    EmbeddingBagPackedSumParams(const ov::PartialShape& iShape,
+                                const ov::element::Type& iType,
+                                const std::vector<IT>& iValues,
+                                const ov::PartialShape& oShape,
+                                const ov::element::Type& oType,
+                                const std::vector<IT>& oValues,
+                                const std::shared_ptr<ngraph::opset1::Constant>& indices,
+                                const std::shared_ptr<ngraph::opset1::Constant>& per_sample_weights = nullptr)
+        : _iShape(iShape),
+          _iType(iType),
+          _iData(CreateTensor(iType, iValues)),
+          _refShape(oShape),
+          _refType(oType),
+          _refData(CreateTensor(oType, oValues)) {
+        _indices = indices;
+        _perSampleWeights = per_sample_weights;
+    }
+    ov::PartialShape _iShape;
+    ov::element::Type _iType;
+    ov::runtime::Tensor _iData;
+
+    ov::PartialShape _refShape;
+    ov::element::Type _refType;
+    ov::runtime::Tensor _refData;
+
+    std::shared_ptr<ngraph::opset1::Constant> _indices;
+    std::shared_ptr<ngraph::opset1::Constant> _perSampleWeights;  // Optional, default is tensor of ones.
+};
+
+class ReferenceEmbeddingBagPackedSumLayerTest : public testing::TestWithParam<EmbeddingBagPackedSumParams>,
+                                                public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params._iShape, params._iType, params._indices, params._perSampleWeights);
+        inputData = {params._iData};
+        refOutData = {params._refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<EmbeddingBagPackedSumParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "_iShape=" << param._iShape << "_";
+        result << "_iType=" << param._iType << "_";
+        result << "_refShape=" << param._refShape << "_";
+        result << "_refType=" << param._refType;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(
+        const PartialShape& input_shape,
+        const element::Type& input_type,
+        const std::shared_ptr<ngraph::opset1::Constant> indices,
+        const std::shared_ptr<ngraph::opset1::Constant> per_sample_weights) {
+        const auto in = std::make_shared<op::Parameter>(input_type, input_shape);
+
+        if (per_sample_weights) {
+            const auto ess = std::make_shared<op::v3::EmbeddingBagPackedSum>(in, indices, per_sample_weights);
+            return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+        } else {
+            const auto ess = std::make_shared<op::v3::EmbeddingBagPackedSum>(in, indices);
+            return std::make_shared<Function>(NodeVector{ess}, ParameterVector{in});
+        }
+    }
+};
+
+TEST_P(ReferenceEmbeddingBagPackedSumLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <class T>
+inline std::shared_ptr<ngraph::opset1::Constant> CreateConstant(const std::vector<std::vector<T>>& val,
+                                                                  const ov::element::Type& element_type) {
+    if (val.size() > 0) {
+        ov::Shape i_shape({val.size(), val[0].size()});
+
+        size_t i_size = ov::shape_size(i_shape);
+        std::vector<T> i_values(i_size);
+
+        for (size_t i = 0; i < i_shape[0]; i++) {
+            for (size_t j = 0; j < i_shape[1]; j++) {
+                i_values[i * i_shape[1] + j] = val[i][j];
+            }
+        }
+
+        return std::make_shared<ngraph::opset1::Constant>(element_type, i_shape, i_values);
+    } else {
+        return std::make_shared<ngraph::opset1::Constant>(element_type, ov::Shape(), std::vector<T>());
+    }
+}
+
+INSTANTIATE_TEST_SUITE_P(
+    smoke_EmbeddingBagPackedSum_With_Hardcoded_Refs,
+    ReferenceEmbeddingBagPackedSumLayerTest,
+    ::testing::Values(
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::f32,
+                                    std::vector<float>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::f32,
+                                    std::vector<float>{-1.05f, -1.2f, -1.f, -1.1f, -0.1f, 0.4f},
+                                    CreateConstant<int32_t>({{0, 2}, {1, 2}, {3, 4}}, element::i32),
+                                    CreateConstant<float>({{0.5, 0.5}, {0.5, 0.5}, {0.5, 0.5}}, element::f32)),
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::f64,
+                                    std::vector<double>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::f64,
+                                    std::vector<double>{-2.1, -2.4, -2.0, -2.2, -0.2, 0.8},
+                                    CreateConstant<int32_t>({{0, 2}, {1, 2}, {3, 4}}, element::i32)),
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::i32,
+                                    std::vector<int32_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::i32,
+                                    std::vector<int32_t>{-6, -4, -2, -2, 2, 18},
+                                    CreateConstant<int32_t>({{0, 2}, {1, 2}, {3, 4}}, element::i32)),
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::u32,
+                                    std::vector<uint32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::u32,
+                                    std::vector<uint32_t>{6, 8, 8, 10, 16, 18},
+                                    CreateConstant<int32_t>({{0, 2}, {1, 2}, {3, 4}}, element::i32)),
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::f16,
+                                    std::vector<float16>{-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::f16,
+                                    std::vector<float16>{-2.1, -2.4, -2.0, -2.2, -0.2, 0.8},
+                                    CreateConstant<int64_t>({{0, 2}, {1, 2}, {3, 4}}, element::i64)),
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::i64,
+                                    std::vector<int64_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::i64,
+                                    std::vector<int64_t>{-6, -4, -2, -2, 2, 18},
+                                    CreateConstant<int64_t>({{0, 2}, {1, 2}, {3, 4}}, element::i64)),
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::i8,
+                                    std::vector<int8_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::i8,
+                                    std::vector<int8_t>{-12, -8, -4, -4, 4, 36},
+                                    CreateConstant<int64_t>({{0, 2}, {1, 2}, {3, 4}}, element::i64),
+                                    CreateConstant<int8_t>({{2, 2}, {2, 2}, {2, 2}}, element::i8)),
+        EmbeddingBagPackedSumParams(ov::PartialShape{5, 2},
+                                    ov::element::u8,
+                                    std::vector<uint8_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10},
+                                    ov::PartialShape{3, 2},
+                                    ov::element::u8,
+                                    std::vector<uint8_t>{6, 8, 8, 10, 16, 18},
+                                    CreateConstant<int64_t>({{0, 2}, {1, 2}, {3, 4}}, element::i64))),
+    ReferenceEmbeddingBagPackedSumLayerTest::getTestCaseName);
diff --git a/docs/template_plugin/tests/functional/op_reference/experimental_detectron_topkrois.cpp b/docs/template_plugin/tests/functional/op_reference/experimental_detectron_topkrois.cpp
new file mode 100644
index 00000000000000..3fe9dce3ebd209
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/experimental_detectron_topkrois.cpp
@@ -0,0 +1,108 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/experimental_detectron_topkrois.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct ExperimentalDetectronTopKROIsParams {
+    ExperimentalDetectronTopKROIsParams(
+        const Tensor& dataTensor, const Tensor& probsTensor, const int32_t numRois,
+        const Tensor& expectedTensor, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), probsTensor(probsTensor), numRois(numRois),
+        expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor probsTensor;
+    int32_t numRois;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceExperimentalDetectronTopKROIsTest : public testing::TestWithParam<ExperimentalDetectronTopKROIsParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data, params.probsTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<ExperimentalDetectronTopKROIsParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_pType=" << param.probsTensor.type;
+        result << "_pShape=" << param.probsTensor.shape;
+        result << "_numRois=" << param.numRois;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const ExperimentalDetectronTopKROIsParams& params) {
+        std::shared_ptr<Function> function;
+        const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto probs = std::make_shared<op::v0::Parameter>(params.probsTensor.type, params.probsTensor.shape);
+        const auto topkRois = std::make_shared<op::v6::ExperimentalDetectronTopKROIs>(data, probs, params.numRois);
+        function = std::make_shared<ov::Function>(topkRois, ParameterVector{data, probs});
+        return function;
+    }
+};
+
+TEST_P(ReferenceExperimentalDetectronTopKROIsTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET>
+std::vector<ExperimentalDetectronTopKROIsParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<ExperimentalDetectronTopKROIsParams> params {
+        ExperimentalDetectronTopKROIsParams(
+            Tensor(ET, {2, 4}, std::vector<T>{1.0f, 1.0f, 3.0f, 4.0f, 2.0f, 1.0f, 5.0f, 7.0f}),
+            Tensor(ET, {2}, std::vector<T>{0.5f, 0.3f}),
+            1,
+            Tensor(ET, {1, 4}, std::vector<T>{1.0, 1.0, 3.0, 4.0}),
+            "experimental_detectron_topk_rois_eval"),
+        ExperimentalDetectronTopKROIsParams(
+            Tensor(ET, {4, 4}, std::vector<T>{1.0f,  1.0f,  4.0f,  5.0f,  3.0f,  2.0f,  7.0f,  9.0f,
+                                              10.0f, 15.0f, 13.0f, 17.0f, 13.0f, 10.0f, 18.0f, 15.0f}),
+            Tensor(ET, {4}, std::vector<T>{0.1f, 0.7f, 0.5f, 0.9f}),
+            2,
+            Tensor(ET, {2, 4}, std::vector<T>{13.0f, 10.0f, 18.0f, 15.0f, 3.0f, 2.0f, 7.0f, 9.0f}),
+            "experimental_detectron_topk_rois_eval"),
+    };
+    return params;
+}
+
+std::vector<ExperimentalDetectronTopKROIsParams> generateCombinedParams() {
+    const std::vector<std::vector<ExperimentalDetectronTopKROIsParams>> generatedParams {
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<ExperimentalDetectronTopKROIsParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronTopKROIs_With_Hardcoded_Refs, ReferenceExperimentalDetectronTopKROIsTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceExperimentalDetectronTopKROIsTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/gather.cpp b/docs/template_plugin/tests/functional/op_reference/gather.cpp
new file mode 100644
index 00000000000000..a76021ab692964
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/gather.cpp
@@ -0,0 +1,880 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/gather.hpp"
+#include "openvino/op/constant.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct GatherParams {
+    GatherParams(
+        const Tensor& dataTensor, const Tensor& indicesTensor, const Tensor& axisTensor,
+        const Tensor& expectedTensor, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), indicesTensor(indicesTensor), axisTensor(axisTensor),
+        expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor indicesTensor;
+    Tensor axisTensor;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceGatherTest : public testing::TestWithParam<GatherParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data, params.indicesTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<GatherParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_iType=" << param.indicesTensor.type;
+        result << "_iShape=" << param.indicesTensor.shape;
+        result << "_aType=" << param.axisTensor.type;
+        result << "_aShape=" << param.axisTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const GatherParams& params) {
+        const auto P = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto I = std::make_shared<op::v0::Parameter>(params.indicesTensor.type, params.indicesTensor.shape);
+        const auto A = op::v0::Constant::create(params.axisTensor.type, params.axisTensor.shape,
+                                                params.axisTensor.data.data());
+        const auto G = std::make_shared<op::v1::Gather>(P, I, A);
+        const auto f = std::make_shared<Function>(G, ParameterVector{P, I});
+        return f;
+    }
+};
+
+TEST_P(ReferenceGatherTest, CompareWithRefs) {
+    Exec();
+}
+
+struct GatherParamsV7 {
+    GatherParamsV7(
+        const Tensor& dataTensor, const Tensor& indicesTensor, const Tensor& axisTensor, int64_t batchDims,
+        const Tensor& expectedTensor, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), indicesTensor(indicesTensor), axisTensor(axisTensor), batchDims(batchDims),
+        expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor indicesTensor;
+    Tensor axisTensor;
+    int64_t batchDims;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceGatherTestV7 : public testing::TestWithParam<GatherParamsV7>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data, params.indicesTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<GatherParamsV7>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_iType=" << param.indicesTensor.type;
+        result << "_iShape=" << param.indicesTensor.shape;
+        result << "_aType=" << param.axisTensor.type;
+        result << "_aShape=" << param.axisTensor.shape;
+        result << "_batchDims=" << param.batchDims;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const GatherParamsV7& params) {
+        const auto P = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto I = std::make_shared<op::v0::Parameter>(params.indicesTensor.type, params.indicesTensor.shape);
+        const auto A = op::v0::Constant::create(params.axisTensor.type, params.axisTensor.shape,
+                                                params.axisTensor.data.data());
+        const auto G = std::make_shared<op::v7::Gather>(P, I, A, params.batchDims);
+        const auto f = std::make_shared<Function>(G, ParameterVector{P, I});
+        return f;
+    }
+};
+
+TEST_P(ReferenceGatherTestV7, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET, element::Type_t ET_I, element::Type_t ET_A>
+std::vector<GatherParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_I = typename element_type_traits<ET_I>::value_type;
+    using T_A = typename element_type_traits<ET_A>::value_type;
+    std::vector<GatherParams> params {
+        GatherParams(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                10, 11, 20, 21, 30, 31}),
+            Tensor(ET_I, {2, 2, 3, 4}, std::vector<T_I>{
+                0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2,
+                0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2, 2, 3, 4, 2}, std::vector<T>{
+                10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
+                10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
+                10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
+                10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31}),
+            "gather_4d_indices_axis_0"),
+        GatherParams(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                10, 11, 20, 21, 30, 31}),
+            Tensor(ET_I, {2, 2}, std::vector<T_I>{
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2, 2, 2}, std::vector<T>{
+                10, 11, 20, 21, 20, 21, 30, 31}),
+            "gather_axis_0"),
+    };
+    return params;
+}
+
+template <element::Type_t ET, element::Type_t ET_I, element::Type_t ET_A>
+std::vector<GatherParams> generateParamsFloatValue() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_I = typename element_type_traits<ET_I>::value_type;
+    using T_A = typename element_type_traits<ET_A>::value_type;
+    std::vector<GatherParams> params {
+        GatherParams(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 2, 3, 4}, std::vector<T_I>{
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2, 2, 3, 4, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_4d_indices_axis_0_2d_input"),
+        GatherParams(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 3, 4}, std::vector<T_I>{
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2, 3, 4, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_3d_indices_axis_0_2d_input"),
+        GatherParams(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 2}, std::vector<T_I>{
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2, 2, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_2d_indices_axis_0_2d_input"),
+        GatherParams(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 2}, std::vector<T_I>{
+                0, -2, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2, 2, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_2d_negative_and_positive_indices_axis_0_2d_input"),
+        GatherParams(
+            Tensor(ET, {3}, std::vector<T>{
+                1.0f, 2.0f, 3.0f}),
+            Tensor(ET_I, {2}, std::vector<T_I>{
+                1, 0}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2}, std::vector<T>{
+                2.0f, 1.0f}),
+            "gather_1d_indices_axis_0_1d_input"),
+        GatherParams(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f, 2.0f, 2.1f, 3.0f, 3.1f}),
+            Tensor(ET_I, {}, std::vector<T_I>{1}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            Tensor(ET, {2}, std::vector<T>{
+                2.0f, 2.1f}),
+            "gather_scalar_indices_axis_0_2d_input"),
+        GatherParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1.0f, 1.1f, 1.2f,
+                2.0f, 2.1f, 2.2f,
+                3.0f, 3.1f, 3.2f}),
+            Tensor(ET_I, {1, 2}, std::vector<T_I>{
+                0, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            Tensor(ET, {3, 1, 2}, std::vector<T>{
+                1.0f, 1.2f,
+                2.0f, 2.2f,
+                3.0f, 3.2f}),
+            "gather_2d_indices_axis_1_2d_input"),
+        GatherParams(
+            Tensor(ET, {2, 2, 3, 3}, std::vector<T>{
+                1.0f,   1.1f,   1.2f,
+                2.0f,   2.1f,   2.2f,
+                3.0f,   3.1f,   3.2f,
+
+                11.0f,  11.1f,  11.2f,
+                12.0f,  12.1f,  12.2f,
+                13.0f,  13.1f,  13.2f,
+
+
+                101.0f, 101.1f, 101.2f,
+                102.0f, 102.1f, 102.2f,
+                103.0f, 103.1f, 103.2f,
+
+                111.0f, 111.1f, 111.2f,
+                112.0f, 112.1f, 112.2f,
+                113.0f, 113.1f, 113.2f}),
+            Tensor(ET_I, {2}, std::vector<T_I>{
+                0, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{2}),
+            Tensor(ET, {2, 2, 2, 3}, std::vector<T>{
+                1.0f,   1.1f,   1.2f,
+                3.0f,   3.1f,   3.2f,
+
+                11.0f,  11.1f,  11.2f,
+                13.0f,  13.1f,  13.2f,
+
+
+                101.0f, 101.1f, 101.2f,
+                103.0f, 103.1f, 103.2f,
+
+                111.0f, 111.1f, 111.2f,
+                113.0f, 113.1f, 113.2f}),
+            "gather_1d_indices_axis_2_4d_input"),
+        GatherParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f, 3.0f, 3.1f, 3.2f}),
+            Tensor(ET_I, {}, std::vector<T_I>{0}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            Tensor(ET, {3}, std::vector<T>{
+                1.0f, 2.0f, 3.0f}),
+            "gather_scalar_indices_axis_1_2d_input"),
+    };
+    return params;
+}
+
+std::vector<GatherParams> generateCombinedParams() {
+    const std::vector<std::vector<GatherParams>> generatedParams {
+        generateParams<element::Type_t::i8, element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::i16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::i32, element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::i64, element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::u8, element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::u16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::u32, element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::u64, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::bf16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::f16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::f32, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::f64, element::Type_t::i32, element::Type_t::i64>(),
+    };
+    std::vector<GatherParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_Gather_With_Hardcoded_Refs, ReferenceGatherTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceGatherTest::getTestCaseName);
+
+template <element::Type_t ET, element::Type_t ET_I, element::Type_t ET_A>
+std::vector<GatherParamsV7> generateParamsV7() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_I = typename element_type_traits<ET_I>::value_type;
+    using T_A = typename element_type_traits<ET_A>::value_type;
+    std::vector<GatherParamsV7> params {
+        GatherParamsV7(
+            Tensor(ET, {3}, std::vector<T>{
+                1, 2, 3}),
+            Tensor(ET_I, {2}, std::vector<T_I>{
+                2, 0}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2}, std::vector<T>{
+                3, 1}),
+            "gather_v7_1d"),
+        GatherParamsV7(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                10, 11, 20, 21, 30, 31}),
+            Tensor(ET_I, {2, 2}, std::vector<T_I>{
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2, 2, 2}, std::vector<T>{
+                10, 11, 20, 21, 20, 21, 30, 31}),
+            "gather_v7_axis_0"),
+        GatherParamsV7(
+            Tensor(ET, {2, 3}, std::vector<T>{
+                1, 2, 3,
+                4, 5, 6}),
+            Tensor(ET_I, {2, 2, 2}, std::vector<T_I>{
+                0, 1,
+                1, 2,
+
+                2, 0,
+                1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            1,
+            Tensor(ET, {2, 2, 2}, std::vector<T>{
+                1, 2,
+                2, 3,
+
+                6, 4,
+                5, 6}),
+            "gather_v7_data_int32_3d_indices_axis_1_batch_dims_1"),
+        GatherParamsV7(
+            Tensor(ET, {2, 5}, std::vector<T>{
+                1, 2, 3, 4, 5,
+                6, 7, 8, 9, 10}),
+            Tensor(ET_I, {2, 3}, std::vector<T_I>{
+                0, 0, 4,
+                4, 0, 0}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            1,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                1,  1, 5,
+                10, 6, 6}),
+            "gather_v7_data_int32_2d_indices_axis_1_batch_dims_1"),
+        GatherParamsV7(
+            Tensor(ET, {2, 5}, std::vector<T>{
+                1, 2, 3, 4, 5,
+                6, 7, 8, 9, 10}),
+            Tensor(ET_I, {2, 3}, std::vector<T_I>{
+                0, 0, 4,
+                4, 0, 0}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            -1,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                1,  1, 5,
+                10, 6, 6}),
+            "gather_v7_data_int32_2d_indices_axis_1_negative_batch_dims"),
+        GatherParamsV7(
+            Tensor(ET, {2, 1, 5, 4}, std::vector<T>{
+                1,  2,  3,  4,
+                5,  6,  7,  8,
+                9,  10, 11, 12,
+                13, 14, 15, 16,
+                17, 18, 19, 20,
+
+                21, 22, 23, 24,
+                25, 26, 27, 28,
+                29, 30, 31, 32,
+                33, 34, 35, 36,
+                37, 38, 39, 40}),
+            Tensor(ET_I, {2, 3}, std::vector<T_I>{
+                1, 2, 4,
+                4, 3, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{2}),
+            1,
+            Tensor(ET, {2, 1, 3, 4}, std::vector<T>{
+                5,  6,  7,  8,
+                9,  10, 11, 12,
+                17, 18, 19, 20,
+
+                37, 38, 39, 40,
+                33, 34, 35, 36,
+                29, 30, 31, 32}),
+            "gather_v7_4d_data_axis_2_batch_dims_1_int32"),
+    };
+    return params;
+}
+
+template <>
+std::vector<GatherParamsV7> generateParamsV7<element::boolean, element::i32, element::i64>() {
+    std::vector<GatherParamsV7> params {
+        GatherParamsV7(
+            Tensor(element::boolean, {3, 2}, std::vector<char>{
+                1, 1, 1, 0, 0, 1}),
+            Tensor(element::i32, {2, 2}, std::vector<int32_t>{
+                0, 1, 1, 2}),
+            Tensor(element::i64, {}, std::vector<int64_t>{0}),
+            0,
+            Tensor(element::boolean, {2, 2, 2}, std::vector<char>{
+                1, 1, 1, 0, 1, 0, 0, 1}),
+            "gather_v7_axis_0_bool"),
+    };
+    return params;
+}
+
+template <element::Type_t ET, element::Type_t ET_I, element::Type_t ET_A>
+std::vector<GatherParamsV7> generateParamsFloatValueV7() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_I = typename element_type_traits<ET_I>::value_type;
+    using T_A = typename element_type_traits<ET_A>::value_type;
+    std::vector<GatherParamsV7> params {
+        GatherParamsV7(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 2, 3, 4}, std::vector<T_I>{
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2, 2, 3, 4, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_v7_4d_indices_axis_0_2d_input"),
+        GatherParamsV7(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 3, 4}, std::vector<T_I>{
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+
+                0, 1, 1, 2,
+                0, 1, 1, 2,
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2, 3, 4, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f,
+
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_v7_3d_indices_axis_0_2d_input"),
+        GatherParamsV7(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 2}, std::vector<T_I>{
+                0, 1, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2, 2, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_v7_2d_indices_axis_0_2d_input"),
+        GatherParamsV7(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            Tensor(ET_I, {2, 2}, std::vector<T_I>{
+                0, -2, 1, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2, 2, 2}, std::vector<T>{
+                1.0f, 1.1f,
+                2.0f, 2.1f,
+
+                2.0f, 2.1f,
+                3.0f, 3.1f}),
+            "gather_v7_2d_negative_and_positive_indices_axis_0_2d_input"),
+        GatherParamsV7(
+            Tensor(ET, {3}, std::vector<T>{
+                1.0f, 2.0f, 3.0f}),
+            Tensor(ET_I, {2}, std::vector<T_I>{
+                1, 0}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2}, std::vector<T>{
+                2.0f, 1.0f}),
+            "gather_v7_1d_indices_axis_0_1d_input"),
+        GatherParamsV7(
+            Tensor(ET, {3, 2}, std::vector<T>{
+                1.0f, 1.1f, 2.0f, 2.1f, 3.0f, 3.1f}),
+            Tensor(ET_I, {}, std::vector<T_I>{1}),
+            Tensor(ET_A, {}, std::vector<T_A>{0}),
+            0,
+            Tensor(ET, {2}, std::vector<T>{
+                2.0f, 2.1f}),
+            "gather_v7_scalar_indices_axis_0_2d_input"),
+        GatherParamsV7(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1.0f, 1.1f, 1.2f,
+                2.0f, 2.1f, 2.2f,
+                3.0f, 3.1f, 3.2f}),
+            Tensor(ET_I, {1, 2}, std::vector<T_I>{
+                0, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            0,
+            Tensor(ET, {3, 1, 2}, std::vector<T>{
+                1.0f, 1.2f,
+                2.0f, 2.2f,
+                3.0f, 3.2f}),
+            "gather_v7_2d_indices_axis_1_2d_input"),
+        GatherParamsV7(
+            Tensor(ET, {2, 2, 3, 3}, std::vector<T>{
+                1.0f,   1.1f,   1.2f,
+                2.0f,   2.1f,   2.2f,
+                3.0f,   3.1f,   3.2f,
+
+                11.0f,  11.1f,  11.2f,
+                12.0f,  12.1f,  12.2f,
+                13.0f,  13.1f,  13.2f,
+
+
+                101.0f, 101.1f, 101.2f,
+                102.0f, 102.1f, 102.2f,
+                103.0f, 103.1f, 103.2f,
+
+                111.0f, 111.1f, 111.2f,
+                112.0f, 112.1f, 112.2f,
+                113.0f, 113.1f, 113.2f}),
+            Tensor(ET_I, {2}, std::vector<T_I>{
+                0, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{2}),
+            0,
+            Tensor(ET, {2, 2, 2, 3}, std::vector<T>{
+                1.0f,   1.1f,   1.2f,
+                3.0f,   3.1f,   3.2f,
+
+                11.0f,  11.1f,  11.2f,
+                13.0f,  13.1f,  13.2f,
+
+
+                101.0f, 101.1f, 101.2f,
+                103.0f, 103.1f, 103.2f,
+
+                111.0f, 111.1f, 111.2f,
+                113.0f, 113.1f, 113.2f}),
+            "gather_v7_1d_indices_axis_2_4d_input"),
+        GatherParamsV7(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f, 3.0f, 3.1f, 3.2f}),
+            Tensor(ET_I, {}, std::vector<T_I>{0}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            0,
+            Tensor(ET, {3}, std::vector<T>{
+                1.0f, 2.0f, 3.0f}),
+            "gather_v7_scalar_indices_axis_1_2d_input"),
+        GatherParamsV7(
+            Tensor(ET, {2, 5, 2}, std::vector<T>{
+                1.0f, 2.0f,
+                3.0f, 4.0f,
+                5.0f, 6.0f,
+                7.0f, 8.0f,
+                9.0f, 10.0f,
+
+                11.0f, 12.0f,
+                13.0f, 14.0f,
+                15.0f, 16.0f,
+                17.0f, 18.0f,
+                19.0f, 20.0f}),
+            Tensor(ET_I, {2, 2, 3}, std::vector<T_I>{
+                0, 0, 4,
+                4, 0, 0,
+
+                1, 2, 4,
+                4, 3, 2}),
+            Tensor(ET_A, {}, std::vector<T_A>{1}),
+            1,
+            Tensor(ET, {2, 2, 3, 2}, std::vector<T>{
+                1.0f, 2.0f,
+                1.0f, 2.0f,
+                9.0f, 10.0f,
+
+                9.0f, 10.0f,
+                1.0f, 2.0f,
+                1.0f, 2.0f,
+
+
+                13.0f, 14.0f,
+                15.0f, 16.0f,
+                19.0f, 20.0f,
+
+                19.0f, 20.0f,
+                17.0f, 18.0f,
+                15.0f, 16.0f}),
+            "gather_v7_3d_indices_axis_1_batch_dims_1"),
+    };
+    return params;
+}
+
+std::vector<GatherParamsV7> generateCombinedParamsV7() {
+    const std::vector<std::vector<GatherParamsV7>> generatedParams {
+        generateParamsV7<element::Type_t::boolean, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::i8, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::i16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::i32, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::i64, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::u8, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::u16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::u32, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsV7<element::Type_t::u64, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValueV7<element::Type_t::bf16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValueV7<element::Type_t::f16, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValueV7<element::Type_t::f32, element::Type_t::i32, element::Type_t::i64>(),
+        generateParamsFloatValueV7<element::Type_t::f64, element::Type_t::i32, element::Type_t::i64>(),
+    };
+    std::vector<GatherParamsV7> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_Gather_With_Hardcoded_Refs, ReferenceGatherTestV7,
+    testing::ValuesIn(generateCombinedParamsV7()), ReferenceGatherTestV7::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/gather_nd.cpp b/docs/template_plugin/tests/functional/op_reference/gather_nd.cpp
index 31b54247150354..d27d3bebf7f6a3 100644
--- a/docs/template_plugin/tests/functional/op_reference/gather_nd.cpp
+++ b/docs/template_plugin/tests/functional/op_reference/gather_nd.cpp
@@ -25,7 +25,7 @@ struct GatherNDParams {
     std::string testcaseName;
 };
 
-class ReferenceGatherNDTest : public testing::TestWithParam<GatherNDParams>, public CommonReferenceTest {
+class ReferenceGatherND5Test : public testing::TestWithParam<GatherNDParams>, public CommonReferenceTest {
 public:
     void SetUp() override {
         auto params = GetParam();
@@ -59,18 +59,18 @@ class ReferenceGatherNDTest : public testing::TestWithParam<GatherNDParams>, pub
                                                               PartialShape{params.dataTensor.shape});
         const auto indices = std::make_shared<op::v0::Parameter>(params.indicesTensor.type,
                                                                  PartialShape{params.indicesTensor.shape});
-        std::shared_ptr<op::v5::GatherND> gatherElement;
+        std::shared_ptr<op::v5::GatherND> gatherND;
         if (params.batchDims == 0) {
-            gatherElement = std::make_shared<op::v5::GatherND>(data, indices);
+            gatherND = std::make_shared<op::v5::GatherND>(data, indices);
         } else {
-            gatherElement = std::make_shared<op::v5::GatherND>(data, indices, params.batchDims);
+            gatherND = std::make_shared<op::v5::GatherND>(data, indices, params.batchDims);
         }
-        function = std::make_shared<ov::Function>(NodeVector {gatherElement}, ParameterVector {data, indices});
+        function = std::make_shared<ov::Function>(NodeVector {gatherND}, ParameterVector {data, indices});
         return function;
     }
 };
 
-TEST_P(ReferenceGatherNDTest, CompareWithRefs) {
+TEST_P(ReferenceGatherND5Test, CompareWithRefs) {
     Exec();
 }
 
@@ -203,6 +203,207 @@ std::vector<GatherNDParams> generateCombinedParams() {
     return combinedParams;
 }
 
-INSTANTIATE_TEST_SUITE_P(smoke_GatherND_With_Hardcoded_Refs, ReferenceGatherNDTest,
-    testing::ValuesIn(generateCombinedParams()), ReferenceGatherNDTest::getTestCaseName);
-} // namespace
\ No newline at end of file
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND_With_Hardcoded_Refs, ReferenceGatherND5Test,
+    testing::ValuesIn(generateCombinedParams()), ReferenceGatherND5Test::getTestCaseName);
+
+
+class ReferenceGatherND8Test : public testing::TestWithParam<GatherNDParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data, params.indicesTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<GatherNDParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_aType=" << param.indicesTensor.type;
+        result << "_aShape=" << param.indicesTensor.shape;
+        result << "_bDims=" << param.batchDims;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const GatherNDParams& params) {
+        std::shared_ptr<Function> function;
+        const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type,
+                                                              PartialShape{params.dataTensor.shape});
+        const auto indices = std::make_shared<op::v0::Parameter>(params.indicesTensor.type,
+                                                                 PartialShape{params.indicesTensor.shape});
+        std::shared_ptr<op::v8::GatherND> gatherND;
+        if (params.batchDims == 0) {
+            gatherND = std::make_shared<op::v8::GatherND>(data, indices);
+        } else {
+            gatherND = std::make_shared<op::v8::GatherND>(data, indices, params.batchDims);
+        }
+        function = std::make_shared<ov::Function>(NodeVector {gatherND}, ParameterVector {data, indices});
+        return function;
+    }
+};
+
+TEST_P(ReferenceGatherND8Test, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<GatherNDParams> generateParams_v8() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<GatherNDParams> params {
+            GatherNDParams(
+                    Tensor(IN_ET, {3, 3}, std::vector<T>{10, 11, 12, 13, 14, 15, 16, 17, 18}),
+                    Tensor(element::i32, {2}, std::vector<int32_t>{1, 2}),
+                    0,
+                    Tensor(IN_ET, {}, std::vector<T>{15}),
+                    "gather_nd_8_single_indices"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2}, std::vector<T>{10, 11, 12, 13}),
+                    Tensor(element::i32, {2, 2}, std::vector<int32_t>{0, 0, 1, 1}),
+                    0,
+                    Tensor(IN_ET, {2}, std::vector<T>{10, 13}),
+                    "gather_nd_8_scalar_from_2d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2}, std::vector<T>{10, 11, 12, 13}),
+                    Tensor(element::i32, {2, 1}, std::vector<int32_t>{1, 0}),
+                    0,
+                    Tensor(IN_ET, {2, 2}, std::vector<T>{12, 13, 10, 11}),
+                    "gather_nd_8_1d_from_2d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{10, 11, 12, 13, 20, 21, 22, 23}),
+                    Tensor(element::i32, {2, 3}, std::vector<int32_t>{0, 0, 1, 1, 0, 1}),
+                    0,
+                    Tensor(IN_ET, {2}, std::vector<T>{11, 21}),
+                    "gather_nd_8_scalar_from_3d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{10, 11, 12, 13, 20, 21, 22, 23}),
+                    Tensor(element::i32, {2, 2}, std::vector<int32_t>{0, 1, 1, 0}),
+                    0,
+                    Tensor(IN_ET, {2, 2}, std::vector<T>{12, 13, 20, 21}),
+                    "gather_nd_8_1d_from_3d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{10, 11, 12, 13, 20, 21, 22, 23}),
+                    Tensor(element::i32, {1, 1}, std::vector<int32_t>{1}),
+                    0,
+                    Tensor(IN_ET, {1, 2, 2}, std::vector<T>{20, 21, 22, 23}),
+                    "gather_nd_8_2d_from_3d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2}, std::vector<T>{10, 11, 12, 13}),
+                    Tensor(element::i32, {2, 1, 2}, std::vector<int32_t>{0, 0, 0, 1}),
+                    0,
+                    Tensor(IN_ET, {2, 1}, std::vector<T>{10, 11}),
+                    "gather_nd_8_batch_scalar_from_2d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2}, std::vector<T>{10, 11, 12, 13}),
+                    Tensor(element::i32, {2, 1, 1}, std::vector<int32_t>{1, 0}),
+                    0,
+                    Tensor(IN_ET, {2, 1, 2}, std::vector<T>{12, 13, 10, 11}),
+                    "gather_nd_8_batch_1d_from_2d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{10, 11, 12, 13, 20, 21, 22, 23}),
+                    Tensor(element::i32, {2, 2, 3}, std::vector<int32_t>{0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0}),
+                    0,
+                    Tensor(IN_ET, {2, 2}, std::vector<T>{11, 21, 13, 22}),
+                    "gather_nd_8_batch_scalar_from_3d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{10, 11, 12, 13, 20, 21, 22, 23}),
+                    Tensor(element::i32, {2, 2, 2}, std::vector<int32_t>{0, 1, 1, 0, 0, 0, 1, 1}),
+                    0,
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{12, 13, 20, 21, 10, 11, 22, 23}),
+                    "gather_nd_8_batch_1d_from_3d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{10, 11, 12, 13, 20, 21, 22, 23}),
+                    Tensor(element::i32, {2, 2, 2}, std::vector<int32_t>{0, -1, -1, 0, 0, 0, 1, 1}),
+                    0,
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{12, 13, 20, 21, 10, 11, 22, 23}),
+                    "gather_nd_8_batch_1d_from_3d_negative"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 2, 2}, std::vector<T>{10, 11, 12, 13, 20, 21, 22, 23}),
+                    Tensor(element::i32, {2, 1, 1}, std::vector<int32_t>{1, 0}),
+                    0,
+                    Tensor(IN_ET, {2, 1, 2, 2}, std::vector<T>{20, 21, 22, 23, 10, 11, 12, 13}),
+                    "gather_nd_8_batch_2d_from_3d"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 3, 4}, std::vector<T>{1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12,
+                                                            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}),
+                    Tensor(element::i32, {2, 1}, std::vector<int32_t>{1, 0}),
+                    1,
+                    Tensor(IN_ET, {2, 4}, std::vector<T>{5, 6, 7, 8, 13, 14, 15, 16}),
+                    "gather_nd_8_batch_dims1"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 3, 4, 2}, std::vector<T>{
+                            1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
+                            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
+                            33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48}),
+                    Tensor(element::i32, {2, 3, 3, 2}, std::vector<int32_t>{
+                            1, 0, 3, 1, 2, 1, 0, 1, 1, 1, 2, 0, 3, 0, 3, 1, 2, 1,
+                            2, 0, 1, 1, 3, 1, 1, 1, 2, 0, 2, 0, 0, 0, 3, 1, 3, 1}),
+                    2,
+                    Tensor(IN_ET, {2, 3, 3}, std::vector<T>{
+                            3, 8, 6, 10, 12, 13, 23, 24, 22, 29, 28, 32, 36, 37, 37, 41, 48, 48}),
+                    "gather_8_nd_batch_dims2"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 3, 4}, std::vector<T>{1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12,
+                                                            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}),
+                    Tensor(element::i32, {2, 3, 1, 1}, std::vector<int32_t>{1, 0, 2, 0, 2, 2}),
+                    2,
+                    Tensor(IN_ET, {2, 3, 1}, std::vector<T>{2, 5, 11, 13, 19, 23}),
+                    "gather_8_nd_batch_dims2_lead_dims"),
+            GatherNDParams(
+                    Tensor(IN_ET, {2, 3, 4, 5}, std::vector<T>{
+                            1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,
+                            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
+                            33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
+                            49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,
+                            65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
+                            81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
+                            97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,
+                            111, 112, 113, 114, 115, 116, 117, 118, 119, 120}),
+                    Tensor(element::i32, {2, 3, 2, 1}, std::vector<int32_t>{
+                            1, 0, 2, 0, 2, 0, 1, 0, 2, 0, 2, 0}),
+                    2,
+                    Tensor(IN_ET, {2, 3, 2, 5}, std::vector<T>{
+                            6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 31, 32, 33, 34, 35, 21, 22, 23,
+                            24, 25, 51, 52, 53, 54, 55, 41, 42, 43, 44, 45, 66, 67, 68, 69,
+                            70, 61, 62, 63, 64, 65, 91, 92, 93, 94, 95, 81, 82, 83, 84, 85,
+                            111, 112, 113, 114, 115, 101, 102, 103, 104, 105}),
+                    "gather_8_nd_batch_dims2_non_scalar_slices"),
+    };
+    return params;
+}
+
+std::vector<GatherNDParams> generateCombinedParams_v8() {
+    const std::vector<std::vector<GatherNDParams>> generatedParams {
+            generateParams_v8<element::Type_t::i8>(),
+            generateParams_v8<element::Type_t::i16>(),
+            generateParams_v8<element::Type_t::i32>(),
+            generateParams_v8<element::Type_t::i64>(),
+            generateParams_v8<element::Type_t::u8>(),
+            generateParams_v8<element::Type_t::u16>(),
+            generateParams_v8<element::Type_t::u32>(),
+            generateParams_v8<element::Type_t::u64>(),
+            generateParams_v8<element::Type_t::bf16>(),
+            generateParams_v8<element::Type_t::f16>(),
+            generateParams_v8<element::Type_t::f32>(),
+            generateParams_v8<element::Type_t::f64>(),
+    };
+    std::vector<GatherNDParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND_With_Hardcoded_Refs, ReferenceGatherND8Test,
+        testing::ValuesIn(generateCombinedParams_v8()), ReferenceGatherND8Test::getTestCaseName);
+} // namespace
diff --git a/docs/template_plugin/tests/functional/op_reference/gru_cell.cpp b/docs/template_plugin/tests/functional/op_reference/gru_cell.cpp
new file mode 100644
index 00000000000000..f520ea0f77e7a2
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/gru_cell.cpp
@@ -0,0 +1,294 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/gru_cell.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct GRUCellParams {
+    GRUCellParams(
+        const int32_t batchSize, const int32_t inputSize, const int32_t hiddenSize, const int32_t gatesCount, const bool linearBeforeReset,
+        const Tensor& X, const Tensor& H_t, const Tensor& W, const Tensor& R, const Tensor& B,
+        const Tensor& Ho, const std::string& testcaseName = "") :
+        batchSize(batchSize), inputSize(inputSize), hiddenSize(hiddenSize), gatesCount(gatesCount), linearBeforeReset(linearBeforeReset),
+        X(X), H_t(H_t), W(W), R(R), B(B), Ho(Ho), testcaseName(testcaseName) {}
+
+    int32_t batchSize;
+    int32_t inputSize;
+    int32_t hiddenSize;
+    int32_t gatesCount;
+    bool linearBeforeReset;
+    Tensor X;
+    Tensor H_t;
+    Tensor W;
+    Tensor R;
+    Tensor B;
+    Tensor Ho;
+    std::string testcaseName;
+};
+
+class ReferenceGRUCellTest : public testing::TestWithParam<GRUCellParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.X.data, params.H_t.data, params.W.data, params.R.data, params.B.data};
+        refOutData = {params.Ho.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<GRUCellParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "bSize=" << param.batchSize;
+        result << "_iSize=" << param.inputSize;
+        result << "_hSize=" << param.hiddenSize;
+        result << "_gCount=" << param.gatesCount;
+        result << "_lbReset=" << param.linearBeforeReset;
+        result << "_xType=" << param.X.type;
+        result << "_xShape=" << param.X.shape;
+        result << "_htType=" << param.H_t.type;
+        result << "_htShape=" << param.H_t.shape;
+        result << "_wType=" << param.W.type;
+        result << "_wShape=" << param.W.shape;
+        result << "_rType=" << param.R.type;
+        result << "_rShape=" << param.R.shape;
+        result << "_hoType=" << param.Ho.type;
+        if (param.testcaseName != "") {
+            result << "_hoShape=" << param.Ho.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_hoShape=" << param.Ho.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const GRUCellParams& params) {
+        float clip = 2.88f;
+
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto gru_cell = std::make_shared<op::v3::GRUCell>(X,
+                                                                H_t,
+                                                                W,
+                                                                R,
+                                                                B,
+                                                                params.hiddenSize,
+                                                                std::vector<std::string>{"sigmoid", "tanh"},
+                                                                std::vector<float>{},
+                                                                std::vector<float>{},
+                                                                clip,
+                                                                params.linearBeforeReset);
+
+        auto function = std::make_shared<Function>(NodeVector{gru_cell}, ParameterVector{X, H_t, W, R, B});
+        return function;
+    }
+};
+
+// Hard Sigmoid activation function is unsupprted with v3::GRUCell
+class ReferenceGRUCellTestHardsigmoidActivationFunction : public ReferenceGRUCellTest {
+public:
+    void SetUp() override {
+        threshold = 1e-1f;
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.X.data, params.H_t.data, params.W.data, params.R.data, params.B.data};
+        refOutData = {params.Ho.data};
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const GRUCellParams& params) {
+        float clip = 2.88f;
+
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto gru_cell = std::make_shared<op::v3::GRUCell>(X,
+                                                                H_t,
+                                                                W,
+                                                                R,
+                                                                B,
+                                                                params.hiddenSize,
+                                                                std::vector<std::string>{"hardsigmoid", "hardsigmoid"},
+                                                                std::vector<float>{1.8345f, 1.8345f},
+                                                                std::vector<float>{3.05f, 3.05f},
+                                                                clip,
+                                                                params.linearBeforeReset);
+
+        auto function = std::make_shared<Function>(NodeVector{gru_cell}, ParameterVector{X, H_t, W, R, B});
+        return function;
+    }
+};
+
+TEST_P(ReferenceGRUCellTest, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceGRUCellTestHardsigmoidActivationFunction, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET>
+std::vector<GRUCellParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<GRUCellParams> params {
+        GRUCellParams(
+            2, 3, 3, 3, false,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.52421564f, 0.78845507f, 0.9372873f, 0.59783894f, 0.18278378f, 0.2084126f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.45738035f, 0.996877f, 0.82882977f, 0.47492632f, 0.88471466f, 0.57833236f}),
+            Tensor(ET, {3 * 3, 3}, std::vector<T>{
+                0.5815369f, 0.16559383f, 0.08464007f, 0.843122f,   0.73968244f, 0.11359601f,
+                0.8295078f, 0.9240567f,  0.10007995f, 0.20573162f, 0.09002485f, 0.2839569f,
+                0.3096991f, 0.5638341f,  0.5787327f,  0.84552664f, 0.16263747f, 0.7243242f,
+                0.8049057f, 0.43966424f, 0.46294412f, 0.9833361f,  0.31369713f, 0.1719934f,
+                0.4937093f, 0.6353004f,  0.77982515f}),
+            Tensor(ET, {3 * 3, 3}, std::vector<T>{
+                0.16510165f, 0.52435565f, 0.2788478f, 0.99427545f, 0.1623331f,  0.01389796f,
+                0.99669236f, 0.53901845f, 0.8737506f, 0.9254788f,  0.21172932f, 0.11634306f,
+                0.40111724f, 0.37497616f, 0.2903471f, 0.6796794f,  0.65131867f, 0.78163475f,
+                0.12058706f, 0.45591718f, 0.791677f,  0.76497287f, 0.9895242f,  0.7845312f,
+                0.51267904f, 0.49030215f, 0.08498167f}),
+            Tensor(ET, {3 * 3}, std::vector<T>{
+                0.8286678f + 0.9175602f,
+                0.9153158f + 0.14958014f,
+                0.9581612f + 0.49230585f,
+                0.6639213f + 0.63162816f,
+                0.84239805f + 0.4161903f,
+                0.5282445f + 0.22148274f,
+                0.14153397f + 0.50496656f,
+                0.22404431f + 0.34798595f,
+                0.6549655f + 0.6699164f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.48588726f, 0.99670005f, 0.83759373f, 0.5023099f, 0.89410484f, 0.60011315f}),
+            "gru_cell_bias_clip"),
+        GRUCellParams(
+            2, 3, 3, 3, true,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.12249453f, 0.6127907f, 0.5001741f, 0.5124603f, 0.04329684f, 0.023834f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f}),
+            Tensor(ET, {3 * 3, 3}, std::vector<T>{
+                0.72259396f, 0.11561195f, 0.9457856f,  0.19037509f, 0.6964006f,  0.33459795f,
+                0.5468904f,  0.85646594f, 0.5101311f,  0.9712257f,  0.3687071f,  0.60280246f,
+                0.56943774f, 0.7475505f,  0.2490578f,  0.86977345f, 0.85542053f, 0.29660386f,
+                0.49717373f, 0.7473479f,  0.53454477f, 0.15974349f, 0.5804805f,  0.14303213f,
+                0.07514781f, 0.5865731f,  0.76409274f}),
+            Tensor(ET, {3 * 3, 3}, std::vector<T>{
+                0.91382647f, 0.41527033f, 0.28040004f, 0.23601337f, 0.04471736f, 0.03888785f,
+                0.06308217f, 0.44844428f, 0.29384327f, 0.49037653f, 0.50421673f, 0.7366393f,
+                0.63143945f, 0.00277612f, 0.37198433f, 0.06966069f, 0.4613444f,  0.10999731f,
+                0.78273284f, 0.21453214f, 0.10751773f, 0.18332677f, 0.1326976f,  0.9998985f,
+                0.19263928f, 0.10979804f, 0.52575564f}),
+            Tensor(ET, {(3 + 1) * 3}, std::vector<T>{
+                0.61395123f,  // 0.09875853f + 0.5151927f,
+                1.08667738f,  // 0.37801138f + 0.708666f,
+                1.32600244f,  // 0.7729636f + 0.55303884f,
+                0.81917698f,  // 0.78493553f + 0.03424145f,
+                1.37736335f,  // 0.5662702f + 0.81109315f,
+                0.42931147f,  // 0.12406381f + 0.30524766f,
+                0.66729516f,
+                0.7752771f,
+                0.78819966f,
+                0.6606634f,
+                0.99040645f,
+                0.21112025f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.8709214f, 0.48411977f, 0.74495184f, 0.6074972f, 0.44572943f, 0.1467715f}),
+            "gru_cell_linear_before_reset"),
+    };
+    return params;
+}
+
+std::vector<GRUCellParams> generateCombinedParams() {
+    const std::vector<std::vector<GRUCellParams>> generatedParams {
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<GRUCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<GRUCellParams> generateParamsHardsigmoidActivationFunction() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<GRUCellParams> params {
+        GRUCellParams(
+            2, 3, 3, 3, true,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.12249453f, 0.6127907f, 0.5001741f, 0.5124603f, 0.04329684f, 0.023834f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f}),
+            Tensor(ET, {3 * 3, 3}, std::vector<T>{
+                0.72259396f, 0.11561195f, 0.9457856f,  0.19037509f, 0.6964006f,  0.33459795f,
+                0.5468904f,  0.85646594f, 0.5101311f,  0.9712257f,  0.3687071f,  0.60280246f,
+                0.56943774f, 0.7475505f,  0.2490578f,  0.86977345f, 0.85542053f, 0.29660386f,
+                0.49717373f, 0.7473479f,  0.53454477f, 0.15974349f, 0.5804805f,  0.14303213f,
+                0.07514781f, 0.5865731f,  0.76409274f}),
+            Tensor(ET, {3 * 3, 3}, std::vector<T>{
+                0.91382647f, 0.41527033f, 0.28040004f, 0.23601337f, 0.04471736f, 0.03888785f,
+                0.06308217f, 0.44844428f, 0.29384327f, 0.49037653f, 0.50421673f, 0.7366393f,
+                0.63143945f, 0.00277612f, 0.37198433f, 0.06966069f, 0.4613444f,  0.10999731f,
+                0.78273284f, 0.21453214f, 0.10751773f, 0.18332677f, 0.1326976f,  0.9998985f,
+                0.19263928f, 0.10979804f, 0.52575564f}),
+            Tensor(ET, {(3 + 1) * 3}, std::vector<T>{
+                0.09875853f + 0.5151927f,
+                0.37801138f + 0.708666f,
+                0.7729636f + 0.55303884f,
+                0.78493553f + 0.03424145f,
+                0.5662702f + 0.81109315f,
+                0.12406381f + 0.30524766f,
+                0.66729516f,
+                0.7752771f,
+                0.78819966f,
+                0.6606634f,
+                0.99040645f,
+                0.21112025f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f}),
+            "gru_cell_hardsigmoid_activation_function"),
+    };
+    return params;
+}
+
+std::vector<GRUCellParams> generateCombinedParamsHardsigmoidActivationFunction() {
+    const std::vector<std::vector<GRUCellParams>> generatedParams {
+        generateParamsHardsigmoidActivationFunction<element::Type_t::bf16>(),
+        generateParamsHardsigmoidActivationFunction<element::Type_t::f16>(),
+        generateParamsHardsigmoidActivationFunction<element::Type_t::f32>(),
+        generateParamsHardsigmoidActivationFunction<element::Type_t::f64>(),
+    };
+    std::vector<GRUCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_GRUCell_With_Hardcoded_Refs, ReferenceGRUCellTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceGRUCellTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_GRUCell_With_Hardcoded_Refs, ReferenceGRUCellTestHardsigmoidActivationFunction,
+    testing::ValuesIn(generateCombinedParamsHardsigmoidActivationFunction()), ReferenceGRUCellTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/lstm_cell.cpp b/docs/template_plugin/tests/functional/op_reference/lstm_cell.cpp
new file mode 100644
index 00000000000000..7279cee6b66cae
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/lstm_cell.cpp
@@ -0,0 +1,379 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/lstm_cell.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct LSTMCellParams {
+    LSTMCellParams(
+        int32_t batchSize, int32_t inputSize, int32_t hiddenSize, int32_t gatesCount,
+        const Tensor& X, const Tensor& W, const Tensor& R, const Tensor& H_t, const Tensor& C_t, const Tensor& B,
+        const Tensor& Ho, const Tensor& Co, const std::string& testcaseName = "") :
+        batchSize(batchSize), inputSize(inputSize), hiddenSize(hiddenSize), gatesCount(gatesCount),
+        X(X), W(W), R(R), H_t(H_t), C_t(C_t), B(B), Ho(Ho), Co(Co), testcaseName(testcaseName) {}
+
+    int32_t batchSize;
+    int32_t inputSize;
+    int32_t hiddenSize;
+    int32_t gatesCount;
+    Tensor X;
+    Tensor W;
+    Tensor R;
+    Tensor H_t;
+    Tensor C_t;
+    Tensor B;
+    Tensor Ho;
+    Tensor Co;
+    std::string testcaseName;
+};
+
+class ReferenceLSTMCellTest : public testing::TestWithParam<LSTMCellParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.X.data, params.H_t.data, params.C_t.data, params.W.data, params.R.data, params.B.data};
+        refOutData = {params.Ho.data, params.Co.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<LSTMCellParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "bSize=" << param.batchSize;
+        result << "_iSize=" << param.inputSize;
+        result << "_hSize=" << param.hiddenSize;
+        result << "_gCount=" << param.gatesCount;
+        result << "_xType=" << param.X.type;
+        result << "_xShape=" << param.X.shape;
+        result << "_wType=" << param.W.type;
+        result << "_wShape=" << param.W.shape;
+        result << "_rType=" << param.R.type;
+        result << "_rShape=" << param.R.shape;
+        result << "_htType=" << param.H_t.type;
+        result << "_htShape=" << param.H_t.shape;
+        result << "_ctType=" << param.C_t.type;
+        result << "_ctShape=" << param.C_t.shape;
+        result << "_hoType=" << param.Ho.type;
+        result << "_hoShape=" << param.Ho.shape;
+        result << "_coType=" << param.Co.type;
+        if (param.testcaseName != "") {
+            result << "_coShape=" << param.Co.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_coShape=" << param.Co.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const LSTMCellParams& params) {
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<op::v0::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto lstm_cell =
+            std::make_shared<op::v4::LSTMCell>(X,
+                                               H_t,
+                                               C_t,
+                                               op::util::convert_lstm_node_format(W, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(R, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(B, op::util::LSTMWeightsFormat::IOFC),
+                                               params.hiddenSize);
+
+        auto function = std::make_shared<Function>(lstm_cell->outputs(), ParameterVector{X, H_t, C_t, W, R, B});
+        return function;
+    }
+};
+
+class ReferenceLSTMCellTestBiasDefaultAttrs : public ReferenceLSTMCellTest {
+public:
+    void SetUp() override {
+        threshold = 1e-1f;
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.X.data, params.H_t.data, params.C_t.data, params.W.data, params.R.data, params.B.data};
+        refOutData = {params.Ho.data, params.Co.data};
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const LSTMCellParams& params) {
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<op::v0::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto lstm_cell =
+            std::make_shared<op::v4::LSTMCell>(X,
+                                               H_t,
+                                               C_t,
+                                               op::util::convert_lstm_node_format(W, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(R, op::util::LSTMWeightsFormat::IOFC),
+                                               op::util::convert_lstm_node_format(B, op::util::LSTMWeightsFormat::IOFC),
+                                               params.hiddenSize);
+
+        auto function = std::make_shared<Function>(lstm_cell->outputs(), ParameterVector{X, H_t, C_t, W, R, B});
+        return function;
+    }
+};
+
+class ReferenceLSTMCellTestBiasClip : public ReferenceLSTMCellTest {
+public:
+    void SetUp() override {
+        threshold = 1e-1f;
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.X.data, params.H_t.data, params.C_t.data, params.W.data, params.R.data, params.B.data};
+        refOutData = {params.Ho.data, params.Co.data};
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const LSTMCellParams& params) {
+        const float clip_threshold = 3.5f;
+
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto C_t = std::make_shared<op::v0::Parameter>(params.C_t.type, params.C_t.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto lstm_cell =
+            std::make_shared<op::v4::LSTMCell>(X,
+                                               H_t,
+                                               C_t,
+                                               W,
+                                               R,
+                                               B,
+                                               params.hiddenSize,
+                                               std::vector<std::string>{"sigmoid", "tanh", "tanh"},
+                                               std::vector<float>{},
+                                               std::vector<float>{},
+                                               clip_threshold);
+
+        auto function = std::make_shared<Function>(lstm_cell->outputs(), ParameterVector{X, H_t, C_t, W, R, B});
+        return function;
+    }
+};
+
+TEST_P(ReferenceLSTMCellTest, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceLSTMCellTestBiasDefaultAttrs, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceLSTMCellTestBiasClip, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET>
+std::vector<LSTMCellParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<LSTMCellParams> params {
+        LSTMCellParams(
+            2, 3, 3, 4,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}),
+            Tensor(ET, {4 * 3, 3}, std::vector<T>{
+                3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+                9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+                6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+                8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+                5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+                3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}),
+            Tensor(ET, {4 * 3, 3}, std::vector<T>{
+                0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
+                0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
+                0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+                0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+                0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
+                0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}),
+            Tensor(ET, {4 * 3}, std::vector<T>(4 * 3, 0.f)),
+            Tensor(ET, {2, 3}, std::vector<T>{0.81457126f, 0.61109227f, 0.769522f, 0.52239674f, 0.4324641f, 0.63183f}),
+            Tensor(ET, {2, 3}, std::vector<T>{1.4444952f, 0.9635685f, 1.2875274f, 0.8053419f, 0.7184521f, 0.95803297f}),
+            "lstm_cell_zero_bias_default_attrs"),
+    };
+    return params;
+}
+
+std::vector<LSTMCellParams> generateCombinedParams() {
+    const std::vector<std::vector<LSTMCellParams>> generatedParams {
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<LSTMCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<LSTMCellParams> generateParamsBiasDefaultAttrs() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<LSTMCellParams> params {
+        LSTMCellParams(
+            2, 3, 3, 4,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}),
+            Tensor(ET, {4 * 3, 3}, std::vector<T>{
+                3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+                9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+                6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+                8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+                5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+                3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}),
+            Tensor(ET, {4 * 3, 3}, std::vector<T>{
+                0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
+                0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
+                0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+                0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+                0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
+                0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}),
+            Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
+                                               1.15248052f,
+                                               1.16671345f,
+                                               0.21450312f,
+                                               1.2380678f,
+                                               1.51688835f,
+                                               0.46718366f,
+                                               0.91810346f,
+                                               1.1274234f,
+                                               0.51022074f,
+                                               1.11389844f,
+                                               0.74174305f}),
+            Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
+                                              0.76665538549423218,
+                                              0.82509011030197144,
+                                              0.6479143500328064,
+                                              0.66586339473724365,
+                                              0.74838578701019287}),
+            Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
+                                              1.1150213479995728,
+                                              1.4578367471694946,
+                                              1.0649888515472412,
+                                              0.93761754035949707,
+                                              1.3659683465957642}),
+            "lstm_cell_bias_default_attrs"),
+    };
+    return params;
+}
+
+std::vector<LSTMCellParams> generateCombinedParamsBiasDefaultAttrs() {
+    const std::vector<std::vector<LSTMCellParams>> generatedParams {
+        generateParamsBiasDefaultAttrs<element::Type_t::bf16>(),
+        generateParamsBiasDefaultAttrs<element::Type_t::f16>(),
+        generateParamsBiasDefaultAttrs<element::Type_t::f32>(),
+        generateParamsBiasDefaultAttrs<element::Type_t::f64>(),
+    };
+    std::vector<LSTMCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<LSTMCellParams> generateParamsBiasClip() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<LSTMCellParams> params {
+        LSTMCellParams(
+            2, 3, 3, 4,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f}),
+            Tensor(ET, {4 * 3, 3}, std::vector<T>{
+                3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
+                9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
+                6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
+                8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
+                5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
+                3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f}),
+            Tensor(ET, {4 * 3, 3}, std::vector<T>{
+                0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
+                0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
+                0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
+                0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
+                0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
+                0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f}),
+            Tensor(ET, {4 * 3}, std::vector<T>{1.07393714f,
+                                               1.15248052f,
+                                               1.16671345f,
+                                               0.21450312f,
+                                               1.2380678f,
+                                               1.51688835f,
+                                               0.46718366f,
+                                               0.91810346f,
+                                               1.1274234f,
+                                               0.51022074f,
+                                               1.11389844f,
+                                               0.74174305f}),
+            Tensor(ET, {2, 3}, std::vector<T>{0.81014400720596313,
+                                              0.76665538549423218,
+                                              0.82387429475784302,
+                                              0.6479143500328064,
+                                              0.66586339473724365,
+                                              0.74838578701019287}),
+            Tensor(ET, {2, 3}, std::vector<T>{1.6800162792205811,
+                                              1.1150213479995728,
+                                              1.4510968923568726,
+                                              1.0649888515472412,
+                                              0.93761754035949707,
+                                              1.3659683465957642}),
+            "lstm_cell_bias_clip"),
+    };
+    return params;
+}
+
+std::vector<LSTMCellParams> generateCombinedParamsBiasClip() {
+    const std::vector<std::vector<LSTMCellParams>> generatedParams {
+        generateParamsBiasClip<element::Type_t::bf16>(),
+        generateParamsBiasClip<element::Type_t::f16>(),
+        generateParamsBiasClip<element::Type_t::f32>(),
+        generateParamsBiasClip<element::Type_t::f64>(),
+    };
+    std::vector<LSTMCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_LSTMCell_With_Hardcoded_Refs, ReferenceLSTMCellTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceLSTMCellTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_LSTMCell_With_Hardcoded_Refs, ReferenceLSTMCellTestBiasDefaultAttrs,
+    testing::ValuesIn(generateCombinedParamsBiasDefaultAttrs()), ReferenceLSTMCellTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_LSTMCell_With_Hardcoded_Refs, ReferenceLSTMCellTestBiasClip,
+    testing::ValuesIn(generateCombinedParamsBiasClip()), ReferenceLSTMCellTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/one_hot.cpp b/docs/template_plugin/tests/functional/op_reference/one_hot.cpp
new file mode 100644
index 00000000000000..7dbcd5788e55a6
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/one_hot.cpp
@@ -0,0 +1,226 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/one_hot.hpp"
+#include "openvino/op/constant.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct OneHotParams {
+    OneHotParams(
+        const Tensor& dataTensor, const int32_t axis,
+        const Tensor& depthTensor, const Tensor& onValueTensor, const Tensor& offValueTensor,
+        const Tensor& expectedTensor, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), axis(axis),
+        depthTensor(depthTensor), onValueTensor(onValueTensor), offValueTensor(offValueTensor),
+        expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    int32_t axis;
+    Tensor depthTensor;
+    Tensor onValueTensor;
+    Tensor offValueTensor;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceOneHotTest : public testing::TestWithParam<OneHotParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<OneHotParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_axis=" << param.axis;
+        result << "_deType=" << param.depthTensor.type;
+        result << "_deShape=" << param.depthTensor.shape;
+        result << "_onType=" << param.onValueTensor.type;
+        result << "_onShape=" << param.onValueTensor.shape;
+        result << "_offType=" << param.offValueTensor.type;
+        result << "_offShape=" << param.offValueTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const OneHotParams& params) {
+        std::shared_ptr<Function> function;
+        const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto depth = std::make_shared<op::v0::Constant>(params.depthTensor.type, params.depthTensor.shape,
+                                                              params.depthTensor.data.data());
+        const auto onValue = std::make_shared<op::v0::Constant>(params.onValueTensor.type, params.onValueTensor.shape,
+                                                              params.onValueTensor.data.data());
+        const auto offValue = std::make_shared<op::v0::Constant>(params.offValueTensor.type, params.offValueTensor.shape,
+                                                              params.offValueTensor.data.data());
+        const auto oneHot = std::make_shared<op::v1::OneHot>(data, depth, onValue, offValue, params.axis);
+        function = std::make_shared<ov::Function>(oneHot, ParameterVector{data});
+        return function;
+    }
+};
+
+TEST_P(ReferenceOneHotTest, CompareWithRefs) {
+    Exec();
+}
+
+template<typename T>
+std::vector<T> generateExpectedValues(const Shape& input_shape, std::vector<T> input, uint32_t category_count) {
+//    std::vector<T> input{0, 11, 101, 1001, 10001, static_cast<int32_t>(category_count - 1)};
+    std::vector<T> output(shape_size(input_shape), 0);
+    for (size_t i = 0; i < input.size(); ++i) {
+        output[i * category_count + input[i]] = 1;
+    }
+    return output;
+}
+
+template <element::Type_t ET1, element::Type_t ET2>
+std::vector<OneHotParams> generateParams() {
+    using T1 = typename element_type_traits<ET1>::value_type;
+    using T2 = typename element_type_traits<ET2>::value_type;
+    std::vector<OneHotParams> params {
+        OneHotParams(
+            Tensor(ET1, {}, std::vector<T1>{2}),
+            0,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {3}, std::vector<T2>{0, 0, 1}),
+            "one_hot_scalar_2_in_3"),
+        OneHotParams(
+            Tensor(ET1, {}, std::vector<T1>{1}),
+            0,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {3}, std::vector<T2>{0, 1, 0}),
+            "one_hot_scalar_1_in_3"),
+        OneHotParams(
+            Tensor(ET1, {}, std::vector<T1>{0}),
+            0,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {3}, std::vector<T2>{1, 0, 0}),
+            "one_hot_scalar_0_in_3"),
+        OneHotParams(
+            Tensor(ET1, {8}, std::vector<T1>{2, 1, 0, 0, 2, 2, 1, 0}),
+            0,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {3, 8}, std::vector<T2>{0, 0, 1, 1, 0, 0, 0, 1,
+                                                0, 1, 0, 0, 0, 0, 1, 0,
+                                                1, 0, 0, 0, 1, 1, 0, 0}),
+            "one_hot_vector_0"),
+        OneHotParams(
+            Tensor(ET1, {8}, std::vector<T1>{2, 1, 0, 0, 2, 2, 1, 0}),
+            1,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {8, 3}, std::vector<T2>{0, 0, 1, 0, 1, 0, 1, 0,
+                                                0, 1, 0, 0, 0, 0, 1, 0,
+                                                0, 1, 0, 1, 0, 1, 0, 0}),
+            "one_hot_vector_1"),
+        OneHotParams(
+            Tensor(ET1, {8}, std::vector<T1>{2, 1, 0, 0, 3, 2, 1, 0}),
+            1,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {8, 3}, std::vector<T2>{0, 0, 1, 0, 1, 0, 1, 0,
+                                                0, 1, 0, 0, 0, 0, 0, 0,
+                                                0, 1, 0, 1, 0, 1, 0, 0}),
+            "one_hot_vector_1_barely_oob"),
+        OneHotParams(
+            Tensor(ET1, {3, 3}, std::vector<T1>{0, 1, 1, 2, 1, 0, 0, 2, 1}),
+            0,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {3, 3, 3}, std::vector<T2>{1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1,
+                                                   0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0}),
+            "one_hot_matrix_0"),
+        OneHotParams(
+            Tensor(ET1, {6}, std::vector<T1>{0, 11, 101, 1001, 10001, 19999}),
+            1,
+            Tensor(ET1, {}, std::vector<T1>{20000}),
+            Tensor(ET2, {}, std::vector<T2>{1}),
+            Tensor(ET2, {}, std::vector<T2>{0}),
+            Tensor(ET2, {6, 20000}, generateExpectedValues({6, 20000},
+                                                           std::vector<T2>{0, 11, 101, 1001, 10001, 19999},
+                                                           20000)),
+            "one_hot_vector_many_categories"),
+    };
+    return params;
+}
+
+template <element::Type_t ET1, element::Type_t ET2>
+std::vector<OneHotParams> generateParamsFloat() {
+    using T1 = typename element_type_traits<ET1>::value_type;
+    using T2 = typename element_type_traits<ET2>::value_type;
+    std::vector<OneHotParams> params {
+        OneHotParams(
+            Tensor(ET1, {3, 3}, std::vector<T1>{0, 1, 1, 2, 1, 0, 0, 2, 1}),
+            0,
+            Tensor(ET1, {}, std::vector<T1>{3}),
+            Tensor(ET2, {}, std::vector<T2>{2.5}),
+            Tensor(ET2, {}, std::vector<T2>{0.5}),
+            Tensor(ET2, {3, 3, 3}, std::vector<T2>{
+                2.5, 0.5, 0.5, 0.5, 0.5, 2.5, 2.5, 0.5, 0.5, 0.5, 2.5, 2.5, 0.5, 2.5,
+                0.5, 0.5, 0.5, 2.5, 0.5, 0.5, 0.5, 2.5, 0.5, 0.5, 0.5, 2.5, 0.5}),
+            "one_hot_on_off_float"),
+    };
+    return params;
+}
+
+std::vector<OneHotParams> generateCombinedParams() {
+    const std::vector<std::vector<OneHotParams>> generatedParams {
+        generateParams<element::Type_t::i32, element::Type_t::i16>(),
+        generateParams<element::Type_t::i32, element::Type_t::i32>(),
+        generateParams<element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::i32, element::Type_t::u16>(),
+        generateParams<element::Type_t::i32, element::Type_t::u32>(),
+        generateParams<element::Type_t::i32, element::Type_t::u64>(),
+        generateParams<element::Type_t::i64, element::Type_t::i16>(),
+        generateParams<element::Type_t::i64, element::Type_t::i32>(),
+        generateParams<element::Type_t::i64, element::Type_t::i64>(),
+        generateParams<element::Type_t::i64, element::Type_t::u16>(),
+        generateParams<element::Type_t::i64, element::Type_t::u32>(),
+        generateParams<element::Type_t::i64, element::Type_t::u64>(),
+        generateParamsFloat<element::Type_t::i32, element::Type_t::bf16>(),
+        generateParamsFloat<element::Type_t::i32, element::Type_t::f16>(),
+        generateParamsFloat<element::Type_t::i32, element::Type_t::f32>(),
+        generateParamsFloat<element::Type_t::i32, element::Type_t::f64>(),
+        generateParamsFloat<element::Type_t::i64, element::Type_t::f32>(),
+        generateParamsFloat<element::Type_t::i64, element::Type_t::f64>(),
+    };
+    std::vector<OneHotParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_OneHot_With_Hardcoded_Refs, ReferenceOneHotTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceOneHotTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/pad.cpp b/docs/template_plugin/tests/functional/op_reference/pad.cpp
new file mode 100644
index 00000000000000..dfe16f69ad6e93
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/pad.cpp
@@ -0,0 +1,1101 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+#include <functional_test_utils/skip_tests_config.hpp>
+
+#include "openvino/op/pad.hpp"
+#include "openvino/op/constant.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct PadParams {
+    PadParams(
+        const Tensor& inputData, const Tensor& padsBegin, const Tensor& padsEnd,
+        const Tensor& expectedOutput, op::PadMode padMode, const Tensor& constantValue,
+        const std::string& testcaseName = "") :
+        inputData(inputData), padsBegin(padsBegin), padsEnd(padsEnd),
+        expectedOutput(expectedOutput), padMode(padMode),
+        useConstValue{true}, constantValue(constantValue),
+        testcaseName(testcaseName) {}
+
+    PadParams(
+        const Tensor& inputData, const Tensor& padsBegin, const Tensor& padsEnd,
+        const Tensor& expectedOutput, op::PadMode padMode,
+        const std::string& testcaseName = "") :
+        inputData(inputData), padsBegin(padsBegin), padsEnd(padsEnd),
+        expectedOutput(expectedOutput), padMode(padMode),
+        testcaseName(testcaseName) {}
+
+    Tensor inputData;
+    Tensor padsBegin;
+    Tensor padsEnd;
+    Tensor expectedOutput;
+    op::PadMode padMode;
+    bool useConstValue{false};
+    Tensor constantValue;
+    std::string testcaseName;
+};
+
+class ReferencePadTest : public testing::TestWithParam<PadParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        SKIP_IF_CURRENT_TEST_IS_DISABLED();
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.inputData.data};
+        refOutData = {params.expectedOutput.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<PadParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "iType=" << param.inputData.type;
+        result << "_iShape=" << param.inputData.shape;
+        result << "_pbType=" << param.padsBegin.type;
+        result << "_pbShape=" << param.padsBegin.shape;
+        result << "_peType=" << param.padsEnd.type;
+        result << "_peShape=" << param.padsEnd.shape;
+        result << "_oType=" << param.expectedOutput.type;
+        result << "_oShape=" << param.expectedOutput.shape;
+        result << "_=" << param.testcaseName;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const PadParams& params) {
+        const auto data = std::make_shared<op::v0::Parameter>(params.inputData.type,
+                                                              params.inputData.shape);
+        const auto padsBegin = op::v0::Constant::create(params.padsBegin.type,
+                                                        params.padsBegin.shape,
+                                                        params.padsBegin.data.data());
+        const auto padsEnd = op::v0::Constant::create(params.padsEnd.type,
+                                                      params.padsEnd.shape,
+                                                      params.padsEnd.data.data());
+        const auto f = [&] {
+            if (params.useConstValue) {
+                // pad_value should be used only in CONSTANT mode
+                const auto padVal = op::v0::Constant::create(params.constantValue.type,
+                                                             params.constantValue.shape,
+                                                             params.constantValue.data.data());
+                return std::make_shared<Function>(std::make_shared<op::v1::Pad>(data,
+                                                                                padsBegin,
+                                                                                padsEnd,
+                                                                                padVal,
+                                                                                params.padMode),
+                                                  ParameterVector{data});
+            }
+
+            return std::make_shared<Function>(std::make_shared<op::v1::Pad>(data,
+                                                                            padsBegin,
+                                                                            padsEnd,
+                                                                            params.padMode),
+                                              ParameterVector{data});
+        }();
+        return f;
+    }
+};
+
+TEST_P(ReferencePadTest, CompareWithRefs) {
+    Exec();
+}
+
+class ReferencePadTestParamsTooLarge : public ReferencePadTest {};
+
+TEST_P(ReferencePadTestParamsTooLarge, CompareWithRefs) {
+    EXPECT_ANY_THROW(Exec());
+}
+
+class ReferencePadTestParamsOk : public ReferencePadTest {};
+
+TEST_P(ReferencePadTestParamsOk, CompareWithRefs) {
+    EXPECT_NO_THROW(Exec());
+}
+
+template <element::Type_t ET, element::Type_t ET_INT>
+std::vector<PadParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_INT = typename element_type_traits<ET_INT>::value_type;
+    std::vector<PadParams> params {
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{4}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{5}),
+            Tensor(ET, {15}, std::vector<T>{
+                2112, 2112, 2112, 2112, 1, 2, 3, 4, 5, 6, 2112, 2112, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_1d_constant_const_value_provided_0"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{4}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET, {10}, std::vector<T>{
+                2112, 2112, 2112, 2112, 1, 2, 3, 4, 5, 6,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_1d_constant_const_value_provided_1"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {9}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_1d_constant_const_value_provided_2"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{4}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{5}),
+            Tensor(ET, {15}, std::vector<T>{
+                0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0,
+            }),
+            op::PadMode::CONSTANT,
+            "pad_1d_constant_use_default_const_0"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{4}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET, {10}, std::vector<T>{
+                0, 0, 0, 0, 1, 2, 3, 4, 5, 6,
+            }),
+            op::PadMode::CONSTANT,
+            "pad_1d_constant_use_default_const_1"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {9}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 0, 0, 0,
+            }),
+            op::PadMode::CONSTANT,
+            "pad_1d_constant_use_default_const_2"),
+
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{3, 4}),
+            Tensor(ET, {6, 8}, std::vector<T>{
+                2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 1,    2,    2112, 2112, 2112, 2112,
+                2112, 2112, 3,    4,    2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_2d_constant_const_value_provided_0"),
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {3, 4}, std::vector<T>{
+                2112, 2112, 2112, 2112,
+                2112, 2112, 1,    2,
+                2112, 2112, 3,    4,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_2d_constant_const_value_provided_1"),
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {3, 4}, std::vector<T>{
+                1,    2,    2112, 2112,
+                3,    4,    2112, 2112,
+                2112, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_2d_constant_const_value_provided_2"),
+
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{3, 4}),
+            Tensor(ET, {6, 8}, std::vector<T>{
+                0, 0, 0, 0, 0, 0, 0, 0,
+                0, 0, 1, 2, 0, 0, 0, 0,
+                0, 0, 3, 4, 0, 0, 0, 0,
+                0, 0, 0, 0, 0, 0, 0, 0,
+                0, 0, 0, 0, 0, 0, 0, 0,
+                0, 0, 0, 0, 0, 0, 0, 0,
+            }),
+            op::PadMode::CONSTANT,
+            "pad_2d_constant_use_default_const_0"),
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {3, 4}, std::vector<T>{
+                0, 0, 0, 0,
+                0, 0, 1, 2,
+                0, 0, 3, 4,
+            }),
+            op::PadMode::CONSTANT,
+            "pad_2d_constant_use_default_const_1"),
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {3, 4}, std::vector<T>{
+                1, 2, 0, 0,
+                3, 4, 0, 0,
+                0, 0, 0, 0,
+            }),
+            op::PadMode::CONSTANT,
+            "pad_2d_constant_use_default_const_2"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {11}, std::vector<T>{
+                1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6,
+            }),
+            op::PadMode::EDGE,
+            "pad_1d_edge_0"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{1}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET, {7}, std::vector<T>{
+                1, 1, 2, 3, 4, 5, 6,
+            }),
+            op::PadMode::EDGE,
+            "pad_1d_edge_1"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET, {8}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 6, 6,
+            }),
+            op::PadMode::EDGE,
+            "pad_1d_edge_2"),
+
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 1}),
+            Tensor(ET, {5, 5}, std::vector<T>{
+                1, 1, 1, 2, 2,
+                1, 1, 1, 2, 2,
+                3, 3, 3, 4, 4,
+                3, 3, 3, 4, 4,
+                3, 3, 3, 4, 4,
+            }),
+            op::PadMode::EDGE,
+            "pad_2d_edge_0"),
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {3, 4}, std::vector<T>{
+                1, 1, 1, 2,
+                1, 1, 1, 2,
+                3, 3, 3, 4,
+            }),
+            op::PadMode::EDGE,
+            "pad_2d_edge_1"),
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 3, 4,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 1}),
+            Tensor(ET, {4, 3}, std::vector<T>{
+                1, 2, 2,
+                3, 4, 4,
+                3, 4, 4,
+                3, 4, 4,
+            }),
+            op::PadMode::EDGE,
+            "pad_2d_edge_2"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {11}, std::vector<T>{
+                3, 2, 1, 2, 3, 4, 5, 6, 5, 4, 3,
+            }),
+            op::PadMode::REFLECT,
+            "pad_1d_reflect_0"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{1}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET, {7}, std::vector<T>{
+                2, 1, 2, 3, 4, 5, 6,
+            }),
+            op::PadMode::REFLECT,
+            "pad_1d_reflect_1"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET, {8}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 5, 4,
+            }),
+            op::PadMode::REFLECT,
+            "pad_1d_reflect_2"),
+
+        PadParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 1}),
+            Tensor(ET, {6, 6}, std::vector<T>{
+                6, 5, 4, 5, 6, 5,
+                3, 2, 1, 2, 3, 2,
+                6, 5, 4, 5, 6, 5,
+                9, 8, 7, 8, 9, 8,
+                6, 5, 4, 5, 6, 5,
+                3, 2, 1, 2, 3, 2,
+            }),
+            op::PadMode::REFLECT,
+            "pad_2d_reflect_0"),
+        PadParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {4, 5}, std::vector<T>{
+                6, 5, 4, 5, 6,
+                3, 2, 1, 2, 3,
+                6, 5, 4, 5, 6,
+                9, 8, 7, 8, 9,
+            }),
+            op::PadMode::REFLECT,
+            "pad_2d_reflect_1"),
+        PadParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 1}),
+            Tensor(ET, {5, 4}, std::vector<T>{
+                1, 2, 3, 2,
+                4, 5, 6, 5,
+                7, 8, 9, 8,
+                4, 5, 6, 5,
+                1, 2, 3, 2,
+            }),
+            op::PadMode::REFLECT,
+            "pad_2d_reflect_2"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {11}, std::vector<T>{
+                2, 1, 1, 2, 3, 4, 5, 6, 6, 5, 4,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_1d_symmetric_0"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{1}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET, {7}, std::vector<T>{
+                1, 1, 2, 3, 4, 5, 6,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_1d_symmetric_1"),
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{0}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET, {8}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 6, 5,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_1d_symmetric_2"),
+
+        PadParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 1}),
+            Tensor(ET, {6, 6}, std::vector<T>{
+                2, 1, 1, 2, 3, 3,
+                2, 1, 1, 2, 3, 3,
+                5, 4, 4, 5, 6, 6,
+                8, 7, 7, 8, 9, 9,
+                8, 7, 7, 8, 9, 9,
+                5, 4, 4, 5, 6, 6,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_2d_symmetric_0"),
+        PadParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {4, 5}, std::vector<T>{
+                2, 1, 1, 2, 3,
+                2, 1, 1, 2, 3,
+                5, 4, 4, 5, 6,
+                8, 7, 7, 8, 9,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_2d_symmetric_1"),
+        PadParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 1}),
+            Tensor(ET, {5, 4}, std::vector<T>{
+                1, 2, 3, 3,
+                4, 5, 6, 6,
+                7, 8, 9, 9,
+                7, 8, 9, 9,
+                4, 5, 6, 6,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_2d_symmetric"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{4}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{5}),
+            Tensor(ET, {15}, std::vector<T>{
+                2112, 2112, 2112, 2112, 1, 2, 3, 4, 5, 6, 2112, 2112, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_exterior_1d"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{4}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-2}),
+            Tensor(ET, {8}, std::vector<T>{
+                2112, 2112, 2112, 2112, 1, 2, 3, 4,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_negative_exterior_1d"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{4}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-7}),
+            Tensor(ET, {3}, std::vector<T>{
+                2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_negative_exterior_1d_check_limits"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {11}, std::vector<T>{
+                1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6,
+            }),
+            op::PadMode::EDGE,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_edge_1d"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-3}),
+            Tensor(ET, {5}, std::vector<T>{
+                1, 1, 1, 2, 3,
+            }),
+            op::PadMode::EDGE,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_edge_1d_top_neg"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-7}),
+            Tensor(ET, {1}, std::vector<T>{
+                1,
+            }),
+            op::PadMode::EDGE,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_edge_1d_top_neg_bigger_than_tensor"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {7}, std::vector<T>{
+                3, 4, 5, 6, 6, 6, 6,
+            }),
+            op::PadMode::EDGE,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_edge_1d_bottom_neg"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-7}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {2}, std::vector<T>{
+                6, 6,
+            }),
+            op::PadMode::EDGE,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_edge_1d_bottom_neg_bigger_than_tensor"),
+
+        PadParams(
+            Tensor(ET, {3, 4}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 3}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {6, 9}, std::vector<T>{
+                1, 1, 1, 1, 2,  3,  4,  4,  4,
+                1, 1, 1, 1, 2,  3,  4,  4,  4,
+                1, 1, 1, 1, 2,  3,  4,  4,  4,
+                5, 5, 5, 5, 6,  7,  8,  8,  8,
+                9, 9, 9, 9, 10, 11, 12, 12, 12,
+                9, 9, 9, 9, 10, 11, 12, 12, 12,
+            }),
+            op::PadMode::EDGE,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_edge_2d"),
+
+        PadParams(
+            Tensor(ET, {3, 4}, std::vector<T>{
+                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, -1}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {6, 5}, std::vector<T>{
+                2,  3,  4,  4,  4,
+                2,  3,  4,  4,  4,
+                2,  3,  4,  4,  4,
+                6,  7,  8,  8,  8,
+                10, 11, 12, 12, 12,
+                10, 11, 12, 12, 12,
+            }),
+            op::PadMode::EDGE,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_edge_2d_with_neg"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {11}, std::vector<T>{
+                3, 2, 1, 2, 3, 4, 5, 6, 5, 4, 3,
+            }),
+            op::PadMode::REFLECT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_reflect_1d"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-3}),
+            Tensor(ET, {5}, std::vector<T>{
+                3, 2, 1, 2, 3,
+            }),
+            op::PadMode::REFLECT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_reflect_1d_top_neg"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-7}),
+            Tensor(ET, {1}, std::vector<T>{
+                3,
+            }),
+            op::PadMode::REFLECT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_reflect_1d_top_neg_bigger_than_tensor"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-2}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {7}, std::vector<T>{
+                3, 4, 5, 6, 5, 4, 3,
+            }),
+            op::PadMode::REFLECT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_reflect_1d_bottom_neg"),
+
+        PadParams(
+            Tensor(ET, {6}, std::vector<T>{
+                1, 2, 3, 4, 5, 6,
+            }),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{-7}),
+            Tensor(ET_INT, {1}, std::vector<T_INT>{3}),
+            Tensor(ET, {2}, std::vector<T>{
+                4, 3,
+            }),
+            op::PadMode::REFLECT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_reflect_1d_bottom_neg_bigger_than_tensor"),
+
+        PadParams(
+            Tensor(ET, {3, 4}, std::vector<T>{
+                1, 2,  3,  4,
+                5, 6,  7,  8,
+                9, 10, 11, 12,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 3}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {6, 9}, std::vector<T>{
+                12, 11, 10, 9, 10, 11, 12, 11, 10,
+                8,  7,  6,  5, 6,  7,  8,  7,  6,
+                4,  3,  2,  1, 2,  3,  4,  3,  2,
+                8,  7,  6,  5, 6,  7,  8,  7,  6,
+                12, 11, 10, 9, 10, 11, 12, 11, 10,
+                8,  7,  6,  5, 6,  7,  8,  7,  6,
+            }),
+            op::PadMode::REFLECT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_reflect_2d"),
+
+        PadParams(
+            Tensor(ET, {3, 4}, std::vector<T>{
+                1, 2,  3,  4,
+                5, 6,  7,  8,
+                9, 10, 11, 12,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, -1}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {6, 5}, std::vector<T>{
+                10, 11, 12, 11, 10,
+                6,  7,  8,  7,  6,
+                2,  3,  4,  3,  2,
+                6,  7,  8,  7,  6,
+                10, 11, 12, 11, 10,
+                6,  7,  8,  7,  6,
+            }),
+            op::PadMode::REFLECT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_reflect_2d_with_neg"),
+
+        PadParams(
+            Tensor(ET, {2, 3}, std::vector<T>{
+                1, 2, 3,
+                4, 5, 6,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, -1}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 0}),
+            Tensor(ET, {5, 2}, std::vector<T>{
+                9, 9,
+                2, 3,
+                5, 6,
+                9, 9,
+                9, 9,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{9}),
+            "pad_negative_exterior_2d"),
+
+        PadParams(
+            Tensor(ET, {3, 3}, std::vector<T>{
+                1, 2, 3,
+                4, 5, 6,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{-1, -1}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{-1, -1}),
+            Tensor(ET, {1, 1}, std::vector<T>{5}),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{9}),
+            "pad_negative_exterior_2d_all_negative"),
+
+        PadParams(
+            Tensor(ET, {0, 0}, std::vector<T>{}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 3}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{3, 2}),
+            Tensor(ET, {5, 5}, std::vector<T>{
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_exterior_2d_0x0"),
+
+        PadParams(
+            Tensor(ET, {0, 3}, std::vector<T>{}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{2, 1}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{3, 1}),
+            Tensor(ET, {5, 5}, std::vector<T>{
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_exterior_2d_0x3"),
+
+        PadParams(
+            Tensor(ET, {3, 0}, std::vector<T>{}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 3}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {5, 5}, std::vector<T>{
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+                2112, 2112, 2112, 2112, 2112,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_exterior_2d_3x0"),
+
+        PadParams(
+            Tensor(ET, {2, 2, 4, 4}, std::vector<T>{
+                0, 1, 0, 2,
+                0, 3, 2, 0,
+                2, 0, 0, 0,
+                0, 2, 1, 0,
+
+                0, 0, 0, 2,
+                0, 2, 3, 0,
+                2, 0, 1, 0,
+                2, 0, 0, 0,
+
+                0, 2, 1, 1,
+                0, 0, 2, 0,
+                0, 0, 1, 2,
+                0, 0, 0, 0,
+
+                2, 1, 0, 0,
+                0, 2, 0, 0,
+                1, 1, 2, 0,
+                1, 0, 0, 0,
+            }),
+            Tensor(ET_INT, {4}, std::vector<T_INT>{0, 0, 0, 0}),
+            Tensor(ET_INT, {4}, std::vector<T_INT>{0, 0, 2, 2}),
+            Tensor(ET, {2, 2, 6, 6}, std::vector<T>{
+                0,  1,  0,  2,  42, 42,
+                0,  3,  2,  0,  42, 42,
+                2,  0,  0,  0,  42, 42,
+                0,  2,  1,  0,  42, 42,
+                42, 42, 42, 42, 42, 42,
+                42, 42, 42, 42, 42, 42,
+
+                0,  0,  0,  2,  42, 42,
+                0,  2,  3,  0,  42, 42,
+                2,  0,  1,  0,  42, 42,
+                2,  0,  0,  0,  42, 42,
+                42, 42, 42, 42, 42, 42,
+                42, 42, 42, 42, 42, 42,
+
+                0,  2,  1,  1,  42, 42,
+                0,  0,  2,  0,  42, 42,
+                0,  0,  1,  2,  42, 42,
+                0,  0,  0,  0,  42, 42,
+                42, 42, 42, 42, 42, 42,
+                42, 42, 42, 42, 42, 42,
+
+                2,  1,  0,  0,  42, 42,
+                0,  2,  0,  0,  42, 42,
+                1,  1,  2,  0,  42, 42,
+                1,  0,  0,  0,  42, 42,
+                42, 42, 42, 42, 42, 42,
+                42, 42, 42, 42, 42, 42,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{42}),
+            "pad_2channel_2image_asym"),
+
+        PadParams(
+            Tensor(ET, {2, 3}, std::vector<T>{
+                1, 2, 3,
+                4, 5, 6,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{1, 2}),
+            Tensor(ET, {4, 7}, std::vector<T>{
+                2, 1, 1, 2, 3, 3, 2,
+                2, 1, 1, 2, 3, 3, 2,
+                5, 4, 4, 5, 6, 6, 5,
+                5, 4, 4, 5, 6, 6, 5,
+            }),
+            op::PadMode::SYMMETRIC,
+            Tensor(ET, {}, std::vector<T>{2112}),
+            "pad_symmetric"),
+    };
+    return params;
+}
+
+template <element::Type_t ET, element::Type_t ET_INT>
+std::vector<PadParams> generateParamsFloatValue() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_INT = typename element_type_traits<ET_INT>::value_type;
+    std::vector<PadParams> params {
+        PadParams(
+            Tensor(ET, {1, 2, 2, 2}, std::vector<T>{
+                0.0f, 0.0f,
+                0.0f, 0.0f,
+                0.0f, 0.0f,
+                0.0f, 0.0f,
+            }),
+            Tensor(ET_INT, {4}, std::vector<T_INT>{0, 0, 1, 1}),
+            Tensor(ET_INT, {4}, std::vector<T_INT>{0, 0, 1, 1}),
+            Tensor(ET, {1, 2, 4, 4}, std::vector<T>{
+                42.0f, 42.0f, 42.0f, 42.0f,
+                42.0f, 0.0f, 0.0f, 42.0f,
+                42.0f, 0.0f, 0.0f, 42.0f,
+                42.0f, 42.0f, 42.0f, 42.0f,
+                42.0f, 42.0f, 42.0f, 42.0f,
+                42.0f, 0.0f, 0.0f, 42.0f,
+                42.0f, 0.0f, 0.0f, 42.0f,
+                42.0f, 42.0f, 42.0f, 42.0f,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{42}),
+            "pad_exterior_4d_1x2x2x2"),
+
+        PadParams(
+            Tensor(ET, {1, 3, 2, 2}, std::vector<T>{
+                0.0f, 0.0f,
+                0.0f, 0.0f,
+                1.0f, 1.0f,
+                1.0f, 1.0f,
+                2.0f, 2.0f,
+                2.0f, 2.0f,
+            }),
+            Tensor(ET_INT, {4}, std::vector<T_INT>{0, -1, 1, 1}),
+            Tensor(ET_INT, {4}, std::vector<T_INT>{0, -1, 1, 1}),
+            Tensor(ET, {1, 1, 4, 4}, std::vector<T>{
+                42.0f, 42.0f, 42.0f, 42.0f,
+                42.0f, 1.0f,  1.0f,  42.0f,
+                42.0f, 1.0f,  1.0f,  42.0f,
+                42.0f, 42.0f, 42.0f, 42.0f,
+            }),
+            op::PadMode::CONSTANT,
+            Tensor(ET, {}, std::vector<T>{42}),
+            "pad_negative_exterior_4d"),
+    };
+    return params;
+}
+
+std::vector<PadParams> generateCombinedParams() {
+    const std::vector<std::vector<PadParams>> generatedParams {
+        generateParams<element::Type_t::i16, element::Type_t::i32>(),
+        generateParams<element::Type_t::i32, element::Type_t::i32>(),
+        generateParams<element::Type_t::i64, element::Type_t::i32>(),
+        generateParams<element::Type_t::u16, element::Type_t::i32>(),
+        generateParams<element::Type_t::u32, element::Type_t::i32>(),
+        generateParams<element::Type_t::u64, element::Type_t::i32>(),
+        generateParams<element::Type_t::bf16, element::Type_t::i32>(),
+        generateParams<element::Type_t::f16, element::Type_t::i32>(),
+        generateParams<element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::f64, element::Type_t::i32>(),
+        generateParams<element::Type_t::i16, element::Type_t::i64>(),
+        generateParams<element::Type_t::i32, element::Type_t::i64>(),
+        generateParams<element::Type_t::i64, element::Type_t::i64>(),
+        generateParams<element::Type_t::u16, element::Type_t::i64>(),
+        generateParams<element::Type_t::u32, element::Type_t::i64>(),
+        generateParams<element::Type_t::u64, element::Type_t::i64>(),
+        generateParams<element::Type_t::bf16, element::Type_t::i64>(),
+        generateParams<element::Type_t::f16, element::Type_t::i64>(),
+        generateParams<element::Type_t::f32, element::Type_t::i64>(),
+        generateParams<element::Type_t::f64, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::bf16, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::f16, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::f32, element::Type_t::i64>(),
+        generateParamsFloatValue<element::Type_t::f64, element::Type_t::i64>(),
+    };
+    std::vector<PadParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_Pad_With_Hardcoded_Refs, ReferencePadTest,
+    testing::ValuesIn(generateCombinedParams()), ReferencePadTest::getTestCaseName);
+
+template <element::Type_t ET, element::Type_t ET_INT>
+std::vector<PadParams> generateParamsTooLarge() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_INT = typename element_type_traits<ET_INT>::value_type;
+    std::vector<PadParams> params {
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 4, 5,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 3}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {2, 5}, std::vector<T>{
+                0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_to_large_symmetric_padding"),
+
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 4, 5,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {2, 4}, std::vector<T>{
+                0, 0, 0, 0, 0, 0, 0, 0,
+            }),
+            op::PadMode::REFLECT,
+            "pad_to_large_reflect_padding"),
+    };
+    return params;
+}
+
+std::vector<PadParams> generateCombinedParamsTooLarge() {
+    const std::vector<std::vector<PadParams>> generatedParams {
+        generateParamsTooLarge<element::Type_t::f32, element::Type_t::i64>(),
+    };
+    std::vector<PadParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_Pad_With_Hardcoded_Refs, ReferencePadTestParamsTooLarge,
+    testing::ValuesIn(generateCombinedParamsTooLarge()), ReferencePadTest::getTestCaseName);
+
+template <element::Type_t ET, element::Type_t ET_INT>
+std::vector<PadParams> generateParamsOk() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_INT = typename element_type_traits<ET_INT>::value_type;
+    std::vector<PadParams> params {
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 4, 5,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 2}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {2, 4}, std::vector<T>{
+                2, 1, 1, 2, 5, 4, 4, 5,
+            }),
+            op::PadMode::SYMMETRIC,
+            "pad_ok_symmetric_padding"),
+
+        PadParams(
+            Tensor(ET, {2, 2}, std::vector<T>{
+                1, 2, 4, 5,
+            }),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 1}),
+            Tensor(ET_INT, {2}, std::vector<T_INT>{0, 0}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                2, 1, 2, 5, 4, 5,
+            }),
+            op::PadMode::REFLECT,
+            "pad_ok_reflect_padding"),
+    };
+    return params;
+}
+
+std::vector<PadParams> generateCombinedParamsOk() {
+    const std::vector<std::vector<PadParams>> generatedParams {
+        generateParamsOk<element::Type_t::f32, element::Type_t::i64>(),
+    };
+    std::vector<PadParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_Pad_With_Hardcoded_Refs, ReferencePadTestParamsOk,
+    testing::ValuesIn(generateCombinedParamsOk()), ReferencePadTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/prior_box.cpp b/docs/template_plugin/tests/functional/op_reference/prior_box.cpp
new file mode 100644
index 00000000000000..e24ae7a72b05a4
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/prior_box.cpp
@@ -0,0 +1,126 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/prior_box.hpp"
+#include "base_reference_test.hpp"
+#include "openvino/opsets/opset1.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct PriorBoxParams {
+    template <class IT>
+    PriorBoxParams(const std::vector<float>& min_size,
+                   const std::vector<float>& aspect_ratio,
+                   const bool scale_all_size,
+                   const ov::Shape& layerShapeShape, const ov::Shape& imageShapeShape,
+                   const ov::element::Type& iType,
+                   const std::vector<IT>& layerShapeValues, const std::vector<IT>& imageShapeValues,
+                   const std::vector<float>& oValues,
+                   const std::string& testcaseName = "")
+        : layerShapeShape(layerShapeShape),
+          imageShapeShape(imageShapeShape),
+          inType(iType),
+          outType(ov::element::Type_t::f32),
+          layerShapeData(CreateTensor(iType, layerShapeValues)),
+          imageShapeData(CreateTensor(iType, imageShapeValues)),
+          refData(CreateTensor(outType, oValues)),
+          testcaseName(testcaseName) {
+              attrs.min_size = min_size;
+              attrs.aspect_ratio = aspect_ratio;
+              attrs.scale_all_sizes = scale_all_size;
+          }
+
+    ov::op::v0::PriorBox::Attributes attrs;
+    ov::Shape layerShapeShape;
+    ov::Shape imageShapeShape;
+    ov::element::Type inType;
+    ov::element::Type outType;
+    ov::runtime::Tensor layerShapeData;
+    ov::runtime::Tensor imageShapeData;
+    ov::runtime::Tensor refData;
+    std::string testcaseName;
+};
+
+class ReferencePriorBoxLayerTest : public testing::TestWithParam<PriorBoxParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {};
+        refOutData = {params.refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<PriorBoxParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "layerShapeShape=" << param.layerShapeShape << "_";
+        result << "imageShapeShape=" << param.imageShapeShape << "_";
+        result << "iType=" << param.inType << "_";
+        result << "oType=" << param.outType;
+        if (param.testcaseName != "")
+            result << "_" << param.testcaseName;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const PriorBoxParams& params) {
+        auto LS = std::make_shared<opset1::Constant>(params.inType, params.layerShapeShape, params.layerShapeData.data());
+        auto IS = std::make_shared<opset1::Constant>(params.inType, params.imageShapeShape, params.imageShapeData.data());
+        const auto PriorBox = std::make_shared<op::v0::PriorBox>(LS, IS, params.attrs);
+        return std::make_shared<ov::Function>(NodeVector {PriorBox}, ParameterVector {});
+    }
+};
+
+TEST_P(ReferencePriorBoxLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<PriorBoxParams> generatePriorBoxFloatParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+
+    std::vector<PriorBoxParams> priorBoxParams {
+        PriorBoxParams({2.0f}, {1.5f}, false,
+                       {2}, {2},
+                       IN_ET,
+                       std::vector<T>{2, 2},
+                       std::vector<T>{10, 10},
+                       std::vector<float>{-0.75, -0.75, 1.25, 1.25, -0.974745, -0.566497,  1.47474, 1.0665,
+                                          -0.25, -0.75, 1.75, 1.25, -0.474745, -0.566497,  1.97474, 1.0665,
+                                          -0.75, -0.25, 1.25, 1.75, -0.974745, -0.0664966, 1.47474, 1.5665,
+                                          -0.25, -0.25, 1.75, 1.75, -0.474745, -0.0664966, 1.97474, 1.5665,
+                                          0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1,
+                                          0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1,
+                                          0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1,
+                                          0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1}),
+    };
+    return priorBoxParams;
+}
+
+std::vector<PriorBoxParams> generatePriorBoxCombinedParams() {
+    const std::vector<std::vector<PriorBoxParams>> priorBoxTypeParams {
+        generatePriorBoxFloatParams<element::Type_t::i64>(),
+        generatePriorBoxFloatParams<element::Type_t::i32>(),
+        generatePriorBoxFloatParams<element::Type_t::i16>(),
+        generatePriorBoxFloatParams<element::Type_t::i8>(),
+        generatePriorBoxFloatParams<element::Type_t::u64>(),
+        generatePriorBoxFloatParams<element::Type_t::u32>(),
+        generatePriorBoxFloatParams<element::Type_t::u16>(),
+        generatePriorBoxFloatParams<element::Type_t::u8>(),
+        };
+    std::vector<PriorBoxParams> combinedParams;
+
+    for (const auto& params : priorBoxTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_PriorBox_With_Hardcoded_Refs, ReferencePriorBoxLayerTest,
+    testing::ValuesIn(generatePriorBoxCombinedParams()), ReferencePriorBoxLayerTest::getTestCaseName);
+
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/prior_box_clustered.cpp b/docs/template_plugin/tests/functional/op_reference/prior_box_clustered.cpp
new file mode 100644
index 00000000000000..33b2ce5bdabb57
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/prior_box_clustered.cpp
@@ -0,0 +1,134 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/prior_box.hpp"
+#include "base_reference_test.hpp"
+#include "openvino/opsets/opset1.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct PriorBoxClusteredParams {
+    template <class IT>
+    PriorBoxClusteredParams(const std::vector<float>& widths,
+                            const std::vector<float>& heights,
+                            const bool clip,
+                            const ov::Shape& layerShapeShape, const ov::Shape& imageShapeShape,
+                            const ov::element::Type& iType,
+                            const std::vector<IT>& layerShapeValues, const std::vector<IT>& imageShapeValues,
+                            const std::vector<float>& oValues,
+                            const std::vector<float>& variances = {},
+                            const std::string& testcaseName = "")
+        : layerShapeShape(layerShapeShape),
+          imageShapeShape(imageShapeShape),
+          inType(iType),
+          outType(ov::element::Type_t::f32),
+          layerShapeData(CreateTensor(iType, layerShapeValues)),
+          imageShapeData(CreateTensor(iType, imageShapeValues)),
+          refData(CreateTensor(outType, oValues)),
+          testcaseName(testcaseName) {
+              attrs.widths = widths;
+              attrs.heights = heights;
+              attrs.clip = clip;
+              if ( variances.size() != 0)
+                attrs.variances = variances;
+          }
+
+    ov::op::v0::PriorBoxClustered::Attributes attrs;
+    ov::Shape layerShapeShape;
+    ov::Shape imageShapeShape;
+    ov::element::Type inType;
+    ov::element::Type outType;
+    ov::runtime::Tensor layerShapeData;
+    ov::runtime::Tensor imageShapeData;
+    ov::runtime::Tensor refData;
+    std::string testcaseName;
+};
+
+class ReferencePriorBoxClusteredLayerTest : public testing::TestWithParam<PriorBoxClusteredParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {};
+        refOutData = {params.refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<PriorBoxClusteredParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "layerShapeShape=" << param.layerShapeShape << "_";
+        result << "imageShapeShape=" << param.imageShapeShape << "_";
+        result << "variancesSize=" << param.attrs.variances.size() << "_";
+        result << "iType=" << param.inType << "_";
+        result << "oType=" << param.outType;
+        if (param.testcaseName != "")
+            result << "_" << param.testcaseName;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const PriorBoxClusteredParams& params) {
+        auto LS = std::make_shared<opset1::Constant>(params.inType, params.layerShapeShape, params.layerShapeData.data());
+        auto IS = std::make_shared<opset1::Constant>(params.inType, params.imageShapeShape, params.imageShapeData.data());
+        const auto PriorBoxClustered = std::make_shared<op::v0::PriorBoxClustered>(LS, IS, params.attrs);
+        return std::make_shared<ov::Function>(NodeVector {PriorBoxClustered}, ParameterVector {});
+    }
+};
+
+TEST_P(ReferencePriorBoxClusteredLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<PriorBoxClusteredParams> generatePriorBoxClusteredFloatParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+
+    std::vector<PriorBoxClusteredParams> priorBoxClusteredParams {
+        PriorBoxClusteredParams({3.0f}, {3.0f}, true,
+                       {2}, {2},
+                       IN_ET,
+                       std::vector<T>{2, 2},
+                       std::vector<T>{10, 10},
+                       std::vector<float>{0,        0,        0.15f,    0.15f,    0.34999f, 0,    0.64999f, 0.15f, 0,    0.34999f, 0.15f,
+                                          0.64999f, 0.34999f, 0.34999f, 0.64999f, 0.64999f, 0.1f, 0.1f,     0.1f,  0.1f, 0.1f,     0.1f,
+                                          0.1f,     0.1f,     0.1f,     0.1f,     0.1f,     0.1f, 0.1f,     0.1f,  0.1f, 0.1f}),
+        PriorBoxClusteredParams({3.0f}, {3.0f}, true,
+                       {2}, {2},
+                       IN_ET,
+                       std::vector<T>{2, 2},
+                       std::vector<T>{10, 10},
+                       std::vector<float>{0,        0,        0.15f,    0.15f,    0.34999f, 0,    0.64999f, 0.15f, 0,    0.34999f, 0.15f,
+                                          0.64999f, 0.34999f, 0.34999f, 0.64999f, 0.64999f, 0.1f, 0.2f,     0.3f,  0.4f, 0.1f,     0.2f,
+                                          0.3f,     0.4f,     0.1f,     0.2f,     0.3f,     0.4f, 0.1f,     0.2f,  0.3f, 0.4f},
+                       {0.1f, 0.2f, 0.3f, 0.4f}),
+    };
+    return priorBoxClusteredParams;
+}
+
+std::vector<PriorBoxClusteredParams> generatePriorBoxClusteredCombinedParams() {
+    const std::vector<std::vector<PriorBoxClusteredParams>> priorBoxClusteredTypeParams {
+        generatePriorBoxClusteredFloatParams<element::Type_t::i64>(),
+        generatePriorBoxClusteredFloatParams<element::Type_t::i32>(),
+        generatePriorBoxClusteredFloatParams<element::Type_t::i16>(),
+        generatePriorBoxClusteredFloatParams<element::Type_t::i8>(),
+        generatePriorBoxClusteredFloatParams<element::Type_t::u64>(),
+        generatePriorBoxClusteredFloatParams<element::Type_t::u32>(),
+        generatePriorBoxClusteredFloatParams<element::Type_t::u16>(),
+        generatePriorBoxClusteredFloatParams<element::Type_t::u8>(),
+        };
+    std::vector<PriorBoxClusteredParams> combinedParams;
+
+    for (const auto& params : priorBoxClusteredTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_PriorBoxClustered_With_Hardcoded_Refs, ReferencePriorBoxClusteredLayerTest,
+    testing::ValuesIn(generatePriorBoxClusteredCombinedParams()), ReferencePriorBoxClusteredLayerTest::getTestCaseName);
+
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/reorg_yolo.cpp b/docs/template_plugin/tests/functional/op_reference/reorg_yolo.cpp
new file mode 100644
index 00000000000000..b9c2618e56ad6a
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/reorg_yolo.cpp
@@ -0,0 +1,123 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/reorg_yolo.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct ReorgYoloParams {
+    template <class IT>
+    ReorgYoloParams(const ov::Strides& stride,
+                    const ov::PartialShape& inputShape,
+                    const ov::element::Type& iType,
+                    const std::vector<IT>& oValues,
+                    const std::string& testcaseName = "")
+        : stride(stride),
+          inputShape(inputShape),
+          inType(iType),
+          outType(iType),
+          refData(CreateTensor(iType, oValues)),
+          testcaseName(testcaseName) {
+              std::vector<IT> iValues(shape_size(inputShape.get_shape()));
+              std::iota(iValues.begin(), iValues.end(), 0);
+              inputData = CreateTensor(iType, iValues);
+          }
+
+    ov::Strides stride;
+    ov::PartialShape inputShape;
+    ov::element::Type inType;
+    ov::element::Type outType;
+    ov::runtime::Tensor inputData;
+    ov::runtime::Tensor refData;
+    std::string testcaseName;
+};
+
+class ReferenceReorgYoloLayerTest : public testing::TestWithParam<ReorgYoloParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.inputData};
+        refOutData = {params.refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<ReorgYoloParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "inputShape=" << param.inputShape << "_";
+        result << "iType=" << param.inType << "_";
+        result << "oType=" << param.outType << "_";
+        result << "stride=" << param.stride;
+        if (param.testcaseName != "")
+            result << "_" << param.testcaseName;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const ReorgYoloParams& params) {
+        const auto p = std::make_shared<op::v0::Parameter>(params.inType, params.inputShape);
+        const auto ReorgYolo = std::make_shared<op::v0::ReorgYolo>(p, params.stride);
+        return std::make_shared<ov::Function>(NodeVector {ReorgYolo}, ParameterVector {p});
+    }
+};
+
+TEST_P(ReferenceReorgYoloLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<ReorgYoloParams> generateReorgYoloParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+
+    std::vector<ReorgYoloParams> reorgYoloParams {
+        ReorgYoloParams({2},
+                        PartialShape {1, 8, 4, 4},
+                        IN_ET,
+                        std::vector<T>{0,   2,   4,   6,   16,  18,  20,  22,  32,  34,  36,  38,  48,  50,  52,  54,  64,  66,  68,  70,  80, 82,
+                                       84,  86,  96,  98,  100, 102, 112, 114, 116, 118, 1,   3,   5,   7,   17,  19,  21,  23,  33,  35,  37, 39,
+                                       49,  51,  53,  55,  65,  67,  69,  71,  81,  83,  85,  87,  97,  99,  101, 103, 113, 115, 117, 119, 8,  10,
+                                       12,  14,  24,  26,  28,  30,  40,  42,  44,  46,  56,  58,  60,  62,  72,  74,  76,  78,  88,  90,  92, 94,
+                                       104, 106, 108, 110, 120, 122, 124, 126, 9,   11,  13,  15,  25,  27,  29,  31,  41,  43,  45,  47,  57, 59,
+                                       61,  63,  73,  75,  77,  79,  89,  91,  93,  95,  105, 107, 109, 111, 121, 123, 125, 127}),
+        ReorgYoloParams({3},
+                        PartialShape {1, 9, 3, 3},
+                        IN_ET,
+                        std::vector<T>{0,  3,  6,  27, 30, 33, 54, 57, 60, 1,  4,  7,  28, 31, 34, 55, 58, 61, 2,  5,  8,  29, 32, 35, 56, 59, 62,
+                                       9,  12, 15, 36, 39, 42, 63, 66, 69, 10, 13, 16, 37, 40, 43, 64, 67, 70, 11, 14, 17, 38, 41, 44, 65, 68, 71,
+                                       18, 21, 24, 45, 48, 51, 72, 75, 78, 19, 22, 25, 46, 49, 52, 73, 76, 79, 20, 23, 26, 47, 50, 53, 74, 77, 80}),
+    };
+    return reorgYoloParams;
+}
+
+std::vector<ReorgYoloParams> generateReorgYoloCombinedParams() {
+    const std::vector<std::vector<ReorgYoloParams>> reorgYoloTypeParams {
+        generateReorgYoloParams<element::Type_t::f64>(),
+        generateReorgYoloParams<element::Type_t::f32>(),
+        generateReorgYoloParams<element::Type_t::f16>(),
+        generateReorgYoloParams<element::Type_t::bf16>(),
+        generateReorgYoloParams<element::Type_t::i64>(),
+        generateReorgYoloParams<element::Type_t::i32>(),
+        generateReorgYoloParams<element::Type_t::i16>(),
+        generateReorgYoloParams<element::Type_t::i8>(),
+        generateReorgYoloParams<element::Type_t::u64>(),
+        generateReorgYoloParams<element::Type_t::u32>(),
+        generateReorgYoloParams<element::Type_t::u16>(),
+        generateReorgYoloParams<element::Type_t::u8>(),
+        };
+    std::vector<ReorgYoloParams> combinedParams;
+
+    for (const auto& params : reorgYoloTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_ReorgYolo_With_Hardcoded_Refs, ReferenceReorgYoloLayerTest,
+    testing::ValuesIn(generateReorgYoloCombinedParams()), ReferenceReorgYoloLayerTest::getTestCaseName);
+
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/rnn_cell.cpp b/docs/template_plugin/tests/functional/op_reference/rnn_cell.cpp
new file mode 100644
index 00000000000000..5bb43841029794
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/rnn_cell.cpp
@@ -0,0 +1,304 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/rnn_cell.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct RNNCellParams {
+    RNNCellParams(
+        int32_t batchSize, int32_t inputSize, int32_t hiddenSize,
+        const Tensor& X, const Tensor& H_t, const Tensor& W, const Tensor& R, const Tensor& B,
+        const Tensor& Ho, const std::string& testcaseName = "") :
+        batchSize(batchSize), inputSize(inputSize), hiddenSize(hiddenSize),
+        X(X), H_t(H_t), W(W), R(R), B(B), Ho(Ho), testcaseName(testcaseName) {}
+
+    int32_t batchSize;
+    int32_t inputSize;
+    int32_t hiddenSize;
+    Tensor X;
+    Tensor H_t;
+    Tensor W;
+    Tensor R;
+    Tensor B;
+    Tensor Ho;
+    std::string testcaseName;
+};
+
+class ReferenceRNNCellTest : public testing::TestWithParam<RNNCellParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.X.data, params.H_t.data, params.W.data, params.R.data, params.B.data};
+        refOutData = {params.Ho.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<RNNCellParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "bSize=" << param.batchSize;
+        result << "_iSize=" << param.inputSize;
+        result << "_hSize=" << param.hiddenSize;
+        result << "_xType=" << param.X.type;
+        result << "_xShape=" << param.X.shape;
+        result << "_htType=" << param.H_t.type;
+        result << "_htShape=" << param.H_t.shape;
+        result << "_wType=" << param.W.type;
+        result << "_wShape=" << param.W.shape;
+        result << "_rType=" << param.R.type;
+        result << "_rShape=" << param.R.shape;
+        result << "_hoType=" << param.Ho.type;
+        if (param.testcaseName != "") {
+            result << "_hoShape=" << param.Ho.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_hoShape=" << param.Ho.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const RNNCellParams& params) {
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto rnn_cell = std::make_shared<op::v0::RNNCell>(X, H_t, W, R, B, params.hiddenSize);
+        auto function = std::make_shared<Function>(NodeVector{rnn_cell}, ParameterVector{X, H_t, W, R, B});
+        return function;
+    }
+};
+
+class ReferenceRNNCellTestBiasClip : public ReferenceRNNCellTest {
+private:
+    static std::shared_ptr<Function> CreateFunction(const RNNCellParams& params) {
+        float clip = 2.88f;
+
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto rnn_cell = std::make_shared<op::v0::RNNCell>(
+            X, H_t, W, R, B, params.hiddenSize,
+            std::vector<std::string>{"tanh"}, std::vector<float>{}, std::vector<float>{}, clip);
+        auto function = std::make_shared<Function>(NodeVector{rnn_cell}, ParameterVector{X, H_t, W, R, B});
+        return function;
+    }
+};
+
+class ReferenceRNNCellTestSigmoidActivationFunction : public ReferenceRNNCellTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.X.data, params.H_t.data, params.W.data, params.R.data, params.B.data};
+        refOutData = {params.Ho.data};
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const RNNCellParams& params) {
+        float clip = 2.88f;
+
+        const auto X = std::make_shared<op::v0::Parameter>(params.X.type, params.X.shape);
+        const auto H_t = std::make_shared<op::v0::Parameter>(params.H_t.type, params.H_t.shape);
+        const auto W = std::make_shared<op::v0::Parameter>(params.W.type, params.W.shape);
+        const auto R = std::make_shared<op::v0::Parameter>(params.R.type, params.R.shape);
+        const auto B = std::make_shared<op::v0::Parameter>(params.B.type, params.B.shape);
+
+        const auto rnn_cell = std::make_shared<op::v0::RNNCell>(X,
+                                                                H_t,
+                                                                W,
+                                                                R,
+                                                                B,
+                                                                params.hiddenSize,
+                                                                std::vector<std::string>{"sigmoid"},
+                                                                std::vector<float>{},
+                                                                std::vector<float>{},
+                                                                clip);
+        auto function = std::make_shared<Function>(NodeVector{rnn_cell}, ParameterVector{X, H_t, W, R, B});
+        return function;
+    }
+};
+
+TEST_P(ReferenceRNNCellTest, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceRNNCellTestBiasClip, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceRNNCellTestSigmoidActivationFunction, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET>
+std::vector<RNNCellParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<RNNCellParams> params {
+        RNNCellParams(
+            2, 3, 3,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.3432185f, 0.612268f, 0.20272376f, 0.9513413f, 0.30585995f, 0.7265472f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.12444675f, 0.52055854f, 0.46489045f, 0.4983964f, 0.7730452f, 0.28439692f}),
+            Tensor(ET, {3, 3}, std::vector<T>{0.41930267f,
+                                              0.7872176f,
+                                              0.89940447f,
+                                              0.23659843f,
+                                              0.24676207f,
+                                              0.17101714f,
+                                              0.3147149f,
+                                              0.6555601f,
+                                              0.4559603f}),
+            Tensor(ET, {3, 3}, std::vector<T>{0.8374871f,
+                                              0.86660194f,
+                                              0.82114047f,
+                                              0.71549815f,
+                                              0.18775631f,
+                                              0.3182116f,
+                                              0.25392973f,
+                                              0.38301638f,
+                                              0.85531586f}),
+            Tensor(ET, {3}, std::vector<T>{0.0f, 0.0f, 0.0f}),
+            Tensor(ET, {2, 3}, std::vector<T>{0.9408395f, 0.53823817f, 0.84270686f, 0.98932856f, 0.768665f, 0.90461975f}),
+            "rnn_cell_zero_bias_default_attrs"),
+    };
+    return params;
+}
+
+std::vector<RNNCellParams> generateCombinedParams() {
+    const std::vector<std::vector<RNNCellParams>> generatedParams {
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<RNNCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<RNNCellParams> generateParamsBiasClip() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<RNNCellParams> params {
+        RNNCellParams(
+            2, 3, 3,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.3432185f, 0.612268f, 0.20272376f, 0.9513413f, 0.30585995f, 0.7265472f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.12444675f, 0.52055854f, 0.46489045f, 0.4983964f, 0.7730452f, 0.28439692f}),
+            Tensor(ET, {3, 3}, std::vector<T>{0.41930267f,
+                                              0.7872176f,
+                                              0.89940447f,
+                                              0.23659843f,
+                                              0.24676207f,
+                                              0.17101714f,
+                                              0.3147149f,
+                                              0.6555601f,
+                                              0.4559603f}),
+            Tensor(ET, {3, 3}, std::vector<T>{0.8374871f,
+                                              0.86660194f,
+                                              0.82114047f,
+                                              0.71549815f,
+                                              0.18775631f,
+                                              0.3182116f,
+                                              0.25392973f,
+                                              0.38301638f,
+                                              0.85531586f}),
+            Tensor(ET, {3}, std::vector<T>{1.0289404f, 1.6362579f, 0.4370661f}),
+            Tensor(ET, {2, 3}, std::vector<T>{0.9922437f, 0.97749525f, 0.9312212f, 0.9937176f, 0.9901317f, 0.95906746f}),
+            "rnn_cell_bias_clip"),
+    };
+    return params;
+}
+
+std::vector<RNNCellParams> generateCombinedParamsBiasClip() {
+    const std::vector<std::vector<RNNCellParams>> generatedParams {
+        generateParamsBiasClip<element::Type_t::bf16>(),
+        generateParamsBiasClip<element::Type_t::f16>(),
+        generateParamsBiasClip<element::Type_t::f32>(),
+        generateParamsBiasClip<element::Type_t::f64>(),
+    };
+    std::vector<RNNCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET>
+std::vector<RNNCellParams> generateParamsSigmoidActivationFunction() {
+    using T = typename element_type_traits<ET>::value_type;
+    std::vector<RNNCellParams> params {
+        RNNCellParams(
+            2, 3, 3,
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.3432185f, 0.612268f, 0.20272376f, 0.9513413f, 0.30585995f, 0.7265472f}),
+            Tensor(ET, {2, 3}, std::vector<T>{
+                0.12444675f, 0.52055854f, 0.46489045f, 0.4983964f, 0.7730452f, 0.28439692f}),
+            Tensor(ET, {3, 3}, std::vector<T>{0.41930267f,
+                                              0.7872176f,
+                                              0.89940447f,
+                                              0.23659843f,
+                                              0.24676207f,
+                                              0.17101714f,
+                                              0.3147149f,
+                                              0.6555601f,
+                                              0.4559603f}),
+            Tensor(ET, {3, 3}, std::vector<T>{0.8374871f,
+                                              0.86660194f,
+                                              0.82114047f,
+                                              0.71549815f,
+                                              0.18775631f,
+                                              0.3182116f,
+                                              0.25392973f,
+                                              0.38301638f,
+                                              0.85531586f}),
+            Tensor(ET, {3}, std::vector<T>{1.0289404f, 1.6362579f, 0.4370661f}),
+            Tensor(ET, {2, 3}, std::vector<T>{0.94126844f, 0.9036043f, 0.841243f, 0.9468489f, 0.934215f, 0.873708f}),
+            "rnn_cell_sigmoid_activation_function"),
+    };
+    return params;
+}
+
+std::vector<RNNCellParams> generateCombinedParamsSigmoidActivationFunction() {
+    const std::vector<std::vector<RNNCellParams>> generatedParams {
+        generateParamsSigmoidActivationFunction<element::Type_t::bf16>(),
+        generateParamsSigmoidActivationFunction<element::Type_t::f16>(),
+        generateParamsSigmoidActivationFunction<element::Type_t::f32>(),
+        generateParamsSigmoidActivationFunction<element::Type_t::f64>(),
+    };
+    std::vector<RNNCellParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_RNNCell_With_Hardcoded_Refs, ReferenceRNNCellTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceRNNCellTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_RNNCell_With_Hardcoded_Refs, ReferenceRNNCellTestBiasClip,
+    testing::ValuesIn(generateCombinedParamsBiasClip()), ReferenceRNNCellTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_RNNCell_With_Hardcoded_Refs, ReferenceRNNCellTestSigmoidActivationFunction,
+    testing::ValuesIn(generateCombinedParamsSigmoidActivationFunction()), ReferenceRNNCellTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/roll.cpp b/docs/template_plugin/tests/functional/op_reference/roll.cpp
new file mode 100644
index 00000000000000..3fdb1f4e217865
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/roll.cpp
@@ -0,0 +1,212 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/opsets/opset1.hpp"
+#include "openvino/opsets/opset7.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct RollParams {
+    RollParams(const Tensor& dataTensor, const Tensor& shiftTensor, const Tensor& axesTensor,
+               const Tensor& expectedTensor, const std::string& testcaseName = "") :
+               dataTensor(dataTensor), shiftTensor(shiftTensor), axesTensor(axesTensor),
+               expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor shiftTensor;
+    Tensor axesTensor;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceRollLayerTest : public testing::TestWithParam<RollParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<RollParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_sType=" << param.shiftTensor.type;
+        result << "_sShape=" << param.shiftTensor.shape;
+        result << "_aType=" << param.axesTensor.type;
+        result << "_aShape=" << param.axesTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const RollParams& params) {
+        const auto data = std::make_shared<opset1::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto shift = std::make_shared<opset1::Constant>(params.shiftTensor.type,
+                                                              params.shiftTensor.shape,
+                                                              params.shiftTensor.data.data());
+        const auto axes = std::make_shared<opset1::Constant>(params.axesTensor.type,
+                                                             params.axesTensor.shape,
+                                                             params.axesTensor.data.data());
+        const auto roll = std::make_shared<opset7::Roll>(data, shift, axes);
+        return std::make_shared<Function>(NodeVector {roll}, ParameterVector {data});
+    }
+};
+
+TEST_P(ReferenceRollLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<RollParams> generateRollParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<RollParams> rollParams {
+        // roll_repeated_axes
+        RollParams(
+            Tensor({4, 3}, IN_ET, std::vector<T>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}),
+            Tensor({3}, element::i64, std::vector<int64_t>{1, 2, 1}),
+            Tensor({3}, element::i64, std::vector<int64_t>{0, 1, 0}),
+            Tensor({4, 3}, IN_ET, std::vector<T>{8, 9, 7, 11, 12, 10, 2, 3, 1, 5, 6, 4}),
+            "roll_repeated_axes"),
+
+        // roll_negative_axes
+        RollParams(
+            Tensor({4, 2, 3}, IN_ET, std::vector<T>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}),
+            Tensor({3}, element::i64, std::vector<int64_t>{2, -1, -7}),
+            Tensor({3}, element::i64, std::vector<int64_t>{-1, -1, -2}),
+            Tensor({4, 2, 3}, IN_ET, std::vector<T>{6, 4, 5, 3, 1, 2, 12, 10, 11, 9, 7, 8, 18, 16, 17, 15, 13, 14, 24, 22, 23, 21, 19, 20}),
+            "roll_negative_axes"),
+    };
+    return rollParams;
+}
+
+std::vector<RollParams> generateRollFloatingPointParams() {
+    std::vector<RollParams> rollParams {
+        // roll_2d_input
+        RollParams(
+            Tensor({4, 3}, element::f32, std::vector<float>{50.2907,
+                                                            70.8054,
+                                                            -68.3403,
+                                                            62.6444,
+                                                            4.9748,
+                                                            -18.5551,
+                                                            40.5383,
+                                                            -15.3859,
+                                                            -4.5881,
+                                                            -43.3479,
+                                                            94.1676,
+                                                            -95.7097}),
+            Tensor({1}, element::i64, std::vector<int64_t>{1}),
+            Tensor({1}, element::i64, std::vector<int64_t>{0}),
+            Tensor({4, 3}, element::f32, std::vector<float>{-43.3479,
+                                                            94.1676,
+                                                            -95.7097,
+                                                            50.2907,
+                                                            70.8054,
+                                                            -68.3403,
+                                                            62.6444,
+                                                            4.9748,
+                                                            -18.5551,
+                                                            40.5383,
+                                                            -15.3859,
+                                                            -4.5881}),
+            "roll_2d_input"),
+
+        // roll_2d_input_negative_shift
+        RollParams(
+            Tensor({4, 3}, element::f32, std::vector<float>{50.2907,
+                                                            70.8054,
+                                                            -68.3403,
+                                                            62.6444,
+                                                            4.9748,
+                                                            -18.5551,
+                                                            40.5383,
+                                                            -15.3859,
+                                                            -4.5881,
+                                                            -43.3479,
+                                                            94.1676,
+                                                            -95.7097}),
+            Tensor({2}, element::i64, std::vector<int64_t>{-1, 2}),
+            Tensor({2}, element::i64, std::vector<int64_t>{0, 1}),
+            Tensor({4, 3}, element::f32, std::vector<float>{4.9748,
+                                                            -18.5551,
+                                                            62.6444,
+                                                            -15.3859,
+                                                            -4.5881,
+                                                            40.5383,
+                                                            94.1676,
+                                                            -95.7097,
+                                                            -43.3479,
+                                                            70.8054,
+                                                            -68.3403,
+                                                            50.2907}),
+            "roll_2d_input_negative_shift"),
+
+        // roll_3d_input
+        RollParams(
+            Tensor({4, 2, 3}, element::f32, std::vector<float>{94.0773,  33.0599, 58.1724,  -20.3640, 54.5372, -54.3023, 10.4662, 11.7532,
+                                                               -11.7692, 56.4223, -95.3774, 8.8978,   1.9305,  13.8025,  12.0827, 81.4669,
+                                                               19.5321,  -8.9553, -75.3226, 20.8033,  20.7660, 62.7361,  14.9372, -33.0825}),
+            Tensor({3}, element::i64, std::vector<int64_t>{2, 1, 3}),
+            Tensor({3}, element::i64, std::vector<int64_t>{0, 1, 2}),
+            Tensor({4, 2, 3}, element::f32, std::vector<float>{81.4669,  19.5321,  -8.9553, 1.9305,   13.8025,  12.0827, 62.7361,  14.9372,
+                                                               -33.0825, -75.3226, 20.8033, 20.7660,  -20.3640, 54.5372, -54.3023, 94.0773,
+                                                               33.0599,  58.1724,  56.4223, -95.3774, 8.8978,   10.4662, 11.7532,  -11.7692}),
+            "roll_3d_input"),
+
+        // roll_3d_input_negative_shift
+        RollParams(
+            Tensor({4, 2, 3}, element::f32, std::vector<float>{94.0773,  33.0599, 58.1724,  -20.3640, 54.5372, -54.3023, 10.4662, 11.7532,
+                                                               -11.7692, 56.4223, -95.3774, 8.8978,   1.9305,  13.8025,  12.0827, 81.4669,
+                                                               19.5321,  -8.9553, -75.3226, 20.8033,  20.7660, 62.7361,  14.9372, -33.0825}),
+            Tensor({3}, element::i64, std::vector<int64_t>{-5, 1, 3}),
+            Tensor({3}, element::i64, std::vector<int64_t>{0, 1, 1}),
+            Tensor({4, 2, 3}, element::f32, std::vector<float>{10.4662, 11.7532,  -11.7692, 56.4223, -95.3774, 8.8978,   1.9305,  13.8025,
+                                                               12.0827, 81.4669,  19.5321,  -8.9553, -75.3226, 20.8033,  20.7660, 62.7361,
+                                                               14.9372, -33.0825, 94.0773,  33.0599, 58.1724,  -20.3640, 54.5372, -54.3023}),
+            "roll_3d_input_negative_shift"),
+    };
+    return rollParams;
+}
+
+std::vector<RollParams> generateRollCombinedParams() {
+    const std::vector<std::vector<RollParams>> rollTypeParams {
+        generateRollParams<element::Type_t::i8>(),
+        generateRollParams<element::Type_t::i16>(),
+        generateRollParams<element::Type_t::i32>(),
+        generateRollParams<element::Type_t::i64>(),
+        generateRollParams<element::Type_t::u8>(),
+        generateRollParams<element::Type_t::u16>(),
+        generateRollParams<element::Type_t::u32>(),
+        generateRollParams<element::Type_t::u64>(),
+        generateRollParams<element::Type_t::bf16>(),
+        generateRollParams<element::Type_t::f16>(),
+        generateRollParams<element::Type_t::f32>(),
+        generateRollParams<element::Type_t::f64>(),
+        generateRollFloatingPointParams(),
+    };
+    std::vector<RollParams> combinedParams;
+
+    for (const auto& params : rollTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_Roll_With_Hardcoded_Refs, ReferenceRollLayerTest,
+    testing::ValuesIn(generateRollCombinedParams()), ReferenceRollLayerTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/shuffle_channels.cpp b/docs/template_plugin/tests/functional/op_reference/shuffle_channels.cpp
new file mode 100644
index 00000000000000..1c6c613dba70be
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/shuffle_channels.cpp
@@ -0,0 +1,209 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/opsets/opset1.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct ShuffleChannelsParams {
+    ShuffleChannelsParams(const Tensor& dataTensor, const int32_t axis, const int32_t group,
+               const Tensor& expectedTensor, const std::string& testcaseName = "") :
+               dataTensor(dataTensor), axis(axis), group(group),
+               expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    int32_t axis;
+    int32_t group;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceShuffleChannelsLayerTest : public testing::TestWithParam<ShuffleChannelsParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<ShuffleChannelsParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_axis=" << param.axis;
+        result << "_group=" << param.group;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const ShuffleChannelsParams& params) {
+        const auto data = std::make_shared<opset1::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto function = std::make_shared<opset1::ShuffleChannels>(data, params.axis, params.group);
+        return std::make_shared<Function>(NodeVector {function}, ParameterVector {data});
+    }
+};
+
+TEST_P(ReferenceShuffleChannelsLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<ShuffleChannelsParams> generateParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<ShuffleChannelsParams> params {
+        // shuffle_channels_simple
+        ShuffleChannelsParams(
+            Tensor({1, 15, 2, 2}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59}),
+            1,
+            5,
+            Tensor({1, 15, 2, 2}, IN_ET, std::vector<T>{
+                0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
+                4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
+                8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59}),
+            "shuffle_channels_simple"),
+
+        // shuffle_channels_negative_axis
+        ShuffleChannelsParams(
+            Tensor({15, 2, 1, 2}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59}),
+            -4,
+            5,
+            Tensor({15, 2, 1, 2}, IN_ET, std::vector<T>{
+                0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
+                4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
+                8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59}),
+            "shuffle_channels_negative_axis"),
+
+        // shuffle_channels_float
+        ShuffleChannelsParams(
+            Tensor({6, 1, 1, 1}, IN_ET, std::vector<T>{0, 1, 2, 3, 4, 5}),
+            0,
+            2,
+            Tensor({6, 1, 1, 1}, IN_ET, std::vector<T>{0, 3, 1, 4, 2, 5}),
+            "shuffle_channels_float"),
+
+        // shuffle_channels_1d
+        ShuffleChannelsParams(
+            Tensor({15}, IN_ET, std::vector<T>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}),
+            0,
+            5,
+            Tensor({15}, IN_ET, std::vector<T>{0, 3, 6, 9, 12, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14}),
+            "shuffle_channels_1d"),
+
+        // shuffle_channels_2d
+        ShuffleChannelsParams(
+            Tensor({15, 4}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59}),
+            0,
+            5,
+            Tensor({15, 4}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38,
+                39, 48, 49, 50, 51, 4,  5,  6,  7,  16, 17, 18, 19, 28, 29,
+                30, 31, 40, 41, 42, 43, 52, 53, 54, 55, 8,  9,  10, 11, 20,
+                21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59}),
+            "shuffle_channels_2d"),
+
+        // shuffle_channels_3d
+        ShuffleChannelsParams(
+            Tensor({15, 2, 2}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59}),
+            0,
+            5,
+            Tensor({15, 2, 2}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38,
+                39, 48, 49, 50, 51, 4,  5,  6,  7,  16, 17, 18, 19, 28, 29,
+                30, 31, 40, 41, 42, 43, 52, 53, 54, 55, 8,  9,  10, 11, 20,
+                21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59}),
+            "shuffle_channels_3d"),
+
+        // shuffle_channels_5d
+        ShuffleChannelsParams(
+            Tensor({2, 2, 15, 2, 2}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
+                40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59}),
+            2,
+            5,
+            Tensor({2, 2, 15, 2, 2}, IN_ET, std::vector<T>{
+                0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
+                4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
+                8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59,
+
+                0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
+                4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
+                8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59,
+
+                0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
+                4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
+                8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59,
+
+                0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
+                4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
+                8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59}),
+            "shuffle_channels_5d"),
+    };
+    return params;
+}
+
+std::vector<ShuffleChannelsParams> generateShuffleChannelsCombinedParams() {
+    const std::vector<std::vector<ShuffleChannelsParams>> generatedParams {
+        generateParams<element::Type_t::i8>(),
+        generateParams<element::Type_t::i16>(),
+        generateParams<element::Type_t::i32>(),
+        generateParams<element::Type_t::i64>(),
+        generateParams<element::Type_t::u8>(),
+        generateParams<element::Type_t::u16>(),
+        generateParams<element::Type_t::u32>(),
+        generateParams<element::Type_t::u64>(),
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<ShuffleChannelsParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_ShuffleChannels_With_Hardcoded_Refs, ReferenceShuffleChannelsLayerTest,
+    testing::ValuesIn(generateShuffleChannelsCombinedParams()), ReferenceShuffleChannelsLayerTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/space_to_batch.cpp b/docs/template_plugin/tests/functional/op_reference/space_to_batch.cpp
new file mode 100644
index 00000000000000..42a5c950648734
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/space_to_batch.cpp
@@ -0,0 +1,143 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/opsets/opset1.hpp"
+#include "openvino/opsets/opset2.hpp"
+#include "openvino/op/constant.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct SpaceToBatchParams {
+    SpaceToBatchParams(const Tensor& dataTensor, const Tensor& blockShapeTensor,
+                       const Tensor& padsBeginTensor, const Tensor& padsEndTensor,
+                       const Tensor& expectedTensor, const std::string& testcaseName = "") :
+                  dataTensor(dataTensor), blockShapeTensor(blockShapeTensor),
+                  padsBeginTensor(padsBeginTensor), padsEndTensor(padsEndTensor),
+                  expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    Tensor blockShapeTensor;
+    Tensor padsBeginTensor;
+    Tensor padsEndTensor;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceSpaceToBatchLayerTest : public testing::TestWithParam<SpaceToBatchParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<SpaceToBatchParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_bsType=" << param.blockShapeTensor.type;
+        result << "_bsShape=" << param.blockShapeTensor.shape;
+        result << "_pbType=" << param.padsBeginTensor.type;
+        result << "_pbShape=" << param.padsBeginTensor.shape;
+        result << "_peType=" << param.padsEndTensor.type;
+        result << "_peShape=" << param.padsEndTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const SpaceToBatchParams& params) {
+        const auto data = std::make_shared<opset1::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto blockShape = std::make_shared<opset1::Constant>(element::i64, params.blockShapeTensor.shape, params.blockShapeTensor.data.data());
+        const auto padsBegin = std::make_shared<opset1::Constant>(element::i64, params.padsBeginTensor.shape, params.padsBeginTensor.data.data());
+        const auto padsEnd = std::make_shared<opset1::Constant>(element::i64, params.padsEndTensor.shape, params.padsEndTensor.data.data());
+        const auto batchToSpace = std::make_shared<opset2::SpaceToBatch>(data, blockShape, padsBegin, padsEnd);
+        return std::make_shared<ov::Function>(NodeVector {batchToSpace}, ParameterVector {data});
+    }
+};
+
+TEST_P(ReferenceSpaceToBatchLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<SpaceToBatchParams> generateParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<SpaceToBatchParams> batchToSpaceParams {
+        // space_to_batch_4D
+        SpaceToBatchParams(
+            Tensor({1, 1, 2, 2}, IN_ET, std::vector<T>{1, 1, 1, 1}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 1, 1}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({1, 1, 2, 2}, IN_ET, std::vector<T>{1, 1, 1, 1}),
+            "space_to_batch_4D"),
+
+        // space_to_batch_5D
+         SpaceToBatchParams(
+             Tensor({1, 1, 3, 2, 1}, IN_ET, std::vector<T>{1, 1, 1, 1, 1, 1}),
+             Tensor({5}, element::i64, std::vector<int64_t>{1, 1, 3, 2, 2}),
+             Tensor({5}, element::i64, std::vector<int64_t>{0, 0, 1, 0, 3}),
+             Tensor({5}, element::i64, std::vector<int64_t>{0, 0, 2, 0, 0}),
+             Tensor({12, 1, 2, 1, 2}, IN_ET, std::vector<T>{
+                 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
+                 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
+                 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
+                 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0}),
+             "space_to_batch_5D"),
+
+        // space_to_batch_4x4
+        SpaceToBatchParams(
+            Tensor({1, 1, 4, 4}, IN_ET, std::vector<T>{
+                1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1}),
+            Tensor({4}, element::i64, std::vector<int64_t>{1, 1, 1, 1}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 1, 0}),
+            Tensor({4}, element::i64, std::vector<int64_t>{0, 0, 0, 0}),
+            Tensor({1, 1, 5, 4}, IN_ET, std::vector<T>{
+                0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
+                0, 0, 0, 0, 1, 0, 0, 0, 0, 1}),
+            "space_to_batch_4x4"),
+    };
+    return batchToSpaceParams;
+}
+
+std::vector<SpaceToBatchParams> generateCombinedParams() {
+    const std::vector<std::vector<SpaceToBatchParams>> batchToSpaceTypeParams {
+        generateParams<element::Type_t::i8>(),
+        generateParams<element::Type_t::i16>(),
+        generateParams<element::Type_t::i32>(),
+        generateParams<element::Type_t::i64>(),
+        generateParams<element::Type_t::u8>(),
+        generateParams<element::Type_t::u16>(),
+        generateParams<element::Type_t::u32>(),
+        generateParams<element::Type_t::u64>(),
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<SpaceToBatchParams> combinedParams;
+
+    for (const auto& params : batchToSpaceTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_SpaceToBatch_With_Hardcoded_Refs, ReferenceSpaceToBatchLayerTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceSpaceToBatchLayerTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/space_to_depth.cpp b/docs/template_plugin/tests/functional/op_reference/space_to_depth.cpp
new file mode 100644
index 00000000000000..b834fbeee7d243
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/space_to_depth.cpp
@@ -0,0 +1,169 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/opsets/opset1.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct SpaceToDepthParams {
+    SpaceToDepthParams(const Tensor& dataTensor, const std::string mode, const int32_t blockSize,
+                       const Tensor& expectedTensor, const std::string& testcaseName = "") :
+        dataTensor(dataTensor), mode(mode), blockSize(blockSize), expectedTensor(expectedTensor),
+        testcaseName(testcaseName) {}
+
+    Tensor dataTensor;
+    std::string mode;
+    int32_t blockSize;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceSpaceToDepthLayerTest : public testing::TestWithParam<SpaceToDepthParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.dataTensor.data};
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<SpaceToDepthParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const SpaceToDepthParams& params) {
+        opset1::SpaceToDepth::SpaceToDepthMode mode = params.mode == "DEPTH_FIRST" ?
+            opset1::SpaceToDepth::SpaceToDepthMode::DEPTH_FIRST : opset1::SpaceToDepth::SpaceToDepthMode::BLOCKS_FIRST;
+        const auto data = std::make_shared<opset1::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+        const auto SpaceToDepth = std::make_shared<opset1::SpaceToDepth>(data, mode, params.blockSize);
+        return std::make_shared<Function>(NodeVector {SpaceToDepth}, ParameterVector {data});
+    }
+};
+
+TEST_P(ReferenceSpaceToDepthLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<SpaceToDepthParams> generateParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<SpaceToDepthParams> params {
+        // space_to_depth_block_first_K2_BS2
+        SpaceToDepthParams(
+            Tensor({1, 2, 4, 4}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10,
+                11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
+                22, 23, 24, 25, 26, 27, 28, 29, 30, 31}),
+            "BLOCKS_FIRST",
+            2,
+            Tensor({1, 8, 2, 2}, IN_ET, std::vector<T>{
+                0, 2, 8,  10, 16, 18, 24, 26, 1, 3, 9,  11, 17, 19, 25, 27,
+                4, 6, 12, 14, 20, 22, 28, 30, 5, 7, 13, 15, 21, 23, 29, 31}),
+            "space_to_depth_block_first_K2_BS2"),
+
+        // space_to_depth_block_first_K2_BS3
+        SpaceToDepthParams(
+            Tensor({1, 2, 6, 3}, IN_ET, std::vector<T>{
+                0,  4,  8,  12, 16, 20, 24, 28, 32, 1,  5,  9,
+                13, 17, 21, 25, 29, 33, 2,  6,  10, 14, 18, 22,
+                26, 30, 34, 3,  7,  11, 15, 19, 23, 27, 31, 35}),
+            "BLOCKS_FIRST",
+            3,
+            Tensor({1, 18, 2, 1}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35}),
+            "space_to_depth_block_first_K2_BS3"),
+
+        // space_to_depth_block_first_K1_BS3
+        SpaceToDepthParams(
+            Tensor({1, 2, 6}, IN_ET, std::vector<T>{
+                0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11}),
+            "BLOCKS_FIRST",
+            3,
+            Tensor({1, 6, 2}, IN_ET, std::vector<T>{
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}),
+            "space_to_depth_block_first_K1_BS3"),
+
+        // space_to_depth_depth_first_K2_BS2
+        SpaceToDepthParams(
+            Tensor({1, 2, 4, 4}, IN_ET, std::vector<T>{
+                0,  16, 2,  18, 1,  17, 3,  19, 8,  24, 10,
+                26, 9,  25, 11, 27, 4,  20, 6,  22, 5,  21,
+                7,  23, 12, 28, 14, 30, 13, 29, 15, 31}),
+            "DEPTH_FIRST",
+            2,
+            Tensor({1, 8, 2, 2}, IN_ET, std::vector<T>{
+                0, 2, 8,  10, 16, 18, 24, 26, 1, 3, 9,  11, 17, 19, 25, 27,
+                4, 6, 12, 14, 20, 22, 28, 30, 5, 7, 13, 15, 21, 23, 29, 31}),
+            "space_to_depth_depth_first_K2_BS2"),
+
+        // space_to_depth_depth_first_K2_BS3
+        SpaceToDepthParams(
+            Tensor({1, 2, 6, 3}, IN_ET, std::vector<T>{
+                0,  2,  4,  6,  8,  10, 12, 14, 16, 1,  3,  5,
+                7,  9,  11, 13, 15, 17, 18, 20, 22, 24, 26, 28,
+                30, 32, 34, 19, 21, 23, 25, 27, 29, 31, 33, 35}),
+            "DEPTH_FIRST",
+            3,
+            Tensor({1, 18, 2, 1}, IN_ET, std::vector<T>{
+                0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35}),
+            "space_to_depth_depth_first_K2_BS3"),
+
+        // space_to_depth_depth_first_K1_BS3
+        SpaceToDepthParams(
+            Tensor({1, 2, 6}, IN_ET, std::vector<T>{
+                0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11}),
+            "DEPTH_FIRST",
+            3,
+            Tensor({1, 6, 2}, IN_ET, std::vector<T>{
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}),
+            "space_to_depth_depth_first_K1_BS3"),
+    };
+    return params;
+}
+
+std::vector<SpaceToDepthParams> generateCombinedParams() {
+    const std::vector<std::vector<SpaceToDepthParams>> generatedParams {
+        generateParams<element::Type_t::i8>(),
+        generateParams<element::Type_t::i16>(),
+        generateParams<element::Type_t::i32>(),
+        generateParams<element::Type_t::i64>(),
+        generateParams<element::Type_t::u8>(),
+        generateParams<element::Type_t::u16>(),
+        generateParams<element::Type_t::u32>(),
+        generateParams<element::Type_t::u64>(),
+        generateParams<element::Type_t::bf16>(),
+        generateParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<SpaceToDepthParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_SpaceToDepth_With_Hardcoded_Refs, ReferenceSpaceToDepthLayerTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceSpaceToDepthLayerTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/strided_slice.cpp b/docs/template_plugin/tests/functional/op_reference/strided_slice.cpp
new file mode 100644
index 00000000000000..4e9d6b0ef6c640
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/strided_slice.cpp
@@ -0,0 +1,424 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/strided_slice.hpp"
+#include "openvino/op/constant.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct StridedSliceParams {
+    StridedSliceParams(const PartialShape& dynamicDataShape,
+                       const Tensor& dataTensor,
+                       const Tensor& beginTensor,
+                       const Tensor& endTensor,
+                       const Tensor& stridesTensor,
+                       const std::vector<int64_t>& beginMask,
+                       const std::vector<int64_t>& endMask,
+                       const std::vector<int64_t>& newAxisMask,
+                       const std::vector<int64_t>& shrinkAxisMask,
+                       const std::vector<int64_t>& ellipsisMask,
+                       const Tensor& expectedTensor,
+                       const std::string& testcaseName = "") :
+        dynamicDataShape(dynamicDataShape), dataTensor(dataTensor),
+        beginTensor(beginTensor), endTensor(endTensor),
+        stridesTensor(stridesTensor), beginMask(beginMask), endMask(endMask),
+        newAxisMask(newAxisMask), shrinkAxisMask(shrinkAxisMask), ellipsisMask(ellipsisMask),
+        expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    PartialShape dynamicDataShape;
+    Tensor dataTensor;
+    Tensor beginTensor;
+    Tensor endTensor;
+    Tensor stridesTensor;
+    std::vector<int64_t> beginMask;
+    std::vector<int64_t> endMask;
+    std::vector<int64_t> newAxisMask;
+    std::vector<int64_t> shrinkAxisMask;
+    std::vector<int64_t> ellipsisMask;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+struct StridedSliceStrideOptionalParams {
+    StridedSliceStrideOptionalParams(const PartialShape& dynamicDataShape,
+                       const Tensor& dataTensor,
+                       const Tensor& beginTensor,
+                       const Tensor& endTensor,
+                       const std::vector<int64_t>& beginMask,
+                       const std::vector<int64_t>& endMask,
+                       const std::vector<int64_t>& newAxisMask,
+                       const std::vector<int64_t>& shrinkAxisMask,
+                       const std::vector<int64_t>& ellipsisMask,
+                       const Tensor& expectedTensor,
+                       const std::string& testcaseName = "") :
+        dynamicDataShape(dynamicDataShape), dataTensor(dataTensor),
+        beginTensor(beginTensor), endTensor(endTensor),
+        beginMask(beginMask), endMask(endMask),
+        newAxisMask(newAxisMask), shrinkAxisMask(shrinkAxisMask), ellipsisMask(ellipsisMask),
+        expectedTensor(expectedTensor), testcaseName(testcaseName) {}
+
+    PartialShape dynamicDataShape;
+    Tensor dataTensor;
+    Tensor beginTensor;
+    Tensor endTensor;
+    std::vector<int64_t> beginMask;
+    std::vector<int64_t> endMask;
+    std::vector<int64_t> newAxisMask;
+    std::vector<int64_t> shrinkAxisMask;
+    std::vector<int64_t> ellipsisMask;
+    Tensor expectedTensor;
+    std::string testcaseName;
+};
+
+class ReferenceStridedSliceLayerTest : public testing::TestWithParam<StridedSliceParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        if (params.dynamicDataShape.is_static()) {
+            inputData = {params.dataTensor.data};
+        } else {
+            inputData = {params.dataTensor.data, params.beginTensor.data, params.endTensor.data, params.stridesTensor.data};
+        }
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<StridedSliceParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "ddShape=" << param.dynamicDataShape;
+        result << "_dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_bType=" << param.beginTensor.type;
+        result << "_bShape=" << param.beginTensor.shape;
+        result << "_eType=" << param.endTensor.type;
+        result << "_eShape=" << param.endTensor.shape;
+        result << "_sType=" << param.stridesTensor.type;
+        result << "_sShape=" << param.stridesTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const StridedSliceParams& params) {
+        std::shared_ptr<Function> function;
+        if (params.dynamicDataShape.is_static()) {
+            const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+            const auto beginOp = std::make_shared<op::v0::Constant>(params.beginTensor.type, params.beginTensor.shape,
+                                                                    params.beginTensor.data.data());
+            const auto endOp = std::make_shared<op::v0::Constant>(params.endTensor.type, params.endTensor.shape,
+                                                                  params.endTensor.data.data());
+            const auto stridesOp = std::make_shared<op::v0::Constant>(params.stridesTensor.type, params.stridesTensor.shape,
+                                                                      params.stridesTensor.data.data());
+            const auto StridedSlice = std::make_shared<op::v1::StridedSlice>(data, beginOp, endOp, stridesOp,
+                                                                             params.beginMask, params.endMask, params.newAxisMask,
+                                                                             params.shrinkAxisMask, params.ellipsisMask);
+            function = std::make_shared<ov::Function>(NodeVector{StridedSlice}, ParameterVector{data});
+        } else {
+            const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, PartialShape::dynamic());
+            const auto beginOp = std::make_shared<op::v0::Parameter>(params.beginTensor.type, params.beginTensor.shape);
+            const auto endOp = std::make_shared<op::v0::Parameter>(params.endTensor.type, params.endTensor.shape);
+            const auto stridesOp = std::make_shared<op::v0::Parameter>(params.stridesTensor.type, params.stridesTensor.shape);
+            const auto StridedSlice = std::make_shared<op::v1::StridedSlice>(data, beginOp, endOp, stridesOp,
+                                                                             params.beginMask, params.endMask, params.newAxisMask,
+                                                                             params.shrinkAxisMask, params.ellipsisMask);
+            function = std::make_shared<ov::Function>(NodeVector{StridedSlice}, ParameterVector{data, beginOp, endOp, stridesOp});
+        }
+        return function;
+    }
+};
+
+class ReferenceStridedSliceLayerTestStrideOptional : public testing::TestWithParam<StridedSliceStrideOptionalParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        if (params.dynamicDataShape.is_static()) {
+            inputData = {params.dataTensor.data};
+        } else {
+            inputData = {params.dataTensor.data, params.beginTensor.data, params.endTensor.data};
+        }
+        refOutData = {params.expectedTensor.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<StridedSliceStrideOptionalParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "ddShape=" << param.dynamicDataShape;
+        result << "_dType=" << param.dataTensor.type;
+        result << "_dShape=" << param.dataTensor.shape;
+        result << "_bType=" << param.beginTensor.type;
+        result << "_bShape=" << param.beginTensor.shape;
+        result << "_eType=" << param.endTensor.type;
+        result << "_eShape=" << param.endTensor.shape;
+        result << "_eType=" << param.expectedTensor.type;
+        if (param.testcaseName != "") {
+            result << "_eShape=" << param.expectedTensor.shape;
+            result << "_" << param.testcaseName;
+        } else {
+            result << "_eShape=" << param.expectedTensor.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const StridedSliceStrideOptionalParams& params) {
+        std::shared_ptr<Function> function;
+        if (params.dynamicDataShape.is_static()) {
+            const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, params.dataTensor.shape);
+            const auto beginOp = std::make_shared<op::v0::Constant>(params.beginTensor.type, params.beginTensor.shape,
+                                                                    params.beginTensor.data.data());
+            const auto endOp = std::make_shared<op::v0::Constant>(params.endTensor.type, params.endTensor.shape,
+                                                                  params.endTensor.data.data());
+            const auto StridedSlice = std::make_shared<op::v1::StridedSlice>(data, beginOp, endOp,
+                                                                             params.beginMask, params.endMask, params.newAxisMask,
+                                                                             params.shrinkAxisMask, params.ellipsisMask);
+            function = std::make_shared<ov::Function>(NodeVector{StridedSlice}, ParameterVector{data});
+        } else {
+            const auto data = std::make_shared<op::v0::Parameter>(params.dataTensor.type, PartialShape::dynamic());
+            const auto beginOp = std::make_shared<op::v0::Parameter>(params.beginTensor.type, params.beginTensor.shape);
+            const auto endOp = std::make_shared<op::v0::Parameter>(params.endTensor.type, params.endTensor.shape);
+            const auto StridedSlice = std::make_shared<op::v1::StridedSlice>(data, beginOp, endOp,
+                                                                             params.beginMask, params.endMask, params.newAxisMask,
+                                                                             params.shrinkAxisMask, params.ellipsisMask);
+            function = std::make_shared<ov::Function>(NodeVector{StridedSlice}, ParameterVector{data, beginOp, endOp});
+        }
+        return function;
+    }
+};
+
+TEST_P(ReferenceStridedSliceLayerTest, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceStridedSliceLayerTestStrideOptional, CompareWithRefs) {
+    Exec();
+}
+
+template<typename T>
+std::vector<T> generateInputValues(const Shape& input_shape, T initial) {
+    std::vector<T> input_values(shape_size(input_shape));
+    std::iota(input_values.begin(), input_values.end(), static_cast<T>(initial));
+    return input_values;
+}
+
+template <element::Type_t IN_ET>
+std::vector<StridedSliceParams> generateSmallParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<StridedSliceParams> params {
+        // strided_slice_0
+        StridedSliceParams(
+            {},
+            Tensor(IN_ET, {2, 3, 4}, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11,
+                                                    12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{0, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 1}),
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 1, 0},
+            std::vector<int64_t>{1, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            Tensor(IN_ET, {1, 3, 4}, std::vector<T>{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            "strided_slice_0"),
+        // strided_slice_0_dynamic
+        StridedSliceParams(
+            PartialShape::dynamic(),
+            Tensor(IN_ET, {2, 3, 4}, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11,
+                                                    12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{0, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 1}),
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 1, 0},
+            std::vector<int64_t>{1, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            Tensor(IN_ET, {1, 3, 4}, std::vector<T>{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            "strided_slice_0_dynamic"),
+    };
+    return params;
+}
+
+template <element::Type_t IN_ET>
+std::vector<StridedSliceParams> generateParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<StridedSliceParams> params {
+        // strided_slice_0
+        StridedSliceParams(
+            {},
+            Tensor(IN_ET, {2, 3, 4}, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11,
+                                                    12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{0, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 1}),
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 1, 0},
+            std::vector<int64_t>{1, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            Tensor(IN_ET, {1, 3, 4}, std::vector<T>{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            "strided_slice_0"),
+        // strided_slice_0_dynamic
+        StridedSliceParams(
+            PartialShape::dynamic(),
+            Tensor(IN_ET, {2, 3, 4}, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11,
+                                                    12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{0, 0}),
+            Tensor(element::i64, {2}, std::vector<int64_t>{1, 1}),
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 1, 0},
+            std::vector<int64_t>{1, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            Tensor(IN_ET, {1, 3, 4}, std::vector<T>{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            "strided_slice_0_dynamic"),
+        // strided_slice_1
+        StridedSliceParams(
+            {},
+            Tensor(IN_ET, {2, 4, 6, 8, 2, 2, 2}, generateInputValues<T>({2, 4, 6, 8, 2, 2, 2}, 0)),
+            Tensor(element::i64, {7}, std::vector<int64_t>{0, 0, 2, 7, 0, 0, 1}),
+            Tensor(element::i64, {7}, std::vector<int64_t>{0, 4, 6, 3, 0, 0, 0}),
+            Tensor(element::i64, {7}, std::vector<int64_t>{1, 1, 2, -2, 1, 1, 1}),
+            std::vector<int64_t>{0, 1, 0, 0, 0, 0, 0},
+            std::vector<int64_t>{1, 0, 0, 0, 0, 0, 0},
+            std::vector<int64_t>{0, 0, 0, 0, 1, 0, 0},
+            std::vector<int64_t>{0, 0, 0, 0, 0, 0, 1},
+            std::vector<int64_t>{0, 0, 0, 0, 0, 1, 0},
+            Tensor(IN_ET, {2, 4, 2, 2, 1, 2, 2}, std::vector<T>{
+                185,  187,  189,  191,  169,  171,  173,  175,  313,  315,  317,  319,  297,  299,  301,
+                303,  569,  571,  573,  575,  553,  555,  557,  559,  697,  699,  701,  703,  681,  683,
+                685,  687,  953,  955,  957,  959,  937,  939,  941,  943,  1081, 1083, 1085, 1087, 1065,
+                1067, 1069, 1071, 1337, 1339, 1341, 1343, 1321, 1323, 1325, 1327, 1465, 1467, 1469, 1471,
+                1449, 1451, 1453, 1455, 1721, 1723, 1725, 1727, 1705, 1707, 1709, 1711, 1849, 1851, 1853,
+                1855, 1833, 1835, 1837, 1839, 2105, 2107, 2109, 2111, 2089, 2091, 2093, 2095, 2233, 2235,
+                2237, 2239, 2217, 2219, 2221, 2223, 2489, 2491, 2493, 2495, 2473, 2475, 2477, 2479, 2617,
+                2619, 2621, 2623, 2601, 2603, 2605, 2607, 2873, 2875, 2877, 2879, 2857, 2859, 2861, 2863,
+                3001, 3003, 3005, 3007, 2985, 2987, 2989, 2991}),
+            "strided_slice_1"),
+        // strided_slice_1_dynamic
+        StridedSliceParams(
+            PartialShape::dynamic(),
+            Tensor(IN_ET, {2, 4, 6, 8, 2, 2, 2}, generateInputValues<T>({2, 4, 6, 8, 2, 2, 2}, 0)),
+            Tensor(element::i64, {7}, std::vector<int64_t>{0, 0, 2, 7, 0, 0, 1}),
+            Tensor(element::i64, {7}, std::vector<int64_t>{0, 4, 6, 3, 0, 0, 0}),
+            Tensor(element::i64, {7}, std::vector<int64_t>{1, 1, 2, -2, 1, 1, 1}),
+            std::vector<int64_t>{0, 1, 0, 0, 0, 0, 0},
+            std::vector<int64_t>{1, 0, 0, 0, 0, 0, 0},
+            std::vector<int64_t>{0, 0, 0, 0, 1, 0, 0},
+            std::vector<int64_t>{0, 0, 0, 0, 0, 0, 1},
+            std::vector<int64_t>{0, 0, 0, 0, 0, 1, 0},
+            Tensor(IN_ET, {2, 4, 2, 2, 1, 2, 2}, std::vector<T>{
+                185,  187,  189,  191,  169,  171,  173,  175,  313,  315,  317,  319,  297,  299,  301,
+                303,  569,  571,  573,  575,  553,  555,  557,  559,  697,  699,  701,  703,  681,  683,
+                685,  687,  953,  955,  957,  959,  937,  939,  941,  943,  1081, 1083, 1085, 1087, 1065,
+                1067, 1069, 1071, 1337, 1339, 1341, 1343, 1321, 1323, 1325, 1327, 1465, 1467, 1469, 1471,
+                1449, 1451, 1453, 1455, 1721, 1723, 1725, 1727, 1705, 1707, 1709, 1711, 1849, 1851, 1853,
+                1855, 1833, 1835, 1837, 1839, 2105, 2107, 2109, 2111, 2089, 2091, 2093, 2095, 2233, 2235,
+                2237, 2239, 2217, 2219, 2221, 2223, 2489, 2491, 2493, 2495, 2473, 2475, 2477, 2479, 2617,
+                2619, 2621, 2623, 2601, 2603, 2605, 2607, 2873, 2875, 2877, 2879, 2857, 2859, 2861, 2863,
+                3001, 3003, 3005, 3007, 2985, 2987, 2989, 2991}),
+            "strided_slice_1_dynamic"),
+    };
+    return params;
+}
+
+template <element::Type_t IN_ET>
+std::vector<StridedSliceStrideOptionalParams> generateStrideOptionalParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<StridedSliceStrideOptionalParams> params {
+        // strided_slice_stride_optional
+        StridedSliceStrideOptionalParams(
+            {},
+            Tensor(IN_ET, {2, 3, 4}, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11,
+                                                    12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            Tensor(element::i64, {3}, std::vector<int64_t>{-1, -1, 0}),
+            Tensor(element::i64, {3}, std::vector<int64_t>{0, 0, 0}),
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 1},
+            std::vector<int64_t>{1, 1, 0},
+            std::vector<int64_t>{0, 0, 0},
+            Tensor(IN_ET, {1, 4}, std::vector<T>{20, 21, 22, 23}),
+            "strided_slice_stride_optional"),
+        // strided_slice_stride_optional_dynamic
+        StridedSliceStrideOptionalParams(
+            PartialShape::dynamic(),
+            Tensor(IN_ET, {2, 3, 4}, std::vector<T>{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11,
+                                                    12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}),
+            Tensor(element::i64, {3}, std::vector<int64_t>{-1, -1, 0}),
+            Tensor(element::i64, {3}, std::vector<int64_t>{0, 0, 0}),
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 0},
+            std::vector<int64_t>{0, 0, 1},
+            std::vector<int64_t>{1, 1, 0},
+            std::vector<int64_t>{0, 0, 0},
+            Tensor(IN_ET, {1, 4}, std::vector<T>{20, 21, 22, 23}),
+            "strided_slice_stride_optional_dynamic"),
+    };
+    return params;
+}
+
+std::vector<StridedSliceParams> generateCombinedParams() {
+    const std::vector<std::vector<StridedSliceParams>> generatedParams {
+        generateSmallParams<element::Type_t::i8>(),
+        generateParams<element::Type_t::i16>(),
+        generateParams<element::Type_t::i32>(),
+        generateParams<element::Type_t::i64>(),
+        generateSmallParams<element::Type_t::u8>(),
+        generateParams<element::Type_t::u16>(),
+        generateParams<element::Type_t::u32>(),
+        generateParams<element::Type_t::u64>(),
+        generateSmallParams<element::Type_t::bf16>(),
+        generateSmallParams<element::Type_t::f16>(),
+        generateParams<element::Type_t::f32>(),
+        generateParams<element::Type_t::f64>(),
+    };
+    std::vector<StridedSliceParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+std::vector<StridedSliceStrideOptionalParams> generateCombinedStrideOptionalParams() {
+    const std::vector<std::vector<StridedSliceStrideOptionalParams>> generatedParams {
+        generateStrideOptionalParams<element::Type_t::i8>(),
+        generateStrideOptionalParams<element::Type_t::i16>(),
+        generateStrideOptionalParams<element::Type_t::i32>(),
+        generateStrideOptionalParams<element::Type_t::i64>(),
+        generateStrideOptionalParams<element::Type_t::u8>(),
+        generateStrideOptionalParams<element::Type_t::u16>(),
+        generateStrideOptionalParams<element::Type_t::u32>(),
+        generateStrideOptionalParams<element::Type_t::u64>(),
+        generateStrideOptionalParams<element::Type_t::f32>(),
+        generateStrideOptionalParams<element::Type_t::f64>(),
+    };
+    std::vector<StridedSliceStrideOptionalParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_StridedSlice_With_Hardcoded_Refs, ReferenceStridedSliceLayerTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceStridedSliceLayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_StridedSlice_With_Hardcoded_Refs, ReferenceStridedSliceLayerTestStrideOptional,
+    testing::ValuesIn(generateCombinedStrideOptionalParams()), ReferenceStridedSliceLayerTestStrideOptional::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp b/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp
index 17f14d66277bfd..be09f97667176c 100644
--- a/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp
+++ b/docs/template_plugin/tests/functional/shared_tests_instances/hetero/synthetic.cpp
@@ -11,6 +11,9 @@
 namespace {
 using namespace HeteroTests;
 
+// this tests load plugin by library name: this is not available during static linkage
+#ifndef OPENVINO_STATIC_LIBRARY
+
 INSTANTIATE_TEST_SUITE_P(smoke_SingleMajorNode, HeteroSyntheticTest,
                         ::testing::Combine(
                                 ::testing::Values(std::vector<PluginParameter>{{"TEMPLATE0", "templatePlugin"}, {"TEMPLATE1", "templatePlugin"}}),
@@ -69,4 +72,6 @@ INSTANTIATE_TEST_SUITE_P(nightly_RandomMajorNodes_dynamic_batch, HeteroSynthetic
                                         dynamicBuilders, true))),
                         HeteroSyntheticTest::getTestCaseName);
 
+#endif // !OPENVINO_STATIC_LIBRARY
+
 }  // namespace
diff --git a/docs/template_plugin/tests/functional/skip_tests_config.cpp b/docs/template_plugin/tests/functional/skip_tests_config.cpp
index fe8a939820bfe5..310aba60f53a45 100644
--- a/docs/template_plugin/tests/functional/skip_tests_config.cpp
+++ b/docs/template_plugin/tests/functional/skip_tests_config.cpp
@@ -59,6 +59,35 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*ReferenceExperimentalGPLayerTest.*bf16.*)",
         // CVS-64105
         R"(.*ReferenceGatherElementsTestNegative.*)",
+        // CVS-64052
+        R"(.*ReferenceStridedSliceLayerTest.*strided_slice_stride_optional_dynamic)",
+        // CVS-64017
+        R"(.*ReferenceGatherTest.*dType=i16.*)",
+        R"(.*ReferenceGatherTest.*dType=u16.*)",
+        R"(.*ReferenceGatherTest.*dType=bf16.*)",
+        R"(.*ReferenceGatherTest.*dType=f64.*)",
+        // CVS-64110
+        R"(.*ReferenceGatherTestV7.*dType=i16.*)",
+        R"(.*ReferenceGatherTestV7.*dType=u16.*)",
+        R"(.*ReferenceGatherTestV7.*dType=bf16.*)",
+        R"(.*ReferenceGatherTestV7.*dType=f64.*)",
+        // CVS-64037
+        R"(.*ReferencePadTest.*pad_exterior_2d_0x0)",
+        R"(.*ReferencePadTest.*pad_exterior_2d_0x3)",
+        R"(.*ReferencePadTest.*pad_exterior_2d_3x0)",
+        // CVS-70975
+        R"(.*ReferencePadTestParamsTooLarge.*)",
+        // CVS-64006
+        R"(.*ReferenceBatchToSpaceLayerTest.*dType=i4.*)",
+        R"(.*ReferenceBatchToSpaceLayerTest.*dType=u4.*)",
+        // CVS-64113
+        R"(.*ReferenceRollLayerTest.*dType=i4.*)",
+        R"(.*ReferenceRollLayerTest.*dType=u4.*)",
+        // CVS-64050
+        R"(.*ReferenceSpaceToBatchLayerTest.*dType=i4.*)",
+        R"(.*ReferenceSpaceToBatchLayerTest.*dType=u4.*)",
+        // CVS-64066
+        R"(.*ReferenceGRUCellTestHardsigmoidActivationFunction.*gru_cell_hardsigmoid_activation_function)",
     };
 
 #ifdef _WIN32
diff --git a/docs/template_plugin/tests/functional/subgraph_reference/preprocess.cpp b/docs/template_plugin/tests/functional/subgraph_reference/preprocess.cpp
index 4ee210dab8d501..8ed19e49a89146 100644
--- a/docs/template_plugin/tests/functional/subgraph_reference/preprocess.cpp
+++ b/docs/template_plugin/tests/functional/subgraph_reference/preprocess.cpp
@@ -97,7 +97,7 @@ static RefPreprocessParams simple_mean_scale() {
     RefPreprocessParams res("simple_mean_scale");
     res.function = []() {
         auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-        f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().mean(1.f).scale(2.f))).build(f);
+        f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().mean(1.f).scale(2.f))).build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 3, 2, 2}, element::f32, std::vector<float>{1., 3., 5., 7., 9., 11., 13., 15., 17., 19., 21., 23.});
@@ -109,7 +109,7 @@ static RefPreprocessParams scale_then_mean() {
     RefPreprocessParams res("scale_then_mean");
     res.function = []() {
         auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-        f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().scale(2.0f).mean(2.0f))).build(f);
+        f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().scale(2.0f).mean(2.0f))).build();
         return f;
     };
 
@@ -122,14 +122,14 @@ static RefPreprocessParams convert_only() {
     RefPreprocessParams res("convert_only");
     res.function = []() {
         auto f = create_simple_function(element::f32, Shape{1, 1, 2, 2});
-        f = PrePostProcessor().input(InputInfo()
+        f = PrePostProcessor(f).input(InputInfo()
                 .tensor(InputTensorInfo().set_element_type(element::i16))
                 .preprocess(PreProcessSteps()
                 .convert_element_type(element::f32)
                 .scale(3.f)
                 .convert_element_type(element::u8)
                 .convert_element_type(element::f32)))
-                        .build(f);
+                        .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 1, 2, 2}, element::i16, std::vector<int16_t>{2, 3, 4, 5});
@@ -141,14 +141,14 @@ static RefPreprocessParams convert_element_type_and_scale() {
     RefPreprocessParams res("convert_element_type_and_scale");
     res.function = []() {
         auto f = create_simple_function(element::u8, Shape{1, 3, 2, 2});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_element_type(element::i16))
                                .preprocess(PreProcessSteps()
                                                    .convert_element_type(element::f32)
                                                    .scale(2.f)
                                                    .convert_element_type(element::u8)))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -163,11 +163,11 @@ static RefPreprocessParams tensor_element_type_and_scale() {
     RefPreprocessParams res("tensor_element_type_and_scale");
     res.function = []() {
         auto f = create_simple_function(element::i8, Shape{1, 3, 1, 1});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_element_type(element::f32))
                            .preprocess(PreProcessSteps().scale(2.0f).convert_element_type(element::i8)))
-            .build(f);
+            .build();
         return f;
     };
 
@@ -180,13 +180,13 @@ static RefPreprocessParams custom_preprocessing() {
     RefPreprocessParams res("custom_preprocessing");
     res.function = []() {
         auto f = create_simple_function(element::i32, Shape{1, 3, 1, 1});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
             .input(InputInfo().preprocess(PreProcessSteps().custom([](const Output<Node>& node) {
                 auto abs = std::make_shared<op::v0::Abs>(node);
                 abs->set_friendly_name(node.get_node_shared_ptr()->get_friendly_name() + "/abs");
                 return abs;
             })))
-            .build(f);
+            .build();
         return f;
     };
 
@@ -199,7 +199,7 @@ static RefPreprocessParams test_lvalue() {
     RefPreprocessParams res("test_lvalue");
     res.function = []() {
         auto f = create_simple_function(element::i8, Shape{1, 3, 1, 1});
-        auto p = PrePostProcessor();
+        auto p = PrePostProcessor(f);
         auto p1 = std::move(p);
         p = std::move(p1);
         auto inputInfo = InputInfo();
@@ -230,7 +230,7 @@ static RefPreprocessParams test_lvalue() {
             inputInfo.preprocess(std::move(same));
         }
         p.input(std::move(inputInfo));
-        f = p.build(f);
+        f = p.build();
         return f;
     };
 
@@ -243,7 +243,7 @@ static RefPreprocessParams test_2_inputs_basic() {
     RefPreprocessParams res("test_2_inputs_basic");
     res.function = []() {
         auto f = create_2inputs(element::f32, Shape{1, 3, 1, 1});
-        f = PrePostProcessor().input(InputInfo(0)
+        f = PrePostProcessor(f).input(InputInfo(0)
                                              .preprocess(
                                                      PreProcessSteps()
                                                              .mean(1.f)))
@@ -252,7 +252,7 @@ static RefPreprocessParams test_2_inputs_basic() {
                                 .preprocess(PreProcessSteps()
                                                     .mean(1.f)
                                                     .scale(2.0f)))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -267,11 +267,11 @@ static RefPreprocessParams mean_scale_vector_tensor_layout() {
     RefPreprocessParams res("mean_scale_vector_tensor_layout");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 3, 2, 1});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_layout("NC??"))
                                .preprocess(PreProcessSteps().mean({1.f, 2.f, 3.f}).scale({2.f, 3.f, 4.f})))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -284,11 +284,11 @@ static RefPreprocessParams mean_scale_dynamic_layout() {
     RefPreprocessParams res("mean_scale_dynamic_layout");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 2, 1, 3});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_layout("N...C"))
                                .preprocess(PreProcessSteps().mean({1.f, 2.f, 3.f}).scale({2.f, 3.f, 4.f})))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -301,13 +301,13 @@ static RefPreprocessParams resize_to_network_height() {
     RefPreprocessParams res("resize_to_network_height");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 2, 1, 1});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_spatial_dynamic_shape())
                                .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                .network(InputNetworkInfo().set_layout("NHWC"))
                 )
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(element::f32, Shape{1, 4, 1, 1}, std::vector<float>{0., 2., 4., 6.});
@@ -319,12 +319,12 @@ static RefPreprocessParams resize_to_network_width() {
     RefPreprocessParams res("resize_to_network_width");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 1, 2, 2});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_spatial_dynamic_shape())
                                .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                .network(InputNetworkInfo().set_layout("NCHW")))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(element::f32, Shape{1, 1, 2, 6}, std::vector<float>{0., 1., 2., 3., 4., 5.,
@@ -339,12 +339,12 @@ static RefPreprocessParams resize_from_spatial_dims() {
         auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 1, 1, 1});
         auto t = InputTensorInfo();
         t.set_spatial_static_shape(1, 4);
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(std::move(t))
                                .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_CUBIC))
                                .network(InputNetworkInfo().set_layout("NCHW")))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(element::f32, Shape{1, 1, 1, 7}, std::vector<float>{0., 0.25, 1., 2.25, 4., 6.25, 9});
@@ -356,13 +356,13 @@ static RefPreprocessParams resize_i8() {
     RefPreprocessParams res("resize_i8");
     res.function = []() {
         auto f = create_simple_function(element::i8, PartialShape{1, 3, 1, 1});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo()
                                     .set_spatial_dynamic_shape())
                                .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                .network(InputNetworkInfo().set_layout("NCHW")))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(element::i8, Shape{1, 3, 2, 2}, std::vector<int8_t>{0, 0, 0, 0,
@@ -376,12 +376,12 @@ static RefPreprocessParams resize_to_network_width_height() {
     RefPreprocessParams res("resize_to_network_width_height");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 1, 4, 4});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_spatial_static_shape(5, 5))
                                .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_NEAREST))
                                .network(InputNetworkInfo().set_layout("...HW")))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -406,12 +406,12 @@ static RefPreprocessParams resize_to_specified_width_height() {
     RefPreprocessParams res("resize_to_specified_width_height");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 1, Dimension::dynamic(), Dimension::dynamic()});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_spatial_dynamic_shape())
                                .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_NEAREST, 4, 4))
                                .network(InputNetworkInfo().set_layout("...HW")))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -448,9 +448,9 @@ static RefPreprocessParams resize_lvalues() {
         i.tensor(std::move(t));
         i.preprocess(std::move(s));
         i.network(std::move(n));
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(std::move(i))
-                .build(f);
+                .build();
         return f;
     };
     // clang-format off
@@ -473,11 +473,11 @@ static RefPreprocessParams convert_layout_nhwc_to_nchw_lvalue() {
         auto p = PreProcessSteps();
         p.convert_layout("NCHW");
 
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_layout("NHWC"))
                                .preprocess(std::move(p)))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 2, 2, 3}, element::u8, std::vector<uint8_t>{1,  2,  3,       // [H=0, W=0, RGB]
@@ -497,11 +497,11 @@ static RefPreprocessParams convert_layout_nhwc_to_net_no_tensor_shape() {
         f->get_parameters()[0]->set_layout("NCHW");
         auto p = PreProcessSteps();
         p.convert_layout();
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo().set_layout("NHWC"))
                                .preprocess(std::move(p)))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 2, 2, 3}, element::u8, std::vector<uint8_t>{1,  2,  3,       // [H=0, W=0, RGB]
@@ -518,10 +518,10 @@ static RefPreprocessParams convert_layout_by_dims() {
     RefPreprocessParams res("convert_layout_by_dims");
     res.function = []() {
         auto f = create_simple_function(element::u8, {1, 3, 2, 2});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .preprocess(PreProcessSteps().convert_layout({0, 3, 1, 2})))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 2, 2, 3}, element::u8, std::vector<uint8_t>{1,  2,  3,       // [H=0, W=0, RGB]
@@ -541,9 +541,9 @@ static RefPreprocessParams convert_layout_by_dims_multi() {
         auto p = PreProcessSteps();
         p.convert_layout({0, 1, 3, 2}); // NHWC->NHCW
         p.convert_layout({0, 2, 1, 3}); // NHCW->NCHW
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo().preprocess(std::move(p)))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 2, 2, 3}, element::f32, std::vector<float>{1,  2,  3,       // [H=0, W=0]
@@ -564,10 +564,10 @@ static RefPreprocessParams convert_layout_by_dims_multi_layout() {
         p.convert_layout({0, 1, 3, 2}); // NHWC->NHCW
         p.mean({1, 2, 2});              // Apply means to 'C' channel
         p.convert_layout({0, 2, 1, 3}); // NHCW->NCHW
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo().tensor(InputTensorInfo().set_layout("N??C"))
                                .preprocess(std::move(p)))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 2, 2, 3}, element::f32, std::vector<float>{1,  2,  3,       // [H=0, W=0, RGB]
@@ -584,7 +584,7 @@ static RefPreprocessParams resize_and_convert_layout() {
     RefPreprocessParams res("resize_and_convert_layout");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 2, 2, 2});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo()
                                                .set_layout("NCHW")
@@ -593,7 +593,7 @@ static RefPreprocessParams resize_and_convert_layout() {
                                                    .resize(ResizeAlgorithm::RESIZE_LINEAR)
                                                    .convert_layout())
                                .network(InputNetworkInfo().set_layout("NHWC")))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -622,13 +622,13 @@ static RefPreprocessParams convert_color_nv12_to_bgr_two_planes() {
     res.rel_threshold = 1.f; // Ignore relative pixel values comparison (100%)
     res.function = []() {
         auto f = create_simple_function(element::u8, PartialShape{1, 4, 4, 3});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo()
                                                .set_color_format(ColorFormat::NV12_TWO_PLANES))
                                .preprocess(PreProcessSteps()
                                                    .convert_color(ColorFormat::BGR)))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -661,13 +661,13 @@ static RefPreprocessParams convert_color_nv12_single_plane() {
     res.rel_threshold = 1.f; // Ignore relative pixel values comparison (100%)
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 4, 4, 3});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo()
                                                .set_color_format(ColorFormat::NV12_SINGLE_PLANE))
                                .preprocess(PreProcessSteps()
                                                    .convert_color(ColorFormat::RGB)))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -696,7 +696,7 @@ static RefPreprocessParams convert_color_nv12_layout_resize() {
     res.rel_threshold = 1.f; // Ignore relative pixel values comparison (100%)
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 3, 2, 2});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo()
                                                .set_color_format(ColorFormat::NV12_SINGLE_PLANE)
@@ -708,7 +708,7 @@ static RefPreprocessParams convert_color_nv12_layout_resize() {
                                                    .convert_element_type(element::f32)
                                                    .resize(ResizeAlgorithm::RESIZE_NEAREST))
                                .network(InputNetworkInfo().set_layout("NCHW")))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -736,7 +736,7 @@ static RefPreprocessParams element_type_before_convert_color_nv12() {
     res.rel_threshold = 1.f; // Ignore relative pixel values comparison (100%)
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 2, 2, 3});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                                .tensor(InputTensorInfo()
                                                .set_element_type(element::u8)
@@ -745,7 +745,7 @@ static RefPreprocessParams element_type_before_convert_color_nv12() {
                                                    .convert_element_type(element::f32)
                                                    .convert_color(ColorFormat::RGB))
                                .network(InputNetworkInfo().set_layout("NHWC")))
-                .build(f);
+                .build();
         return f;
     };
 
@@ -767,7 +767,7 @@ static RefPreprocessParams postprocess_2_inputs_basic() {
     RefPreprocessParams res("postprocess_2_inputs_basic");
     res.function = []() {
         auto f = create_2inputs(element::f32, Shape{1, 3, 1, 2});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .output(OutputInfo("tensor_output1")
                                 .network(OutputNetworkInfo().set_layout("NCHW"))
                                 .postprocess(PostProcessSteps().convert_layout())
@@ -775,7 +775,7 @@ static RefPreprocessParams postprocess_2_inputs_basic() {
                 .output(OutputInfo("tensor_output2")
                                 .postprocess(PostProcessSteps().convert_element_type())
                                 .tensor(OutputTensorInfo().set_element_type(element::u8)))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 3, 1, 2}, element::f32, std::vector<float>{1.1, 2.1, 3.1, 4.1, 5.1, 6.1});
@@ -789,10 +789,10 @@ static RefPreprocessParams post_convert_layout_by_dims() {
     RefPreprocessParams res("post_convert_layout_by_dims");
     res.function = []() {
         auto f = create_simple_function(element::u8, {1, 2, 2, 3});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .output(OutputInfo()
                                .postprocess(PostProcessSteps().convert_layout({0, 3, 1, 2})))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 2, 2, 3}, element::u8, std::vector<uint8_t>{1,  2,  3,       // [H=0, W=0, RGB]
@@ -812,9 +812,9 @@ static RefPreprocessParams post_convert_layout_by_dims_multi() {
         auto p = PostProcessSteps();
         p.convert_layout({0, 1, 3, 2}); // NHWC->NHCW
         p.convert_layout({0, 2, 1, 3}); // NHCW->NCHW
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .output(OutputInfo().postprocess(std::move(p)))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 2, 2, 3}, element::f32, std::vector<float>{1,  2,  3,       // [H=0, W=0]
@@ -831,7 +831,7 @@ static RefPreprocessParams pre_and_post_processing() {
     RefPreprocessParams res("pre_and_post_processing");
     res.function = []() {
         auto f = create_2inputs(element::f32, Shape{1, 3, 1, 2});
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo(0)
                                 .tensor(InputTensorInfo().set_element_type(element::u8))
                                 .preprocess(PreProcessSteps().convert_element_type(element::f32).mean(1.f)))
@@ -844,7 +844,7 @@ static RefPreprocessParams pre_and_post_processing() {
                 .output(OutputInfo("tensor_output2")
                                 .postprocess(PostProcessSteps().convert_element_type())
                                 .tensor(OutputTensorInfo().set_element_type(element::u8)))
-                .build(f);
+                .build();
         return f;
     };
     res.inputs.emplace_back(Shape{1, 3, 1, 2}, element::u8, std::vector<uint8_t>{1, 2, 3, 4, 5, 6});
@@ -858,9 +858,9 @@ static RefPreprocessParams rgb_to_bgr() {
     RefPreprocessParams res("rgb_to_bgr");
     res.function = []() {
         auto f = create_simple_function(element::f32, Shape{2, 1, 1, 3});
-        f = PrePostProcessor().input(InputInfo()
+        f = PrePostProcessor(f).input(InputInfo()
                                              .tensor(InputTensorInfo().set_color_format(ColorFormat::RGB))
-                                             .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR))).build(f);
+                                             .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR))).build();
         return f;
     };
 
@@ -873,9 +873,9 @@ static RefPreprocessParams bgr_to_rgb() {
     RefPreprocessParams res("bgr_to_rgb");
     res.function = []() {
         auto f = create_simple_function(element::f32, Shape{2, 1, 1, 3});
-        f = PrePostProcessor().input(InputInfo()
+        f = PrePostProcessor(f).input(InputInfo()
                 .tensor(InputTensorInfo().set_color_format(ColorFormat::BGR))
-                .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB))).build(f);
+                .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB))).build();
         return f;
     };
 
@@ -888,9 +888,9 @@ static RefPreprocessParams reverse_channels_nchw() {
     RefPreprocessParams res("reverse_channels_nchw");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 2, 2, 2});
-        f = PrePostProcessor().input(InputInfo()
+        f = PrePostProcessor(f).input(InputInfo()
                                              .tensor(InputTensorInfo().set_layout("NCHW"))
-                                             .preprocess(PreProcessSteps().reverse_channels())).build(f);
+                                             .preprocess(PreProcessSteps().reverse_channels())).build();
         return f;
     };
 
@@ -903,9 +903,9 @@ static RefPreprocessParams reverse_channels_dyn_layout() {
     RefPreprocessParams res("reverse_channels_dyn_layout");
     res.function = []() {
         auto f = create_simple_function(element::f32, PartialShape{1, 1, 3, 2});
-        f = PrePostProcessor().input(InputInfo()
+        f = PrePostProcessor(f).input(InputInfo()
                 .tensor(InputTensorInfo().set_color_format(ColorFormat::BGR).set_layout("...CN"))
-                .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB))).build(f);
+                .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB))).build();
         return f;
     };
 
@@ -921,9 +921,9 @@ static RefPreprocessParams reverse_dyn_shape() {
                                                                    Dimension::dynamic(),
                                                                    Dimension::dynamic(),
                                                                    Dimension::dynamic()});
-        f = PrePostProcessor().input(InputInfo()
+        f = PrePostProcessor(f).input(InputInfo()
                                              .tensor(InputTensorInfo().set_layout("NCHW"))
-                                             .preprocess(PreProcessSteps().reverse_channels())).build(f);
+                                             .preprocess(PreProcessSteps().reverse_channels())).build();
         return f;
     };
 
@@ -938,9 +938,9 @@ static RefPreprocessParams reverse_fully_dyn_shape() {
         auto f = create_simple_function(element::u8, PartialShape::dynamic());
         auto p = PreProcessSteps();
         p.reverse_channels();
-        f = PrePostProcessor().input(InputInfo()
+        f = PrePostProcessor(f).input(InputInfo()
                                              .tensor(InputTensorInfo().set_layout("...C??"))
-                                             .preprocess(std::move(p))).build(f);
+                                             .preprocess(std::move(p))).build();
         return f;
     };
 
diff --git a/docs/template_plugin/tests/functional/subgraph_reference/preprocess_legacy.cpp b/docs/template_plugin/tests/functional/subgraph_reference/preprocess_legacy.cpp
index 6c41db9bd96a3b..e0bfa67f9028af 100644
--- a/docs/template_plugin/tests/functional/subgraph_reference/preprocess_legacy.cpp
+++ b/docs/template_plugin/tests/functional/subgraph_reference/preprocess_legacy.cpp
@@ -54,7 +54,7 @@ static std::shared_ptr<Function> create_simple_function_nv12(const PartialShape&
 
 TEST_F(ReferencePreprocessLegacyTest, mean) {
     function = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-    function = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().mean(1.f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo().preprocess(PreProcessSteps().mean(1.f))).build();
 
     auto f2 = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     legacy_network = InferenceEngine::CNNNetwork(f2);
@@ -72,7 +72,7 @@ TEST_F(ReferencePreprocessLegacyTest, mean) {
 
 TEST_F(ReferencePreprocessLegacyTest, mean_scale) {
     function = create_simple_function(element::f32, Shape{1, 3, 20, 20});
-    function = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().scale(2.f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo().preprocess(PreProcessSteps().scale(2.f))).build();
 
     auto f2 = create_simple_function(element::f32, Shape{1, 3, 20, 20});
     legacy_network = InferenceEngine::CNNNetwork(f2);
@@ -93,11 +93,11 @@ TEST_F(ReferencePreprocessLegacyTest, resize) {
     auto f2 = create_simple_function(element::f32, Shape{1, 3, 5, 5});
     legacy_network = InferenceEngine::CNNNetwork(f2);
 
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
             .tensor(InputTensorInfo().set_layout("NCHW").set_spatial_static_shape(42, 30))
             .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
             .network(InputNetworkInfo().set_layout("NCHW")))
-                    .build(function);
+                    .build();
 
     auto &preProcess = legacy_network.getInputsInfo().begin()->second->getPreProcess();
     preProcess.setResizeAlgorithm(InferenceEngine::ResizeAlgorithm::RESIZE_BILINEAR);
@@ -114,12 +114,12 @@ class ConvertNV12WithLegacyTest: public ReferencePreprocessLegacyTest {
         inputData.clear();
         legacy_input_blobs.clear();
 
-        function = PrePostProcessor().input(InputInfo()
+        function = PrePostProcessor(function).input(InputInfo()
                                                     .tensor(InputTensorInfo().set_color_format(
                                                             ColorFormat::NV12_SINGLE_PLANE))
                                                     .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR))
                                                     .network(InputNetworkInfo().set_layout("NCHW")))
-                .build(function);
+                .build();
 
         const auto &param = function->get_parameters()[0];
         inputData.emplace_back(param->get_element_type(), param->get_shape(), ov20_input_yuv.data());
diff --git a/docs/template_plugin/tests/functional/subgraph_reference/preprocess_opencv.cpp b/docs/template_plugin/tests/functional/subgraph_reference/preprocess_opencv.cpp
index cf9abb1adae08a..14ff3f3ac27ae7 100644
--- a/docs/template_plugin/tests/functional/subgraph_reference/preprocess_opencv.cpp
+++ b/docs/template_plugin/tests/functional/subgraph_reference/preprocess_opencv.cpp
@@ -72,11 +72,11 @@ TEST_F(PreprocessOpenCVReferenceTest_NV12, convert_nv12_full_color_range) {
 
     inputData.clear();
 
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_color_format(
                                                         ColorFormat::NV12_SINGLE_PLANE))
                                                 .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR)))
-            .build(function);
+            .build();
 
     const auto &param = function->get_parameters()[0];
     inputData.emplace_back(param->get_element_type(), param->get_shape(), ov20_input_yuv.data());
@@ -101,12 +101,12 @@ TEST_F(PreprocessOpenCVReferenceTest_NV12, convert_nv12_colored) {
 
     inputData.clear();
 
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_color_format(
                                                         ColorFormat::NV12_SINGLE_PLANE))
                                                 .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR))
                                                 )
-            .build(function);
+            .build();
 
     const auto &param = function->get_parameters()[0];
     inputData.emplace_back(param->get_element_type(), param->get_shape(), input_yuv.data());
@@ -128,12 +128,12 @@ TEST_F(PreprocessOpenCVReferenceTest, resize_u8_simple_linear) {
 
     inputData.clear();
 
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_spatial_static_shape(2, 2))
                                                 .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                                 .network(InputNetworkInfo().set_layout("NCHW"))
             )
-            .build(function);
+            .build();
 
     const auto &param = function->get_parameters()[0];
     inputData.emplace_back(param->get_element_type(), param->get_shape(), input_img.data());
@@ -167,12 +167,12 @@ TEST_F(PreprocessOpenCVReferenceTest, resize_u8_large_picture_linear) {
 
     inputData.clear();
 
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_spatial_static_shape(input_height, input_width))
                                                 .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                                 .network(InputNetworkInfo().set_layout("NCHW"))
             )
-            .build(function);
+            .build();
 
     const auto &param = function->get_parameters()[0];
     inputData.emplace_back(param->get_element_type(), param->get_shape(), input_img.data());
@@ -205,12 +205,12 @@ TEST_F(PreprocessOpenCVReferenceTest, resize_f32_large_picture_linear) {
 
     inputData.clear();
 
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_spatial_static_shape(input_height, input_width))
                                                 .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                                 .network(InputNetworkInfo().set_layout("NCHW"))
             )
-            .build(function);
+            .build();
 
     const auto &param = function->get_parameters()[0];
     inputData.emplace_back(param->get_element_type(), param->get_shape(), input_img.data());
@@ -234,12 +234,12 @@ TEST_F(PreprocessOpenCVReferenceTest, DISABLED_resize_f32_large_picture_cubic_sm
     auto element_type = element::f32;
     auto input_img = std::vector<float> {1.f, 2.f, 3.f, 4.f, 4.f, 3.f, 2.f, 1.f, 1.f, 2.f, 3.f, 4.f, 4.f, 3.f, 2.f, 1.f};
     function = create_simple_function(element_type, func_shape);
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_spatial_static_shape(input_height, input_width))
                                                 .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_CUBIC))
                                                 .network(InputNetworkInfo().set_layout("NCHW"))
             )
-            .build(function);
+            .build();
 
     inputData.emplace_back(element_type, input_shape, input_img.data());
 
diff --git a/inference-engine/CMakeLists.txt b/inference-engine/CMakeLists.txt
index 304098403a6eba..942907895b4bfd 100644
--- a/inference-engine/CMakeLists.txt
+++ b/inference-engine/CMakeLists.txt
@@ -63,4 +63,3 @@ elseif(WIN32)
             PATTERN speech_libs_and_demos EXCLUDE
             PATTERN .clang-format EXCLUDE)
 endif()
-
diff --git a/inference-engine/cmake/ie_parallel.cmake b/inference-engine/cmake/ie_parallel.cmake
index 6c87d9b4eb940d..4f724840d377d9 100644
--- a/inference-engine/cmake/ie_parallel.cmake
+++ b/inference-engine/cmake/ie_parallel.cmake
@@ -91,7 +91,10 @@ function(set_ie_threading_interface_for TARGET_NAME)
             if(TARGET ${library})
                 get_target_property(include_directories ${library} INTERFACE_INCLUDE_DIRECTORIES)
                 if(include_directories)
-                    target_include_directories(${TARGET_NAME} SYSTEM BEFORE ${LINK_TYPE} ${include_directories})
+                    foreach(include_directory IN LISTS include_directories)
+                        target_include_directories(${TARGET_NAME} SYSTEM BEFORE
+                            ${LINK_TYPE} $<BUILD_INTERFACE:${include_directory}>)
+                    endforeach()
                 endif()
             endif()
         endforeach()
@@ -178,6 +181,6 @@ function(set_ie_threading_interface_for TARGET_NAME)
 
     if (NOT THREADING STREQUAL "SEQ")
         find_package(Threads REQUIRED)
-        ie_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} ${CMAKE_THREAD_LIBS_INIT})
+        ie_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} Threads::Threads)
     endif()
 endfunction(set_ie_threading_interface_for)
diff --git a/inference-engine/ie_bridges/c/include/c_api/ie_c_api.h b/inference-engine/ie_bridges/c/include/c_api/ie_c_api.h
index f46e59c38265fa..fdc50ae2efe2c8 100644
--- a/inference-engine/ie_bridges/c/include/c_api/ie_c_api.h
+++ b/inference-engine/ie_bridges/c/include/c_api/ie_c_api.h
@@ -372,7 +372,7 @@ INFERENCE_ENGINE_C_API(void) ie_core_versions_free(ie_core_versions_t *vers);
 /**
  * @brief Reads the model from the .xml and .bin files of the IR. Use the ie_network_free() method to free memory.
  * @ingroup Core
- * @param core A pointer to ie_core_t instance.
+ * @param core A pointer to the ie_core_t instance.
  * @param xml .xml file's path of the IR.
  * @param weights_file .bin file's path of the IR, if path is empty, will try to read bin file with the same name as xml and
  * if bin file with the same name was not found, will load IR without weights.
@@ -384,7 +384,7 @@ INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_core_read_network(ie_core_t
 /**
  * @brief Reads the model from an xml string and a blob of the bin part of the IR. Use the ie_network_free() method to free memory.
  * @ingroup Core
- * @param core A pointer to ie_core_t instance.
+ * @param core A pointer to the ie_core_t instance.
  * @param xml_content Xml content of the IR.
  * @param xml_content_size Number of bytes in the xml content of the IR.
  * @param weight_blob Blob containing the bin part of the IR.
@@ -395,12 +395,50 @@ INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_core_read_network_from_memo
     const ie_blob_t *weight_blob, ie_network_t **network);
 
 /**
- * @brief Creates an executable network from a network object. Users can create as many networks as they need and use
+* @brief Creates an executable network from a network previously exported to a file. Users can create as many networks as they need and use
+* them simultaneously (up to the limitation of the hardware resources). Use the ie_exec_network_free() method to free memory.
+* @ingroup Core
+* @param core A pointer to the ie_core_t instance.
+* @param file_name A path to the location of the exported file.
+* @param device_name A name of the device to load the network to.
+* @param config Device configuration.
+* @param exe_network A pointer to the newly created executable network.
+* @return Status code of the operation: OK(0) for success.
+*/
+INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_core_import_network(ie_core_t *core, const char *file_name, const char *device_name, \
+        const ie_config_t *config, ie_executable_network_t **exe_network);
+
+/**
+* @brief Creates an executable network from a network previously exported to memory. Users can create as many networks as they need and use
+* them simultaneously (up to the limitation of the hardware resources). Use the ie_exec_network_free() method to free memory.
+* @ingroup Core
+* @param core A pointer to the ie_core_t instance.
+* @param content A pointer to content of the exported network.
+* @param content_size Number of bytes in the exported network.
+* @param device_name A name of the device to load the network to.
+* @param config Device configuration.
+* @param exe_network A pointer to the newly created executable network.
+* @return Status code of the operation: OK(0) for success.
+*/
+INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_core_import_network_from_memory(ie_core_t *core, const uint8_t *content, size_t content_size,
+        const char *device_name, const ie_config_t *config, ie_executable_network_t **exe_network);
+
+/**
+* @brief Exports an executable network to a .bin file.
+* @ingroup Core
+* @param exe_network A pointer to the newly created executable network.
+* @param file_name Path to the file to export the network to.
+* @return Status code of the operation: OK(0) for success.
+*/
+INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_core_export_network(ie_executable_network_t *exe_network, const char *file_name);
+
+/**
+ * @brief Creates an executable network from a given network object. Users can create as many networks as they need and use
  * them simultaneously (up to the limitation of the hardware resources). Use the ie_exec_network_free() method to free memory.
  * @ingroup Core
- * @param core A pointer to ie_core_t instance.
- * @param network A pointer to ie_network instance.
- * @param device_name Name of device to load network to.
+ * @param core A pointer to the ie_core_t instance.
+ * @param network A pointer to the input ie_network instance to create the executable network from.
+ * @param device_name Name of the device to load the network to.
  * @param config Device configuration.
  * @param exe_network A pointer to the newly created executable network.
  * @return Status code of the operation: OK(0) for success.
@@ -412,7 +450,7 @@ INFERENCE_ENGINE_C_API(IE_NODISCARD IEStatusCode) ie_core_load_network(ie_core_t
 * @brief Reads model and creates an executable network from IR or ONNX file. Users can create as many networks as they need and use
 * them simultaneously (up to the limitation of the hardware resources). Use the ie_exec_network_free() method to free memory.
 * @ingroup Core
-* @param core A pointer to ie_core_t instance.
+* @param core A pointer to the ie_core_t instance.
 * @param xml .xml file's path of the IR. Weights file name will be calculated automatically
 * @param device_name Name of device to load network to.
 * @param config Device configuration.
diff --git a/inference-engine/ie_bridges/c/src/CMakeLists.txt b/inference-engine/ie_bridges/c/src/CMakeLists.txt
index ecc8165ca349fa..4a60879bb52073 100644
--- a/inference-engine/ie_bridges/c/src/CMakeLists.txt
+++ b/inference-engine/ie_bridges/c/src/CMakeLists.txt
@@ -17,6 +17,10 @@ target_link_libraries(${TARGET_NAME} PRIVATE inference_engine)
 target_include_directories(${TARGET_NAME} PUBLIC
     $<BUILD_INTERFACE:${InferenceEngine_C_API_SOURCE_DIR}/include>)
 
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME} PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
+
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
 
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
diff --git a/inference-engine/ie_bridges/c/src/ie_c_api.cpp b/inference-engine/ie_bridges/c/src/ie_c_api.cpp
index f3b0e8f6b32dad..4eaf8539e81d12 100644
--- a/inference-engine/ie_bridges/c/src/ie_c_api.cpp
+++ b/inference-engine/ie_bridges/c/src/ie_c_api.cpp
@@ -9,10 +9,11 @@
 #include <vector>
 #include <set>
 #include <algorithm>
-#include <sstream>
 #include <chrono>
 #include <tuple>
 #include <memory>
+#include <streambuf>
+#include <istream>
 #include <ie_extension.h>
 #include "inference_engine.hpp"
 #include "ie_compound_blob.h"
@@ -60,6 +61,47 @@ struct ie_network {
     IE::CNNNetwork object;
 };
 
+/**
+ * @struct mem_stringbuf
+ * @brief This struct puts memory buffer to stringbuf.
+ */
+struct mem_stringbuf : std::streambuf {
+    mem_stringbuf(const char *buffer, size_t sz) {
+        char * bptr(const_cast<char *>(buffer));
+        setg(bptr, bptr, bptr + sz);
+    }
+
+    pos_type seekoff(off_type off, std::ios_base::seekdir dir, std::ios_base::openmode which = std::ios_base::in) override {
+        switch (dir) {
+            case std::ios_base::beg:
+                setg(eback(), eback() + off, egptr());
+                break;
+            case std::ios_base::end:
+                setg(eback(), egptr() + off, egptr());
+                break;
+            case std::ios_base::cur:
+                setg(eback(), gptr() + off, egptr());
+                break;
+            default:
+                return pos_type(off_type(-1));
+        }
+        return (gptr() < eback() || gptr() > egptr()) ? pos_type(off_type(-1)) : pos_type(gptr() - eback());
+    }
+
+    pos_type seekpos(pos_type pos, std::ios_base::openmode which) override {
+        return seekoff(pos, std::ios_base::beg, which);
+    }
+};
+
+/**
+ * @struct mem_istream
+ * @brief This struct puts stringbuf buffer to istream.
+ */
+struct mem_istream: virtual mem_stringbuf, std::istream {
+    mem_istream(const char * buffer, size_t sz) : mem_stringbuf(buffer, sz), std::istream(static_cast<std::streambuf *>(this)) {
+    }
+};
+
 std::map<IE::StatusCode, IEStatusCode> status_map = {{IE::StatusCode::GENERAL_ERROR, IEStatusCode::GENERAL_ERROR},
                                                         {IE::StatusCode::INFER_NOT_STARTED, IEStatusCode::INFER_NOT_STARTED},
                                                         {IE::StatusCode::NETWORK_NOT_LOADED,  IEStatusCode::NETWORK_NOT_LOADED},
@@ -143,7 +185,6 @@ std::map<IE::ColorFormat, colorformat_e> colorformat_map = {{IE::ColorFormat::RA
 std::map<std::string, std::string> config2Map(const ie_config_t *config) {
     std::map<std::string, std::string> m;
     const ie_config_t *tmp = config;
-
     while (tmp && tmp->name && tmp->value) {
         m[tmp->name] = tmp->value;
         tmp = tmp->next;
@@ -335,18 +376,71 @@ IEStatusCode ie_core_read_network_from_memory(ie_core_t *core, const uint8_t *xm
     return status;
 }
 
+IEStatusCode ie_core_import_network(ie_core_t *core, const char *file_name, const char *device_name,
+        const ie_config_t *config, ie_executable_network_t **exe_network) {
+    IEStatusCode status = IEStatusCode::OK;
+
+    if (core == nullptr || file_name == nullptr || device_name == nullptr || exe_network == nullptr) {
+        status = IEStatusCode::GENERAL_ERROR;
+        return status;
+    }
+
+    try {
+        std::map<std::string, std::string> conf_map = config2Map(config);
+        std::unique_ptr<ie_executable_network_t> exe_net(new ie_executable_network_t);
+
+        exe_net->object = core->object.ImportNetwork(file_name, device_name, conf_map);
+        *exe_network = exe_net.release();
+    } CATCH_IE_EXCEPTIONS
+
+    return status;
+}
+
+IEStatusCode ie_core_import_network_from_memory(ie_core_t *core, const uint8_t *content, size_t content_size, const char *device_name,
+       const ie_config_t *config, ie_executable_network_t **exe_network) {
+    if (core == nullptr || content == nullptr || device_name == nullptr || exe_network == nullptr) {
+        return IEStatusCode::GENERAL_ERROR;
+    }
+
+    IEStatusCode status = IEStatusCode::OK;
+    try {
+        mem_istream model_stream(reinterpret_cast<const char*>(content), content_size);
+
+        std::map<std::string, std::string> conf_map = config2Map(config);
+        std::unique_ptr<ie_executable_network_t> exe_net(new ie_executable_network_t);
+
+        exe_net->object = core->object.ImportNetwork(model_stream, device_name, conf_map);
+        *exe_network = exe_net.release();
+    } CATCH_IE_EXCEPTIONS
+
+    return status;
+}
+
+IEStatusCode ie_core_export_network(ie_executable_network_t *exe_network, const char *file_name) {
+    IEStatusCode status = IEStatusCode::OK;
+
+    if (file_name == nullptr || exe_network == nullptr) {
+        status = IEStatusCode::GENERAL_ERROR;
+        return status;
+    }
+    try {
+        exe_network->object.Export(file_name);
+    } CATCH_IE_EXCEPTIONS
+
+    return status;
+}
+
 IEStatusCode ie_core_load_network(ie_core_t *core, const ie_network_t *network, const char *device_name, \
         const ie_config_t *config, ie_executable_network_t **exe_network) {
     IEStatusCode status = IEStatusCode::OK;
 
-    if (core == nullptr || network == nullptr || device_name == nullptr || config == nullptr || exe_network == nullptr) {
+    if (core == nullptr || network == nullptr || device_name == nullptr || exe_network == nullptr) {
         status = IEStatusCode::GENERAL_ERROR;
         return status;
     }
 
     try {
-        std::map<std::string, std::string> conf_map;
-        conf_map = config2Map(config);
+        std::map<std::string, std::string> conf_map = config2Map(config);
         std::unique_ptr<ie_executable_network_t> exe_net(new ie_executable_network_t);
 
         // create plugin in the registery and then create ExecutableNetwork.
@@ -361,14 +455,13 @@ IEStatusCode ie_core_load_network_from_file(ie_core_t *core, const char *xml, co
         const ie_config_t *config, ie_executable_network_t **exe_network) {
     IEStatusCode status = IEStatusCode::OK;
 
-    if (core == nullptr || xml == nullptr || device_name == nullptr || config == nullptr || exe_network == nullptr) {
+    if (core == nullptr || xml == nullptr || device_name == nullptr || exe_network == nullptr) {
         status = IEStatusCode::GENERAL_ERROR;
         return status;
     }
 
     try {
-        std::map<std::string, std::string> conf_map;
-        conf_map = config2Map(config);
+        std::map<std::string, std::string> conf_map = config2Map(config);
         std::unique_ptr<ie_executable_network_t> exe_net(new ie_executable_network_t);
 
         exe_net->object = core->object.LoadNetwork(xml, device_name, conf_map);
diff --git a/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp b/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp
index c12f98013dee6b..d422448bc69907 100644
--- a/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp
+++ b/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp
@@ -145,6 +145,9 @@ TEST(ie_core_get_available_devices, getAvailableDevices) {
     ie_core_free(&core);
 }
 
+// TODO: CVS-68982
+#ifndef OPENVINO_STATIC_LIBRARY
+
 TEST(ie_core_register_plugin, registerPlugin) {
     ie_core_t *core = nullptr;
     IE_ASSERT_OK(ie_core_create("", &core));
@@ -213,6 +216,8 @@ TEST(ie_core_unregister_plugin, unregisterPlugin) {
     ie_core_free(&core);
 }
 
+#endif // !OPENVINO_STATIC_LIBRARY
+
 TEST(ie_core_set_config, setConfig) {
     ie_core_t *core = nullptr;
     IE_ASSERT_OK(ie_core_create("", &core));
@@ -312,7 +317,7 @@ TEST(ie_core_read_network_from_memory, networkReadFromMemory) {
 
     if (weights_blob != nullptr) {
         std::vector<uint8_t> xml_content(content_from_file(xml, false));
-        
+
         ie_network_t *network = nullptr;
         IE_EXPECT_OK(ie_core_read_network_from_memory(core, xml_content.data(), xml_content.size(), weights_blob, &network));
         EXPECT_NE(nullptr, network);
@@ -321,6 +326,115 @@ TEST(ie_core_read_network_from_memory, networkReadFromMemory) {
         }
         ie_blob_free(&weights_blob);
     }
+    ie_core_free(&core);
+}
+
+TEST(ie_core_export_network_to_file, exportNetworktoFile) {
+    ie_core_t *core = nullptr;
+    IE_ASSERT_OK(ie_core_create("", &core));
+    ASSERT_NE(nullptr, core);
+
+    ie_config_t config = {nullptr, nullptr, nullptr};
+    ie_executable_network_t *exe_network = nullptr;
+
+    IE_EXPECT_OK(ie_core_load_network_from_file(core, xml, "HETERO:CPU", &config, &exe_network));
+    EXPECT_NE(nullptr, exe_network);
+
+    std::string export_path = TestDataHelpers::generate_model_path("test_model", "exported_model.blob");
+    IE_EXPECT_OK(ie_core_export_network(exe_network, export_path.c_str()));
+    std::ifstream file(export_path.c_str());
+    EXPECT_NE(file.peek(), std::ifstream::traits_type::eof());
+
+    EXPECT_NE(nullptr, exe_network);
+    ie_exec_network_free(&exe_network);
+    ie_core_free(&core);
+}
+
+TEST(ie_core_import_network_from_memory, importNetworkFromMem) {
+    ie_core_t *core = nullptr;
+    IE_ASSERT_OK(ie_core_create("", &core));
+    ASSERT_NE(nullptr, core);
+
+    ie_executable_network_t *exe_network = nullptr;
+
+    IE_EXPECT_OK(ie_core_load_network_from_file(core, xml, "HETERO:CPU", nullptr, &exe_network));
+    EXPECT_NE(nullptr, exe_network);
+
+    std::string export_path = TestDataHelpers::generate_model_path("test_model", "exported_model.blob");
+    IE_EXPECT_OK(ie_core_export_network(exe_network, export_path.c_str()));
+
+    std::vector<uchar> buffer(content_from_file(export_path.c_str(), true));
+    ie_executable_network_t *network = nullptr;
+
+    IE_EXPECT_OK(ie_core_import_network_from_memory(core, buffer.data(), buffer.size(), "HETERO:CPU", nullptr, &network));
+    EXPECT_NE(nullptr, network);
+    if (network != nullptr) {
+        ie_exec_network_free(&network);
+    }
+    if (exe_network != nullptr) {
+        ie_exec_network_free(&exe_network);
+    }
+    ie_core_free(&core);
+}
+
+TEST(ie_core_import_network_from_file, importNetworkFromFile) {
+    ie_core_t *core = nullptr;
+    IE_ASSERT_OK(ie_core_create("", &core));
+    ASSERT_NE(nullptr, core);
+
+    ie_config_t conf = {nullptr, nullptr, nullptr};
+
+    ie_executable_network_t *exe_network = nullptr;
+    IE_EXPECT_OK(ie_core_load_network_from_file(core, xml, "HETERO:CPU", &conf, &exe_network));
+    EXPECT_NE(nullptr, exe_network);
+
+    std::string exported_model = TestDataHelpers::generate_model_path("test_model", "exported_model.blob");
+    IE_EXPECT_OK(ie_core_export_network(exe_network, exported_model.c_str()));
+    std::ifstream file(exported_model);
+    EXPECT_NE(file.peek(), std::ifstream::traits_type::eof());
+
+    IE_EXPECT_OK(ie_core_import_network(core, exported_model.c_str(), "HETERO:CPU", &conf, &exe_network));
+    EXPECT_NE(nullptr, exe_network);
+    ie_exec_network_free(&exe_network);
+    ie_core_free(&core);
+}
+
+TEST(ie_core_import_network_from_file, importNetwork_errorHandling) {
+    ie_core_t *core = nullptr;
+    IE_ASSERT_OK(ie_core_create("", &core));
+    ASSERT_NE(nullptr, core);
+
+    ie_config_t config = {nullptr, nullptr, nullptr};
+
+    ie_executable_network_t *network = nullptr;
+    IE_EXPECT_OK(ie_core_load_network_from_file(core, xml, "HETERO:CPU", &config, &network));
+    EXPECT_NE(nullptr, network);
+
+    std::string exported_model = TestDataHelpers::generate_model_path("test_model", "exported_model.blob");
+    IE_EXPECT_OK(ie_core_export_network(network, exported_model.c_str()));
+
+    ie_executable_network_t *exe_network = nullptr;
+    IE_EXPECT_NOT_OK(ie_core_import_network(core, nullptr, "HETERO:CPU", &config, &exe_network));
+    EXPECT_EQ(nullptr, exe_network);
+
+    IE_EXPECT_NOT_OK(ie_core_import_network(core, exported_model.c_str(), nullptr, &config, &exe_network));
+    EXPECT_EQ(nullptr, exe_network);
+
+    IE_EXPECT_NOT_OK(ie_core_import_network(core, exported_model.c_str(), "HETERO:CPU", &config, nullptr));
+    EXPECT_EQ(nullptr, exe_network);
+
+    IE_EXPECT_NOT_OK(ie_core_import_network(core, exported_model.c_str(), "UnregisteredDevice", &config, &exe_network));
+    EXPECT_EQ(nullptr, exe_network);
+
+    IE_EXPECT_OK(ie_core_import_network(core, exported_model.c_str(), "HETERO:CPU", nullptr, &exe_network));
+    EXPECT_NE(nullptr, exe_network);
+
+    if (network != nullptr) {
+        ie_exec_network_free(&network);
+    }
+    if (exe_network != nullptr) {
+        ie_exec_network_free(&exe_network);
+    }
 
     ie_core_free(&core);
 }
@@ -366,6 +480,24 @@ TEST(ie_core_load_network, loadNetworkNoConfig) {
     ie_core_free(&core);
 }
 
+TEST(ie_core_load_network, loadNetworkNullConfig) {
+    ie_core_t *core = nullptr;
+    IE_ASSERT_OK(ie_core_create("", &core));
+    ASSERT_NE(nullptr, core);
+
+    ie_network_t *network = nullptr;
+    IE_EXPECT_OK(ie_core_read_network(core, xml, bin, &network));
+    EXPECT_NE(nullptr, network);
+
+    ie_executable_network_t *exe_network = nullptr;
+    IE_EXPECT_OK(ie_core_load_network(core, network, "CPU", nullptr, &exe_network));
+    EXPECT_NE(nullptr, exe_network);
+
+    ie_exec_network_free(&exe_network);
+    ie_network_free(&network);
+    ie_core_free(&core);
+}
+
 TEST(ie_core_load_network_from_file, loadNetworkNoConfig) {
     ie_core_t *core = nullptr;
     IE_ASSERT_OK(ie_core_create("", &core));
@@ -380,6 +512,20 @@ TEST(ie_core_load_network_from_file, loadNetworkNoConfig) {
     ie_core_free(&core);
 }
 
+TEST(ie_core_load_network_from_file, loadNetworkNullConfig) {
+    ie_core_t *core = nullptr;
+    IE_ASSERT_OK(ie_core_create("", &core));
+    ASSERT_NE(nullptr, core);
+
+    ie_executable_network_t *exe_network = nullptr;
+    IE_EXPECT_OK(ie_core_load_network_from_file(core, xml, "CPU", nullptr, &exe_network));
+    EXPECT_NE(nullptr, exe_network);
+
+    ie_exec_network_free(&exe_network);
+    ie_core_free(&core);
+}
+
+
 TEST(ie_core_load_network_from_file, loadNetwork_errorHandling) {
     ie_core_t *core = nullptr;
     IE_ASSERT_OK(ie_core_create("", &core));
@@ -396,9 +542,6 @@ TEST(ie_core_load_network_from_file, loadNetwork_errorHandling) {
     IE_EXPECT_NOT_OK(ie_core_load_network_from_file(core, xml, nullptr, &config, &exe_network));
     EXPECT_EQ(nullptr, exe_network);
 
-    IE_EXPECT_NOT_OK(ie_core_load_network_from_file(core, xml, "CPU", nullptr, &exe_network));
-    EXPECT_EQ(nullptr, exe_network);
-
     IE_EXPECT_NOT_OK(ie_core_load_network_from_file(core, xml, "CPU", &config, nullptr));
     EXPECT_EQ(nullptr, exe_network);
 
diff --git a/inference-engine/ie_bridges/c/tests/test_model_repo.hpp b/inference-engine/ie_bridges/c/tests/test_model_repo.hpp
index e6a099f591f126..bd84e76a7ef54a 100644
--- a/inference-engine/ie_bridges/c/tests/test_model_repo.hpp
+++ b/inference-engine/ie_bridges/c/tests/test_model_repo.hpp
@@ -50,4 +50,4 @@ std::string generate_image_path(std::string dir, std::string filename) {
 std::string generate_ieclass_xml_path(std::string filename) {
     return getModelPathNonFatal() + kPathSeparator + "ie_class" + kPathSeparator + filename;
 }
-} // namespace TestDataHelpers
\ No newline at end of file
+} // namespace TestDataHelpers
diff --git a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/CMakeLists.txt b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/CMakeLists.txt
index bc9115229e621c..72a69fb5904ad6 100644
--- a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/CMakeLists.txt
+++ b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/CMakeLists.txt
@@ -29,10 +29,10 @@ if(COMMAND ie_add_vs_version_file)
                            FILEDESCRIPTION "Offline Transformatoins Python library")
 endif()
 
-if(TARGET offline_transformations)
-    list(APPEND link_libraries offline_transformations)
-else()
+if(InferenceEngineDeveloperPackage_FOUND)
     list(APPEND link_libraries IE::offline_transformations)
+else()
+    list(APPEND link_libraries offline_transformations)
 endif()
 
 target_include_directories(${TARGET_NAME} SYSTEM PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/../inference_engine")
diff --git a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api.pyx b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api.pyx
index 2ca15561620a4d..4b42bee7805ef2 100644
--- a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api.pyx
+++ b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api.pyx
@@ -31,6 +31,10 @@ def ApplyLowLatencyTransformation(IENetwork network, bool use_const_initializer
     C.ApplyLowLatencyTransformation(network.impl, use_const_initializer)
 
 
+def CompressModelTransformation(IENetwork network):
+    C.CompressModelTransformation(network.impl)
+
+
 def ApplyPruningTransformation(IENetwork network):
     C.ApplyPruningTransformation(network.impl)
 
diff --git a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.cpp b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.cpp
index bae64e826a7843..9a7d652866643b 100644
--- a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.cpp
+++ b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.cpp
@@ -12,6 +12,8 @@
 #include <openvino/pass/make_stateful.hpp>
 #include <pot_transformations.hpp>
 #include <pruning.hpp>
+#include <transformations/common_optimizations/compress_float_constants.hpp>
+#include <transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp>
 #include <transformations/common_optimizations/moc_transformations.hpp>
 #include <transformations/control_flow/unroll_tensor_iterator.hpp>
 #include <transformations/serialize.hpp>
@@ -56,6 +58,13 @@ void InferenceEnginePython::GenerateMappingFile(InferenceEnginePython::IENetwork
     manager.run_passes(network.actual->getFunction());
 }
 
+void InferenceEnginePython::CompressModelTransformation(InferenceEnginePython::IENetwork network) {
+    ngraph::pass::Manager manager;
+    manager.register_pass<ov::pass::MarkPrecisionSensitiveSubgraphs>();
+    manager.register_pass<ov::pass::CompressFloatConstants>();
+    manager.run_passes(network.actual->getFunction());
+}
+
 void InferenceEnginePython::Serialize(InferenceEnginePython::IENetwork network,
                                       std::string path_to_xml,
                                       std::string path_to_bin) {
@@ -80,4 +89,4 @@ void InferenceEnginePython::CheckAPI() {
     auto reshape = f->get_result()->input_value(0).get_node_shared_ptr();
     assert(std::dynamic_pointer_cast<ngraph::opset6::Parameter>(reshape->input_value(0).get_node_shared_ptr()));
     assert(std::dynamic_pointer_cast<ngraph::opset6::Constant>(reshape->input_value(1).get_node_shared_ptr()));
-}
\ No newline at end of file
+}
diff --git a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.hpp b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.hpp
index c135919a91f638..5de87dc29991fe 100644
--- a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.hpp
+++ b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl.hpp
@@ -25,6 +25,8 @@ void ApplyPruningTransformation(InferenceEnginePython::IENetwork network);
 
 void GenerateMappingFile(InferenceEnginePython::IENetwork network, std::string path, bool extract_names);
 
+void CompressModelTransformation(InferenceEnginePython::IENetwork network);
+
 void Serialize(InferenceEnginePython::IENetwork network, std::string path_to_xml, std::string path_to_bin);
 
 void CheckAPI();
diff --git a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl_defs.pxd b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl_defs.pxd
index 82f6133df887e9..41755a0b2e06af 100644
--- a/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl_defs.pxd
+++ b/inference-engine/ie_bridges/python/src/openvino/offline_transformations/offline_transformations_api_impl_defs.pxd
@@ -17,6 +17,8 @@ cdef extern from "offline_transformations_api_impl.hpp" namespace "InferenceEngi
     cdef void ApplyMakeStatefulTransformation(IENetwork network, map[string, string]& in_out_names)
 
     cdef void ApplyPruningTransformation(IENetwork network)
+    
+    cdef void CompressModelTransformation(IENetwork network)
 
     cdef void GenerateMappingFile(IENetwork network, string path, bool extract_names)
 
diff --git a/inference-engine/ie_bridges/python/src/openvino/test_utils/CMakeLists.txt b/inference-engine/ie_bridges/python/src/openvino/test_utils/CMakeLists.txt
index c6ae33c7951305..86dd25996b90ee 100644
--- a/inference-engine/ie_bridges/python/src/openvino/test_utils/CMakeLists.txt
+++ b/inference-engine/ie_bridges/python/src/openvino/test_utils/CMakeLists.txt
@@ -29,10 +29,10 @@ if(COMMAND ie_add_vs_version_file)
                            FILEDESCRIPTION "Test Utils Python library")
 endif()
 
-if(TARGET commonTestUtils)
-    list(APPEND link_libraries commonTestUtils)
-else()
+if(InferenceEngineDeveloperPackage_FOUND)
     list(APPEND link_libraries IE::commonTestUtils)
+else()
+    list(APPEND link_libraries commonTestUtils)
 endif()
 
 target_include_directories(${TARGET_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}")
diff --git a/inference-engine/ie_bridges/python/tests/conftest.py b/inference-engine/ie_bridges/python/tests/conftest.py
index 9d429a1d9c2885..2be7c5e1783cb2 100644
--- a/inference-engine/ie_bridges/python/tests/conftest.py
+++ b/inference-engine/ie_bridges/python/tests/conftest.py
@@ -45,9 +45,8 @@ def device():
 
 def pytest_configure(config):
     # register an additional markers
-    config.addinivalue_line(
-        "markers", "template_plugin"
-    )
+    config.addinivalue_line("markers", "template_plugin: Skip test on Template plugin")
+    config.addinivalue_line("markers", "dynamic_library: Runs tests only in dynamic libraries case")
 
 
 def create_encoder(input_shape, levels = 4):
diff --git a/inference-engine/ie_bridges/python/tests/test_IECore.py b/inference-engine/ie_bridges/python/tests/test_IECore.py
index 6e57f0958e3841..f7b67e20feb2d1 100644
--- a/inference-engine/ie_bridges/python/tests/test_IECore.py
+++ b/inference-engine/ie_bridges/python/tests/test_IECore.py
@@ -75,6 +75,7 @@ def test_query_network(device):
     assert next(iter(set(query_res.values()))) == device, "Wrong device for some layers"
 
 
+@pytest.mark.dynamic_library
 @pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU", reason="Device dependent test")
 def test_register_plugin():
     ie = IECore()
@@ -86,6 +87,7 @@ def test_register_plugin():
     assert isinstance(exec_net, ExecutableNetwork), "Cannot load the network to the registered plugin with name 'BLA'"
 
 
+@pytest.mark.dynamic_library
 @pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU", reason="Device dependent test")
 def test_register_plugins():
     ie = IECore()
diff --git a/inference-engine/ie_bridges/python/wheel/CMakeLists.txt b/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
index ce1306f3f69fb3..211e63492378ae 100644
--- a/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
+++ b/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
@@ -47,7 +47,7 @@ endif()
 # create target for openvino.wheel
 
 set(openvino_wheel_deps ie_api offline_transformations_api)
-foreach(_target ie_libraries ie_plugins _pyngraph)
+foreach(_target ie_libraries ie_plugins _pyngraph pyopenvino)
     if(TARGET ${_target})
         list(APPEND openvino_wheel_deps ${_target})
     endif()
@@ -70,4 +70,4 @@ add_custom_command(OUTPUT ${openvino_wheel_path}
     COMMENT "Building Python wheel ${openvino_wheel_name}"
     VERBATIM)
 
-add_custom_target(ie_wheel DEPENDS ${openvino_wheel_path})
+add_custom_target(ie_wheel ALL DEPENDS ${openvino_wheel_path})
diff --git a/inference-engine/ie_bridges/python/wheel/requirements-dev.txt b/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
index 8c4ce47c35fcbb..26c6de3b960323 100644
--- a/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
+++ b/inference-engine/ie_bridges/python/wheel/requirements-dev.txt
@@ -1,3 +1,3 @@
-setuptools>=53.0.0
+setuptools>=53.0.0,<=58.4.0
 wheel>=0.36.2
 python-decouple>=3.4
diff --git a/inference-engine/ie_bridges/python/wheel/setup.py b/inference-engine/ie_bridges/python/wheel/setup.py
index dcf7967260c94c..de276d831d970c 100644
--- a/inference-engine/ie_bridges/python/wheel/setup.py
+++ b/inference-engine/ie_bridges/python/wheel/setup.py
@@ -109,6 +109,11 @@
         'prefix': 'site-packages',
         'install_dir': PY_PACKAGES_DIR,
     },
+    'pyopenvino' : {
+        'name': f'pyopenvino_{PYTHON_VERSION}',
+        'prefix': 'site-packages',
+        'install_dir': PY_PACKAGES_DIR,
+    },
 }
 
 
diff --git a/inference-engine/samples/CMakeLists.txt b/inference-engine/samples/CMakeLists.txt
index b78e32b2fa79a3..c64dfbfe0451e3 100644
--- a/inference-engine/samples/CMakeLists.txt
+++ b/inference-engine/samples/CMakeLists.txt
@@ -227,6 +227,13 @@ macro(ie_add_sample)
         target_link_libraries(${IE_SAMPLE_NAME} PRIVATE gflags)
     endif()
 
+    install(
+        TARGETS ${IE_SAMPLE_NAME}
+        RUNTIME DESTINATION samples_bin/
+        COMPONENT samples_bin
+        EXCLUDE_FROM_ALL
+    )
+
     # create global target with all samples / demo apps
     if(NOT TARGET ie_samples)
         add_custom_target(ie_samples ALL)
diff --git a/inference-engine/samples/benchmark_app/CMakeLists.txt b/inference-engine/samples/benchmark_app/CMakeLists.txt
index 4913084f94d872..b040829b422c65 100644
--- a/inference-engine/samples/benchmark_app/CMakeLists.txt
+++ b/inference-engine/samples/benchmark_app/CMakeLists.txt
@@ -4,6 +4,10 @@
 
 set(TARGET_NAME "benchmark_app")
 
+if(ENABLE_OPENCV)
+    set(OPENCV_MODULES core)
+endif()
+
 file (GLOB SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 file (GLOB HDR ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
 
@@ -11,7 +15,7 @@ ie_add_sample(NAME ${TARGET_NAME}
               SOURCES ${SRC}
               HEADERS ${HDR}
               DEPENDENCIES format_reader ie_samples_utils
-              OPENCV_DEPENDENCIES core)
+              OPENCV_DEPENDENCIES ${OPENCV_MODULES})
 
 find_package(OpenCL)
 
diff --git a/inference-engine/samples/build_samples.sh b/inference-engine/samples/build_samples.sh
index ad92920ae50be8..75f59b0c3716ab 100755
--- a/inference-engine/samples/build_samples.sh
+++ b/inference-engine/samples/build_samples.sh
@@ -3,6 +3,44 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+usage() {
+    echo "Build inference engine samples"
+    echo
+    echo "Options:"
+    echo "  -h                       Print the help message"
+    echo "  -b SAMPLE_BUILD_DIR      Specify the sample build directory"
+    echo "  -i SAMPLE_INSTALL_DIR    Specify the sample install directory"
+    echo
+    exit 1
+}
+
+samples_type=$(basename "$PWD")
+build_dir="$HOME/inference_engine_${samples_type}_samples_build"
+sample_install_dir=""
+
+# parse command line options
+while [[ $# -gt 0 ]]
+do
+case "$1" in
+    -b | --build_dir)
+    build_dir="$2"
+    shift
+    ;;
+    -i | --install_dir)
+    sample_install_dir="$2"
+    shift
+    ;;
+    -h | --help)
+    usage
+    ;;
+    *)
+    echo "Unrecognized option specified $1"
+    usage
+    ;;
+esac
+shift
+done
+
 error() {
     local code="${3:-1}"
     if [[ -n "$2" ]];then
@@ -39,9 +77,6 @@ if ! command -v cmake &>/dev/null; then
     exit 1
 fi
 
-samples_type=$(basename "$PWD")
-build_dir="$HOME/inference_engine_${samples_type}_samples_build"
-
 OS_PATH=$(uname -m)
 NUM_THREADS="-j2"
 
@@ -53,9 +88,16 @@ fi
 if [ -e "$build_dir/CMakeCache.txt" ]; then
   rm -rf "$build_dir/CMakeCache.txt"
 fi
+
 mkdir -p "$build_dir"
 cd "$build_dir"
 cmake -DCMAKE_BUILD_TYPE=Release "$SAMPLES_PATH"
 make $NUM_THREADS
 
-printf "\nBuild completed, you can find binaries for all samples in the $build_dir/%s/Release subfolder.\n\n" "$OS_PATH"
+if [ "$sample_install_dir" != "" ]; then
+    cmake -DCMAKE_INSTALL_PREFIX="$sample_install_dir" -DCOMPONENT=samples_bin -P cmake_install.cmake
+    printf "\nBuild completed, you can find binaries for all samples in the %s/samples_bin subfolder.\n\n" "$sample_install_dir"
+else
+    printf "\nBuild completed, you can find binaries for all samples in the $build_dir/%s/Release subfolder.\n\n" "$OS_PATH"
+fi
+
diff --git a/inference-engine/samples/build_samples_msvc.bat b/inference-engine/samples/build_samples_msvc.bat
index d986d7277d0667..4ef67b2901806c 100644
--- a/inference-engine/samples/build_samples_msvc.bat
+++ b/inference-engine/samples/build_samples_msvc.bat
@@ -8,24 +8,26 @@ SETLOCAL EnableDelayedExpansion
 set "ROOT_DIR=%~dp0"
 FOR /F "delims=\" %%i IN ("%ROOT_DIR%") DO set SAMPLES_TYPE=%%~nxi
 
-set "SOLUTION_DIR64=%USERPROFILE%\Documents\Intel\OpenVINO\inference_engine_%SAMPLES_TYPE%_samples_build"
-
-set MSBUILD_BIN=
-set VS_PATH=
-set VS_VERSION=
-
-if not "%1" == "" (
-   if "%1"=="VS2015" (
-      set "VS_VERSION=2015" 
-   ) else if "%1"=="VS2017" (
-      set "VS_VERSION=2017" 
-   ) else if "%1"=="VS2019" (
-      set "VS_VERSION=2019" 
-   ) else (
-      echo Unrecognized option specified "%1"
-      echo Supported command line options: VS2015, VS2017, VS2019
-      goto errorHandling
-   )
+set "SAMPLE_BUILD_DIR=%USERPROFILE%\Documents\Intel\OpenVINO\inference_engine_%SAMPLES_TYPE%_samples_build"
+set SAMPLE_INSTALL_DIR=
+
+:: command line arguments parsing
+:input_arguments_loop
+if not "%1"=="" (
+    if "%1"=="-b" (
+        set SAMPLE_BUILD_DIR=%2
+        shift
+    ) else if "%1"=="-i" (
+        set SAMPLE_INSTALL_DIR=%2
+        shift
+    ) else if "%1"=="-h" (
+        goto usage
+    ) else (
+        echo Unrecognized option specified "%1"
+        goto usage
+    )
+    shift
+    goto :input_arguments_loop
 )
 
 if "%INTEL_OPENVINO_DIR%"=="" (
@@ -36,7 +38,6 @@ if "%INTEL_OPENVINO_DIR%"=="" (
          echo To fix, run the following command: ^<INSTALL_DIR^>\setupvars.bat
          echo where INSTALL_DIR is the OpenVINO installation directory.
          GOTO errorHandling
-      )
     )
 )
 
@@ -46,87 +47,31 @@ if "%PROCESSOR_ARCHITECTURE%" == "AMD64" (
    set "PLATFORM=Win32"
 )
 
-set VSWHERE="false"
-if exist "%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" (
-   set VSWHERE="true"
-   cd "%ProgramFiles(x86)%\Microsoft Visual Studio\Installer"
-) else if exist "%ProgramFiles%\Microsoft Visual Studio\Installer\vswhere.exe" (
-      set VSWHERE="true"
-      cd "%ProgramFiles%\Microsoft Visual Studio\Installer"
-) else (
-   echo "vswhere tool is not found"
-)
-
-if !VSWHERE! == "true" (
-   if "!VS_VERSION!"=="" (
-      echo Searching the latest Visual Studio...
-      for /f "usebackq tokens=*" %%i in (`vswhere -latest -products * -requires Microsoft.Component.MSBuild -property installationPath`) do (
-         set VS_PATH=%%i
-      )
-   ) else (
-      echo Searching Visual Studio !VS_VERSION!...
-      for /f "usebackq tokens=*" %%i in (`vswhere -products * -requires Microsoft.Component.MSBuild -property installationPath`) do (
-         set CUR_VS=%%i
-         if not "!CUR_VS:%VS_VERSION%=!"=="!CUR_VS!" (
-            set VS_PATH=!CUR_VS!
-         )
-      )
-   )
-   if exist "C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe" (
-      set "MSBUILD_BIN=C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe"
-      set "MSBUILD_VERSION=14 2015"
-   )
-   if exist "!VS_PATH!\MSBuild\15.0\Bin\MSBuild.exe" (
-      set "MSBUILD_BIN=!VS_PATH!\MSBuild\15.0\Bin\MSBuild.exe"
-   )
-   if exist "!VS_PATH!\MSBuild\Current\Bin\MSBuild.exe" (
-      set "MSBUILD_BIN=!VS_PATH!\MSBuild\Current\Bin\MSBuild.exe"
-   )
-)
-
-if "!MSBUILD_BIN!" == "" (
-   if "!VS_VERSION!"=="2015" (
-      if exist "C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe" (
-         set "MSBUILD_BIN=C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe"
-         set "MSBUILD_VERSION=14 2015"
-      )
-   ) else if "!VS_VERSION!"=="2017" (
-      if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\MSBuild\15.0\Bin\MSBuild.exe" (
-         set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\MSBuild\15.0\Bin\MSBuild.exe"
-         set "MSBUILD_VERSION=15 2017"
-      ) else if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\MSBuild.exe" (
-         set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\MSBuild.exe"
-         set "MSBUILD_VERSION=15 2017"
-      ) else if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\MSBuild\15.0\Bin\MSBuild.exe" (
-         set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\MSBuild\15.0\Bin\MSBuild.exe"
-         set "MSBUILD_VERSION=15 2017"
-      )
-   )
-) else (
-   if not "!MSBUILD_BIN:2019=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=16 2019"
-   if not "!MSBUILD_BIN:2017=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=15 2017"
-   if not "!MSBUILD_BIN:2015=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=14 2015"
-)
-
-if "!MSBUILD_BIN!" == "" (
-   echo Build tools for Microsoft Visual Studio !VS_VERSION! cannot be found. If you use Visual Studio 2017, please download and install build tools from https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017
-   GOTO errorHandling
-)
-
-if exist "%SOLUTION_DIR64%\CMakeCache.txt" del "%SOLUTION_DIR64%\CMakeCache.txt"
+if exist "%SAMPLE_BUILD_DIR%\CMakeCache.txt" del "%SAMPLE_BUILD_DIR%\CMakeCache.txt"
 
-echo Creating Visual Studio %MSBUILD_VERSION% %PLATFORM% files in %SOLUTION_DIR64%... && ^
-cd "%ROOT_DIR%" && cmake -E make_directory "%SOLUTION_DIR64%" && cd "%SOLUTION_DIR64%" && cmake -G "Visual Studio !MSBUILD_VERSION!" -A %PLATFORM% "%ROOT_DIR%"
+cd "%ROOT_DIR%" && cmake -E make_directory "%SAMPLE_BUILD_DIR%" && cd "%SAMPLE_BUILD_DIR%" && cmake -G "Visual Studio 16 2019" -A %PLATFORM% "%ROOT_DIR%"
 
 echo.
 echo ###############^|^| Build Inference Engine samples using MS Visual Studio (MSBuild.exe) ^|^|###############
 echo.
-echo "!MSBUILD_BIN!" Samples.sln /p:Configuration=Release
-"!MSBUILD_BIN!" Samples.sln /p:Configuration=Release
+
+echo cmake --build . --config Release
+cmake --build . --config Release
 if ERRORLEVEL 1 GOTO errorHandling
 
+if NOT "%SAMPLE_INSTALL_DIR%"=="" cmake -DCMAKE_INSTALL_PREFIX="%SAMPLE_INSTALL_DIR%" -DCOMPONENT=samples_bin -P cmake_install.cmake
+
 echo Done.
-goto :eof
+exit /b
+
+:usage
+echo Build inference engine samples
+echo.
+echo Options:
+echo   -h                       Print the help message
+echo   -b SAMPLE_BUILD_DIR      Specify the sample build directory
+echo   -i SAMPLE_INSTALL_DIR    Specify the sample install directory
+exit /b
 
 :errorHandling
 echo Error
diff --git a/inference-engine/samples/classification_sample_async/main.cpp b/inference-engine/samples/classification_sample_async/main.cpp
index 56d942c1515387..9c8c168f0cda8b 100644
--- a/inference-engine/samples/classification_sample_async/main.cpp
+++ b/inference-engine/samples/classification_sample_async/main.cpp
@@ -104,28 +104,22 @@ int main(int argc, char* argv[]) {
         // -------- Step 3. Apply preprocessing --------
         const ov::Layout tensor_layout{"NHWC"};
 
-        // clang-format off
-        model = PrePostProcessor().
-            // 1) InputInfo() with no args assumes a model has a single input
-            input(InputInfo().
-                // 2) Set input tensor information:
-                // - precision of tensor is supposed to be 'u8'
-                // - layout of data is 'NHWC'
-                tensor(InputTensorInfo().
-                    set_element_type(ov::element::u8).
-                    set_layout(tensor_layout)).
-                // 3) Here we suppose model has 'NCHW' layout for input
-                network(InputNetworkInfo().
-                    set_layout("NCHW"))).
-            output(OutputInfo().
-                // 4) Set output tensor information:
-                // - precision of tensor is supposed to be 'f32'
-                tensor(OutputTensorInfo().
-                    set_element_type(ov::element::f32))).
-            // 5) Once the build() method is called, the preprocessing steps
-            // for layout and precision conversions are inserted automatically
-        build(model);
-        // clang-format on
+        PrePostProcessor proc(model);
+        // 1) input() with no args assumes a model has a single input
+        InputInfo& input_info = proc.input();
+        // 2) Set input tensor information:
+        // - precision of tensor is supposed to be 'u8'
+        // - layout of data is 'NHWC'
+        input_info.tensor().set_element_type(ov::element::u8).set_layout(tensor_layout);
+        // 3) Here we suppose model has 'NCHW' layout for input
+        input_info.network().set_layout("NCHW");
+        // 4) output() with no args assumes a model has a single result
+        // - output() with no args assumes a model has a single result
+        // - precision of tensor is supposed to be 'f32'
+        proc.output().tensor().set_element_type(ov::element::f32);
+        // 5) Once the build() method is called, the pre(post)processing steps
+        // for layout and precision conversions are inserted automatically
+        model = proc.build();
 
         // -------- Step 4. read input images --------
         slog::info << "Read input images" << slog::endl;
diff --git a/inference-engine/samples/common/format_reader/CMakeLists.txt b/inference-engine/samples/common/format_reader/CMakeLists.txt
index f4a3dfd0c7dd61..2bc71df4b2f611 100644
--- a/inference-engine/samples/common/format_reader/CMakeLists.txt
+++ b/inference-engine/samples/common/format_reader/CMakeLists.txt
@@ -40,3 +40,9 @@ set_target_properties(${TARGET_NAME} PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}
 if(COMMAND add_clang_format_target)
     add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 endif()
+
+install(
+    TARGETS ${TARGET_NAME}
+    RUNTIME DESTINATION samples_bin/ COMPONENT samples_bin EXCLUDE_FROM_ALL
+    LIBRARY DESTINATION samples_bin/ COMPONENT samples_bin EXCLUDE_FROM_ALL
+)
diff --git a/inference-engine/samples/common/format_reader/opencv_wrapper.cpp b/inference-engine/samples/common/format_reader/opencv_wrapper.cpp
index 26161e082fe71c..c302782e0be0d2 100644
--- a/inference-engine/samples/common/format_reader/opencv_wrapper.cpp
+++ b/inference-engine/samples/common/format_reader/opencv_wrapper.cpp
@@ -36,7 +36,7 @@ std::shared_ptr<unsigned char> OCVReader::getData(size_t width = 0, size_t heigh
     size_t size = width * height * img.channels();
     _data.reset(new unsigned char[size], std::default_delete<unsigned char[]>());
 
-    cv::Mat resized(width, height, img.type(), _data.get());
+    cv::Mat resized(cv::Size(width, height), img.type(), _data.get());
 
     if (width != img.cols || height != img.rows) {
         slog::warn << "Image is resized from (" << img.cols << ", " << img.rows << ") to (" << width << ", " << height
diff --git a/inference-engine/samples/common/utils/include/samples/args_helper.hpp b/inference-engine/samples/common/utils/include/samples/args_helper.hpp
index f216f370f5d2e5..e05f1ee905e567 100644
--- a/inference-engine/samples/common/utils/include/samples/args_helper.hpp
+++ b/inference-engine/samples/common/utils/include/samples/args_helper.hpp
@@ -38,3 +38,4 @@ void processLayout(InferenceEngine::CNNNetwork& network,
                    const std::string& iol);
 
 void printInputAndOutputsInfo(const InferenceEngine::CNNNetwork& network);
+void printInputAndOutputsInfo(const ov::Function& network);
diff --git a/inference-engine/samples/common/utils/include/samples/common.hpp b/inference-engine/samples/common/utils/include/samples/common.hpp
index 397e1f0ab35fbd..728071441e0003 100644
--- a/inference-engine/samples/common/utils/include/samples/common.hpp
+++ b/inference-engine/samples/common/utils/include/samples/common.hpp
@@ -33,6 +33,36 @@
 #    endif
 #endif
 
+/**
+ * @brief Unicode string wrappers
+ */
+#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+#    define tchar                wchar_t
+#    define tstring              std::wstring
+#    define tmain                wmain
+#    define TSTRING2STRING(tstr) wstring2string(tstr)
+#else
+#    define tchar                char
+#    define tstring              std::string
+#    define tmain                main
+#    define TSTRING2STRING(tstr) tstr
+#endif
+
+#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+
+/**
+ * @brief Convert wstring to string
+ * @param ref on wstring
+ * @return string
+ */
+inline std::string wstring2string(const std::wstring& wstr) {
+    std::string str;
+    for (auto&& wc : wstr)
+        str += static_cast<char>(wc);
+    return str;
+}
+#endif
+
 /**
  * @brief trim from start (in place)
  * @param s - string to trim
diff --git a/inference-engine/samples/common/utils/src/args_helper.cpp b/inference-engine/samples/common/utils/src/args_helper.cpp
index 43bf96d86a695a..a1cf3219054658 100644
--- a/inference-engine/samples/common/utils/src/args_helper.cpp
+++ b/inference-engine/samples/common/utils/src/args_helper.cpp
@@ -326,3 +326,35 @@ void printInputAndOutputsInfo(const InferenceEngine::CNNNetwork& network) {
                   << layer.second->getLayout() << std::endl;
     }
 }
+
+void printInputAndOutputsInfo(const ov::Function& network) {
+    slog::info << "model name: " << network.get_friendly_name() << slog::endl;
+
+    const std::vector<ov::Output<const ov::Node>> inputs = network.inputs();
+    for (const ov::Output<const ov::Node> input : inputs) {
+        slog::info << "    inputs" << slog::endl;
+
+        const std::string name = input.get_names().empty() ? "NONE" : input.get_any_name();
+        slog::info << "        input name: " << name << slog::endl;
+
+        const ov::element::Type type = input.get_element_type();
+        slog::info << "        input type: " << type << slog::endl;
+
+        const ov::Shape shape = input.get_shape();
+        slog::info << "        input shape: " << shape << slog::endl;
+    }
+
+    const std::vector<ov::Output<const ov::Node>> outputs = network.outputs();
+    for (const ov::Output<const ov::Node> output : outputs) {
+        slog::info << "    outputs" << slog::endl;
+
+        const std::string name = output.get_names().empty() ? "NONE" : output.get_any_name();
+        slog::info << "        output name: " << name << slog::endl;
+
+        const ov::element::Type type = output.get_element_type();
+        slog::info << "        output type: " << type << slog::endl;
+
+        const ov::Shape shape = output.get_shape();
+        slog::info << "        output shape: " << shape << slog::endl;
+    }
+}
diff --git a/inference-engine/samples/hello_classification/CMakeLists.txt b/inference-engine/samples/hello_classification/CMakeLists.txt
index 9b0509bab82c23..bef82baa3cecee 100644
--- a/inference-engine/samples/hello_classification/CMakeLists.txt
+++ b/inference-engine/samples/hello_classification/CMakeLists.txt
@@ -4,6 +4,5 @@
 
 ie_add_sample(NAME hello_classification
               SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/main.cpp"
-              DEPENDENCIES ie_samples_utils
-              OPENCV_DEPENDENCIES core imgcodecs)
+              DEPENDENCIES format_reader ie_samples_utils)
 
diff --git a/inference-engine/samples/hello_classification/main.cpp b/inference-engine/samples/hello_classification/main.cpp
index baa359cbbd6901..2169652695f9cf 100644
--- a/inference-engine/samples/hello_classification/main.cpp
+++ b/inference-engine/samples/hello_classification/main.cpp
@@ -4,122 +4,75 @@
 
 #include <iterator>
 #include <memory>
-#include <samples/slog.hpp>
 #include <string>
 #include <vector>
 
-#include "openvino/core/layout.hpp"
+// clang-format off
 #include "openvino/openvino.hpp"
-#include "samples/classification_results.h"
+
+#include "samples/args_helper.hpp"
 #include "samples/common.hpp"
-#include "samples/ocv_common.hpp"
+#include "samples/classification_results.h"
+#include "samples/slog.hpp"
+#include "format_reader_ptr.h"
+// clang-format on
 
 using namespace ov::preprocess;
 
-/**
- * @brief Define names based depends on Unicode path support
- */
-#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-#    define tcout                  std::wcout
-#    define file_name_t            std::wstring
-#    define imread_t               imreadW
-#    define ClassificationResult_t ClassificationResultW
-#else
-#    define tcout                  std::cout
-#    define file_name_t            std::string
-#    define imread_t               cv::imread
-#    define ClassificationResult_t ClassificationResult
-#endif
-
-#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-/**
- * @brief Realization cv::imread with support Unicode paths
- */
-cv::Mat imreadW(std::wstring input_image_path) {
-    cv::Mat image;
-    std::ifstream input_image_stream;
-    input_image_stream.open(input_image_path.c_str(), std::iostream::binary | std::ios_base::ate | std::ios_base::in);
-    if (input_image_stream.is_open()) {
-        if (input_image_stream.good()) {
-            input_image_stream.seekg(0, std::ios::end);
-            std::size_t file_size = input_image_stream.tellg();
-            input_image_stream.seekg(0, std::ios::beg);
-            std::vector<char> buffer(0);
-            std::copy(std::istreambuf_iterator<char>(input_image_stream),
-                      std::istreambuf_iterator<char>(),
-                      std::back_inserter(buffer));
-            image = cv::imdecode(cv::Mat(1, file_size, CV_8UC1, &buffer[0]), cv::IMREAD_COLOR);
-        } else {
-            tcout << "Input file '" << input_image_path << "' processing error" << std::endl;
-        }
-        input_image_stream.close();
-    } else {
-        tcout << "Unable to read input file '" << input_image_path << "'" << std::endl;
-    }
-    return image;
-}
-
-/**
- * @brief Convert wstring to string
- * @param ref on wstring
- * @return string
- */
-std::string simpleConvert(const std::wstring& wstr) {
-    std::string str;
-    for (auto&& wc : wstr)
-        str += static_cast<char>(wc);
-    return str;
-}
-
 /**
  * @brief Main with support Unicode paths, wide strings
  */
-int wmain(int argc, wchar_t* argv[]) {
-#else
-
-int main(int argc, char* argv[]) {
-#endif
+int tmain(int argc, tchar* argv[]) {
     try {
-        // -------- Get OpenVINO Runtime version --------
-        slog::info << "OpenVINO runtime: " << ov::get_openvino_version() << slog::endl;
+        // -------- Get OpenVINO runtime version --------
+        slog::info << ov::get_openvino_version() << slog::endl;
 
         // -------- Parsing and validation of input arguments --------
         if (argc != 4) {
-            tcout << "Usage : " << argv[0] << " <path_to_model> <path_to_image> <device_name>" << std::endl;
+            slog::info << "Usage : " << argc << " <path_to_model> <path_to_image> <device_name>" << slog::endl;
             return EXIT_FAILURE;
         }
 
-        const file_name_t input_model{argv[1]};
-        const file_name_t input_image_path{argv[2]};
-#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-        const std::string device_name = simpleConvert(argv[3]);
-#else
-        const std::string device_name{argv[3]};
-#endif
+        const std::string args = TSTRING2STRING(argv[0]);
+        const std::string model_path = TSTRING2STRING(argv[1]);
+        const std::string image_path = TSTRING2STRING(argv[2]);
+        const std::string device_name = TSTRING2STRING(argv[3]);
 
         // -------- Step 1. Initialize OpenVINO Runtime Core --------
         ov::runtime::Core core;
 
         // -------- Step 2. Read a model --------
-        auto model = core.read_model(input_model);
+        slog::info << "Loading model files: " << model_path << slog::endl;
+        auto model = core.read_model(model_path);
+        printInputAndOutputsInfo(*model);
 
         OPENVINO_ASSERT(model->get_parameters().size() == 1, "Sample supports models with 1 input only");
         OPENVINO_ASSERT(model->get_results().size() == 1, "Sample supports models with 1 output only");
 
-        // -------- Step 3. Initialize inference engine core
+        // -------- Step 3. Set up input
 
         // Read input image to a tensor and set it to an infer request
         // without resize and layout conversions
-        cv::Mat image = imread_t(input_image_path);
-        // just wrap Mat data by ov::runtime::Tensor without allocating of new memory
-        ov::runtime::Tensor input_tensor = wrapMat2Tensor(image);
+        FormatReader::ReaderPtr reader(image_path.c_str());
+        if (reader.get() == nullptr) {
+            slog::warn << "Image " + image_path + " cannot be read!" << slog::endl;
+            throw std::logic_error("");
+        }
+
+        ov::element::Type input_type = ov::element::u8;
+        ov::Shape input_shape = {1, reader->height(), reader->width(), 3};
+        std::shared_ptr<unsigned char> input_data = reader->getData();
+
+        // just wrap image data by ov::runtime::Tensor without allocating of new memory
+        ov::runtime::Tensor input_tensor = ov::runtime::Tensor(input_type, input_shape, input_data.get());
+
         const ov::Shape tensor_shape = input_tensor.get_shape();
+        const ov::Layout tensor_layout{"NHWC"};
 
         // -------- Step 4. Apply preprocessing --------
-        const ov::Layout tensor_layout{"NHWC"};
 
         // clang-format off
-        model = PrePostProcessor().
+        model = PrePostProcessor(model).
             // 1) InputInfo() with no args assumes a model has a single input
             input(InputInfo().
                 // 2) Set input tensor information:
@@ -147,8 +100,8 @@ int main(int argc, char* argv[]) {
                 // - precision of tensor is supposed to be 'f32'
                 tensor(OutputTensorInfo().
                     set_element_type(ov::element::f32))).
-        // 6) Apply preprocessing modifing the original 'model'
-        build(model);
+            // 6) Apply preprocessing modifing the original 'model'
+            build();
         // clang-format on
 
         // -------- Step 5. Loading a model to the device --------
@@ -165,18 +118,16 @@ int main(int argc, char* argv[]) {
         infer_request.infer();
 
         // -------- Step 9. Process output
-        ov::runtime::Tensor output_tensor = infer_request.get_output_tensor();
+        const ov::runtime::Tensor& output_tensor = infer_request.get_output_tensor();
 
         // Print classification results
-        ClassificationResult_t classification_result(output_tensor, {input_image_path});
+        ClassificationResult classification_result(output_tensor, {image_path});
         classification_result.show();
         // -----------------------------------------------------------------------------------------------------
     } catch (const std::exception& ex) {
         std::cerr << ex.what() << std::endl;
         return EXIT_FAILURE;
     }
-    std::cout << "This sample is an API example, for any performance measurements "
-                 "please use the dedicated benchmark_app tool"
-              << std::endl;
+
     return EXIT_SUCCESS;
 }
diff --git a/inference-engine/samples/hello_reshape_ssd/CMakeLists.txt b/inference-engine/samples/hello_reshape_ssd/CMakeLists.txt
index 5be6137ab2a716..ae7283c3634ca5 100644
--- a/inference-engine/samples/hello_reshape_ssd/CMakeLists.txt
+++ b/inference-engine/samples/hello_reshape_ssd/CMakeLists.txt
@@ -5,5 +5,4 @@
 ie_add_sample(NAME hello_reshape_ssd
               SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/main.cpp"
               HEADERS "${CMAKE_CURRENT_SOURCE_DIR}/reshape_ssd_extension.hpp"
-              DEPENDENCIES ie_samples_utils
-              OPENCV_DEPENDENCIES core imgproc imgcodecs)
+              DEPENDENCIES format_reader ie_samples_utils)
diff --git a/inference-engine/samples/hello_reshape_ssd/main.cpp b/inference-engine/samples/hello_reshape_ssd/main.cpp
index b91c2e1118699f..13d30a8c50bb08 100644
--- a/inference-engine/samples/hello_reshape_ssd/main.cpp
+++ b/inference-engine/samples/hello_reshape_ssd/main.cpp
@@ -2,10 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include <format_reader_ptr.h>
+
 #include <inference_engine.hpp>
 #include <memory>
 #include <ngraph/ngraph.hpp>
-#include <samples/ocv_common.hpp>
+#include <samples/common.hpp>
 #include <string>
 #include <vector>
 
@@ -54,12 +56,19 @@ int main(int argc, char* argv[]) {
         std::string input_name;
         SizeVector input_shape;
         std::tie(input_name, input_shape) = *input_shapes.begin();
-        cv::Mat image = cv::imread(input_image_path);
+        FormatReader::ReaderPtr reader(input_image_path.c_str());
+        if (reader.get() == nullptr) {
+            std::cout << "Image " + input_image_path + " cannot be read!" << std::endl;
+            return 1;
+        }
+        size_t image_width, image_height;
+        image_width = reader->width();
+        image_height = reader->height();
         input_shape[0] = batch_size;
-        input_shape[2] = static_cast<size_t>(image.rows);
-        input_shape[3] = static_cast<size_t>(image.cols);
+        input_shape[2] = image_height;
+        input_shape[3] = image_width;
         input_shapes[input_name] = input_shape;
-        std::cout << "Resizing network to the image size = [" << image.rows << "x" << image.cols << "] "
+        std::cout << "Resizing network to the image size = [" << image_height << "x" << image_width << "] "
                   << "with batch = " << batch_size << std::endl;
         network.reshape(input_shapes);
         // -----------------------------------------------------------------------------------------------------
@@ -132,10 +141,51 @@ int main(int argc, char* argv[]) {
 
         // --------------------------- Step 6. Prepare input
         // --------------------------------------------------------
-        Blob::Ptr input = infer_request.GetBlob(input_name);
-        for (size_t b = 0; b < batch_size; b++) {
-            matU8ToBlob<uint8_t>(image, input, b);
+        /** Collect images data ptrs **/
+        std::shared_ptr<unsigned char> image_data, original_image_data;
+        /** Store image data **/
+        std::shared_ptr<unsigned char> original_data(reader->getData());
+        std::shared_ptr<unsigned char> data_reader(
+            reader->getData(input_info->getTensorDesc().getDims()[3], input_info->getTensorDesc().getDims()[2]));
+        if (data_reader.get() != nullptr) {
+            original_image_data = original_data;
+            image_data = data_reader;
+        } else {
+            throw std::logic_error("Valid input images were not found!");
+        }
+
+        /** Creating input blob **/
+        Blob::Ptr image_input = infer_request.GetBlob(input_name);
+
+        /** Filling input tensor with images. First b channel, then g and r channels **/
+        MemoryBlob::Ptr mimage = as<MemoryBlob>(image_input);
+        if (!mimage) {
+            std::cout << "We expect image blob to be inherited from MemoryBlob, but by fact we were not able "
+                         "to cast imageInput to MemoryBlob"
+                      << std::endl;
+            return 1;
+        }
+        // locked memory holder should be alive all time while access to its buffer happens
+        auto minputHolder = mimage->wmap();
+
+        size_t num_channels = mimage->getTensorDesc().getDims()[1];
+        size_t image_size = mimage->getTensorDesc().getDims()[3] * mimage->getTensorDesc().getDims()[2];
+
+        unsigned char* data = minputHolder.as<unsigned char*>();
+
+        /** Iterate over all input images **/
+        for (size_t image_id = 0; image_id < batch_size; ++image_id) {
+            /** Iterate over all pixel in image (b,g,r) **/
+            for (size_t pid = 0; pid < image_size; pid++) {
+                /** Iterate over all channels **/
+                for (size_t ch = 0; ch < num_channels; ++ch) {
+                    /**          [images stride + channels stride + pixel id ] all in bytes            **/
+                    data[image_id * image_size * num_channels + ch * image_size + pid] =
+                        image_data.get()[pid * num_channels + ch];
+                }
+            }
         }
+
         // -----------------------------------------------------------------------------------------------------
 
         // --------------------------- Step 7. Do inference
@@ -156,37 +206,51 @@ int main(int argc, char* argv[]) {
         auto moutputHolder = moutput->rmap();
         const float* detection = moutputHolder.as<const float*>();
 
+        std::vector<std::vector<int>> boxes(batch_size);
+        std::vector<std::vector<int>> classes(batch_size);
+
         /* Each detection has image_id that denotes processed image */
         for (size_t cur_proposal = 0; cur_proposal < max_proposal_count; cur_proposal++) {
-            float image_id = detection[cur_proposal * object_size + 0];
-            float label = detection[cur_proposal * object_size + 1];
-            float confidence = detection[cur_proposal * object_size + 2];
-            /* CPU and GPU devices have difference in DetectionOutput layer, so we
-             * need both checks */
-            if (image_id < 0 || confidence == 0.0f) {
-                continue;
+            auto image_id = static_cast<int>(detection[cur_proposal * object_size + 0]);
+            if (image_id < 0) {
+                break;
             }
 
-            float xmin = detection[cur_proposal * object_size + 3] * image.cols;
-            float ymin = detection[cur_proposal * object_size + 4] * image.rows;
-            float xmax = detection[cur_proposal * object_size + 5] * image.cols;
-            float ymax = detection[cur_proposal * object_size + 6] * image.rows;
+            float confidence = detection[cur_proposal * object_size + 2];
+            auto label = static_cast<int>(detection[cur_proposal * object_size + 1]);
+            auto xmin = detection[cur_proposal * object_size + 3] * image_width;
+            auto ymin = detection[cur_proposal * object_size + 4] * image_height;
+            auto xmax = detection[cur_proposal * object_size + 5] * image_width;
+            auto ymax = detection[cur_proposal * object_size + 6] * image_height;
 
             if (confidence > 0.5f) {
                 /** Drawing only objects with >50% probability **/
-                std::ostringstream conf;
-                conf << ":" << std::fixed << std::setprecision(3) << confidence;
-                cv::rectangle(image, cv::Point2f(xmin, ymin), cv::Point2f(xmax, ymax), cv::Scalar(0, 0, 255));
+                classes[image_id].push_back(label);
+                boxes[image_id].push_back(static_cast<int>(xmin));
+                boxes[image_id].push_back(static_cast<int>(ymin));
+                boxes[image_id].push_back(static_cast<int>(xmax - xmin));
+                boxes[image_id].push_back(static_cast<int>(ymax - ymin));
+
                 std::cout << "[" << cur_proposal << "," << label << "] element, prob = " << confidence << ", bbox = ("
                           << xmin << "," << ymin << ")-(" << xmax << "," << ymax << ")"
                           << ", batch id = " << image_id << std::endl;
             }
         }
 
-        cv::imwrite("hello_reshape_ssd_output.jpg", image);
-        std::cout << "The resulting image was saved in the file: "
-                     "hello_reshape_ssd_output.jpg"
-                  << std::endl;
+        for (size_t batch_id = 0; batch_id < batch_size; ++batch_id) {
+            addRectangles(original_image_data.get(),
+                          image_height,
+                          image_width,
+                          boxes[batch_id],
+                          classes[batch_id],
+                          BBOX_THICKNESS);
+            const std::string image_path = "hello_reshape_ssd_output.bmp";
+            if (writeOutputBmp(image_path, original_image_data.get(), image_height, image_width)) {
+                std::cout << "The resulting image was saved in the file: " + image_path << std::endl;
+            } else {
+                throw std::logic_error(std::string("Can't create a file: ") + image_path);
+            }
+        }
         // -----------------------------------------------------------------------------------------------------
     } catch (const std::exception& ex) {
         std::cerr << ex.what() << std::endl;
diff --git a/inference-engine/samples/hello_reshape_ssd/reshape_ssd_extension.hpp b/inference-engine/samples/hello_reshape_ssd/reshape_ssd_extension.hpp
index 1b1d6c934ff094..fdc4837e31bee6 100644
--- a/inference-engine/samples/hello_reshape_ssd/reshape_ssd_extension.hpp
+++ b/inference-engine/samples/hello_reshape_ssd/reshape_ssd_extension.hpp
@@ -12,6 +12,9 @@
 
 #define CUSTOM_RELU_TYPE "CustomReLU"
 
+/* thickness of a line (in pixels) to be used for bounding boxes */
+#define BBOX_THICKNESS 2
+
 class CustomReLUImpl : public InferenceEngine::ILayerExecImpl {
 public:
     explicit CustomReLUImpl(const std::shared_ptr<ngraph::Node>& node) : _node(node) {}
diff --git a/inference-engine/samples/ngraph_function_creation_sample/main.cpp b/inference-engine/samples/ngraph_function_creation_sample/main.cpp
index 14a37c4d3ef929..24db7a4e0a3890 100644
--- a/inference-engine/samples/ngraph_function_creation_sample/main.cpp
+++ b/inference-engine/samples/ngraph_function_creation_sample/main.cpp
@@ -278,7 +278,7 @@ int main(int argc, char* argv[]) {
         // apply preprocessing
         // clang-format off
         using namespace ov::preprocess;
-        model = PrePostProcessor()
+        model = PrePostProcessor(model)
             // 1) InputInfo() with no args assumes a model has a single input
             .input(InputInfo()
                 // 2) Set input tensor information:
@@ -292,7 +292,7 @@ int main(int argc, char* argv[]) {
                     .set_layout("NCHW")))
         // 4) Once the build() method is called, the preprocessing steps
         // for layout and precision conversions are inserted automatically
-        .build(model);
+        .build();
         // clang-format on
 
         // -------- Step 4. Read input images --------
diff --git a/inference-engine/src/CMakeLists.txt b/inference-engine/src/CMakeLists.txt
index 7db652e7a2e1fe..b74d4b2830b4a4 100644
--- a/inference-engine/src/CMakeLists.txt
+++ b/inference-engine/src/CMakeLists.txt
@@ -9,8 +9,6 @@ endif()
 
 add_subdirectory(transformations)
 
-add_subdirectory(legacy_api)
-
 add_subdirectory(low_precision_transformations)
 
 add_subdirectory(offline_transformations)
@@ -43,6 +41,8 @@ endif()
 
 add_subdirectory(inference_engine)
 
+add_subdirectory(legacy_api)
+
 add_subdirectory(readers)
 
 add_subdirectory(preprocessing)
@@ -59,17 +59,21 @@ if(ENABLE_IR_V7_READER)
 endif()
 
 if(NGRAPH_IR_FRONTEND_ENABLE)
-    add_dependencies(ie_libraries ir_ngraph_frontend)
+    if(BUILD_SHARED_LIBS)
+        add_dependencies(ie_libraries ir_ngraph_frontend)
+    endif()
+    # use this one once CVS-69781 is fixed
+    # add_dependencies(inference_engine ir_ngraph_frontend)
 endif()
 
 if(NGRAPH_ONNX_FRONTEND_ENABLE)
-    add_dependencies(ie_libraries onnx_ngraph_frontend)
+    add_dependencies(inference_engine onnx_ngraph_frontend)
 endif()
 
 if(NGRAPH_PDPD_FRONTEND_ENABLE)
-    add_dependencies(ie_libraries paddlepaddle_ngraph_frontend)
+    add_dependencies(inference_engine paddlepaddle_ngraph_frontend)
 endif()
 
 if(NGRAPH_TF_FRONTEND_ENABLE)
-    add_dependencies(ie_libraries tensorflow_ngraph_frontend)
+    add_dependencies(inference_engine tensorflow_ngraph_frontend)
 endif()
diff --git a/inference-engine/src/cldnn_engine/CMakeLists.txt b/inference-engine/src/cldnn_engine/CMakeLists.txt
index 07fdb08df843c1..c28ff772540bb4 100644
--- a/inference-engine/src/cldnn_engine/CMakeLists.txt
+++ b/inference-engine/src/cldnn_engine/CMakeLists.txt
@@ -43,6 +43,11 @@ target_include_directories(${TARGET_NAME} PRIVATE
 
 set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
+# Workaround to avoid warnings during LTO build
+if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+    set_target_properties(${TARGET_NAME} PROPERTIES LINK_FLAGS_RELEASE "-Wno-error=maybe-uninitialized -Wno-maybe-uninitialized")
+endif()
+
 # Failed because of OpenCL
 # ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
diff --git a/inference-engine/src/cldnn_engine/cldnn_async_infer_request.cpp b/inference-engine/src/cldnn_engine/cldnn_async_infer_request.cpp
index 9e69ddeb0c8f28..c6c5faf11c42fb 100644
--- a/inference-engine/src/cldnn_engine/cldnn_async_infer_request.cpp
+++ b/inference-engine/src/cldnn_engine/cldnn_async_infer_request.cpp
@@ -17,29 +17,34 @@ CLDNNPlugin::CLDNNAsyncInferRequest::CLDNNAsyncInferRequest(const CLDNNInferRequ
         _pipeline.push_back({taskExecutor,
                     [this] {
                         OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNAsyncInferRequest::PreprocessingAndStartPipeline");
+                        _inferRequest->setup_stream_graph();
                         _inferRequest->preprocess();
                         _inferRequest->enqueue();
+                        _inferRequest->wait();
         } });
+    } else {
+        _pipeline.push_back({ _waitExecutor,
+                        [this] {
+                            OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNAsyncInferRequest::WaitPipeline");
+                            _inferRequest->wait_notify();
+                        } });
     }
-    _pipeline.push_back({_waitExecutor,
-                    [this] {
-                        OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNAsyncInferRequest::WaitPipeline");
-                        _inferRequest->wait();
-                    }});
 }
 
 void CLDNNPlugin::CLDNNAsyncInferRequest::Infer_ThreadUnsafe() {
     if (_inferRequest->use_external_queue()) {
-        _inferRequest->preprocess();
-        _inferRequest->enqueue();
+        _inferRequest->setup_stream_graph();
+        _inferRequest->preprocess_notify();
+        _inferRequest->enqueue_notify();
     }
     Parent::Infer_ThreadUnsafe();
 }
 
 void CLDNNPlugin::CLDNNAsyncInferRequest::StartAsync_ThreadUnsafe() {
     if (_inferRequest->use_external_queue()) {
-        _inferRequest->preprocess();
-        _inferRequest->enqueue();
+        _inferRequest->setup_stream_graph();
+        _inferRequest->preprocess_notify();
+        _inferRequest->enqueue_notify();
     }
     Parent::StartAsync_ThreadUnsafe();
 }
diff --git a/inference-engine/src/cldnn_engine/cldnn_graph.h b/inference-engine/src/cldnn_engine/cldnn_graph.h
index d65d4183d90a76..27a9a3f029a005 100644
--- a/inference-engine/src/cldnn_engine/cldnn_graph.h
+++ b/inference-engine/src/cldnn_engine/cldnn_graph.h
@@ -70,6 +70,7 @@ class CLDNNGraph {
         }
         m_cv.notify_one();
     }
+    std::mutex& get_mutex() { return m_infer_mutex; }
 
     bool use_external_queue() const;
 
diff --git a/inference-engine/src/cldnn_engine/cldnn_infer_request.cpp b/inference-engine/src/cldnn_engine/cldnn_infer_request.cpp
index c71acf4e6d975a..c98b06ce4cffcb 100644
--- a/inference-engine/src/cldnn_engine/cldnn_infer_request.cpp
+++ b/inference-engine/src/cldnn_engine/cldnn_infer_request.cpp
@@ -450,10 +450,7 @@ void CLDNNInferRequest::SetBatch(int new_batch) {
 
 CLDNNInferRequest::CLDNNInferRequest(InputsDataMap networkInputs, OutputsDataMap networkOutputs,
                                      const CLDNNExecNetwork::Ptr& execNetwork)
-        : IInferRequestInternal(networkInputs, networkOutputs)
-        , m_useProfiling(false)
-        , m_useStreams(false)
-        , m_useExternalQueue(false) {
+        : IInferRequestInternal(networkInputs, networkOutputs) {
     IE_ASSERT(nullptr != execNetwork);
     streamExecutor = dynamic_cast<InferenceEngine::IStreamsExecutor*>(execNetwork->m_taskExecutor.get());
 }
@@ -461,9 +458,7 @@ CLDNNInferRequest::CLDNNInferRequest(InputsDataMap networkInputs, OutputsDataMap
 CLDNNInferRequest::CLDNNInferRequest(const std::vector<std::shared_ptr<const ov::Node>>& inputs,
                                      const std::vector<std::shared_ptr<const ov::Node>>& outputs,
                                      const CLDNNExecNetwork::Ptr& execNetwork)
-        : IInferRequestInternal(inputs, outputs)
-        , m_useProfiling(false)
-        , m_useStreams(false) {
+        : IInferRequestInternal(inputs, outputs) {
     IE_ASSERT(nullptr != execNetwork);
     streamExecutor = dynamic_cast<InferenceEngine::IStreamsExecutor*>(execNetwork->m_taskExecutor.get());
 }
@@ -471,28 +466,30 @@ CLDNNInferRequest::CLDNNInferRequest(const std::vector<std::shared_ptr<const ov:
 // ----------------------------------------------------------------------------------------- //
 // ---------------------------- internal pipeline stages ----------------------------------- //
 // ----------------------------------------------------------------------------------------- //
-
-void CLDNNInferRequest::preprocess() {
-    int streamID = 0;
-    auto& streamGraphs = static_cast<CLDNNExecNetwork*>(_exeNetwork.get())->m_graphs;
-    if (nullptr != streamExecutor) {
-        streamID = streamExecutor->GetStreamId();
-        int numGraphs = streamGraphs.size();
-        streamID = streamID % numGraphs;
-    }
-    m_graph = streamGraphs[streamID];
-
+void CLDNNInferRequest::preprocess_notify() {
     m_graph->wait(CLDNNGraph::Stage::PREPROC);
     if (m_graph->GetMaxDynamicBatchSize() > 1) {
         preprocess_dynamic();
-        return;
+    } else {
+        execDataPreprocessing(_inputs, true);  // "true" stands for serial preprocessing in case of OpenMP
     }
-    execDataPreprocessing(_inputs, true);  // "true" stands for serial preprocessing in case of OpenMP
     m_graph->notify(CLDNNGraph::Stage::PREPROC);
 }
 
-void CLDNNInferRequest::enqueue() {
+void CLDNNInferRequest::preprocess() {
+    if (m_graph->GetMaxDynamicBatchSize() > 1) {
+        preprocess_dynamic();
+    } else {
+        execDataPreprocessing(_inputs, true);  // "true" stands for serial preprocessing in case of OpenMP
+    }
+}
+
+void CLDNNInferRequest::enqueue_notify() {
     m_graph->wait(CLDNNGraph::Stage::EXECUTE);
+    enqueue();
+}
+
+void CLDNNInferRequest::enqueue() {
     if (m_graph->GetMaxDynamicBatchSize() > 1) {
         enqueue_dynamic();
         return;
@@ -541,6 +538,11 @@ void CLDNNInferRequest::enqueue() {
     internal_outputs = m_graph->GetNetwork()->execute(dependencies);
 }
 
+void CLDNNInferRequest::wait_notify() {
+    wait();
+    m_graph->notify(CLDNNGraph::Stage::EXECUTE);
+}
+
 void CLDNNInferRequest::wait() {
     if (m_graph->GetMaxDynamicBatchSize() > 1) {
         wait_dynamic();
@@ -568,13 +570,11 @@ void CLDNNInferRequest::wait() {
     if (m_useProfiling) {
         m_graph->UpdatePerfStatistics();
     }
-    m_graph->notify(CLDNNGraph::Stage::EXECUTE);
 }
 
 void CLDNNInferRequest::preprocess_dynamic() {
     // execute input pre-processing.
     execDataPreprocessing(_inputs, true);  // "true" stands for serial preprocessing in case of OpenMP
-    m_graph->notify(CLDNNGraph::Stage::PREPROC);
 }
 
 void CLDNNInferRequest::enqueue_dynamic() {
@@ -619,12 +619,21 @@ void CLDNNInferRequest::wait_dynamic() {
             }
         }
     }
-    m_graph->notify(CLDNNGraph::Stage::EXECUTE);
 }
 
 // ----------------------------------------------------------------------------------------- //
 // ---------------------------- internal utils --------- ----------------------------------- //
 // ----------------------------------------------------------------------------------------- //
+void CLDNNInferRequest::setup_stream_graph() {
+    int streamID = 0;
+    auto& streamGraphs = static_cast<CLDNNExecNetwork*>(_exeNetwork.get())->m_graphs;
+    if (nullptr != streamExecutor) {
+        streamID = streamExecutor->GetStreamId();
+        int numGraphs = streamGraphs.size();
+        streamID = streamID % numGraphs;
+    }
+    m_graph = streamGraphs[streamID];
+}
 
 Blob::Ptr CLDNNInferRequest::create_host_blob(const TensorDesc& desc, uint8_t* mem_ptr) {
     OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNInferRequest::create_host_blob");
@@ -746,6 +755,21 @@ void CLDNNInferRequest::copy_input_data(std::shared_ptr<cldnn::network> network,
     }
 }
 
+Blob::Ptr CLDNNInferRequest::host_blob_from_device_blob(Blob::Ptr blobPtr) {
+    uint8_t* bufferMem = nullptr;
+    auto clblobPtr = std::dynamic_pointer_cast<InferenceEngine::gpu::ClBlob>(blobPtr);
+    if (clblobPtr) {
+        const auto memPtr = getBlobImpl(clblobPtr.get())->getMemory();
+        if (memPtr->get_allocation_type() == cldnn::allocation_type::usm_host) {
+            bufferMem = reinterpret_cast<uint8_t*>(memPtr->get_internal_params().mem);
+        }
+    }
+    Blob::Ptr hostBlob = create_host_blob(blobPtr->getTensorDesc(), bufferMem);
+    hostBlob->allocate();
+
+    return hostBlob;
+}
+
 void CLDNNInferRequest::allocate_inputs() {
     OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNInferRequest::allocate_inputs");
     auto inputLayouts = m_graph->GetInputLayouts();
@@ -777,7 +801,7 @@ void CLDNNInferRequest::allocate_inputs() {
             } else {
                 auto blobPtr = create_device_blob(desc, litr->second);
                 _deviceInputs[name] = blobPtr;
-                _inputs[name] = blobPtr;
+                _inputs[name] = host_blob_from_device_blob(blobPtr);
             }
         }
     }
@@ -823,7 +847,7 @@ void CLDNNInferRequest::allocate_outputs() {
         }
         auto blobPtr = create_device_blob(desc, output_layout);
         _deviceOutputs[no.first] = blobPtr;
-        _outputs[no.first] = blobPtr;
+        _outputs[no.first] = host_blob_from_device_blob(blobPtr);
         outputsMap[no.first] = outputID;
     }
 }
@@ -852,7 +876,8 @@ void CLDNNInferRequest::allocate_outputs_dynamic() {
 
 void CLDNNInferRequest::InferImpl() {
     OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNInferRequest::InferImpl");
-
+    setup_stream_graph();
+    std::lock_guard<std::mutex> lk(m_graph->get_mutex());
     preprocess();
     enqueue();
     wait();
diff --git a/inference-engine/src/cldnn_engine/cldnn_infer_request.h b/inference-engine/src/cldnn_engine/cldnn_infer_request.h
index 72c924b015e964..ae152855b45b59 100644
--- a/inference-engine/src/cldnn_engine/cldnn_infer_request.h
+++ b/inference-engine/src/cldnn_engine/cldnn_infer_request.h
@@ -49,6 +49,11 @@ class CLDNNInferRequest : public InferenceEngine::IInferRequestInternal {
     void EnableProfiling() { m_useProfiling = true; }
     void EnableStreams() { m_useStreams = true; }
 
+    void setup_stream_graph();
+    void preprocess_notify();
+    void enqueue_notify();
+    void wait_notify();
+
     void preprocess();
     void enqueue();
     void wait();
@@ -65,9 +70,9 @@ class CLDNNInferRequest : public InferenceEngine::IInferRequestInternal {
     std::map<std::string, cldnn::primitive_id> inputsMap;
     std::map<std::string, cldnn::primitive_id> outputsMap;
 
-    bool m_useProfiling;
-    bool m_useStreams;
-    bool m_useExternalQueue;
+    bool m_useProfiling = false;
+    bool m_useStreams = false;
+    bool m_useExternalQueue = false;
     std::shared_ptr<CLDNNGraph> m_graph;
 
     // dynamic batch stuff
@@ -87,6 +92,7 @@ class CLDNNInferRequest : public InferenceEngine::IInferRequestInternal {
                          const cldnn::layout& inputLayout, const InferenceEngine::Blob &inputBlob,
                          buf_info* bi = nullptr);
 
+    InferenceEngine::Blob::Ptr host_blob_from_device_blob(const InferenceEngine::Blob::Ptr blobPtr);
     void allocate_inputs();
     void allocate_outputs();
     void allocate_inputs_dynamic();
diff --git a/inference-engine/src/cldnn_engine/cldnn_program.cpp b/inference-engine/src/cldnn_engine/cldnn_program.cpp
index 22a55d743bd153..1841631937b171 100644
--- a/inference-engine/src/cldnn_engine/cldnn_program.cpp
+++ b/inference-engine/src/cldnn_engine/cldnn_program.cpp
@@ -6,6 +6,7 @@
 #include "ngraph/ops.hpp"
 #include "ngraph_ops/nms_ie_internal.hpp"
 #include "cldnn_itt.h"
+#include "cldnn/runtime/debug_configuration.hpp"
 
 using namespace InferenceEngine;
 using namespace InferenceEngine::details;
@@ -231,6 +232,12 @@ void Program::CreateSingleLayerPrimitive(cldnn::topology& topology, const std::s
     OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "Program::CreateSingleLayerPrimitive");
     InitProfileInfo(op->get_friendly_name(), op->get_type_name());
 
+    GPU_DEBUG_GET_INSTANCE(debug_config);
+    GPU_DEBUG_IF(debug_config->verbose >= 2) {
+        GPU_DEBUG_COUT << "Process " << "op::v" << op->get_type_info().version << "::" << op->get_type_name() << " operation "
+                       << "(friendly_name=" << op->get_friendly_name() << ")" << std::endl;
+    }
+
     bool is_created = false;
     const ngraph::NodeTypeInfo* op_type_info = &op->get_type_info();
     while (op_type_info != nullptr) {
@@ -251,8 +258,8 @@ void Program::CreateSingleLayerPrimitive(cldnn::topology& topology, const std::s
 
     if (!is_created) {
         IE_THROW() << "Operation: " << op->get_friendly_name()
-                           << " of type " << op->get_type_name()
-                           << "(op::v" << op->get_type_info().version << ") is not supported";
+                   << " of type " << op->get_type_name()
+                   << "(op::v" << op->get_type_info().version << ") is not supported";
     }
 }
 
diff --git a/inference-engine/src/cldnn_engine/cldnn_remote_context.cpp b/inference-engine/src/cldnn_engine/cldnn_remote_context.cpp
index 3b015a94e57be0..f043b6e4a891b0 100644
--- a/inference-engine/src/cldnn_engine/cldnn_remote_context.cpp
+++ b/inference-engine/src/cldnn_engine/cldnn_remote_context.cpp
@@ -204,6 +204,7 @@ CLDNNExecutionContextImpl::CLDNNExecutionContextImpl(const std::shared_ptr<IInfe
     lock.clear(std::memory_order_relaxed);
     gpu_handle_param _context_id = nullptr;
     gpu_handle_param _va_device = nullptr;
+    int ctx_device_id = 0;
     int target_tile_id = -1;
 
     if (params.size()) {
@@ -212,6 +213,12 @@ CLDNNExecutionContextImpl::CLDNNExecutionContextImpl(const std::shared_ptr<IInfe
 
         if (GPU_PARAM_VALUE(OCL) == contextTypeStr) {
             _context_id = _ObjFromParamSimple<gpu_handle_param>(params, GPU_PARAM_KEY(OCL_CONTEXT));
+
+            if (params.find(GPU_PARAM_KEY(OCL_QUEUE)) != params.end())
+                m_external_queue = _ObjFromParamSimple<gpu_handle_param>(params, GPU_PARAM_KEY(OCL_QUEUE));
+
+            if (params.find(GPU_PARAM_KEY(OCL_CONTEXT_DEVICE_ID)) != params.end())
+                ctx_device_id = _ObjFromParamSimple<int>(params, GPU_PARAM_KEY(OCL_CONTEXT_DEVICE_ID));
         } else if (GPU_PARAM_VALUE(VA_SHARED) == contextTypeStr) {
             m_va_display = _va_device = _ObjFromParamSimple<gpu_handle_param>(params, GPU_PARAM_KEY(VA_DEVICE));
             m_type = ContextType::DEV_SHARED;
@@ -222,16 +229,13 @@ CLDNNExecutionContextImpl::CLDNNExecutionContextImpl(const std::shared_ptr<IInfe
         if (tile_id_itr != params.end()) {
             target_tile_id = tile_id_itr->second.as<int>();
         }
-
-        if (params.find(GPU_PARAM_KEY(OCL_QUEUE)) != params.end())
-            m_external_queue = _ObjFromParamSimple<gpu_handle_param>(params, GPU_PARAM_KEY(OCL_QUEUE));
     }
 
     // TODO: Parameterize this based on plugin config and compilation options
     auto engine_type = cldnn::engine_types::ocl;
     auto runtime_type = cldnn::runtime_types::ocl;
     // Use actual runtime and engine types
-    cldnn::device_query device_query(engine_type, runtime_type, _context_id, _va_device, target_tile_id);
+    cldnn::device_query device_query(engine_type, runtime_type, _context_id, _va_device, ctx_device_id, target_tile_id);
     auto device_map = device_query.get_available_devices();
 
     auto iter = device_map.find(m_config.device_id);
@@ -273,6 +277,7 @@ ParamMap CLDNNExecutionContextImpl::getParams() const {
     switch (m_type) {
     case OCL:
         ret[GPU_PARAM_KEY(CONTEXT_TYPE)] = GPU_PARAM_VALUE(OCL);
+        ret[GPU_PARAM_KEY(OCL_QUEUE)] = static_cast<gpu_handle_param>(m_external_queue);
         break;
     case DEV_SHARED:
         ret[GPU_PARAM_KEY(CONTEXT_TYPE)] = GPU_PARAM_VALUE(VA_SHARED);
@@ -287,6 +292,19 @@ ParamMap CLDNNExecutionContextImpl::getParams() const {
 
 std::string CLDNNExecutionContextImpl::getDeviceName() const noexcept {
     auto devName = m_plugin.lock()->GetName();
+
+    auto engine_type = cldnn::engine_types::ocl;
+    auto runtime_type = cldnn::runtime_types::ocl;
+    // Use actual runtime and engine types
+    cldnn::device_query device_query(engine_type, runtime_type);
+    auto all_devices = device_query.get_available_devices();
+    auto current_device = m_engine->get_device();
+
+    for (auto& kv : all_devices) {
+        if (current_device->is_same(kv.second))
+            return devName + "." + kv.first;
+    }
+
     if (!m_config.device_id.empty())
         devName += "." + m_config.device_id;
     return devName;
diff --git a/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp b/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp
index c979d19f2626ab..d1c0eb38a01a16 100644
--- a/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp
+++ b/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp
@@ -34,6 +34,7 @@
 #include <transformations/common_optimizations/lin_op_sequence_fusion.hpp>
 #include <transformations/common_optimizations/weights_dequantize_to_fake_quantize.hpp>
 #include "transformations/common_optimizations/convert_quantize_dequantize.hpp"
+#include "transformations/common_optimizations/convert_compression_only_to_legacy.hpp"
 #include <transformations/op_conversions/convert_depth_to_space.hpp>
 #include <transformations/op_conversions/convert_space_to_depth.hpp>
 #include <transformations/op_conversions/convert_gelu.hpp>
@@ -163,6 +164,8 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Function> func) {
 
         auto pass_config = manager.get_pass_config();
 
+        pass_config->enable<ov::pass::ConvertCompressedOnlyToLegacy>();
+
         // SpaceToDepth/DepthToSpace node implementation supports only equal input/output tensors with rank <= 5
         pass_config->set_callback<ngraph::pass::ConvertSpaceToDepth,
                                   ngraph::pass::ConvertDepthToSpace>(
diff --git a/inference-engine/src/cldnn_engine/ops/constant.cpp b/inference-engine/src/cldnn_engine/ops/constant.cpp
index d2b4a37e63cd4d..26e8c2845ee5d6 100644
--- a/inference-engine/src/cldnn_engine/ops/constant.cpp
+++ b/inference-engine/src/cldnn_engine/ops/constant.cpp
@@ -103,7 +103,7 @@ static void CreateConstantOp(Program& p, const std::shared_ptr<ngraph::op::v0::C
                     bool all_inputs_1d = true;
                     for (size_t j = 0; j < outOp->get_input_size(); j++) {
                         auto& in_shape = outOp->get_input_shape(j);
-                        if (in_shape.size() != 1)
+                        if (in_shape.size() > 1)
                             all_inputs_1d = false;
                     }
                     needsBatchInterpretation = all_inputs_1d;
diff --git a/inference-engine/src/gna_plugin/CMakeLists.txt b/inference-engine/src/gna_plugin/CMakeLists.txt
index a476318dc03e89..3ec2d5b089c6c3 100644
--- a/inference-engine/src/gna_plugin/CMakeLists.txt
+++ b/inference-engine/src/gna_plugin/CMakeLists.txt
@@ -65,10 +65,13 @@ target_compile_definitions(${TARGET_NAME}_test_static
             INTEGER_LOW_P
             USE_STATIC_IE)
 
-target_link_libraries(${TARGET_NAME}_test_static PUBLIC inference_engine_preproc_s inference_engine_transformations libGNA::API)
-target_include_directories(${TARGET_NAME}_test_static PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}
-    $<TARGET_PROPERTY:inference_engine_legacy,INTERFACE_INCLUDE_DIRECTORIES>
-    PRIVATE $<TARGET_PROPERTY:openvino::conditional_compilation,INTERFACE_INCLUDE_DIRECTORIES>)
+target_link_libraries(${TARGET_NAME}_test_static PUBLIC inference_engine_s inference_engine_preproc_s inference_engine_transformations libGNA::API)
+target_include_directories(${TARGET_NAME}_test_static
+    PUBLIC
+        ${CMAKE_CURRENT_SOURCE_DIR}
+        $<TARGET_PROPERTY:inference_engine_legacy,INTERFACE_INCLUDE_DIRECTORIES>
+    PRIVATE
+        $<TARGET_PROPERTY:openvino::conditional_compilation,INTERFACE_INCLUDE_DIRECTORIES>)
 set_target_properties(${TARGET_NAME}_test_static PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}_test_static)
 
 set_target_properties(${TARGET_NAME} ${TARGET_NAME}_test_static
diff --git a/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp b/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp
index d246d3406d5fa5..826cbfc3151062 100644
--- a/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp
+++ b/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp
@@ -270,7 +270,8 @@ void GNAPluginNS::backend::AMIntelDNN::updateNumberOfOutputsIfPoolingEnabled(Gna
         IE_ASSERT(gnaOp.Parameters != nullptr || gnaOp.NumberOfParameters == 0);
         if (gnaOp.Type == Gna2OperationTypeConvolution && inputShape.NumberOfDimensions == 2 &&
             gnaOp.NumberOfParameters >= PoolStrideParamIdx &&
-            gnaOp.Parameters[PoolWinParamIdx]!= nullptr &&
+            gnaOp.Parameters != nullptr &&
+            gnaOp.Parameters[PoolWinParamIdx] != nullptr &&
             gnaOp.Parameters[PoolStrideParamIdx] != nullptr) {
             IE_ASSERT(gnaOp.Operands[OutOpIdx] != nullptr);
             IE_ASSERT(gnaOp.Operands[FilterOpIdx] != nullptr);
diff --git a/inference-engine/src/gna_plugin/gna_executable_network.hpp b/inference-engine/src/gna_plugin/gna_executable_network.hpp
index d7a9cd1bd71701..923fcc92f5ae08 100644
--- a/inference-engine/src/gna_plugin/gna_executable_network.hpp
+++ b/inference-engine/src/gna_plugin/gna_executable_network.hpp
@@ -67,13 +67,6 @@ class GNAExecutableNetwork : public InferenceEngine::IExecutableNetworkInternal
         return std::make_shared<GNAInferRequest>(plg, inputs, outputs);
     }
 
-    INFERENCE_ENGINE_DEPRECATED("Use InferRequest::QueryState instead")
-    std::vector<InferenceEngine::IVariableStateInternal::Ptr>  QueryState() override {
-        IE_SUPPRESS_DEPRECATED_START
-        return plg->QueryState();
-        IE_SUPPRESS_DEPRECATED_END
-    }
-
     void Export(const std::string &modelFileName) override {
         plg->Export(modelFileName);
     }
diff --git a/inference-engine/src/gna_plugin/gna_plugin.cpp b/inference-engine/src/gna_plugin/gna_plugin.cpp
index fde061f91b7424..83c2c0dbaea57e 100644
--- a/inference-engine/src/gna_plugin/gna_plugin.cpp
+++ b/inference-engine/src/gna_plugin/gna_plugin.cpp
@@ -70,6 +70,7 @@
 #include "transformations/op_conversions/lstm_cell_decomposition.hpp"
 #include "transformations/remove_single_input_concat.hpp"
 #include "transformations/broadcast_const.hpp"
+#include "transformations/substitute_softsign.hpp"
 
 #include <ngraph/opsets/opset7.hpp>
 
@@ -713,6 +714,7 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         manager.register_pass<InsertTransposeAfterConvOrPool>();
         manager.register_pass<ReorderActivationAndPooling>();
         manager.register_pass<RemoveSingleInputConcat>();
+        manager.register_pass<SubstituteSoftsign>();
         manager.register_pass<ngraph::pass::ConvertOpSet3ToOpSet2>();
         manager.register_pass<ngraph::pass::ConvertOpSet2ToOpSet1>();
         manager.register_pass<ngraph::pass::ConvertOpSet1ToLegacy>();
@@ -781,6 +783,9 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
             passes->registerPass<SubstituteScaleShiftBroadCastPass>();
         }
 
+        if (fake_quantized)
+            passes->registerPass<SubstituteSoftSignPass>();
+
         // fake quantisation aware passes
         passes->registerPass<FuseFQIntoWeightsPass>();
         passes->registerPass<MoveFakeQuantizeLayerIntoQuantParamsPass>();
@@ -788,7 +793,6 @@ void GNAPlugin::LoadNetwork(CNNNetwork & _network) {
         passes->registerPass<TransposeWeightsFromNCHWToNHWCPass>();
 
         passes->registerPass<SubstitutePReluPass>();
-        passes->registerPass<SubstituteSoftSignPass>();
 
         passes->registerPass<ReorderMaxPoolPass>();
         passes->registerPass<EltwiseSplitOverChannelsPass>();
diff --git a/inference-engine/src/gna_plugin/ops/softsign.cpp b/inference-engine/src/gna_plugin/ops/softsign.cpp
new file mode 100644
index 00000000000000..eb10bffea2798b
--- /dev/null
+++ b/inference-engine/src/gna_plugin/ops/softsign.cpp
@@ -0,0 +1,79 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "softsign.hpp"
+
+#include <ngraph/validation_util.hpp>
+
+#include "ngraph/attribute_visitor.hpp"
+#include "ngraph/runtime/host_tensor.hpp"
+
+#include <cmath>
+#include <cstddef>
+
+NGRAPH_RTTI_DEFINITION(GNAPluginNS::SoftSign, "SoftSign", 0);
+
+namespace GNAPluginNS {
+
+template <typename T>
+void softsign(const T* arg, T* out, size_t count) {
+    for (size_t i = 0; i < count; i++) {
+        out[i] = 1 / (1 + std::abs(arg[i]));
+    }
+}
+
+SoftSign::SoftSign(const ngraph::Output<ngraph::Node>& arg) : ov::op::util::UnaryElementwiseArithmetic(arg) {
+    constructor_validate_and_infer_types();
+}
+
+std::shared_ptr<ngraph::Node> SoftSign::clone_with_new_inputs(const ngraph::OutputVector& new_args) const {
+    check_new_args_count(this, new_args);
+    return std::make_shared<SoftSign>(new_args.at(0));
+}
+
+template <ngraph::element::Type_t ET>
+inline bool evaluate(const ov::runtime::Tensor& arg, ov::runtime::Tensor& out, const size_t count) {
+    using T = typename ngraph::element_type_traits<ET>::value_type;
+    softsign<T>(arg.data<T>(), out.data<T>(), count);
+    return true;
+}
+
+namespace {
+bool evaluate_softsign(const ov::runtime::Tensor& arg, ov::runtime::Tensor& out) {
+    bool rc = true;
+    size_t count = shape_size(arg.get_shape());
+
+    switch (arg.get_element_type()) {
+    case ov::element::Type_t::f16:
+        rc = evaluate<ov::element::Type_t::f16>(arg, out, count);
+        break;
+    case ov::element::Type_t::f32:
+        rc = evaluate<ov::element::Type_t::f32>(arg, out, count);
+        break;
+    default:
+        rc = false;
+        break;
+    }
+    return rc;
+}
+} // namespace
+
+bool SoftSign::evaluate(ov::runtime::TensorVector& outputs,
+                        const ov::runtime::TensorVector& inputs,
+                        const ov::EvaluationContext& evaluation_context) const {
+    return evaluate_softsign(inputs[0], outputs[0]);
+}
+
+bool SoftSign::has_evaluate() const {
+    switch (get_input_element_type(0)) {
+    case ngraph::element::f16:
+    case ngraph::element::f32:
+        return true;
+    default:
+        break;
+    }
+    return false;
+}
+
+} // namespace GNAPluginNS
diff --git a/inference-engine/src/gna_plugin/ops/softsign.hpp b/inference-engine/src/gna_plugin/ops/softsign.hpp
new file mode 100644
index 00000000000000..ffbcbc9dbb959e
--- /dev/null
+++ b/inference-engine/src/gna_plugin/ops/softsign.hpp
@@ -0,0 +1,30 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/op/op.hpp"
+#include "ngraph/node.hpp"
+#include "openvino/op/util/unary_elementwise_arithmetic.hpp"
+
+namespace GNAPluginNS {
+/// \brief Neural Activation Function
+/// f(x) =  x/(1.0 + |x|)
+///
+class SoftSign : public ov::op::util::UnaryElementwiseArithmetic {
+public:
+    NGRAPH_RTTI_DECLARATION;
+
+    SoftSign() = default;
+    /// \brief Constructs an SoftSign operation.
+    ///
+    /// \param data Input tensor
+    SoftSign(const ngraph::Output<ngraph::Node>& arg);
+    std::shared_ptr<Node> clone_with_new_inputs(const ngraph::OutputVector& new_args) const override;
+    bool evaluate(ov::runtime::TensorVector& output_values,
+                  const ov::runtime::TensorVector& input_values,
+                  const ov::EvaluationContext & evaluation_context) const override;
+    bool has_evaluate() const override;
+};
+}  // namespace GNAPluginNS
diff --git a/inference-engine/src/gna_plugin/runtime/pwl.cpp b/inference-engine/src/gna_plugin/runtime/pwl.cpp
index d214f06413b9cd..de577d0a029cc0 100644
--- a/inference-engine/src/gna_plugin/runtime/pwl.cpp
+++ b/inference-engine/src/gna_plugin/runtime/pwl.cpp
@@ -61,7 +61,8 @@ double pivot_search(std::vector<pwl_t>& result,
                     const double alpha_0,
                     const double alpha_N,
                     const double threshold,
-                    const bool negative) {
+                    const bool negative,
+                    size_t iter_num) {
     std::vector<std::vector<double>> t(N + 1);
     std::vector<std::vector<double>> alpha(N + 1);
     std::vector<std::vector<double>> epsilon(N + 1);
@@ -114,7 +115,7 @@ double pivot_search(std::vector<pwl_t>& result,
             if (fabs(epsilon[i][j]) > max_epsilon) max_epsilon = fabs(epsilon[i][j]);
             if (fabs(epsilon[i][j]) < min_epsilon) min_epsilon = fabs(epsilon[i][j]);
         }
-        if ((j == PWL_MAX_ITERATIONS) || (max_epsilon - min_epsilon < threshold * min_epsilon)) {
+        if ((j == iter_num) || (max_epsilon - min_epsilon < threshold * min_epsilon)) {
             pwl_t value;
             result.resize(0);
             epsilon_final = (max_epsilon + min_epsilon) / 4.0;  // Andrzej's modification
@@ -133,7 +134,7 @@ double pivot_search(std::vector<pwl_t>& result,
             value.alpha = alpha[N][j];
             value.beta = sgn * first_deriv_f(t[N - 1][j]) * (alpha[N][j] - t[N - 1][j]) + sgn * f(t[N - 1][j]) - epsilon_final;
             result.push_back(value);
-            if (j == PWL_MAX_ITERATIONS) {
+            if (j == iter_num) {
                 THROW_GNA_EXCEPTION << "Failed to converge in pivot_search!";
             }
             return(epsilon_final);
@@ -178,7 +179,8 @@ double pivot_search(std::vector<pwl_t>& result, double(*f)(const double),
                     const double alpha_0,
                     const double alpha_N,
                     const double threshold,
-                    const bool negative) {
+                    const bool negative,
+                    size_t iter_num) {
     double epsilon_final = 0.0;
 
     if (f == nullptr ||
@@ -189,7 +191,7 @@ double pivot_search(std::vector<pwl_t>& result, double(*f)(const double),
 
     auto fun = [&f](double x) -> double { return f(x); };
     auto first_deriv = [&first_deriv_f](double x) -> double { return first_deriv_f(x); };
-    return pivot_search(result, fun, first_deriv, N, alpha_0, alpha_N, threshold, negative);
+    return pivot_search(result, fun, first_deriv, N, alpha_0, alpha_N, threshold, negative, iter_num);
 }
 
 double calculate_error_pct(const DnnActivation& activation_type,
@@ -406,30 +408,37 @@ std::vector<pwl_t> pwl_search(const DnnActivation& activation_type,
             switch (activation_type) {
                 case kActSigmoid:
                     if (u_bound == 0) negative = true;  // make left half convex
-                    err = pivot_search(pwl, sigmoid, first_deriv_sigmoid, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, sigmoid, first_deriv_sigmoid, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                     break;
                 case kActTanh:
                     if (u_bound == 0) negative = true;  // make left half convex
-                    err = pivot_search(pwl, tanh, first_deriv_tanh, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, tanh, first_deriv_tanh, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                     break;
                 case kActSoftSign:
                     if (u_bound == 0) negative = true;  // make left half convex
-                    err = pivot_search(pwl, softsign, first_deriv_softsign, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, softsign, first_deriv_softsign, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                     break;
                 case kActExp:
                     negative = true;  // make function convex
-                    err = pivot_search(pwl, exp, first_deriv_exp, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, exp, first_deriv_exp, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                     break;
                 case kActLog:
-                    err = pivot_search(pwl, log, first_deriv_log, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, log, first_deriv_log, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_LOG);
                     break;
                 case kActNegLog:
                     negative = true;  // make function convex
-                    err = pivot_search(pwl, neglog, first_deriv_neglog, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, neglog, first_deriv_neglog, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_LOG);
                     break;
                 case kActNegHalfLog:
                     negative = true;  // make function convex
-                    err = pivot_search(pwl, neghalflog, first_deriv_neghalflog, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, neghalflog, first_deriv_neghalflog, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_LOG);
                     break;
                 case kActPow: {
                     negative = (fmod(activation_type.args.pow.exponent, 1.0) == 0) ? true : false;
@@ -438,7 +447,8 @@ std::vector<pwl_t> pwl_search(const DnnActivation& activation_type,
                                                                     activation_type.args.pow.offset };
                     auto fun = [&args](double x) -> double { return power(x, args); };
                     auto first_deriv = [&args](double x) -> double { return first_deriv_power(x, args); };
-                    err = pivot_search(pwl, fun, first_deriv, n_segments, l_bound, u_bound, threshold, negative);
+                    err = pivot_search(pwl, fun, first_deriv, n_segments, l_bound, u_bound,
+                        threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                     break;
                 }
                 default:
@@ -446,29 +456,36 @@ std::vector<pwl_t> pwl_search(const DnnActivation& activation_type,
             }
             err_pct = calculate_error_pct(activation_type, l_bound, u_bound, err, samples);
 
-            while ((n_segments < PWL_MAX_ITERATIONS) && (allowed_err_pct < err_pct)) {
+            while ((n_segments < PWL_MAX_NUM_SEGMENTS) && (allowed_err_pct < err_pct)) {
                 n_segments += 1;
                 switch (activation_type) {
                     case kActSigmoid:
-                        err = pivot_search(pwl, sigmoid, first_deriv_sigmoid, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, sigmoid, first_deriv_sigmoid, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                         break;
                     case kActTanh:
-                        err = pivot_search(pwl, tanh, first_deriv_tanh, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, tanh, first_deriv_tanh, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                         break;
                     case kActSoftSign:
-                        err = pivot_search(pwl, softsign, first_deriv_softsign, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, softsign, first_deriv_softsign, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                             break;
                     case kActExp:
-                        err = pivot_search(pwl, exp, first_deriv_exp, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, exp, first_deriv_exp, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                         break;
                     case kActLog:
-                        err = pivot_search(pwl, log, first_deriv_log, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, log, first_deriv_log, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_LOG);
                         break;
                     case kActNegLog:
-                        err = pivot_search(pwl, neglog, first_deriv_neglog, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, neglog, first_deriv_neglog, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_LOG);
                         break;
                     case kActNegHalfLog:
-                        err = pivot_search(pwl, neghalflog, first_deriv_neghalflog, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, neghalflog, first_deriv_neghalflog, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_LOG);
                         break;
                     case kActPow: {
                         auto args = std::tuple<double, double, double>{ activation_type.args.pow.exponent,
@@ -476,7 +493,8 @@ std::vector<pwl_t> pwl_search(const DnnActivation& activation_type,
                                                                         activation_type.args.pow.offset };
                         auto fun = [&args](double x) { return power(x, args); };
                         auto first_deriv = [&args](double x) { return first_deriv_power(x, args); };
-                        err = pivot_search(pwl, fun, first_deriv, n_segments, l_bound, u_bound, threshold, negative);
+                        err = pivot_search(pwl, fun, first_deriv, n_segments, l_bound, u_bound,
+                            threshold, negative, PWL_MAX_ITERATIONS_DEFAULT);
                         break;
                     }
                     default:
@@ -485,7 +503,7 @@ std::vector<pwl_t> pwl_search(const DnnActivation& activation_type,
                 err_pct = calculate_error_pct(activation_type, l_bound, u_bound, err, samples);
             }
 
-            if (n_segments >= PWL_MAX_ITERATIONS) {
+            if (n_segments >= PWL_MAX_NUM_SEGMENTS) {
                 THROW_GNA_EXCEPTION << "Failed to converge in pwl_search!";
             }
         }
diff --git a/inference-engine/src/gna_plugin/runtime/pwl.h b/inference-engine/src/gna_plugin/runtime/pwl.h
index cf908f9e7652cb..1d11543e60320f 100644
--- a/inference-engine/src/gna_plugin/runtime/pwl.h
+++ b/inference-engine/src/gna_plugin/runtime/pwl.h
@@ -21,7 +21,8 @@
 #define IDENTITY_NUM_SEGMENTS 3
 #define IDENTITY_DOMAIN 10.0f
 #define PWL_MAX_ERR_PERCENT 1.0f
-#define PWL_MAX_ITERATIONS 2000
+#define PWL_MAX_ITERATIONS_DEFAULT 2000
+#define PWL_MAX_ITERATIONS_LOG 5000
 #define PWL_MAX_NUM_SEGMENTS 128
 #define PWL_DESIGN_THRESHOLD 0.1f
 #define PWL_DESIGN_SAMPLES 500
@@ -61,7 +62,8 @@ double pivot_search(std::vector<pwl_t>& result, double(*f)(const double),
                     const double alpha_0,
                     const double alpha_N,
                     const double threshold,
-                    const bool negative);
+                    const bool negative,
+                    size_t iter_num);
 
 inline std::vector<pwl_t> negative_pwl(const std::vector<pwl_t>& pwl);
 
diff --git a/inference-engine/src/gna_plugin/transformations/remove_single_input_concat.hpp b/inference-engine/src/gna_plugin/transformations/remove_single_input_concat.hpp
index 7730c36d9afe07..364d48a8458db4 100644
--- a/inference-engine/src/gna_plugin/transformations/remove_single_input_concat.hpp
+++ b/inference-engine/src/gna_plugin/transformations/remove_single_input_concat.hpp
@@ -10,6 +10,18 @@ namespace GNAPluginNS {
 
 /**
  * @brief remove concat layers with single input
+ *
+ * Searches for next pattern
+ *     Any input layer
+ *           |
+ *         Concat
+ *           |
+ *     Any output layer
+ *
+ * And transforms to
+ *     Any input layer
+ *           |
+ *     Any output layer
  */
 class RemoveSingleInputConcat : public ngraph::pass::MatcherPass {
 public:
diff --git a/inference-engine/src/gna_plugin/transformations/substitute_softsign.cpp b/inference-engine/src/gna_plugin/transformations/substitute_softsign.cpp
new file mode 100644
index 00000000000000..36329ed7c34b6b
--- /dev/null
+++ b/inference-engine/src/gna_plugin/transformations/substitute_softsign.cpp
@@ -0,0 +1,89 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <openvino/cc/ngraph/itt.hpp>
+
+#include "transformations/substitute_softsign.hpp"
+
+#include "transformations/utils/transformation_helper.hpp"
+#include "transformations/utils/utils.hpp"
+
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/pattern/op/or.hpp>
+#include <ngraph/rt_info.hpp>
+#include <ops/softsign.hpp>
+
+using namespace GNAPluginNS;
+
+NGRAPH_RTTI_DEFINITION(SubstituteSoftsign, "SubstituteSoftsign", 0);
+
+using Node = std::shared_ptr<ngraph::Node>;
+
+namespace {
+
+void DoTransformation(Node start_node, Node last_node) {
+    auto activation = std::make_shared<SoftSign>(start_node);
+    activation->set_friendly_name(last_node->get_friendly_name());
+    ngraph::copy_runtime_info(last_node, activation);
+    ngraph::replace_node(last_node, activation);
+}
+
+class IsConstValueAcceptable {
+public:
+    IsConstValueAcceptable(double expected_value) :
+        m_expected_value(expected_value) {}
+
+    bool operator()(const ngraph::Output<ngraph::Node>& output) const {
+        auto node = std::dynamic_pointer_cast<ngraph::opset8::Constant>(output.get_node_shared_ptr());
+        if (!node)
+            return false;
+
+        float value;
+        if (!ngraph::op::util::get_single_value(node, value)) {
+            return false;
+        }
+
+        return (value == m_expected_value);
+    }
+
+private:
+    const double m_expected_value;
+};
+
+} // namespace
+
+SubstituteSoftsign::SubstituteSoftsign() {
+    MATCHER_SCOPE(SubstituteSoftsign);
+
+    auto root = ngraph::pattern::any_input();
+    auto abs = ngraph::pattern::wrap_type<ngraph::opset8::Abs>({root});
+
+    auto add_const = ngraph::pattern::wrap_type<ngraph::opset8::Constant>(IsConstValueAcceptable(1.0));
+    auto add = ngraph::pattern::wrap_type<ngraph::opset8::Add>({abs, add_const});
+
+    auto power_const = ngraph::pattern::wrap_type<ngraph::opset8::Constant>(IsConstValueAcceptable(-1.0));
+    auto power = ngraph::pattern::wrap_type<ngraph::opset8::Power>({add, power_const});
+
+    auto multiply = ngraph::pattern::wrap_type<ngraph::opset8::Multiply>({root, power});
+    auto divide = ngraph::pattern::wrap_type<ngraph::opset8::Divide>({root, add});
+    auto last =  std::make_shared<ngraph::pattern::op::Or>(ngraph::OutputVector{multiply, divide});
+
+    ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
+        const auto& pattern_map = m.get_pattern_value_map();
+        auto root_node = pattern_map.at(root).get_node_shared_ptr();
+
+        auto last_node_it = pattern_map.find(multiply);
+        if (last_node_it == pattern_map.end())
+            last_node_it = pattern_map.find(divide);
+        auto last_node = last_node_it->second.get_node_shared_ptr();
+
+        DoTransformation(root_node, last_node);
+
+        return true;
+    };
+
+    auto m = std::make_shared<ngraph::pattern::Matcher>(last, matcher_name);
+    this->register_matcher(m, callback);
+}
diff --git a/inference-engine/src/gna_plugin/transformations/substitute_softsign.hpp b/inference-engine/src/gna_plugin/transformations/substitute_softsign.hpp
new file mode 100644
index 00000000000000..fe11241c04f003
--- /dev/null
+++ b/inference-engine/src/gna_plugin/transformations/substitute_softsign.hpp
@@ -0,0 +1,45 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/pass/graph_rewrite.hpp>
+
+namespace GNAPluginNS {
+
+/**
+ * @brief Current version of ModelOptimizer substitutes SoftSign activation
+ * function with next subgraph
+ *  a layer
+ *  |  \
+ * abs  \
+ *  |    |
+ * add   |
+ *  |    |
+ * power |
+ *  \   /
+ *  Divide
+ * 
+ * See model-optimizer/extensions/front/softsign_replacer.py
+ * 
+ * The ConvertDivide transformation from CommonOptimizations
+ * substitutes Divide with {-1} and add constant {1}
+ * - GNA supports Power [0, 2.8]
+ * - Add, Power, Divide layers are more perfomance expensive in GNA
+ *   than SoftSign PWL
+ * 
+ * Legacy SubstituteSoftSignPass supports irv7 model where SoftSign subgraph
+ * could have been without add layer. Current ModelOptimezer always generates
+ * SoftSign subgraph with that layer.
+ * 
+ * SubstituteSoftsign transformation does backward substitution to SoftSign.
+ * TODO: remove that pass as soon as ModelOptimizer will not substitute SoftSign activation
+ */
+class SubstituteSoftsign : public ngraph::pass::MatcherPass {
+public:
+  NGRAPH_RTTI_DECLARATION;
+  SubstituteSoftsign();
+};
+
+} // namespace GNAPluginNS
diff --git a/inference-engine/src/hetero_plugin/CMakeLists.txt b/inference-engine/src/hetero_plugin/CMakeLists.txt
index c7fbe6eecf8b65..39aa7191bd3fd0 100644
--- a/inference-engine/src/hetero_plugin/CMakeLists.txt
+++ b/inference-engine/src/hetero_plugin/CMakeLists.txt
@@ -16,7 +16,7 @@ ie_faster_build(${TARGET_NAME}
     UNITY
 )
 
-target_link_libraries(${TARGET_NAME} PRIVATE pugixml inference_engine
+target_link_libraries(${TARGET_NAME} PRIVATE pugixml::static inference_engine
     ngraph inference_engine_transformations)
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
diff --git a/inference-engine/src/inference_engine/CMakeLists.txt b/inference-engine/src/inference_engine/CMakeLists.txt
index 4dc8fe70d705bf..0430c5336616cb 100644
--- a/inference-engine/src/inference_engine/CMakeLists.txt
+++ b/inference-engine/src/inference_engine/CMakeLists.txt
@@ -4,10 +4,16 @@
 
 set (TARGET_NAME "inference_engine")
 
-if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO")
+if(ENABLE_TBBBIND_2_5)
+    # try to find prebuilt version of tbbbind_2_5
     find_package(TBBBIND_2_5 QUIET)
-    if (TBBBIND_2_5_FOUND)
-        message(STATUS "Static tbbbind_2_5 package was found")
+    if(TBBBIND_2_5_FOUND)
+        message(STATUS "Static tbbbind_2_5 package is found")
+        set_target_properties(${TBBBIND_2_5_IMPORTED_TARGETS} PROPERTIES
+            INTERFACE_COMPILE_DEFINITIONS TBBBIND_2_5_AVAILABLE)
+        if(NOT BUILD_SHARED_LIBS)
+            set(install_tbbbind ON)
+        endif()
     endif()
 endif()
 
@@ -101,10 +107,10 @@ source_group("include" FILES ${LIBRARY_HEADERS} ${PUBLIC_HEADERS})
 add_library(${TARGET_NAME}_plugin_api INTERFACE)
 
 target_include_directories(${TARGET_NAME}_plugin_api INTERFACE
-    "${IE_MAIN_SOURCE_DIR}/src/plugin_api"
     $<TARGET_PROPERTY:${TARGET_NAME}_preproc,INTERFACE_INCLUDE_DIRECTORIES>
-    ${PUBLIC_HEADERS_DIR}
-    ${PUBLIC_HEADERS_DIR}/ie)
+    $<BUILD_INTERFACE:${IE_MAIN_SOURCE_DIR}/src/plugin_api>
+    $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
+    $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}/ie>)
 
 target_link_libraries(${TARGET_NAME}_plugin_api INTERFACE pugixml::static openvino::itt openvino::util)
 
@@ -137,7 +143,7 @@ target_include_directories(${TARGET_NAME}_obj SYSTEM PRIVATE $<TARGET_PROPERTY:n
 
 target_include_directories(${TARGET_NAME}_obj PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src"
                                                       "${CMAKE_CURRENT_BINARY_DIR}" # for static ie_plugins.hpp
-                                                      "${IE_MAIN_SOURCE_DIR}/src/readers/ir_reader_v7" # for ie_ir_version.hpp
+                                                      $<$<TARGET_EXISTS:inference_engine_ir_v7_reader>:$<TARGET_PROPERTY:inference_engine_ir_v7_reader,SOURCE_DIR>> # for ie_ir_version.hpp
                                                       $<TARGET_PROPERTY:${TARGET_NAME}_legacy,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:${TARGET_NAME}_transformations,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:${TARGET_NAME}_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>)
@@ -146,7 +152,6 @@ target_link_libraries(${TARGET_NAME}_obj PRIVATE ${TARGET_NAME}_reader_api openv
 
 set_ie_threading_interface_for(${TARGET_NAME}_obj)
 if (TBBBIND_2_5_FOUND)
-    target_compile_definitions(${TARGET_NAME}_obj PRIVATE -DTBBBIND_2_5_AVAILABLE)
     target_link_libraries(${TARGET_NAME}_obj PRIVATE ${TBBBIND_2_5_IMPORTED_TARGETS})
 endif()
 
@@ -171,7 +176,6 @@ ie_add_vs_version_file(NAME ${TARGET_NAME}
 
 set_ie_threading_interface_for(${TARGET_NAME})
 if (TBBBIND_2_5_FOUND)
-    target_compile_definitions(${TARGET_NAME} PRIVATE -DTBBBIND_2_5_AVAILABLE)
     target_link_libraries(${TARGET_NAME} PRIVATE ${TBBBIND_2_5_IMPORTED_TARGETS})
 endif()
 
@@ -198,44 +202,33 @@ ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
 # Static library used for unit tests which are always built
 
-if(BUILD_SHARED_LIBS)
-    add_library(${TARGET_NAME}_s STATIC EXCLUDE_FROM_ALL
-                $<TARGET_OBJECTS:${TARGET_NAME}_legacy_obj>
-                $<TARGET_OBJECTS:${TARGET_NAME}_obj>
-                ${IE_STATIC_DEPENDENT_FILES})
-
-    set_ie_threading_interface_for(${TARGET_NAME}_s)
-    if (TBBBIND_2_5_FOUND)
-        target_compile_definitions(${TARGET_NAME}_s PRIVATE -DTBBBIND_2_5_AVAILABLE)
-        target_link_libraries(${TARGET_NAME}_s PRIVATE ${TBBBIND_2_5_IMPORTED_TARGETS})
-    endif()
+add_library(${TARGET_NAME}_s STATIC EXCLUDE_FROM_ALL
+            $<TARGET_OBJECTS:${TARGET_NAME}_legacy_obj>
+            $<TARGET_OBJECTS:${TARGET_NAME}_obj>
+            ${IE_STATIC_DEPENDENT_FILES})
 
-    target_include_directories(${TARGET_NAME}_s PUBLIC
-        $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
-        "${CMAKE_CURRENT_SOURCE_DIR}/src"
-        "${IE_MAIN_SOURCE_DIR}/src/legacy_api/src")
+set_ie_threading_interface_for(${TARGET_NAME}_s)
+if (TBBBIND_2_5_FOUND)
+    target_link_libraries(${TARGET_NAME}_s PRIVATE ${TBBBIND_2_5_IMPORTED_TARGETS})
+endif()
 
-    if(WIN32)
-        set_target_properties(${TARGET_NAME}_s PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}_s)
-    endif()
+target_include_directories(${TARGET_NAME}_s PUBLIC
+    $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/src>
+    $<BUILD_INTERFACE:$<TARGET_PROPERTY:${TARGET_NAME}_legacy,SOURCE_DIR>/src>)
 
-    target_link_libraries(${TARGET_NAME}_s PRIVATE openvino::itt ${CMAKE_DL_LIBS} ngraph
-        frontend_manager::static inference_engine_transformations pugixml::static)
+if(WIN32)
+    set_target_properties(${TARGET_NAME}_s PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}_s)
+endif()
 
-    target_compile_definitions(${TARGET_NAME}_s PUBLIC USE_STATIC_IE)
+target_link_libraries(${TARGET_NAME}_s PRIVATE openvino::itt ${CMAKE_DL_LIBS} ngraph
+    frontend_manager::static inference_engine_preproc_s inference_engine_transformations pugixml::static)
 
-    set_target_properties(${TARGET_NAME}_s PROPERTIES
-        EXCLUDE_FROM_ALL ON
-        INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
-else()
-    # for static OpenVINO build we can re-use inference_engine which is already static
-    add_library(${TARGET_NAME}_s ALIAS ${TARGET_NAME})
+target_compile_definitions(${TARGET_NAME}_s PUBLIC USE_STATIC_IE)
 
-    target_include_directories(${TARGET_NAME} PUBLIC
-        $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
-        "${CMAKE_CURRENT_SOURCE_DIR}/src"
-        "${IE_MAIN_SOURCE_DIR}/src/legacy_api/src")
-endif()
+set_target_properties(${TARGET_NAME}_s PROPERTIES
+    EXCLUDE_FROM_ALL ON
+    INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 
 # LTO
 
@@ -263,6 +256,11 @@ if(THREADING MATCHES "^(TBB|TBB_AUTO)$")
     list(APPEND PATH_VARS "IE_TBB_DIR")
 endif()
 
+if(install_tbbbind)
+    set(IE_TBBBIND_DIR "${TBBBIND_2_5}")
+    list(APPEND PATH_VARS "IE_TBBBIND_DIR")
+endif()
+
 # install only downloaded TBB, system one is not installed
 if(THREADING MATCHES "^(TBB|TBB_AUTO)$" AND TBBROOT MATCHES ${TEMP})
     ie_cpack_add_component(tbb REQUIRED)
@@ -285,13 +283,26 @@ if(THREADING MATCHES "^(TBB|TBB_AUTO)$" AND TBBROOT MATCHES ${TEMP})
             COMPONENT tbb)
 
     set(IE_TBB_DIR_INSTALL "3rdparty/tbb/cmake")
-
     install(FILES "${TBB}/cmake/TBBConfig.cmake"
                   "${TBB}/cmake/TBBConfigVersion.cmake"
             DESTINATION runtime/${IE_TBB_DIR_INSTALL}
             COMPONENT tbb)
 endif()
 
+if(install_tbbbind)
+    install(DIRECTORY "${TBBBIND_2_5}/lib"
+            DESTINATION runtime/3rdparty/tbb_bind_2_5
+            COMPONENT tbb)
+    install(FILES "${TBBBIND_2_5}/LICENSE"
+            DESTINATION runtime/3rdparty/tbb_bind_2_5
+            COMPONENT tbb)
+
+    set(IE_TBBBIND_DIR_INSTALL "3rdparty/tbb_bind_2_5/cmake")
+    install(FILES "${TBBBIND_2_5}/cmake/TBBBIND_2_5Config.cmake"
+            DESTINATION runtime/${IE_TBBBIND_DIR_INSTALL}
+            COMPONENT tbb)
+endif()
+
 # Install Inference Engine
 
 ie_cpack_add_component(core REQUIRED DEPENDS ${core_components})
@@ -309,28 +320,32 @@ install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
                              # TODO: remove later once samples are updated
                              runtime/include/ie)
 
-install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
-        DESTINATION ${IE_CPACK_RUNTIME_PATH}
-        COMPONENT core)
-
-# for InferenceEngineUnitTest
-if(WIN32)
-    install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
-            DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
-else()
+if(BUILD_SHARED_LIBS)
     install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
-            DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
+            DESTINATION ${IE_CPACK_RUNTIME_PATH}
+            COMPONENT core)
+
+    # for InferenceEngineUnitTest
+    if(WIN32)
+        install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
+                DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+    else()
+        install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
+                DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
+    endif()
 endif()
 
+# Install static libraries for case BUILD_SHARED_LIBS=OFF
+
+ov_install_static_lib(${TARGET_NAME}_plugin_api core)
+
 # Install cmake scripts
 
-if(BUILD_SHARED_LIBS)
-    install(EXPORT OpenVINOTargets
-            FILE OpenVINOTargets.cmake
-            NAMESPACE openvino::
-            DESTINATION runtime/cmake
-            COMPONENT core_dev)
-endif()
+install(EXPORT OpenVINOTargets
+        FILE OpenVINOTargets.cmake
+        NAMESPACE openvino::
+        DESTINATION runtime/cmake
+        COMPONENT core_dev)
 
 set(IE_NGRAPH_DIR "${CMAKE_BINARY_DIR}/ngraph")
 set(IE_INCLUDE_DIR "${PUBLIC_HEADERS_DIR}/ie")
@@ -349,6 +364,7 @@ configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOCo
 set(IE_INCLUDE_DIR "include/ie")
 set(IE_NGRAPH_DIR ".")
 set(IE_TBB_DIR "${IE_TBB_DIR_INSTALL}")
+set(IE_TBBBIND_DIR "${IE_TBBBIND_DIR_INSTALL}")
 set(IE_PARALLEL_CMAKE "cmake/ie_parallel.cmake")
 
 configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig.cmake.in"
diff --git a/inference-engine/src/inference_engine/include/ie/cpp/ie_executable_network.hpp b/inference-engine/src/inference_engine/include/ie/cpp/ie_executable_network.hpp
index 6c8dab11542d78..fc205e966f65ab 100644
--- a/inference-engine/src/inference_engine/include/ie/cpp/ie_executable_network.hpp
+++ b/inference-engine/src/inference_engine/include/ie/cpp/ie_executable_network.hpp
@@ -190,18 +190,6 @@ class INFERENCE_ENGINE_API_CLASS(ExecutableNetwork) {
      */
     INFERENCE_ENGINE_DEPRECATED("Use ExecutableNetwork::CreateInferRequest instead")
     InferRequest::Ptr CreateInferRequestPtr();
-
-    /**
-     * @deprecated Use InferRequest::QueryState instead
-     * @brief Gets state control interface for given executable network.
-     *
-     * State control essential for recurrent networks
-     *
-     * @return A vector of Memory State objects
-     */
-    INFERENCE_ENGINE_DEPRECATED("Use InferRequest::QueryState instead")
-    std::vector<VariableState> QueryState();
-    IE_SUPPRESS_DEPRECATED_END
 };
 
 }  // namespace InferenceEngine
diff --git a/inference-engine/src/inference_engine/include/ie/details/ie_so_loader.h b/inference-engine/src/inference_engine/include/ie/details/ie_so_loader.h
index 555f91542591a6..c0135cfc6983f0 100644
--- a/inference-engine/src/inference_engine/include/ie/details/ie_so_loader.h
+++ b/inference-engine/src/inference_engine/include/ie/details/ie_so_loader.h
@@ -20,14 +20,13 @@ namespace details {
  * @brief This class provides an OS shared module abstraction
  */
 class INFERENCE_ENGINE_API_CLASS(SharedObjectLoader) {
-    class Impl;
-    std::shared_ptr<Impl> _impl;
+    std::shared_ptr<void> _so;
 
 public:
     /**
      * @brief Constructs from existing object
      */
-    SharedObjectLoader(const std::shared_ptr<void>& impl);
+    SharedObjectLoader(const std::shared_ptr<void>& so);
 
     /**
      * @brief Default constructor
diff --git a/inference-engine/src/inference_engine/include/ie/details/ie_so_pointer.hpp b/inference-engine/src/inference_engine/include/ie/details/ie_so_pointer.hpp
index 87c5fbfdb0f616..9158d6f02cd4aa 100644
--- a/inference-engine/src/inference_engine/include/ie/details/ie_so_pointer.hpp
+++ b/inference-engine/src/inference_engine/include/ie/details/ie_so_pointer.hpp
@@ -77,8 +77,8 @@ class SOPointer {
 
     /**
      * @brief Constructs an object with existing reference
-     * @brief Constructs an object with existing loader
-     * @param soLoader Existing pointer to a library loader
+     * @param so Existing pointer to a library loader
+     * @param ptr Existing reference to an object
      */
     SOPointer(const SharedObjectLoader& so, const std::shared_ptr<T>& ptr) : _so{so}, _ptr{ptr} {}
 
@@ -165,8 +165,6 @@ class SOPointer {
                 using CreateF = void(std::shared_ptr<T>&);
                 reinterpret_cast<CreateF*>(create)(_ptr);
             }
-        } catch (const std::runtime_error& ex) {
-            IE_THROW() << ex.what();
         } catch (...) {
             details::Rethrow();
         }
@@ -179,8 +177,6 @@ class SOPointer {
         try {
             using CreateF = void(std::shared_ptr<T>&);
             reinterpret_cast<CreateF*>(_so.get_symbol(SOCreatorTrait<T>::name))(_ptr);
-        } catch (const std::runtime_error& ex) {
-            IE_THROW() << ex.what();
         } catch (...) {
             details::Rethrow();
         }
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/gpu_params.hpp b/inference-engine/src/inference_engine/include/ie/gpu/gpu_params.hpp
index 2ceff349214ba2..d7e36c95ac5df9 100644
--- a/inference-engine/src/inference_engine/include/ie/gpu/gpu_params.hpp
+++ b/inference-engine/src/inference_engine/include/ie/gpu/gpu_params.hpp
@@ -62,6 +62,12 @@ DECLARE_GPU_PARAM_VALUE(VA_SHARED);
  */
 DECLARE_GPU_PARAM_KEY(OCL_CONTEXT, gpu_handle_param);
 
+/**
+ * @brief This key identifies ID of device in OpenCL context
+ * if multiple devices are present in the context
+ */
+DECLARE_GPU_PARAM_KEY(OCL_CONTEXT_DEVICE_ID, int);
+
 /**
  * @brief In case of multi-tile system,
  * this key identifies tile within given context
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/dx.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/dx.hpp
similarity index 86%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/dx.hpp
rename to inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/dx.hpp
index b43f1a780ae9b5..8da0821145a50b 100644
--- a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/dx.hpp
+++ b/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/dx.hpp
@@ -7,20 +7,29 @@
  * shared Video Acceleration device contexts
  * and shared memory tensors which contain Video Acceleration surfaces
  *
- * @file openvino/runtime/gpu/dx.hpp
+ * @file openvino/runtime/gpu/ocl/dx.hpp
  */
 #pragma once
 
+#ifndef NOMINMAX
+#    define NOMINMAX
+#endif
+
+#ifndef _WIN32
+#    error "OpenCL DirectX interoperability is supported only on Windows platforms"
+#endif
+
 #include <d3d11.h>
 
 #include <memory>
 #include <string>
 
-#include "openvin/runtime/gpu/ocl.hpp"
+#include "openvino/runtime/gpu/ocl/ocl.hpp"
 
 namespace ov {
 namespace runtime {
 namespace gpu {
+namespace ocl {
 
 /**
  * @brief This class represents an abstraction for GPU plugin remote tensor
@@ -62,10 +71,10 @@ class D3DSurface2DTensor : public ClImage2DTensor {
      * @param remote_tensor remote tensor to check
      */
     static void type_check(const RemoteTensor& remote_tensor) {
-        remote_type_check(remote_context.get_params(),
-                          {{GPU_PARAM_KEY(DEV_OBJECT_HANDLE), {}},
-                           {GPU_PARAM_KEY(VA_PLANE), {}},
-                           {GPU_PARAM_KEY(SHARED_MEM_TYPE), {GPU_PARAM_VALUE(VA_SURFACE)}}});
+        RemoteTensor::type_check(remote_tensor,
+                                 {{GPU_PARAM_KEY(DEV_OBJECT_HANDLE), {}},
+                                  {GPU_PARAM_KEY(VA_PLANE), {}},
+                                  {GPU_PARAM_KEY(SHARED_MEM_TYPE), {GPU_PARAM_VALUE(VA_SURFACE)}}});
     }
 
     /**
@@ -94,7 +103,6 @@ class D3DSurface2DTensor : public ClImage2DTensor {
  */
 class D3DContext : public ClContext {
     using RemoteContext::create_tensor;
-    static constexpr const char* device_name = "GPU";
 
 public:
     /**
@@ -102,8 +110,8 @@ class D3DContext : public ClContext {
      * @param remote_context remote context to check
      */
     static void type_check(const RemoteContext& remote_context) {
-        remote_type_check(
-            remote_context.get_params(),
+        RemoteContext::type_check(
+            remote_context,
             {{GPU_PARAM_KEY(VA_DEVICE), {}}, {GPU_PARAM_KEY(CONTEXT_TYPE), {GPU_PARAM_VALUE(VA_SHARED)}}});
     }
 
@@ -119,12 +127,15 @@ class D3DContext : public ClContext {
      * @brief Constructs D3DContext remote context object from ID3D11Device
      * @param core OpenVINO Runtime Core object instance
      * @param device A pointer to ID3D11Device to be used to create a remote context
+     * @param target_tile_id Desired tile id within given context for multi-tile system. Default value (-1) means
+     * that root device should be used
      */
-    D3DContext(Core& core, ID3D11Device* device) {
+    D3DContext(Core& core, ID3D11Device* device, int target_tile_id = -1) : ClContext(core, (cl_context) nullptr) {
         // clang-format off
         ParamMap context_params = {
             {GPU_PARAM_KEY(CONTEXT_TYPE), GPU_PARAM_VALUE(VA_SHARED)},
-            {GPU_PARAM_KEY(VA_DEVICE), static_cast<gpu_handle_param>(device)}
+            {GPU_PARAM_KEY(VA_DEVICE), static_cast<gpu_handle_param>(device)},
+            {GPU_PARAM_KEY(TILE_ID), target_tile_id}
         };
         *this = core.create_context(device_name, context_params);
     }
@@ -137,7 +148,7 @@ class D3DContext : public ClContext {
      * @param nv12_surf A ID3D11Texture2D instance to create NV12 tensor from
      * @return A pair of remote tensors for each plane
      */
-    std::pair<D3DSurface2DTensor, D3DSurface2DTensor> create_tensor_nv12(const size_t height, const size_t width, const ID3D11Texture2D* nv12_surf) {
+    std::pair<D3DSurface2DTensor, D3DSurface2DTensor> create_tensor_nv12(const size_t height, const size_t width, ID3D11Texture2D* nv12_surf) {
         ParamMap tensor_params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(VA_SURFACE)},
                                   {GPU_PARAM_KEY(DEV_OBJECT_HANDLE), static_cast<gpu_handle_param>(nv12_surf)},
                                   {GPU_PARAM_KEY(VA_PLANE), uint32_t(0)}};
@@ -155,7 +166,7 @@ class D3DContext : public ClContext {
      * @param buffer A pointer to ID3D11Buffer instance to create remote tensor based on
      * @return A remote tensor instance
      */
-    D3DBufferTensor create_tensor(const element::Type type, const Shape& shape, const ID3D11Buffer* buffer) {
+    D3DBufferTensor create_tensor(const element::Type type, const Shape& shape, ID3D11Buffer* buffer) {
         ParamMap params = {{GPU_PARAM_KEY(SHARED_MEM_TYPE), GPU_PARAM_VALUE(DX_BUFFER)},
                            {GPU_PARAM_KEY(DEV_OBJECT_HANDLE), static_cast<gpu_handle_param>(buffer)}};
         create_tensor(type, shape, params);
@@ -180,6 +191,7 @@ class D3DContext : public ClContext {
         return create_tensor(type, shape, params);
     }
 };
+}  // namespace ocl
 }  // namespace gpu
 }  // namespace runtime
 }  // namespace ov
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl.hpp
similarity index 95%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl.hpp
rename to inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl.hpp
index 24ccf2ae9af3d9..d205b7f0548e0f 100644
--- a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl.hpp
+++ b/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl.hpp
@@ -6,7 +6,7 @@
  * @brief a header that defines wrappers for internal GPU plugin-specific
  * OpenCL context and OpenCL shared memory tensors
  *
- * @file openvino/runtime/gpu/ocl.hpp
+ * @file openvino/runtime/gpu/ocl/ocl.hpp
  */
 #pragma once
 
@@ -15,13 +15,14 @@
 
 #include "gpu/gpu_params.hpp"
 #include "openvino/runtime/core.hpp"
-#include "openvino/runtime/gpu/ocl_wrapper.hpp"
+#include "openvino/runtime/gpu/ocl/ocl_wrapper.hpp"
 #include "openvino/runtime/remote_context.hpp"
 #include "openvino/runtime/remote_tensor.hpp"
 
 namespace ov {
 namespace runtime {
 namespace gpu {
+namespace ocl {
 
 /**
  * @brief Shortcut for defining a handle parameter
@@ -123,7 +124,12 @@ class ClImage2DTensor : public RemoteTensor {
  * ExecutableNetwork::get_context() or Core::create_context() calls.
  */
 class ClContext : public RemoteContext {
+protected:
     using RemoteContext::create_tensor;
+
+    /**
+     * @brief GPU device name
+     */
     static constexpr const char* device_name = "GPU";
 
 public:
@@ -141,10 +147,12 @@ class ClContext : public RemoteContext {
      * @brief Constructs context object from user-supplied OpenCL context handle
      * @param core A reference to OpenVINO Runtime Core object
      * @param ctx A OpenCL context to be used to create shared remote context
+     * @param ctx_device_id An ID of device to be used from ctx
      */
-    ClContext(Core& core, cl_context ctx) {
+    ClContext(Core& core, cl_context ctx, int ctx_device_id = 0) {
         ParamMap context_params = {{GPU_PARAM_KEY(CONTEXT_TYPE), GPU_PARAM_VALUE(OCL)},
-                                   {GPU_PARAM_KEY(OCL_CONTEXT), static_cast<gpu_handle_param>(ctx)}};
+                                   {GPU_PARAM_KEY(OCL_CONTEXT), static_cast<gpu_handle_param>(ctx)},
+                                   {GPU_PARAM_KEY(OCL_CONTEXT_DEVICE_ID), ctx_device_id}};
         *this = core.create_context(device_name, context_params);
     }
 
@@ -245,6 +253,7 @@ class ClContext : public RemoteContext {
         return create_tensor(type, shape, params);
     }
 };
+}  // namespace ocl
 }  // namespace gpu
 }  // namespace runtime
 }  // namespace ov
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl_wrapper.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl_wrapper.hpp
rename to inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/va.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/va.hpp
similarity index 89%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/va.hpp
rename to inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/va.hpp
index fc82d0f5990ce0..91f6c037f694c9 100644
--- a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/va.hpp
+++ b/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/va.hpp
@@ -7,14 +7,18 @@
  * shared Video Acceleration device contexts
  * and shared memory tensors which contain Video Acceleration surfaces
  *
- * @file openvino/runtime/gpu/va.hpp
+ * @file openvino/runtime/gpu/ocl/va.hpp
  */
 #pragma once
 
+#ifdef _WIN32
+#    error "OpenCL VA-API interoperability is supported only on Linux-based platforms"
+#endif
+
 #include <memory>
 #include <string>
 
-#include "openvino/runtime/gpu/ocl.hpp"
+#include "openvino/runtime/gpu/ocl/ocl.hpp"
 
 // clang-format off
 #include <va/va.h>
@@ -23,6 +27,7 @@
 namespace ov {
 namespace runtime {
 namespace gpu {
+namespace ocl {
 
 /**
  * @brief This class represents an abstraction for GPU plugin remote tensor
@@ -68,7 +73,6 @@ class VASurfaceTensor : public ClImage2DTensor {
  */
 class VAContext : public ClContext {
     using RemoteContext::create_tensor;
-    static constexpr const char* device_name = "GPU";
 
 public:
     /**
@@ -76,8 +80,8 @@ class VAContext : public ClContext {
      * @param remote_context remote context to check
      */
     static void type_check(const RemoteContext& remote_context) {
-        remote_type_check(
-            remote_context.get_params(),
+        RemoteContext::type_check(
+            remote_context,
             {{GPU_PARAM_KEY(VA_DEVICE), {}}, {GPU_PARAM_KEY(CONTEXT_TYPE), {GPU_PARAM_VALUE(VA_SHARED)}}});
     }
 
@@ -93,10 +97,13 @@ class VAContext : public ClContext {
      * @brief Constructs remote context object from VA display handle
      * @param core OpenVINO Runtime Core object
      * @param device A `VADisplay` to create remote context from
+     * @param target_tile_id Desired tile id within given context for multi-tile system. Default value (-1) means
+     * that root device should be used
      */
-    VAContext(Core& core, VADisplay device) {
+    VAContext(Core& core, VADisplay device, int target_tile_id = -1) : ClContext(core, (cl_context) nullptr) {
         ParamMap context_params = {{GPU_PARAM_KEY(CONTEXT_TYPE), GPU_PARAM_VALUE(VA_SHARED)},
-                                   {GPU_PARAM_KEY(VA_DEVICE), static_cast<gpu_handle_param>(device)}};
+                                   {GPU_PARAM_KEY(VA_DEVICE), static_cast<gpu_handle_param>(device)},
+                                   {GPU_PARAM_KEY(TILE_ID), target_tile_id}};
         *this = core.create_context(device_name, context_params);
     }
 
@@ -138,6 +145,7 @@ class VAContext : public ClContext {
         return create_tensor(type, shape, params);
     }
 };
+}  // namespace ocl
 }  // namespace gpu
 }  // namespace runtime
 }  // namespace ov
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp b/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp
index d811b8378d4f88..24a6af02d64ced 100644
--- a/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp
+++ b/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp
@@ -65,16 +65,6 @@ ExecutableNetwork::operator IExecutableNetwork::Ptr() {
     return std::make_shared<ExecutableNetworkBase>(_impl);
 }
 
-std::vector<VariableState> ExecutableNetwork::QueryState() {
-    std::vector<VariableState> controller;
-    EXEC_NET_CALL_STATEMENT({
-        for (auto&& state : _impl->QueryState()) {
-            controller.emplace_back(VariableState{_so, state});
-        }
-    });
-    return controller;
-}
-
 InferRequest ExecutableNetwork::CreateInferRequest() {
     EXEC_NET_CALL_STATEMENT(return {_so, _impl->CreateInferRequest()});
 }
diff --git a/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp b/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp
index 9a8abf1eec13c6..47a9d7fab75867 100644
--- a/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp
+++ b/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp
@@ -87,10 +87,6 @@ std::shared_ptr<ngraph::Function> IExecutableNetworkInternal::GetExecGraphInfo()
     IE_THROW(NotImplemented);
 }
 
-std::vector<std::shared_ptr<IVariableStateInternal>> IExecutableNetworkInternal::QueryState() {
-    IE_THROW(NotImplemented);
-}
-
 void IExecutableNetworkInternal::SetPointerToPlugin(const std::shared_ptr<IInferencePlugin>& plugin) {
     _plugin = plugin;
 }
diff --git a/inference-engine/src/inference_engine/src/file_utils.cpp b/inference-engine/src/inference_engine/src/file_utils.cpp
index a646a456d8edfd..ea962e2b679472 100644
--- a/inference-engine/src/inference_engine/src/file_utils.cpp
+++ b/inference-engine/src/inference_engine/src/file_utils.cpp
@@ -16,14 +16,12 @@
 #include <sys/stat.h>
 
 #include "ie_common.h"
+#include "openvino/util/file_util.hpp"
+
 #ifndef _WIN32
 #    include <dlfcn.h>
 #    include <limits.h>
 #    include <unistd.h>
-#    ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-#        include <codecvt>
-#        include <locale>
-#    endif
 #else
 #    if defined(WINAPI_FAMILY) && !WINAPI_PARTITION_DESKTOP
 #        error "Only WINAPI_PARTITION_DESKTOP is supported, because of GetModuleHandleEx[A|W]"
@@ -34,90 +32,20 @@
 #    include <Windows.h>
 #endif
 
-#ifdef _WIN32
-
-#    include <direct.h>
-
-// Copied from linux libc sys/stat.h:
-#    define S_ISDIR(m) (((m)&S_IFMT) == S_IFDIR)
-
-/// @brief Windows-specific 'mkdir' wrapper
-#    define makedir(dir) _mkdir(dir)
-
-/// @brief Max length of absolute file path
-#    define MAX_ABS_PATH _MAX_PATH
-/// @brief Get absolute file path, returns NULL in case of error
-#    define get_absolute_path(result, path) _fullpath(result, path.c_str(), MAX_ABS_PATH)
-
-/// @brief Windows-specific 'stat' wrapper
-#    define stat _stat
-
-#else
-
-#    include <unistd.h>
-
-/// @brief mkdir wrapper
-#    define makedir(dir)                    mkdir(dir, 0755)
-
-/// @brief Max length of absolute file path
-#    define MAX_ABS_PATH                    PATH_MAX
-/// @brief Get absolute file path, returns NULL in case of error
-#    define get_absolute_path(result, path) realpath(path.c_str(), result)
-
-#endif
-
 long long FileUtils::fileSize(const char* charfilepath) {
-#if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
-    std::wstring widefilename = ov::util::string_to_wstring(charfilepath);
-    const wchar_t* fileName = widefilename.c_str();
-#elif defined(__ANDROID__) || defined(ANDROID)
-    std::string fileName = charfilepath;
-    std::string::size_type pos = fileName.find('!');
-    if (pos != std::string::npos) {
-        fileName = fileName.substr(0, pos);
-    }
-#else
-    const char* fileName = charfilepath;
-#endif
-    std::ifstream in(fileName, std::ios_base::binary | std::ios_base::ate);
-    return in.tellg();
+    return ov::util::file_size(charfilepath);
 }
 
 std::string FileUtils::absoluteFilePath(const std::string& filePath) {
-    std::string absolutePath;
-    absolutePath.resize(MAX_ABS_PATH);
-    auto absPath = get_absolute_path(&absolutePath[0], filePath);
-    if (!absPath) {
-        IE_THROW() << "Can't get absolute file path for [" << filePath << "], err = " << strerror(errno);
-    }
-    absolutePath.resize(strlen(absPath));
-    return absolutePath;
+    return ov::util::get_absolute_file_path(filePath);
 }
 
 bool FileUtils::directoryExists(const std::string& path) {
-    struct stat sb;
-
-    if (stat(path.c_str(), &sb) == 0 && S_ISDIR(sb.st_mode)) {
-        return true;
-    }
-    return false;
+    return ov::util::directory_exists(path);
 }
 
 void FileUtils::createDirectoryRecursive(const std::string& dirPath) {
-    if (dirPath.empty() || directoryExists(dirPath)) {
-        return;
-    }
-
-    std::size_t pos = dirPath.rfind(ov::util::FileTraits<char>::file_separator);
-    if (pos != std::string::npos) {
-        createDirectoryRecursive(dirPath.substr(0, pos));
-    }
-
-    int err = makedir(dirPath.c_str());
-    if (err != 0 && errno != EEXIST) {
-        // TODO: in case of exception it may be needed to remove all created sub-directories
-        IE_THROW() << "Couldn't create directory [" << dirPath << "], err=" << strerror(errno) << ")";
-    }
+    ov::util::create_directory_recursive(dirPath);
 }
 
 namespace InferenceEngine {
@@ -148,11 +76,11 @@ static std::string getIELibraryPathA() {
     GetModuleFileNameA(hm, (LPSTR)ie_library_path, sizeof(ie_library_path));
     return getPathName(std::string(ie_library_path));
 #elif defined(__APPLE__) || defined(__linux__)
-#    if defined(OPENVINO_STATIC_LIBRARY) || defined(USE_STATIC_IE)
+#    ifdef USE_STATIC_IE
 #        ifdef __APPLE__
     Dl_info info;
     dladdr(reinterpret_cast<void*>(getIELibraryPath), &info);
-    std::string path = getPathName(std::string(info.dli_fname)).c_str();
+    std::string path = getPathName(std::string(info.dli_fname));
 #        else
     char result[PATH_MAX];
     ssize_t count = readlink("/proc/self/exe", result, PATH_MAX);
@@ -162,8 +90,9 @@ static std::string getIELibraryPathA() {
 #    else
     Dl_info info;
     dladdr(reinterpret_cast<void*>(getIELibraryPath), &info);
-    return getPathName(std::string(info.dli_fname)).c_str();
-#    endif  // OPENVINO_STATIC_LIBRARY || USE_STATIC_IE
+    std::string path = FileUtils::absoluteFilePath(info.dli_fname);
+    return getPathName(path);
+#    endif  // USE_STATIC_IE
 #else
 #    error "Unsupported OS"
 #endif  // _WIN32
@@ -192,6 +121,7 @@ std::wstring getIELibraryPathW() {
 #endif  // OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
 
 std::string getIELibraryPath() {
+    (void)getIELibraryPathA;
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
     return ov::util::wstring_to_string(getIELibraryPathW());
 #else
diff --git a/inference-engine/src/inference_engine/src/ie_core.cpp b/inference-engine/src/inference_engine/src/ie_core.cpp
index 104f1e40ceee5f..0004c48e462f9a 100644
--- a/inference-engine/src/inference_engine/src/ie_core.cpp
+++ b/inference-engine/src/inference_engine/src/ie_core.cpp
@@ -728,11 +728,11 @@ class CoreImpl : public ie::ICore, public std::enable_shared_from_this<ie::ICore
             try {
                 const ie::Parameter p = GetMetric(deviceName, propertyName);
                 devicesIDs = p.as<std::vector<std::string>>();
-            } catch (ie::Exception&) {
+            } catch (const ie::Exception&) {
                 // plugin is not created by e.g. invalid env
-            } catch (ov::Exception&) {
+            } catch (const ov::Exception&) {
                 // plugin is not created by e.g. invalid env
-            } catch (std::runtime_error&) {
+            } catch (const std::runtime_error&) {
                 // plugin is not created by e.g. invalid env
             } catch (const std::exception& ex) {
                 IE_THROW() << "An exception is thrown while trying to create the " << deviceName
@@ -854,8 +854,7 @@ class CoreImpl : public ie::ICore, public std::enable_shared_from_this<ie::ICore
                     TryToRegisterLibraryAsExtensionUnsafe(desc.libraryLocation);
                 }
 
-                auto result = plugins.emplace(deviceName, plugin).first->second;
-                return result;
+                return plugins.emplace(deviceName, plugin).first->second;
             } catch (const ie::Exception& ex) {
                 IE_THROW() << "Failed to create plugin " << ov::util::from_file_path(desc.libraryLocation)
                            << " for device " << deviceName << "\n"
diff --git a/inference-engine/src/inference_engine/src/ie_data.cpp b/inference-engine/src/inference_engine/src/ie_data.cpp
index 269d28fe8aeee7..70677970f65646 100644
--- a/inference-engine/src/inference_engine/src/ie_data.cpp
+++ b/inference-engine/src/inference_engine/src/ie_data.cpp
@@ -133,7 +133,13 @@ void Data::reshape(const ngraph::PartialShape& dims, Layout layout) {
     if (dims.is_static()) {
         reshape(SizeVector(dims.to_shape()), layout);
     } else {
-        tensorDesc = TensorDesc(tensorDesc.getPrecision(), layout);
+        SizeVector d;
+        if (dims.rank().is_static()) {
+            d = SizeVector(dims.rank().get_length(), 0);
+        } else {
+            d = SizeVector{0};
+        }
+        tensorDesc = TensorDesc(tensorDesc.getPrecision(), d, layout);
         _impl->pShape = dims;
     }
 }
diff --git a/inference-engine/src/inference_engine/src/ie_network_reader.cpp b/inference-engine/src/inference_engine/src/ie_network_reader.cpp
index cb55c706c89f1c..a0c4c7b1b2d5aa 100644
--- a/inference-engine/src/inference_engine/src/ie_network_reader.cpp
+++ b/inference-engine/src/inference_engine/src/ie_network_reader.cpp
@@ -20,7 +20,9 @@
 #include "ie_common.h"
 #include "ie_icnn_network.hpp"
 #include "ie_input_info.hpp"
-#include "ie_ir_version.hpp"
+#ifdef ENABLE_IR_V7_READER
+#    include "ie_ir_version.hpp"
+#endif
 #include "ie_itt.hpp"
 #include "ie_reader.hpp"
 #include "ngraph/function.hpp"
@@ -307,7 +309,7 @@ CNNNetwork convert_to_cnnnetwork(std::shared_ptr<ngraph::Function>& function,
     // only for IR cases we need preprocessing or postprocessing steps
     if (is_ir) {
         using namespace ov::preprocess;
-        PrePostProcessor prepost;
+        PrePostProcessor prepost(function);
 
         auto ir_version_impl = std::dynamic_pointer_cast<ngraph::VariantImpl<int64_t>>(it->second);
         OPENVINO_ASSERT(ir_version_impl != nullptr, "Failed to extract IR version from 'version' attribute");
@@ -318,7 +320,7 @@ CNNNetwork convert_to_cnnnetwork(std::shared_ptr<ngraph::Function>& function,
             for (size_t i = 0; i < inputs.size(); ++i) {
                 const auto ngraph_type = inputs[i].get_element_type();
                 const auto legacy_type = details::toLegacyType(ngraph_type, true);
-                prepost.input(ov::preprocess::InputInfo(i).tensor(InputTensorInfo().set_element_type(legacy_type)));
+                prepost.input(i).tensor().set_element_type(legacy_type);
             }
 
             // in order to support the following scenarios for IR v10 cases:
@@ -349,10 +351,10 @@ CNNNetwork convert_to_cnnnetwork(std::shared_ptr<ngraph::Function>& function,
                 const auto ngraph_type = outputs[i].get_element_type();
                 const auto legacy_type = details::toLegacyType(ngraph_type, false);
 
-                prepost.output(OutputInfo(i).tensor(OutputTensorInfo().set_element_type(legacy_type)));
+                prepost.output(i).tensor().set_element_type(legacy_type);
             }
 
-            function = prepost.build(function);
+            function = prepost.build();
 
             // Set version to 10
             rt_info["version"] = std::make_shared<ov::VariantWrapper<int64_t>>(10);
@@ -378,14 +380,14 @@ CNNNetwork convert_to_cnnnetwork(std::shared_ptr<ngraph::Function>& function,
                 if (old_api_type == ov::element::undefined)
                     old_api_type = parameter->get_element_type();
 
-                prepost.input(ov::preprocess::InputInfo(i)
-                                  .tensor(InputTensorInfo().set_element_type(old_api_type))
-                                  .preprocess(PreProcessSteps().convert_layout(old_api_transpose_args)));
+                auto& pre_input = prepost.input(i);
+                pre_input.tensor().set_element_type(old_api_type);
+                pre_input.preprocess().convert_layout(old_api_transpose_args);
             }
 
-            auto& resuls = function->get_results();
-            for (size_t i = 0; i < resuls.size(); ++i) {
-                const auto& result = resuls[i];
+            auto& results = function->get_results();
+            for (size_t i = 0; i < results.size(); ++i) {
+                const auto& result = results[i];
                 ov::RTMap& rtInfo = result->get_rt_info();
                 const auto it = rtInfo.find(old_api_map_key);
                 if (it == rtInfo.end())
@@ -402,9 +404,9 @@ CNNNetwork convert_to_cnnnetwork(std::shared_ptr<ngraph::Function>& function,
                 if (old_api_type == ov::element::undefined)
                     old_api_type = result->get_element_type();
 
-                prepost.output(OutputInfo(i)
-                                   .postprocess(PostProcessSteps().convert_layout(old_api_transpose_args))
-                                   .tensor(OutputTensorInfo().set_element_type(old_api_type)));
+                auto& post_output = prepost.output(i);
+                post_output.postprocess().convert_layout(old_api_transpose_args);
+                post_output.tensor().set_element_type(old_api_type);
 
                 // remove old api once we applied it
                 rtInfo.erase(it);
@@ -413,7 +415,7 @@ CNNNetwork convert_to_cnnnetwork(std::shared_ptr<ngraph::Function>& function,
             // Set version to 10
             rt_info["version"] = std::make_shared<ov::VariantWrapper<int64_t>>(10);
 
-            function = prepost.build(function);
+            function = prepost.build();
         }
     }
 
@@ -422,8 +424,8 @@ CNNNetwork convert_to_cnnnetwork(std::shared_ptr<ngraph::Function>& function,
     OPENVINO_SUPPRESS_DEPRECATED_END
 }
 
-ngraph::frontend::FrontEndManager& get_frontend_manager() {
-    static ngraph::frontend::FrontEndManager manager;
+ov::frontend::FrontEndManager& get_frontend_manager() {
+    static ov::frontend::FrontEndManager manager;
     return manager;
 }
 
@@ -471,8 +473,8 @@ CNNNetwork details::ReadNetwork(const std::string& modelPath,
 
     // Try to load with FrontEndManager
     auto& manager = get_frontend_manager();
-    ngraph::frontend::FrontEnd::Ptr FE;
-    ngraph::frontend::InputModel::Ptr inputModel;
+    ov::frontend::FrontEnd::Ptr FE;
+    ov::frontend::InputModel::Ptr inputModel;
 
     ov::VariantVector params{ov::make_variant(model_path)};
 
@@ -533,8 +535,8 @@ CNNNetwork details::ReadNetwork(const std::string& model,
 
     // Try to load with FrontEndManager
     auto& manager = get_frontend_manager();
-    ngraph::frontend::FrontEnd::Ptr FE;
-    ngraph::frontend::InputModel::Ptr inputModel;
+    ov::frontend::FrontEnd::Ptr FE;
+    ov::frontend::InputModel::Ptr inputModel;
 
     ov::VariantVector params{ov::make_variant(&modelStream)};
     if (weights) {
diff --git a/inference-engine/src/inference_engine/src/shared_object_loader.cpp b/inference-engine/src/inference_engine/src/shared_object_loader.cpp
index 5e794ac1d789f6..85fbbb636185fa 100644
--- a/inference-engine/src/inference_engine/src/shared_object_loader.cpp
+++ b/inference-engine/src/inference_engine/src/shared_object_loader.cpp
@@ -10,47 +10,33 @@
 namespace InferenceEngine {
 namespace details {
 
-struct SharedObjectLoader::Impl {
-    std::shared_ptr<void> shared_object = nullptr;
-
-    explicit Impl(const std::shared_ptr<void>& shared_object_) : shared_object{shared_object_} {}
-
-    explicit Impl(const char* pluginName) : shared_object{ov::util::load_shared_object(pluginName)} {}
-
-#ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-    explicit Impl(const wchar_t* pluginName) : Impl(ov::util::wstring_to_string(pluginName).c_str()) {}
-#endif  // OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-
-    void* get_symbol(const char* symbolName) const {
-        return ov::util::get_symbol(shared_object, symbolName);
-    }
-};
-
-SharedObjectLoader::SharedObjectLoader(const std::shared_ptr<void>& shared_object) {
-    _impl.reset(new Impl(shared_object));
-}
+SharedObjectLoader::SharedObjectLoader(const std::shared_ptr<void>& so) : _so(so) {}
 
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-SharedObjectLoader::SharedObjectLoader(const wchar_t* pluginName) {
-    _impl.reset(new Impl(pluginName));
-}
+SharedObjectLoader::SharedObjectLoader(const wchar_t* pluginName)
+    : SharedObjectLoader(ov::util::wstring_to_string(pluginName).c_str()) {}
 #endif
 
-SharedObjectLoader::SharedObjectLoader(const char* pluginName) {
-    _impl.reset(new Impl(pluginName));
+SharedObjectLoader::SharedObjectLoader(const char* pluginName) : _so{nullptr} {
+    try {
+        _so = ov::util::load_shared_object(pluginName);
+    } catch (const std::runtime_error& ex) {
+        IE_THROW(GeneralError) << ex.what();
+    }
 }
 
 SharedObjectLoader::~SharedObjectLoader() {}
 
 void* SharedObjectLoader::get_symbol(const char* symbolName) const {
-    if (_impl == nullptr) {
-        IE_THROW(NotAllocated) << "SharedObjectLoader is not initialized";
+    try {
+        return ov::util::get_symbol(_so, symbolName);
+    } catch (const std::runtime_error& ex) {
+        IE_THROW(NotFound) << ex.what();
     }
-    return _impl->get_symbol(symbolName);
 }
 
 std::shared_ptr<void> SharedObjectLoader::get() const {
-    return _impl->shared_object;
+    return _so;
 }
 
 }  // namespace details
diff --git a/inference-engine/src/legacy_api/CMakeLists.txt b/inference-engine/src/legacy_api/CMakeLists.txt
index 1bf44deb97cc55..1acd566e5b314e 100644
--- a/inference-engine/src/legacy_api/CMakeLists.txt
+++ b/inference-engine/src/legacy_api/CMakeLists.txt
@@ -26,7 +26,7 @@ endif()
 
 file(TOUCH ${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp)
 
-add_library(${TARGET_NAME}_obj OBJECT
+add_library(${TARGET_NAME}_obj OBJECT EXCLUDE_FROM_ALL
             ${LIBRARY_SRC}
             ${PUBLIC_HEADERS})
 
@@ -39,7 +39,7 @@ target_compile_definitions(${TARGET_NAME}_obj PRIVATE IMPLEMENT_INFERENCE_ENGINE
 target_include_directories(${TARGET_NAME}_obj PRIVATE
     ${PUBLIC_HEADERS_DIR}
     ${CMAKE_CURRENT_SOURCE_DIR}/src
-    ${IE_MAIN_SOURCE_DIR}/src/inference_engine/src # For CNNNetworkNGraphImpl
+    $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src # For CNNNetworkNGraphImpl
     $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>
     $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>
     $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>
@@ -53,7 +53,7 @@ add_cpplint_target(${TARGET_NAME}_obj_cpplint FOR_TARGETS ${TARGET_NAME}_obj)
 
 # Create library
 
-add_library(${TARGET_NAME}
+add_library(${TARGET_NAME} EXCLUDE_FROM_ALL
     ${CMAKE_CURRENT_BINARY_DIR}/dummy.cpp
     $<TARGET_OBJECTS:${TARGET_NAME}_obj>)
 
@@ -64,7 +64,8 @@ target_link_libraries(${TARGET_NAME} PUBLIC inference_engine
                                      PRIVATE pugixml::static openvino::itt
                                              ngraph inference_engine_transformations)
 
-target_include_directories(${TARGET_NAME} INTERFACE ${PUBLIC_HEADERS_DIR})
+target_include_directories(${TARGET_NAME} INTERFACE
+    $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>)
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
@@ -79,6 +80,10 @@ openvino_developer_export_targets(COMPONENT inference_engine TARGETS ${TARGET_NA
 
 # install
 
-install(TARGETS ${TARGET_NAME}
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
+if(BUILD_SHARED_LIBS)
+    install(TARGETS ${TARGET_NAME}
+            RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
+            LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
+else()
+    ov_install_static_lib(${TARGET_NAME} core)
+endif()
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/pad_ie.hpp b/inference-engine/src/legacy_api/include/legacy/ngraph_ops/pad_ie.hpp
index b94af5ada5ddab..6a0d96287d5360 100644
--- a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/pad_ie.hpp
+++ b/inference-engine/src/legacy_api/include/legacy/ngraph_ops/pad_ie.hpp
@@ -23,6 +23,8 @@ class INFERENCE_ENGINE_API_CLASS(PadIE) : public Op {
 
     explicit PadIE(const std::shared_ptr<op::v1::Pad>& pad);
 
+    PadIE(const Output<ngraph::Node>& input, PadMode pad_mode, CoordinateDiff pads_begin, CoordinateDiff pads_end, Shape output_shape, float pad_value);
+
     size_t get_version() const override { return 1; }
 
     void validate_and_infer_types() override;
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/pad_ie.cpp b/inference-engine/src/legacy_api/src/ngraph_ops/pad_ie.cpp
index e3cecc67214517..8d22a28f6f7770 100644
--- a/inference-engine/src/legacy_api/src/ngraph_ops/pad_ie.cpp
+++ b/inference-engine/src/legacy_api/src/ngraph_ops/pad_ie.cpp
@@ -37,12 +37,22 @@ op::PadIE::PadIE(const std::shared_ptr<op::v1::Pad>& pad)
     constructor_validate_and_infer_types();
 }
 
+op::PadIE::PadIE(const Output<ngraph::Node>& input, PadMode pad_mode, CoordinateDiff pads_begin, CoordinateDiff pads_end, Shape output_shape, float pad_value)
+                 : Op({input})
+                 , m_pad_mode(pad_mode)
+                 , m_pads_begin(pads_begin)
+                 , m_pads_end(pads_end)
+                 , m_output_shape(output_shape)
+                 , m_pad_value(pad_value) {
+    constructor_validate_and_infer_types();
+}
+
 void op::PadIE::validate_and_infer_types() {
     set_output_type(0, get_input_element_type(0), m_output_shape);
 }
 
 shared_ptr<Node> op::PadIE::clone_with_new_inputs(const OutputVector& new_args) const {
-    return nullptr;
+    return std::make_shared<PadIE>(new_args.at(0), m_pad_mode, m_pads_begin, m_pads_end, m_output_shape, m_pad_value);
 }
 
 bool op::PadIE::visit_attributes(AttributeVisitor& visitor) {
diff --git a/inference-engine/src/low_precision_transformations/CMakeLists.txt b/inference-engine/src/low_precision_transformations/CMakeLists.txt
index 10c7162ecb7003..4cbb9c1b33ca0f 100644
--- a/inference-engine/src/low_precision_transformations/CMakeLists.txt
+++ b/inference-engine/src/low_precision_transformations/CMakeLists.txt
@@ -17,13 +17,11 @@ source_group("include" FILES ${PUBLIC_HEADERS})
 
 # Create library
 
-add_library(${TARGET_NAME}
+add_library(${TARGET_NAME} EXCLUDE_FROM_ALL
             ${LIBRARY_SRC}
             ${PUBLIC_HEADERS})
 
-ie_faster_build(${TARGET_NAME}
-    UNITY
-)
+ie_faster_build(${TARGET_NAME} UNITY)
 
 ie_add_vs_version_file(NAME ${TARGET_NAME}
                        FILEDESCRIPTION "Inference Engine LP transformations library")
@@ -31,7 +29,8 @@ ie_add_vs_version_file(NAME ${TARGET_NAME}
 target_link_libraries(${TARGET_NAME} PUBLIC inference_engine_transformations
                                      PRIVATE openvino::itt)
 
-target_include_directories(${TARGET_NAME} PUBLIC ${PUBLIC_HEADERS_DIR})
+target_include_directories(${TARGET_NAME} PUBLIC
+    $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>)
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
 
@@ -47,6 +46,10 @@ openvino_developer_export_targets(COMPONENT inference_engine TARGETS ${TARGET_NA
 
 # install
 
-install(TARGETS ${TARGET_NAME}
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
+if(BUILD_SHARED_LIBS)
+    install(TARGETS ${TARGET_NAME}
+            RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
+            LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
+else()
+    ov_install_static_lib(${TARGET_NAME} core)
+endif()
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/common/fake_quantize_dequantization.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/common/fake_quantize_dequantization.hpp
index a9fba5234d1846..0da82810c972cd 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/common/fake_quantize_dequantization.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/common/fake_quantize_dequantization.hpp
@@ -29,7 +29,7 @@ class LP_TRANSFORMATIONS_API FakeQuantizeDequantization {
         const std::shared_ptr<ngraph::opset1::Multiply>& multiply,
         const std::shared_ptr<ngraph::opset1::Constant>& multiplyConstant);
 
-    bool empty() const;
+    bool empty() const noexcept;
     bool multiplyHasZeroOrDenormal() const;
     bool isShared() const;
     bool isLowPrecision() const;
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/convolution.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/convolution.hpp
index 5542d04d70adb3..b49fcc89c4aee4 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/convolution.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/convolution.hpp
@@ -16,8 +16,8 @@ class LP_TRANSFORMATIONS_API ConvolutionTransformation : public WeightableLayerT
     NGRAPH_RTTI_DECLARATION;
     ConvolutionTransformation(const Params& params = Params());
     bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override;
-    bool isQuantized(const std::shared_ptr<const Node>& layer) const noexcept override;
-    static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer) noexcept;
+    bool isQuantized(const std::shared_ptr<const Node>& layer) const override;
+    static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer);
 };
 
 } // namespace low_precision
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/convolution_backprop_data.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/convolution_backprop_data.hpp
index 35b5d806be1a7b..a1176e71efffc7 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/convolution_backprop_data.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/convolution_backprop_data.hpp
@@ -16,8 +16,8 @@ class LP_TRANSFORMATIONS_API ConvolutionBackpropDataTransformation : public Weig
     ConvolutionBackpropDataTransformation(const Params& params = Params());
     bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override;
     bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> op) const override;
-    bool isQuantized(const std::shared_ptr<const Node>& layer) const noexcept override;
-    static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer) noexcept;
+    bool isQuantized(const std::shared_ptr<const Node>& layer) const override;
+    static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer);
 };
 
 } // namespace low_precision
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/group_convolution.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/group_convolution.hpp
index d53c37b8df93b8..b54921faf69a01 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/group_convolution.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/group_convolution.hpp
@@ -16,8 +16,8 @@ class LP_TRANSFORMATIONS_API GroupConvolutionTransformation : public Convolution
     NGRAPH_RTTI_DECLARATION;
     GroupConvolutionTransformation(const Params& params = Params());
     bool transform(TransformationContext& context, ngraph::pattern::Matcher &m) override;
-    bool isQuantized(const std::shared_ptr<const Node>& layer) const noexcept override;
-    static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer) noexcept;
+    bool isQuantized(const std::shared_ptr<const Node>& layer) const override;
+    static bool isQuantizedStatic(const std::shared_ptr<const Node>& layer);
 };
 
 } // namespace low_precision
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/layer_transformation.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/layer_transformation.hpp
index 17a5cf567e131e..81c25e7f28bef4 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/layer_transformation.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/layer_transformation.hpp
@@ -56,6 +56,10 @@ class LP_TRANSFORMATIONS_API DataPrecision {
             max(max),
             hasZeroPoint(hasZeroPoint) {}
 
+    bool empty() const noexcept {
+        return (precision == element::undefined) && (min == 0.f) && (max == 0.f) && (!hasZeroPoint);
+    }
+
     static bool isSupported(const element::Type& precision) {
         static const std::set<element::Type_t> lowPrecision = {
                 element::i8, element::u8,
@@ -76,28 +80,17 @@ class LP_TRANSFORMATIONS_API DataPrecision {
                 return -8.f;
             case element::i8:
                 switch (levels) {
+                    case 16:
+                        return -8.f;
                     case 255:
                         return -127.f;
-                    case 256:
+                    default:
                         return -128.f;
                 }
-                break;
             case element::i16:
-                switch (levels) {
-                    case 65536:
-                        return -32768.f;
-                    case 65535:
-                        return -32767.f;
-                }
-                break;
+                return levels == 65535 ? -32767.f : -32768.f;
             case element::i32:
-                switch (levels) {
-                    case static_cast<size_t>(4294967296):
-                        return -2147483648.f;
-                    case 4294967295:
-                        return -2147483647.f;
-                }
-                break;
+                return -2147483647.f; // -2147483647.f == -2147483648.f
             case element::f16:
                 return -1.0e15f;
             case element::f32:
@@ -113,19 +106,29 @@ class LP_TRANSFORMATIONS_API DataPrecision {
             case element::u4:
                 return 15.f;
             case element::u8:
-                return 255.f;
+                switch (levels) {
+                    case 16:
+                        return 15.f;
+                    default:
+                        return 255.f;
+                }
             case element::u16:
                 return 65535.f;
             case element::u32:
-                return 4294967296.f;
+                return 4294967296.f; // 4294967296.f == 4294967295.f
             case element::i4:
                 return 7.f;
             case element::i8:
-                return 127.f;
+                switch (levels) {
+                    case 16:
+                        return 7.f;
+                    default:
+                        return 127.f;
+                }
             case element::i16:
                 return 32767.f;
             case element::i32:
-                return 2147483647.f;
+                return 2147483648.f;  // 2147483648.f == 2147483647.f
             case element::f16:
                 return 1.0e15f;
             case element::f32:
@@ -141,6 +144,10 @@ class LP_TRANSFORMATIONS_API DataPrecision {
             return 254.f;
         } else if (maxLevelsForPrecision == 256ul) {
             return 255.f;
+        } else if (maxLevelsForPrecision == 16ul) {
+            return 15.f;
+        } else if (maxLevelsForPrecision == 15ul) {
+            return 14.f;
         } else {
             THROW_TRANSFORMATION_EXCEPTION << "unexpected quantization level " << maxLevelsForPrecision;
         }
@@ -249,7 +256,7 @@ class LP_TRANSFORMATIONS_API LayerTransformation : public ngraph::pass::MatcherP
     // return true if operation can be quantized and false otherwise
     // for example: if convolution operation weights are not quantized, then isQuantize returns false and true otherwise
     // note: dequantization operations on activations are absent during method execution
-    virtual bool isQuantized(const std::shared_ptr<const Node>& layer) const noexcept;
+    virtual bool isQuantized(const std::shared_ptr<const Node>& layer) const;
 
     // return true if operation can be preserved for precision
     // note: dequantization operations on activations are absent during method execution
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp
index 1ca49a05d162ef..cb0f56e51fc17e 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp
@@ -210,6 +210,11 @@ class LP_TRANSFORMATIONS_API NetworkHelper {
 
     static bool isPrecisionPreserved(const std::shared_ptr<ngraph::Node>& node);
 
+    static void insertDequantizationAfter(
+        const std::shared_ptr<Node>& originalNode,
+        const std::shared_ptr<Node>& dequantization,
+        const std::shared_ptr<Node>& newNode);
+
     static void replaceAttributeInNodes(
         std::shared_ptr<ngraph::Function> f,
         const std::string& name,
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/quantization_details.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/quantization_details.hpp
index a1c2f1ca4976b1..975dca8cc532ba 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/quantization_details.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/quantization_details.hpp
@@ -51,6 +51,8 @@ class LP_TRANSFORMATIONS_API QuantizationDetails {
     float getOutputLowValue(const size_t channel) const;
     float getOutputHighValue(const size_t channel) const;
 
+    bool empty() const noexcept;
+
     static bool isSupportedLevel(const size_t level);
 
     const size_t levels;
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/reduce_sum.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/reduce_sum.hpp
index 5053545fbff5bb..82e8dd2888321f 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/reduce_sum.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/reduce_sum.hpp
@@ -17,7 +17,7 @@ namespace low_precision {
 class LP_TRANSFORMATIONS_API ReduceSumTransformation : public ReduceBaseTransformation {
 public:
     NGRAPH_RTTI_DECLARATION;
-    ReduceSumTransformation(const Params& params);
+    ReduceSumTransformation(const Params& params = Params());
     bool isPrecisionPreserved(std::shared_ptr<Node> reduce) const noexcept override;
     bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> reduce) const override;
 
diff --git a/inference-engine/src/low_precision_transformations/src/add.cpp b/inference-engine/src/low_precision_transformations/src/add.cpp
index 0928e355e14fd2..a56dc64a585d81 100644
--- a/inference-engine/src/low_precision_transformations/src/add.cpp
+++ b/inference-engine/src/low_precision_transformations/src/add.cpp
@@ -206,7 +206,9 @@ bool AddTransformation::transform(TransformationContext& context, ngraph::patter
         inputs[emptyPathIndex] = dequantizationEmptyPath.data;
         inputs[fullPathIndex] = std::make_shared<opset1::Multiply>(
             newSubtractFullPathValues == nullptr ?
-                fullPathInput :
+                (fullPathInput.get_element_type() != newMultiplyFullPathValues->get_element_type() ?
+                     std::make_shared<opset1::Convert>(fullPathInput, newMultiplyFullPathValues->get_element_type()) :
+                     fullPathInput) :
                 std::make_shared<opset1::Subtract>(
                     // precision on branch with dequantization operations can be different with dequantization precision,
                     // for example: FP16 model with FP32 dequantization
@@ -225,7 +227,7 @@ bool AddTransformation::transform(TransformationContext& context, ngraph::patter
             ngraph::op::TemporaryReplaceOutputType(newAddOrSubtract, element::f32).get(),
             ngraph::op::TemporaryReplaceOutputType(multiplyEmptyPathValues, element::f32).get());
 
-        replace_node(add, newMultiply);
+        NetworkHelper::insertDequantizationAfter(add, newMultiply, newAddOrSubtract);
         NetworkHelper::copyInfo(add, newAddOrSubtract);
         ngraph::copy_runtime_info({ add, newMultiply }, newMultiply);
     }
diff --git a/inference-engine/src/low_precision_transformations/src/clamp.cpp b/inference-engine/src/low_precision_transformations/src/clamp.cpp
index 4c1c10efadead8..c7c10619a8cd3f 100644
--- a/inference-engine/src/low_precision_transformations/src/clamp.cpp
+++ b/inference-engine/src/low_precision_transformations/src/clamp.cpp
@@ -70,8 +70,7 @@ bool ClampTransformation::transform(TransformationContext& context, ngraph::patt
         replacement = std::make_shared<ngraph::opset1::Clamp>(newClamp->input_value(0), min, max);
     }
 
-    replace_node(newClamp, replacement);
-    replacement->set_friendly_name(newClamp->get_friendly_name());
+    replace_node_update_name(newClamp, replacement);
 
     element::Type outputClampType = dequantization.multiply ?
         dequantization.multiply->get_output_element_type(0) :
diff --git a/inference-engine/src/low_precision_transformations/src/concat.cpp b/inference-engine/src/low_precision_transformations/src/concat.cpp
index 19d4f8daa3716e..cc5f933e1d0810 100644
--- a/inference-engine/src/low_precision_transformations/src/concat.cpp
+++ b/inference-engine/src/low_precision_transformations/src/concat.cpp
@@ -160,7 +160,7 @@ bool ConcatTransformation::transform(TransformationContext& context, ngraph::pat
         lastDequantization = multiply;
     }
 
-    replace_node(concat, lastDequantization);
+    NetworkHelper::insertDequantizationAfter(concat, lastDequantization, newConcat);
     NetworkHelper::copyInfo(concat, newConcat);
     updateOutput(context, lastDequantization, newConcat);
     return true;
diff --git a/inference-engine/src/low_precision_transformations/src/convolution.cpp b/inference-engine/src/low_precision_transformations/src/convolution.cpp
index 0884ccc847bf38..8a8ff7f29d5be3 100644
--- a/inference-engine/src/low_precision_transformations/src/convolution.cpp
+++ b/inference-engine/src/low_precision_transformations/src/convolution.cpp
@@ -43,11 +43,11 @@ ConvolutionTransformation::ConvolutionTransformation(const Params& params) : Wei
     this->register_matcher(m, callback);
 }
 
-bool ConvolutionTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const noexcept {
+bool ConvolutionTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const {
     return ConvolutionTransformation::isQuantizedStatic(layer);
 }
 
-bool ConvolutionTransformation::isQuantizedStatic(const std::shared_ptr<const Node>& layer) noexcept {
+bool ConvolutionTransformation::isQuantizedStatic(const std::shared_ptr<const Node>& layer) {
     return WeightableLayerTransformation::isQuantizedStatic(layer, false);
 }
 
@@ -81,6 +81,7 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
     convolution = NetworkHelper::separateInStandaloneBranch(convolution);
     FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(convolution);
 
+    std::shared_ptr<Node> newMultiplyAfter;
     {
         std::shared_ptr<opset1::Subtract> subtract;
         if (dequantization.subtract != nullptr) {
@@ -172,13 +173,13 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
         }
         NetworkHelper::copyInfo(convolution, relaxedNewConvolution);
 
-        std::shared_ptr<ngraph::opset1::Multiply> newMultiplyAfter = std::make_shared<op::TypeRelaxed<opset1::Multiply>>(
+        newMultiplyAfter = std::make_shared<op::TypeRelaxed<opset1::Multiply>>(
             std::vector<element::Type>{ deqPrecision, deqPrecision },
             std::vector<element::Type>{ dequantization.multiply->get_output_element_type(0) },
             ngraph::op::TemporaryReplaceOutputType(relaxedNewConvolution, deqPrecision).get(),
             ngraph::op::TemporaryReplaceOutputType(newMultiplyAfterConst, deqPrecision).get());
 
-        replace_node(convolution, newMultiplyAfter);
+        NetworkHelper::insertDequantizationAfter(convolution, newMultiplyAfter, relaxedNewConvolution);
         convolution = newMultiplyAfter->input_value(0).get_node_shared_ptr();
 
         if (ov::is_type<opset1::Convert>(convolution->get_input_node_ptr(0))) {
@@ -242,7 +243,7 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
             });
             NetworkHelper::copyInfo(convolution, newConvolution);
 
-            auto newMultiplyAfter = std::make_shared<opset1::Multiply>(
+            newMultiplyAfter = std::make_shared<opset1::Multiply>(
                 newConvolution,
                 foldConvert(
                     fold_reshape<opset1::Reshape>(
@@ -250,7 +251,7 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
                         std::make_shared<opset1::Constant>(element::u64, Shape{ newScaleShape.size() }, newScaleShape),
                         false),
                     convolution->get_output_element_type(0)));
-            replace_node(convolution, newMultiplyAfter);
+            NetworkHelper::insertDequantizationAfter(convolution, newMultiplyAfter, newConvolution);
             convolution = newMultiplyAfter->input_value(0).get_node_shared_ptr();
         }
 
@@ -308,8 +309,7 @@ bool ConvolutionTransformation::transform(TransformationContext &context, ngraph
         }
     }
 
-    std::shared_ptr<ngraph::opset1::Multiply> finalDequantization = NetworkHelper::optimizeMultipliesAfter(
-        convolution->output(0).get_target_inputs().begin()->get_node()->shared_from_this());
+    const auto finalDequantization = NetworkHelper::optimizeMultipliesAfter(newMultiplyAfter);
     ngraph::copy_runtime_info({ convolution, finalDequantization }, finalDequantization);
     updateOutput(context, finalDequantization, convolution);
 
diff --git a/inference-engine/src/low_precision_transformations/src/convolution_backprop_data.cpp b/inference-engine/src/low_precision_transformations/src/convolution_backprop_data.cpp
index 2e71e77ac8fb99..288c897de45c04 100644
--- a/inference-engine/src/low_precision_transformations/src/convolution_backprop_data.cpp
+++ b/inference-engine/src/low_precision_transformations/src/convolution_backprop_data.cpp
@@ -53,11 +53,11 @@ ConvolutionBackpropDataTransformation::ConvolutionBackpropDataTransformation(con
     this->register_matcher(m, callback);
 }
 
-bool ConvolutionBackpropDataTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const noexcept {
+bool ConvolutionBackpropDataTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const {
     return ConvolutionBackpropDataTransformation::isQuantizedStatic(layer);
 }
 
-bool ConvolutionBackpropDataTransformation::isQuantizedStatic(const std::shared_ptr<const Node>& layer) noexcept {
+bool ConvolutionBackpropDataTransformation::isQuantizedStatic(const std::shared_ptr<const Node>& layer) {
     return WeightableLayerTransformation::isQuantizedStatic(layer, false);
 }
 
@@ -98,6 +98,7 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
 
     convolutionBackpropData = NetworkHelper::separateInStandaloneBranch(convolutionBackpropData);
     FakeQuantizeDequantization dequantization = NetworkHelper::getDequantization(convolutionBackpropData);
+    std::shared_ptr<Node> newMultiplyAfter;
     {
         if (dequantization.subtract != nullptr) {
             NetworkHelper::optimizeSubtract(dequantization.subtract);
@@ -116,13 +117,13 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
             std::vector<element::Type>{deqPrecision, deqPrecision},
             std::vector<element::Type>{deqPrecision});
 
-        const auto newMultiplyAfter = std::make_shared<op::TypeRelaxed<opset1::Multiply>>(
+        newMultiplyAfter = std::make_shared<op::TypeRelaxed<opset1::Multiply>>(
             std::vector<element::Type>{ deqPrecision, deqPrecision },
             std::vector<element::Type>{ dequantization.multiply->get_output_element_type(0) },
             ngraph::op::TemporaryReplaceOutputType(relaxedConvolutionBackpropData, deqPrecision).get(),
             ngraph::op::TemporaryReplaceOutputType(newMultiplyAfterConst, deqPrecision).get());
+        NetworkHelper::insertDequantizationAfter(convolutionBackpropData, newMultiplyAfter, relaxedConvolutionBackpropData);
 
-        replace_node(convolutionBackpropData, newMultiplyAfter);
         convolutionBackpropData = newMultiplyAfter->get_input_node_shared_ptr(0);
         inputs[0] = convolutionBackpropData->get_input_node_ptr(0)->input_value(0);
         if (ov::is_type<opset1::Convert>(convolutionBackpropData->get_input_node_ptr(0))) {
@@ -154,15 +155,17 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
 
             auto inputs = convolutionBackpropData->input_values();
             inputs[1] = multiplyFromWeights->input_value(0);
-            auto newMultiplyAfter = std::make_shared<opset1::Multiply>(
-                convolutionBackpropData->clone_with_new_inputs(inputs),
+
+            const auto newconvolutionBackpropData = convolutionBackpropData->copy_with_new_inputs(inputs);
+            newMultiplyAfter = std::make_shared<opset1::Multiply>(
+                newconvolutionBackpropData,
                 foldConvert(
                     fold_reshape<opset1::Reshape>(
                         multiplyFromWeights->input_value(1),
                         std::make_shared<opset1::Constant>(element::u64, Shape{ newScaleShape.size() }, newScaleShape),
                         false),
                     convolutionBackpropData->get_output_element_type(0)));
-            replace_node(convolutionBackpropData, newMultiplyAfter);
+            NetworkHelper::insertDequantizationAfter(convolutionBackpropData, newMultiplyAfter, newconvolutionBackpropData);
             convolutionBackpropData = newMultiplyAfter->get_input_node_shared_ptr(0);
         }
 
@@ -202,8 +205,8 @@ bool ConvolutionBackpropDataTransformation::transform(TransformationContext &con
             convolutionBackpropData = newConvolution;
         }
     }
-    std::shared_ptr<ngraph::opset1::Multiply> finalDequantization = NetworkHelper::optimizeMultipliesAfter(
-            convolutionBackpropData->output(0).get_target_inputs().begin()->get_node()->shared_from_this());
+
+    const auto finalDequantization = NetworkHelper::optimizeMultipliesAfter(newMultiplyAfter);
     ngraph::copy_runtime_info({ convolutionBackpropData, finalDequantization }, finalDequantization);
     updateOutput(context, finalDequantization, convolutionBackpropData);
 
diff --git a/inference-engine/src/low_precision_transformations/src/fake_quantize_decomposition.cpp b/inference-engine/src/low_precision_transformations/src/fake_quantize_decomposition.cpp
index 18429587d18518..d13d3b54f2e5c2 100644
--- a/inference-engine/src/low_precision_transformations/src/fake_quantize_decomposition.cpp
+++ b/inference-engine/src/low_precision_transformations/src/fake_quantize_decomposition.cpp
@@ -29,6 +29,7 @@ FakeQuantizeDecompositionTransformation::FakeQuantizeDecompositionTransformation
         if (transformation_callback(op)) {
             return false;
         }
+
         return transform(*context, m);
     };
 
@@ -230,7 +231,7 @@ std::tuple<std::shared_ptr<Node>, std::shared_ptr<Node>> decomposeFakeQuantize(
             deqPrecision,
             newFakeQuantizeLayer);
 
-        replace_node(layer, dequantization.multiply);
+        NetworkHelper::insertDequantizationAfter(layer, dequantization.multiply, newFakeQuantizeLayer);
 
         std::vector<std::shared_ptr<ngraph::Node>> sourceNodes{ layer };
         std::vector<std::shared_ptr<ngraph::Node>> targetNodes{ newFakeQuantizeLayer,  dequantization.multiply };
diff --git a/inference-engine/src/low_precision_transformations/src/fake_quantize_dequantization.cpp b/inference-engine/src/low_precision_transformations/src/fake_quantize_dequantization.cpp
index 52e651b6ca0576..46b56716081cb0 100644
--- a/inference-engine/src/low_precision_transformations/src/fake_quantize_dequantization.cpp
+++ b/inference-engine/src/low_precision_transformations/src/fake_quantize_dequantization.cpp
@@ -34,7 +34,7 @@ FakeQuantizeDequantization::FakeQuantizeDequantization(
     multiplyConstant(multiplyConstant) {
 }
 
-bool FakeQuantizeDequantization::empty() const {
+bool FakeQuantizeDequantization::empty() const noexcept {
     return (subtract == nullptr) && (multiply == nullptr);
 }
 
diff --git a/inference-engine/src/low_precision_transformations/src/group_convolution.cpp b/inference-engine/src/low_precision_transformations/src/group_convolution.cpp
index 42d9600d13c7a0..5eacede1bf3224 100644
--- a/inference-engine/src/low_precision_transformations/src/group_convolution.cpp
+++ b/inference-engine/src/low_precision_transformations/src/group_convolution.cpp
@@ -32,7 +32,7 @@ GroupConvolutionTransformation::GroupConvolutionTransformation(const Params& par
     this->register_matcher(m, callback);
 }
 
-bool GroupConvolutionTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const noexcept {
+bool GroupConvolutionTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const {
     return GroupConvolutionTransformation::isQuantizedStatic(layer);
 }
 
@@ -47,7 +47,7 @@ bool GroupConvolutionTransformation::transform(TransformationContext &context, n
     return true;
 }
 
-bool GroupConvolutionTransformation::isQuantizedStatic(const std::shared_ptr<const Node>& layer) noexcept {
+bool GroupConvolutionTransformation::isQuantizedStatic(const std::shared_ptr<const Node>& layer) {
     return WeightableLayerTransformation::isQuantizedStatic(layer, true);
 }
 
diff --git a/inference-engine/src/low_precision_transformations/src/layer_transformation.cpp b/inference-engine/src/low_precision_transformations/src/layer_transformation.cpp
index 64363ace33ca0e..028e5ee4cbc57d 100644
--- a/inference-engine/src/low_precision_transformations/src/layer_transformation.cpp
+++ b/inference-engine/src/low_precision_transformations/src/layer_transformation.cpp
@@ -199,8 +199,8 @@ LayerTransformation::PrecisionDetails LayerTransformation::getPrecisionDetails(
     const size_t quantizationLevels,
     const std::vector<float>& outputLowValues,
     const std::vector<float>& outputHighValues) {
-    const float zeroThreshold = std::numeric_limits<float>::denorm_min();
     // TODO: workaround: hardcoded values
+    const float zeroThreshold = 1.e-6f;
     const float quantizationIntervalAsymmetryThreshold = 0.002f;
 
     float asymmetricIntervalSideRatio = -static_cast<float>(quantizationLevels) / (quantizationLevels - 2.f);
@@ -209,7 +209,15 @@ LayerTransformation::PrecisionDetails LayerTransformation::getPrecisionDetails(
     bool unsignedPrecision = true;
 
     bool hasZeroPoint = false;
+    bool thereIsAtLeastOneNormalValue = false;
     for (size_t i = 0; i < outputLowValues.size(); ++i) {
+        if ((std::fabs(outputLowValues[i]) < zeroThreshold) && (std::fabs(outputHighValues[i]) < zeroThreshold)) {
+            // both values are too small to identify preferable precision
+            continue;
+        }
+
+        thereIsAtLeastOneNormalValue = true;
+
         const bool signedInterval = std::signbit(outputLowValues[i]) != std::signbit(outputHighValues[i]);
         const bool outputLowValueIsNotZero = std::fabs(outputLowValues[i]) >= zeroThreshold;
         if (signedInterval && outputLowValueIsNotZero) {
@@ -218,8 +226,9 @@ LayerTransformation::PrecisionDetails LayerTransformation::getPrecisionDetails(
             hasNegative = true;
 
             if (outputHighValues[i] != 0.f) {
-                const float expectedRatio = (quantizationLevels == 256 || quantizationLevels == 65536 || quantizationLevels == 4294967296) ?
-                                            asymmetricIntervalSideRatio : -1.f;
+                const float expectedRatio =
+                        (quantizationLevels == 16 || quantizationLevels == 256 ||
+                         quantizationLevels == 65536 || quantizationLevels == 4294967296) ? asymmetricIntervalSideRatio : -1.f;
                 const float actualRatio = outputLowValues[i] / outputHighValues[i];
                 const float actual = std::fabs((actualRatio - expectedRatio) / std::min(actualRatio, expectedRatio));
                 if (actual > quantizationIntervalAsymmetryThreshold) {
@@ -253,16 +262,11 @@ LayerTransformation::PrecisionDetails LayerTransformation::getPrecisionDetails(
         }
     }
 
-    // TODO: use this implementation after merge <= not aligned with master
-//    if (signedPrecision && (!unsignedPrecision)) {
-//        return LayerTransformation::PrecisionDetails(element::i8, hasNegative, hasZeroPoint);
-//    }
-//
-//    if ((!signedPrecision) && unsignedPrecision) {
-//        return LayerTransformation::PrecisionDetails(element::u8, hasNegative, hasZeroPoint);
-//    }
-//
-//    THROW_TRANSFORMATION_EXCEPTION << "unexpected interval";
+    if (!thereIsAtLeastOneNormalValue) {
+        // all values are small and didn't define 'signedPrecision'
+        signedPrecision = std::any_of(outputLowValues.begin(), outputLowValues.end(), [](const float& value) { return value < 0.f; });
+        unsignedPrecision = !signedPrecision;
+    }
 
     element::Type resultPrecision = element::undefined;
     if (!hasZeroPoint) {
@@ -270,6 +274,7 @@ LayerTransformation::PrecisionDetails LayerTransformation::getPrecisionDetails(
             switch (quantizationLevels) {
                 case 256:
                 case 255:
+                case 16:
                     resultPrecision = element::i8;
                     break;
                 case 65536:
@@ -287,6 +292,7 @@ LayerTransformation::PrecisionDetails LayerTransformation::getPrecisionDetails(
             switch (quantizationLevels) {
                 case 256:
                 case 255:
+                case 16:
                     resultPrecision = element::u8;
                     break;
                 case 65536:
@@ -311,10 +317,13 @@ LayerTransformation::PrecisionDetails LayerTransformation::getPrecisionDetails(c
 bool LayerTransformation::isAsymmetricQuantization(const std::shared_ptr<const Node>& layer) {
     const auto nonConstNode = const_cast<ngraph::Node*>(layer.get())->shared_from_this();
     const auto dequantization = NetworkHelper::getDequantization(nonConstNode);
+    if (dequantization.empty()) {
+        return false;
+    }
     return dequantization.subtract != nullptr;
 }
 
-bool LayerTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const noexcept {
+bool LayerTransformation::isQuantized(const std::shared_ptr<const Node>& layer) const {
     return true;
 }
 
diff --git a/inference-engine/src/low_precision_transformations/src/mat_mul.cpp b/inference-engine/src/low_precision_transformations/src/mat_mul.cpp
index ad27caab8b4e6a..1ced4c45188e7e 100644
--- a/inference-engine/src/low_precision_transformations/src/mat_mul.cpp
+++ b/inference-engine/src/low_precision_transformations/src/mat_mul.cpp
@@ -167,7 +167,7 @@ bool MatMulTransformation::transform(TransformationContext &context, ngraph::pat
 
     newMultiply->set_friendly_name(newMatMul->get_friendly_name() + "/DequantizationMultiply");
 
-    replace_node(matMul, newMultiply);
+    NetworkHelper::insertDequantizationAfter(matMul, newMultiply, newMatMul);
     copy_runtime_info({ newMultiply, matMul }, newMultiply);
 
     updateOutput(context, newMultiply, newMatMul);
diff --git a/inference-engine/src/low_precision_transformations/src/mvn.cpp b/inference-engine/src/low_precision_transformations/src/mvn.cpp
index 428d293810266c..a30c6859470246 100644
--- a/inference-engine/src/low_precision_transformations/src/mvn.cpp
+++ b/inference-engine/src/low_precision_transformations/src/mvn.cpp
@@ -159,7 +159,7 @@ bool MVNTransformation::transform(TransformationContext &context, ngraph::patter
         mvn->get_output_element_type(0));
     ngraph::copy_runtime_info({ mvn, newMultiply }, newMultiply);
 
-    replace_node(mvn, newMultiply);
+    NetworkHelper::insertDequantizationAfter(mvn, newMultiply, newMVN);
 
     updateOutput(context, newMultiply, newMVN);
     return true;
diff --git a/inference-engine/src/low_precision_transformations/src/network_helper.cpp b/inference-engine/src/low_precision_transformations/src/network_helper.cpp
index 31d39ddae419c5..50d6da8fc1b38c 100644
--- a/inference-engine/src/low_precision_transformations/src/network_helper.cpp
+++ b/inference-engine/src/low_precision_transformations/src/network_helper.cpp
@@ -1108,7 +1108,7 @@ std::tuple<std::shared_ptr<Node>, std::shared_ptr<Node>> NetworkHelper::decompos
     dequantize->set_friendly_name(newFQ->get_friendly_name() + "/DequantizationMultiply");
     ngraph::copy_runtime_info({ newFQ, dequantize }, dequantize);
 
-    replace_node(fq, dequantize);
+    insertDequantizationAfter(fq, dequantize, newFQ);
 
     return std::make_tuple(newFQ, dequantize);
 }
@@ -1516,12 +1516,24 @@ std::shared_ptr<Node> NetworkHelper::optimizeSubtract(std::shared_ptr<opset1::Su
     }
 
     auto data = convertOnSubtract->input_value(0);
-    const auto subtractParent = subtract->get_input_node_shared_ptr(1);
-    if (ov::is_type<opset1::Constant>(subtractParent)) {
+    // check if shift is a constant
+    std::shared_ptr<Node> shift = subtract->get_input_node_shared_ptr(1);
+    bool isShiftConstant = ov::is_type<opset1::Constant>(shift);
+    if (!isShiftConstant && ov::is_type<opset1::Convert>(shift)) {
+        // if not - we're dealing with shift->Convert (to high precision) -> Subtract
+        shift = shift->get_input_node_shared_ptr(0);
+        isShiftConstant = ov::is_type<opset1::Constant>(shift);
+    }
+    if (isShiftConstant) {
         std::shared_ptr<Node> replacement;
 
-        auto shift = subtract->input_value(1).get_node_shared_ptr();
-        auto roundedShift = NetworkHelper::round(shift, convertInputType);
+        auto shiftConst = ov::as_type_ptr<opset1::Constant>(shift);
+        std::shared_ptr<opset1::Constant> roundedShift;
+        if (shiftConst->get_element_type() != convertInputType) {
+            roundedShift = NetworkHelper::round(shiftConst, convertInputType);
+        } else {
+            roundedShift = shiftConst;
+        }
 
         if (isScalarLike(roundedShift)) {
             roundedShift = toScalar(roundedShift);
@@ -1532,7 +1544,7 @@ std::shared_ptr<Node> NetworkHelper::optimizeSubtract(std::shared_ptr<opset1::Su
         }
 
         if (roundedShift) {
-            NetworkHelper::copyInfo(shift, roundedShift);
+            NetworkHelper::copyInfo(shiftConst, roundedShift);
 
             // Propagate convertInputType down
             replacement = std::make_shared<op::TypeRelaxed<opset1::Subtract>>(data, roundedShift->output(0));
@@ -1541,12 +1553,6 @@ std::shared_ptr<Node> NetworkHelper::optimizeSubtract(std::shared_ptr<opset1::Su
             replace_node(subtract, replacement);
         }
 
-        return replacement;
-    } else if (ov::is_type<opset1::Convert>(subtractParent) && ov::is_type<opset1::Constant>(subtractParent->get_input_node_shared_ptr(0))) {
-        auto replacement = std::make_shared<op::TypeRelaxed<opset1::Subtract>>(data, subtractParent->input_value(0));
-        NetworkHelper::copyInfo(subtract, replacement);
-        NetworkHelper::setOutDataPrecisionForTypeRelaxed(replacement, convertOutputType);
-        replace_node(subtract, replacement);
         return replacement;
     }
 
@@ -1568,36 +1574,32 @@ NetworkHelper::InsertDequantizationResult NetworkHelper::moveDequantizationAfter
 
     assert(operation->get_output_size() == 1);
 
+    // we must have dequantization multiply
+    assert(dequantization.multiply != nullptr);
+
     OutputVector inputs = operation->input_values();
     const size_t dequantizationIndex = getChildInputIndex(dequantization.multiply, operation);
-    inputs[dequantizationIndex] = moveSubtract ?
-        dequantization.data :
-        (dequantization.subtract == nullptr ? dequantization.data : dequantization.subtract);
+    inputs[dequantizationIndex] = (!moveSubtract && dequantization.subtract != nullptr) ?
+        dequantization.subtract :
+        dequantization.data;
 
-    const std::shared_ptr<ngraph::Node> newOperation = operation->clone_with_new_inputs(inputs);
+    const auto newOperation = operation->clone_with_new_inputs(inputs);
     newOperation->set_friendly_name(operation->get_friendly_name());
     ngraph::copy_runtime_info(operation, newOperation);
 
-    auto op = std::dynamic_pointer_cast<ngraph::op::TypeRelaxedBase>(newOperation);
-    if (op != nullptr) {
-        if (updatePrecision) {
-            op->set_overridden_output_type(newOperation->get_input_element_type(0));
-        } else if (dequantization.multiply) {
-            op->set_overridden_output_type(dequantization.multiplyConstant->get_element_type());
-        } else if (dequantization.subtract) {
-            op->set_overridden_output_type(dequantization.subtractConstant->get_element_type());
-        }
-        std::dynamic_pointer_cast<ngraph::Node>(newOperation)->validate_and_infer_types();
+    if (const auto op = std::dynamic_pointer_cast<ngraph::op::TypeRelaxedBase>(newOperation)) {
+        op->set_overridden_output_type(updatePrecision ?
+            newOperation->get_input_element_type(0) :
+            dequantization.multiplyConstant->get_element_type());
+        newOperation->validate_and_infer_types();
     }
 
+    std::shared_ptr<Node> parent = newOperation;
+
     const element::Type deqPrecision = dequantization.multiplyConstant->get_element_type();
     const bool shouldConvert = (newOperation->get_output_element_type(0) != deqPrecision);
-
-    auto parent = newOperation;
     if (shouldConvert) {
-        const auto convertOutputPrecision = dequantization.convert != nullptr ?
-            dequantization.convert->get_output_element_type(0) :
-            deqPrecision;
+        const auto convertOutputPrecision = dequantization.convert ? dequantization.convert->get_element_type() : deqPrecision;
         parent = std::make_shared<opset1::Convert>(parent, convertOutputPrecision);
         ngraph::copy_runtime_info({ newOperation, parent }, parent);
     }
@@ -1613,12 +1615,9 @@ NetworkHelper::InsertDequantizationResult NetworkHelper::moveDequantizationAfter
             }
 
             parent = std::make_shared<op::TypeRelaxed<opset1::Subtract>>(
-                std::vector<element::Type>{element::f32, element::f32}, std::vector<element::Type>{ element::f32 },
-                ngraph::op::TemporaryReplaceOutputType(parent, element::f32).get(),
-                ngraph::op::TemporaryReplaceOutputType(
-                    dequantization.subtractConstant->output(0).get_element_type() == parentPrecision ?
-                        dequantization.subtractConstant :
-                        foldConvert(dequantization.subtractConstant->output(0), parentPrecision), element::f32).get());
+                element::TypeVector{ element::f32, element::f32 }, element::TypeVector{ element::f32 },
+                op::TemporaryReplaceOutputType(parent, element::f32).get(),
+                op::TemporaryReplaceOutputType(foldConvert(dequantization.subtractConstant, parentPrecision), element::f32).get());
             ngraph::copy_runtime_info({ newOperation, parent }, parent);
         } else {
             parent = std::make_shared<opset1::Subtract>(parent, dequantization.subtractConvert);
@@ -1627,23 +1626,21 @@ NetworkHelper::InsertDequantizationResult NetworkHelper::moveDequantizationAfter
     }
 
     if (dequantization.multiply != nullptr) {
-        auto multiplyConstant = dequantization.multiplyConstant;
         const element::Type parentPrecision = parent->get_output_element_type(0);
-        if (parentPrecision.bitwidth() < multiplyConstant->get_element_type().bitwidth()) {
+        if (parentPrecision.bitwidth() < dequantization.multiplyConstant->get_element_type().bitwidth()) {
             THROW_IE_LPT_EXCEPTION(*parent) <<
                 "unexpected precisions: on data " << parent->get_friendly_name() << ":" << parentPrecision <<
-                ", multiply dequantization constant " << multiplyConstant->get_friendly_name() << ":" << multiplyConstant->get_element_type();
+                ", multiply dequantization constant " << dequantization.multiplyConstant->get_friendly_name() << ":" <<
+                dequantization.multiplyConstant->get_element_type();
         }
 
         parent = std::make_shared<op::TypeRelaxed<opset1::Multiply>>(
-            opset1::Multiply(parent,
-                multiplyConstant->output(0).get_element_type() == parentPrecision ?
-                    multiplyConstant :
-                    foldConvert(multiplyConstant->output(0), parentPrecision)),
+            opset1::Multiply(parent, foldConvert(dequantization.multiplyConstant, parentPrecision)),
             dequantization.multiply->get_output_element_type(0));
         ngraph::copy_runtime_info({ newOperation, parent }, parent);
     }
-    replace_node(operation, parent);
+
+    insertDequantizationAfter(operation, parent, newOperation);
 
     if ((!moveSubtract) && (dequantization.convert != nullptr) && (dequantization.subtract != nullptr)) {
         // issue #43088
@@ -1872,6 +1869,21 @@ bool isDisabled(const std::shared_ptr<Node>& node) {
     }
     return false;
 }
+
+void NetworkHelper::insertDequantizationAfter(
+    const std::shared_ptr<Node>& originalNode,
+    const std::shared_ptr<Node>& dequantization,
+    const std::shared_ptr<Node>& newNode) {
+    replace_node(originalNode, dequantization);
+
+    // We do it to avoid dequantization propagation to the shapeOf subgraphs
+    for (const auto& input : dequantization->get_output_target_inputs(0)) {
+        if (const auto shapeOf = as_type_ptr<opset1::ShapeOf>(input.get_node()->shared_from_this())) {
+            const auto newShapeOf = shapeOf->clone_with_new_inputs({ newNode });
+            replace_node_update_name(shapeOf, newShapeOf);
+        }
+    }
+}
 } // namespace low_precision
 } // namespace pass
 } // namespace ngraph
diff --git a/inference-engine/src/low_precision_transformations/src/normalize_l2.cpp b/inference-engine/src/low_precision_transformations/src/normalize_l2.cpp
index 9cc1086c393c4a..7d2a894580084b 100644
--- a/inference-engine/src/low_precision_transformations/src/normalize_l2.cpp
+++ b/inference-engine/src/low_precision_transformations/src/normalize_l2.cpp
@@ -140,7 +140,7 @@ bool NormalizeL2Transformation::transform(TransformationContext &context, ngraph
         ngraph::op::TemporaryReplaceOutputType(newNormalize, element::f32).get(),
         ngraph::op::TemporaryReplaceOutputType(newScalesConst, element::f32).get());
 
-    replace_node(normalize, newMultiply);
+    NetworkHelper::insertDequantizationAfter(normalize, newMultiply, newNormalize);
     ngraph::copy_runtime_info({ normalize, newMultiply }, newMultiply);
 
     updateOutput(context, newMultiply, newNormalize);
diff --git a/inference-engine/src/low_precision_transformations/src/quantization_details.cpp b/inference-engine/src/low_precision_transformations/src/quantization_details.cpp
index 84028c99a15682..ee5ed04ad267a5 100644
--- a/inference-engine/src/low_precision_transformations/src/quantization_details.cpp
+++ b/inference-engine/src/low_precision_transformations/src/quantization_details.cpp
@@ -91,6 +91,10 @@ void QuantizationDetails::getOutputIntervals(
 }
 
 QuantizationDetails QuantizationDetails::getDetails(std::shared_ptr<opset1::FakeQuantize> quantize) {
+    if (!QuantizationDetails::outputLayoutIsSupported(quantize)) {
+        return QuantizationDetails();
+    }
+
     const std::vector<float> inputLowValues = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(1))->cast_vector<float>();
     const std::vector<float> inputHighValues = ov::as_type_ptr<opset1::Constant>(quantize->get_input_node_shared_ptr(2))->cast_vector<float>();
 
@@ -153,8 +157,12 @@ std::vector<float> QuantizationDetails::getBlobValue(std::shared_ptr<Node> const
     return ov::as_type_ptr<opset1::Constant>(constantLayer)->cast_vector<float>();
 }
 
+bool QuantizationDetails::empty() const noexcept {
+    return (levels == 0ul) && inputLowValues.empty() && inputHighValues.empty() && outputLowValues.empty() && outputHighValues.empty();
+}
+
 bool QuantizationDetails::isSupportedLevel(const size_t level) {
-    static const std::unordered_set<size_t> supported_levels = { 255, 256, 65536, 65535, static_cast<size_t>(4294967296), 4294967295 };
+    static const std::unordered_set<size_t> supported_levels = { 16, 255, 256, 65536, 65535, static_cast<size_t>(4294967296), 4294967295 };
     return supported_levels.find(level) != supported_levels.end();
 }
 
diff --git a/inference-engine/src/low_precision_transformations/src/rt_info/intervals_alignment_attribute.cpp b/inference-engine/src/low_precision_transformations/src/rt_info/intervals_alignment_attribute.cpp
index cb8b650bac8611..7beb4722459a8e 100644
--- a/inference-engine/src/low_precision_transformations/src/rt_info/intervals_alignment_attribute.cpp
+++ b/inference-engine/src/low_precision_transformations/src/rt_info/intervals_alignment_attribute.cpp
@@ -166,25 +166,26 @@ void VariantWrapper<IntervalsAlignmentAttributePtr>::merge(
         const auto size = std::abs(sharedValue->minInterval.high - sharedValue->minInterval.low);
         if (resultSize > size) {
             resultSharedValue->minInterval = sharedValue->minInterval;
-
-            float dequantizationMul;
-            float dequantizationSub;
-            float updatedOutputLowValue;
-            float updatedOutputHighValue;
-
-            const size_t minLevels = NetworkHelper::calculateLevels(
-                0.f,
-                DataPrecision::getMaxValue(resultAttribute->levels),
-                resultSharedValue->combinedInterval.low,
-                resultSharedValue->combinedInterval.high,
-                resultSharedValue->minInterval.low,
-                resultSharedValue->minInterval.high,
-                dequantizationMul,
-                dequantizationSub,
-                updatedOutputLowValue,
-                updatedOutputHighValue);
-
-            resultSharedValue->minLevels = minLevels;
+            if (resultAttribute->levels != 0ul) {
+                float dequantizationMul;
+                float dequantizationSub;
+                float updatedOutputLowValue;
+                float updatedOutputHighValue;
+
+                const size_t minLevels = NetworkHelper::calculateLevels(
+                        0.f,
+                        DataPrecision::getMaxValue(resultAttribute->levels),
+                        resultSharedValue->combinedInterval.low,
+                        resultSharedValue->combinedInterval.high,
+                        resultSharedValue->minInterval.low,
+                        resultSharedValue->minInterval.high,
+                        dequantizationMul,
+                        dequantizationSub,
+                        updatedOutputLowValue,
+                        updatedOutputHighValue);
+
+                resultSharedValue->minLevels = minLevels;
+            }
 
 #ifdef LPT_DEBUG
             resultSharedValue->minLevelsOperation = sharedValue->minLevelsOperation;
diff --git a/inference-engine/src/low_precision_transformations/src/split.cpp b/inference-engine/src/low_precision_transformations/src/split.cpp
index 141549dd582859..70c0be993d2d70 100644
--- a/inference-engine/src/low_precision_transformations/src/split.cpp
+++ b/inference-engine/src/low_precision_transformations/src/split.cpp
@@ -109,6 +109,16 @@ bool SplitTransformation::transform(TransformationContext& context, ngraph::patt
         }
     }
 
+    // We do it to avoid dequantization propagation to the shapeOf subgraphs
+    for (size_t i = 0; i < replacement.size(); ++i) {
+        for (const auto& input : replacement[i].get_target_inputs()) {
+            if (const auto shapeOf = as_type_ptr<opset1::ShapeOf>(input.get_node()->shared_from_this())) {
+                const auto newShapeOf = shapeOf->clone_with_new_inputs({ newSplit->output(i) });
+                replace_node_update_name(shapeOf, newShapeOf);
+            }
+        }
+    }
+
     updateOutputs(context, lastNodes, newSplit);
     return true;
 }
diff --git a/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp b/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp
index a8aee81aca6ebf..bc5ee3706baeac 100644
--- a/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp
+++ b/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp
@@ -42,7 +42,7 @@ bool WeightableLayerTransformation::canConvolutionBeTransformed(const Transforma
     if (dequantization.empty()) {
         const auto fqOnWeights = getFakeQuantizeOnWeights(layer);
         const auto dataPrecision = getDataPrecisionOnWeights(layer);
-        if (!NetworkHelper::checkZeroPoint(fqOnWeights, dataPrecision)) {
+        if ((dataPrecision.precision == ngraph::element::undefined) || (!NetworkHelper::checkZeroPoint(fqOnWeights, dataPrecision))) {
             return false;
         }
     } else {
@@ -361,6 +361,9 @@ std::shared_ptr<opset1::FakeQuantize> WeightableLayerTransformation::getFakeQuan
 DataPrecision WeightableLayerTransformation::getDataPrecisionOnWeights(const std::shared_ptr<Node>& node) {
     const auto fq = getFakeQuantizeOnWeights(node);
     const QuantizationDetails quantizationDetails = QuantizationDetails::getDetails(fq);
+    if (quantizationDetails.empty()) {
+        return DataPrecision();
+    }
 
     const auto precisionsAttribute = getAttributeFromOutput<PrecisionsAttributePtr>(fq);
     const auto precisions = precisionsAttribute == nullptr ?
@@ -380,11 +383,15 @@ bool WeightableLayerTransformation::isAsymmetricOnWeights(const std::shared_ptr<
 
     if (dequantization.empty()) {
         const auto dataPrecision = WeightableLayerTransformation::getDataPrecisionOnWeights(n);
+        if (dataPrecision.empty()) {
+            return false;
+        }
+
         if (dataPrecision.hasZeroPoint) {
             return true;
         }
     } else {
-        if (dequantization.subtract != nullptr) {
+        if ((dequantization.subtract != nullptr) && (NetworkHelper::optimizeSubtract(dequantization.subtract) != nullptr)) {
             return true;
         }
     }
diff --git a/inference-engine/src/mkldnn_plugin/cpu_types.cpp b/inference-engine/src/mkldnn_plugin/cpu_types.cpp
index 00b22d9093740e..a475c660fddb45 100644
--- a/inference-engine/src/mkldnn_plugin/cpu_types.cpp
+++ b/inference-engine/src/mkldnn_plugin/cpu_types.cpp
@@ -66,6 +66,7 @@ const InferenceEngine::details::caseless_unordered_map<std::string, Type> type_t
         { "Squeeze", Reshape },
         { "Unsqueeze", Reshape },
         { "ShapeOf", ShapeOf },
+        { "NonZero", NonZero },
         { "Softmax", Softmax },
         { "Reorder", Reorder },
         { "BatchToSpace", BatchToSpace },
@@ -228,6 +229,8 @@ std::string NameFromType(const Type type) {
             return "Reshape";
         case ShapeOf:
             return "ShapeOf";
+        case NonZero:
+            return "NonZero";
         case Tile:
             return "Tile";
         case ROIAlign:
diff --git a/inference-engine/src/mkldnn_plugin/cpu_types.h b/inference-engine/src/mkldnn_plugin/cpu_types.h
index 0062c034c5bdb2..8052b73fe9de50 100644
--- a/inference-engine/src/mkldnn_plugin/cpu_types.h
+++ b/inference-engine/src/mkldnn_plugin/cpu_types.h
@@ -34,6 +34,7 @@ enum Type {
     MatMul,
     Reshape,
     ShapeOf,
+    NonZero,
     Tile,
     ROIAlign,
     ROIPooling,
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.cpp b/inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.cpp
index a7b231c37af35e..873ee413a58fbf 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.cpp
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/blocked_memory_desc.cpp
@@ -19,10 +19,7 @@ bool BlockedMemoryDesc::isCompatible(const BlockedMemoryDesc &rhs) const {
         return false;
     }
 
-    // this check needed to avoid inserting unnecessary reorders if the memory is used in place and the batch size is equal to 1
-    size_t skipAxis = this->getShape().getRank() > 0 && this->getShape().getDims().front() == 1 ? 0 :
-            Shape::UNDEFINED_DIM; //ignore batch axis if batch size == 1
-    if (!dimsEqualWeak(this->getStrides(), rhs.getStrides(), skipAxis)) {
+    if (!dimsEqualWeak(this->getStrides(), rhs.getStrides())) {
         return false;
     }
 
diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp
index b9caab749dd120..bdb5d5bb77bf56 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/dnnl_blocked_memory_desc.cpp
@@ -236,7 +236,7 @@ bool DnnlBlockedMemoryDesc::isCompatible(const DnnlBlockedMemoryDesc& rhs) const
     if (one_of(wrappedThis.format_kind(), format_kind::undef, format_kind::any))
         return false;
 
-    int stride_start = wrappedThis.ndims() > 0 && wrappedThis.dims()[0] == 1 ? 1 : 0;  // ignore batch axis stride if batch size == 1
+    int stride_start = 0;
 
     const auto thisExtra = this->desc.data.extra;
     const auto rhsExtra = rhs.desc.data.extra;
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_edge.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_edge.cpp
index ad45a5d39adedb..5c433a607775b3 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_edge.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_edge.cpp
@@ -65,12 +65,7 @@ void MKLDNNEdge::drop() {
     _drop_from(getChild()->parentEdges);
 }
 
-
-bool MKLDNNEdge::needReorder() {
-    if (!getInputDesc().isCompatible(getOutputDesc())) {
-        return true;
-    }
-
+bool MKLDNNEdge::enforceReorder() {
     bool canBeInPlaceConflicts = false;
     auto parentNode = getParent();
     auto parentSPD = parentNode->getSelectedPrimitiveDescriptor();
@@ -142,6 +137,107 @@ bool MKLDNNEdge::needReorder() {
     return false;
 }
 
+static inline bool isPhycicalMemCompatible(const MemoryDesc& lhsMemDesc, const MemoryDesc& rhsMemDesc) {
+    if (!lhsMemDesc.isDefined() || !rhsMemDesc.isDefined() ||
+        !(lhsMemDesc.getType() & MemoryDescType::Blocked) || !(rhsMemDesc.getType() & MemoryDescType::Blocked) ||
+        (lhsMemDesc.getType() == DnnlBlocked && !lhsMemDesc.as<const DnnlMemoryDesc>()->hasEmptyExtraData()) ||
+        (rhsMemDesc.getType() == DnnlBlocked && !rhsMemDesc.as<const DnnlMemoryDesc>()->hasEmptyExtraData()))
+        return false;
+
+    const auto lhsBlockMemDesc = lhsMemDesc.as<BlockedMemoryDesc>();
+    const auto rhsBlockMemDesc = rhsMemDesc.as<BlockedMemoryDesc>();
+
+    if (lhsBlockMemDesc->getShape() != rhsBlockMemDesc->getShape() || lhsBlockMemDesc->getPrecision() != rhsBlockMemDesc->getPrecision())
+        return false;
+
+    // dims padding check
+    bool isZeroDimsPaddings =
+        std::all_of(lhsBlockMemDesc->getOffsetPaddingToData().begin(), lhsBlockMemDesc->getOffsetPaddingToData().end(), [](size_t x){ return x == 0; }) &&
+        std::all_of(rhsBlockMemDesc->getOffsetPaddingToData().begin(), rhsBlockMemDesc->getOffsetPaddingToData().end(), [](size_t x){ return x == 0; });
+    bool isSameElementsCount = lhsBlockMemDesc->getPaddedElementsCount() == rhsBlockMemDesc->getPaddedElementsCount();
+    if (!isZeroDimsPaddings || !isSameElementsCount)
+        return false;
+
+    // tensor padding check
+    if (lhsBlockMemDesc->getOffsetPadding() != rhsBlockMemDesc->getOffsetPadding()) {
+        return false;
+    }
+
+    // stride check
+    const auto lhsBlockDims = lhsBlockMemDesc->getBlockDims();
+    std::vector<size_t> lhsStridesDefault(lhsBlockDims.size());
+    lhsStridesDefault[lhsBlockDims.size() - 1] = 1;
+    for (size_t i = 2; i <= lhsBlockDims.size(); i++) {
+        lhsStridesDefault[lhsBlockDims.size() - i] = lhsStridesDefault[lhsBlockDims.size() - (i - 1)] * lhsBlockDims[lhsBlockDims.size() - (i - 1)];
+    }
+
+    auto rhsBlockDims = rhsBlockMemDesc->getBlockDims();
+    std::vector<size_t> rhsStridesDefault(rhsBlockDims.size());
+    rhsStridesDefault[rhsBlockDims.size() - 1] = 1;
+    for (size_t i = 2; i <= rhsBlockDims.size(); i++) {
+        rhsStridesDefault[rhsBlockDims.size() - i] =
+             rhsStridesDefault[rhsBlockDims.size() - (i - 1)] * rhsBlockDims[rhsBlockDims.size() - (i - 1)];
+    }
+
+    // this check needed to avoid inserting unnecessary reorders if the memory is used in place and the batch size is equal to 1
+    // in nodes like concate and split
+    size_t lhsSkipAxis = lhsBlockDims.size() > 0 && lhsBlockDims[0] == 1 ? 0 : Shape::UNDEFINED_DIM;
+    size_t rhsSkipAxis = rhsBlockDims.size() > 0 && rhsBlockDims[0] == 1 ? 0 : Shape::UNDEFINED_DIM;
+
+    bool isDenseTensor = dimsEqualStrong(lhsStridesDefault, lhsBlockMemDesc->getStrides(), lhsSkipAxis) &&
+                         dimsEqualStrong(rhsStridesDefault, rhsBlockMemDesc->getStrides(), rhsSkipAxis);
+    if (!isDenseTensor)
+        return false;
+
+    auto getCleanDim = [&](const VectorDims& dims, const VectorDims& flag) {
+        if (dims.size() != flag.size())
+            return dims;
+        std::vector<size_t> ret;
+        for (int i = 0; i < dims.size(); i++) {
+            if (flag[i] != 1) {
+                ret.push_back(dims[i]);
+            }
+        }
+        return ret;
+    };
+
+    // block dim check
+    auto lhsBlockDimsClean = getCleanDim(lhsBlockDims, lhsBlockDims);
+    auto rhsBlockDimsClean = getCleanDim(rhsBlockDims, rhsBlockDims);
+    if (!dimsEqualStrong(lhsBlockDimsClean, rhsBlockDimsClean))
+        return false;
+
+    // order check
+    auto lhsOrderClean = getCleanDim(lhsBlockMemDesc->getOrder(), lhsBlockDims);
+    auto rhsOrderClean = getCleanDim(rhsBlockMemDesc->getOrder(), rhsBlockDims);
+    if (!dimsEqualStrong(lhsOrderClean, rhsOrderClean))
+        return false;
+
+    return true;
+}
+
+MKLDNNEdge::ReorderStatus MKLDNNEdge::needReorder() {
+    bool optimized = false;
+    if (!getInputDesc().isCompatible(getOutputDesc())) {
+        if (isPhycicalMemCompatible(getInputDesc(), getOutputDesc()) && !getParent()->isConstant()) {
+            optimized = true;
+        } else {
+            return ReorderStatus::Regular;
+        }
+    }
+
+    // put here as more costly than compatible check
+    if (enforceReorder()) {
+        return ReorderStatus::Regular;
+    }
+
+    if (optimized) {
+        return ReorderStatus::Optimized;
+    }
+
+    return ReorderStatus::No;
+}
+
 void MKLDNNEdge::reuse(MKLDNNMemoryPtr ptr) {
     if (status != Status::NeedAllocation)
         return;
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_edge.h b/inference-engine/src/mkldnn_plugin/mkldnn_edge.h
index 9c6a4f92506b51..e997f67894378a 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_edge.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_edge.h
@@ -35,6 +35,12 @@ class MKLDNNEdge {
         Validated
     };
 
+    enum class ReorderStatus {
+        Regular = 0,
+        Optimized = 1,
+        No = 2
+    };
+
     inline Status getStatus() const noexcept {
         return status;
     }
@@ -54,7 +60,7 @@ class MKLDNNEdge {
     const MKLDNNMemory& getMemory();
     MKLDNNMemoryPtr& getMemoryPtr();
 
-    bool needReorder();
+    ReorderStatus needReorder();
     bool isDropped() const;
     bool isUseExternalMemory() const;
 
@@ -87,6 +93,7 @@ class MKLDNNEdge {
     const MemoryDesc& getInputDesc() const;
     const MemoryDesc& getOutputDesc() const;
     const MemoryDesc& getDesc() const;
+    bool enforceReorder();
 
     enum LOOK { LOOK_UP = 1, LOOK_DOWN = 2, LOOK_BOTH = LOOK_UP | LOOK_DOWN, LOOK_NO_RECURRENT = 4 };
 
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp
index fdcdf37996fac7..747a2968e73d7a 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp
@@ -283,12 +283,6 @@ bool MKLDNNExecNetwork::CanProcessDynBatch(const InferenceEngine::CNNNetwork &ne
     return true;
 }
 
-IE_SUPPRESS_DEPRECATED_START
-std::vector<IVariableStateInternal::Ptr> MKLDNNExecNetwork::QueryState() {
-    return memoryStates;
-}
-IE_SUPPRESS_DEPRECATED_END
-
 void MKLDNNExecNetwork::Export(std::ostream& modelStream) {
     CNNNetworkSerializer serializer(modelStream, extensionManager);
     serializer <<_network;
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.h b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.h
index 1fab2d16ebb7ce..cbaa419abae1cc 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.h
@@ -44,9 +44,6 @@ class MKLDNNExecNetwork: public InferenceEngine::ExecutableNetworkThreadSafeDefa
 
     std::shared_ptr<ngraph::Function> GetExecGraphInfo() override;
 
-    INFERENCE_ENGINE_DEPRECATED("Use InferRequest::QueryState instead")
-    std::vector<InferenceEngine::IVariableStateInternal::Ptr> QueryState() override;
-
     void Export(std::ostream& modelStream) override;
 
 protected:
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
index c2a5cd739488a1..d56c81ce79cb07 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
@@ -470,12 +470,34 @@ void MKLDNNGraph::InitEdges() {
         uniqueLayerNames.insert(node->getName());
     }
 
-    for (auto i = 0; i < numberOfEdges; i++) {
-        if (graphEdges[i]->needReorder()) {
-            auto edge = graphEdges[i];
-            bool insertReorder = true;
+    auto insertReorder = [&](MKLDNNEdgePtr& edge, bool isOptimized) {
+        std::string basicLayerName = edge->getParent()->getName() + "_" +
+                                     MKLDNNReorderNode::getReorderArgs(edge->getInputDesc(), edge->getOutputDesc()) + "_" +
+                                     edge->getChild()->getName();
+        std::string layerName = basicLayerName;
+        int idx = 0;
+        while (uniqueLayerNames.find(layerName) != uniqueLayerNames.end()) {
+            idx++;
+            layerName = basicLayerName + "_" + std::to_string(idx);
+        }
+        uniqueLayerNames.insert(layerName);
+
+        // optimized flag indicate that just desc update w/o actual physical memory movement.
+        InsertReorder(edge, layerName, edge->getInputDesc(), edge->getOutputDesc(), isOptimized);
+    };
 
-            // Check if there is a reorder that supports the type conversion
+    auto updateEdge = [&](int& i) {
+        graphEdges.erase(graphEdges.begin() + i);
+        i--;
+        numberOfEdges--;
+    };
+
+    for (auto i = 0; i < numberOfEdges; i++) {
+        auto edge = graphEdges[i];
+        auto reorderStatus = graphEdges[i]->needReorder();
+        if (reorderStatus == MKLDNNEdge::ReorderStatus::Regular) {
+            MKLDNNEdge::ReorderStatus reorderStatusInternal = MKLDNNEdge::ReorderStatus::Regular;
+            // Check if there is a reorder that needs the precision conversion
             if (edge->getInputDesc().getPrecision() != edge->getOutputDesc().getPrecision() &&
                     !isReorderAvailable(edge->getInputDesc(), edge->getOutputDesc(), this->getEngine())) {
                 // If we are here, then we need to insert Convert, because there are no reorders that support such type conversion
@@ -491,29 +513,17 @@ void MKLDNNGraph::InitEdges() {
                 InsertNode(edge, convertNode, true);
 
                 //Check if reorder is still needed
-                if (convertNode->getChildEdgeAt(0)->needReorder()) {
+                reorderStatusInternal = convertNode->getChildEdgeAt(0)->needReorder();
+                if (reorderStatusInternal != MKLDNNEdge::ReorderStatus::No)
                     edge = convertNode->getChildEdgeAt(0);
-                } else {
-                    insertReorder = false;
-                }
             }
-
-            if (insertReorder) {
-                std::string basicLayerName = edge->getParent()->getName() + "_" +
-                                             MKLDNNReorderNode::getReorderArgs(edge->getInputDesc(), edge->getOutputDesc()) + "_" +
-                                             edge->getChild()->getName();
-                std::string layerName = basicLayerName;
-                int idx = 0;
-                while (uniqueLayerNames.find(layerName) != uniqueLayerNames.end()) {
-                    idx++;
-                    layerName = basicLayerName + "_" + std::to_string(idx);
-                }
-                uniqueLayerNames.insert(layerName);
-                InsertReorder(edge, layerName, edge->getInputDesc(), edge->getOutputDesc());
+            if (reorderStatusInternal != MKLDNNEdge::ReorderStatus::No) {
+                insertReorder(edge, reorderStatusInternal == MKLDNNEdge::ReorderStatus::Optimized);
             }
-            graphEdges.erase(graphEdges.begin() + i);
-            i--;
-            numberOfEdges--;
+            updateEdge(i);
+        } else if (reorderStatus == MKLDNNEdge::ReorderStatus::Optimized) {
+            insertReorder(edge, true);
+            updateEdge(i);
         }
     }
 }
@@ -769,13 +779,19 @@ void MKLDNNGraph::PullOutputData(BlobMap &out) {
                              std::accumulate(actualDesc.getDims().begin(), actualDesc.getDims().end(), (size_t)1, std::multiplies<size_t>()) == 1);
         }
 
-        if (out[name]->getTensorDesc().getDims() != intr_blob.getStaticDims() && !isScalarOutput) {
+        const auto &outDims = intr_blob.getStaticDims();
+        if (out[name]->getTensorDesc().getDims() != outDims && !isScalarOutput) {
             // WA: because input/output info initially contains non empty dims, order etc.
             // and setDims (called inside setShape) can't correct modify blocked desc for desc with blocked layout
             if (expectedDesc.getLayout() == Layout::BLOCKED) {
                 expectedDesc = TensorDesc(expectedDesc.getPrecision(), expectedDesc.getLayout());
             }
-            out[name]->setShape(intr_blob.getStaticDims());
+            out[name]->setShape(outDims);
+        }
+
+        // check for empty output blob
+        if (std::any_of(outDims.begin(), outDims.end(), [](const Dim dim) {return dim == 0;})) {
+            return;
         }
 
         auto srcPrec = actualDesc.getPrecision();
@@ -791,7 +807,6 @@ void MKLDNNGraph::PullOutputData(BlobMap &out) {
         // That is the same memory. No need to copy
         if (ext_blob_ptr == intr_blob_ptr) continue;
 
-        const auto &outDims = intr_blob.getStaticDims();
         size_t size_to_copy = intr_blob.GetDescWithType<BlockedMemoryDesc>()->getPaddedElementsCount();
         // TODO: Should we support InferenceEngine::PluginConfigParams::KEY_DYN_BATCH_LIMIT???
         // TODO [DS]: phase 2: should we support this behaviour? Looks obsolete in the dynamic shapes paradigm
@@ -1249,7 +1264,11 @@ void MKLDNNGraph::EnforceBF16() {
         if (node->getType() != Input && node->getType() != Output) {
             for (size_t i = 0; i < node->getOriginalInputsNumber(); i++) {
                 const auto &parent = node->getParentEdgesAtPort(i)[0]->getParent();
-                if (!(parent->getType() == Input && parent->isConstant()) &&       // exclude skipNodes after Constant Inputs
+                /* Skip BF16 enforcement for nodes after Constant Inputs for maintaining precision for fusing.
+                 * Precision conversion to BF16 does automatically, if convolution follows up after Constant Inputs
+                 * and if activation is BF16 */
+                if (!(parent->getType() == Input && parent->isConstant() &&
+                      node->getType() != Concatenation) && // Concatenation node is exception because it doesn't change an accuracy for BF16 activation
                     !(parent->getType() == Input && node->getType() == Eltwise) && // exclude Eltwise after Input since it supports conversion to BF16
                     node->getOriginalInputPrecisionAtPort(i) == Precision::FP32)
                     node->setOriginalInputPrecisionAtPort(i, Precision::BF16);
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
index 639932348f0fdd..8b83701f0779e4 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
@@ -11,6 +11,7 @@
 #include "nodes/mkldnn_concat_node.h"
 #include "nodes/mkldnn_reorder_node.h"
 #include "nodes/mkldnn_conv_node.h"
+#include "nodes/mkldnn_deconv_node.h"
 #include "nodes/mkldnn_bin_conv_node.h"
 #include "nodes/mkldnn_fake_quantize_node.h"
 #include "nodes/mkldnn_mvn_node.h"
@@ -277,7 +278,25 @@ void MKLDNNGraphOptimizer::FuseDeconvolutionAndSimpleOperation(MKLDNNGraph &grap
     auto& graphNodes = graph.GetNodes();
 
     auto isSuitableParentNode = [](MKLDNNNodePtr node) {
-        return node->getType() == Deconvolution && node->getChildEdges().size() == 1;
+        if (node->getType() != Deconvolution || node->getChildEdges().size() != 1)
+            return false;
+        const auto deconv = std::dynamic_pointer_cast<MKLDNNDeconvolutionNode>(node);
+        if (deconv == nullptr)
+            IE_THROW() << "Cannot cast to deconvolution node " << node->getName();
+
+        if (deconv->getAlgorithm() != DeconvolutionCommon) {
+            return true;
+        }
+
+        const auto& strides = deconv->getStride();
+        const auto& kernel = deconv->getWeightDims();
+        // WA oneDNN doesn't support fusing post ops after deconvolution with strides over kernel size
+        bool isSupportedParams = strides[strides.size() - 1] <= kernel[kernel.size() - 1];
+        if (strides.size() > 1)
+            isSupportedParams &= strides[strides.size() - 2] <= kernel[kernel.size() - 2];
+        if (strides.size() > 2)
+            isSupportedParams &= strides[strides.size() - 3] <= kernel[kernel.size() - 3];
+        return isSupportedParams;
     };
 
     auto parent = graphNodes.begin();
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
index af900f7170db91..0498eeefd63824 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp
@@ -60,26 +60,20 @@ void MKLDNNPlugin::MKLDNNInferRequest::CreateInferRequest() {
     // Save all MemoryLayer data tensors. Will use insight about mechanics
     // of MemoryLayer implementation. It uses output edge of MemoryLayer
     // producer as storage for tensor to keep it between infer calls.
-    IE_SUPPRESS_DEPRECATED_START
-    if (execNetwork->_numRequests > 1 || execNetwork->QueryState().size() == 0) {
-        for (auto &node : graph->GetNodes()) {
-            if (node->getType() == MemoryInput) {
-                auto memoryNode = dynamic_cast<MKLDNNMemoryInputNode*>(node.get());
-                auto state_store = memoryNode->getStore();
-                auto state_name = memoryNode->getId();
-
-                // Remove suffix with pair ID. Internal information.
-                auto suffix_idx = state_name.find("/id=");
-                if (suffix_idx != std::string::npos)
-                    state_name = state_name.substr(0, suffix_idx);
-
-                memoryStates.emplace_back(new MKLDNNVariableState(state_name, state_store));
-           }
+    for (auto& node : graph->GetNodes()) {
+        if (node->getType() == MemoryInput) {
+            auto memoryNode = dynamic_cast<MKLDNNMemoryInputNode*>(node.get());
+            auto state_store = memoryNode->getStore();
+            auto state_name = memoryNode->getId();
+
+            // Remove suffix with pair ID. Internal information.
+            auto suffix_idx = state_name.find("/id=");
+            if (suffix_idx != std::string::npos)
+                state_name = state_name.substr(0, suffix_idx);
+
+            memoryStates.emplace_back(new MKLDNNVariableState(state_name, state_store));
         }
-    } else {
-        memoryStates = execNetwork->QueryState();
     }
-    IE_SUPPRESS_DEPRECATED_END
 }
 
 MKLDNNPlugin::MKLDNNInferRequest::~MKLDNNInferRequest() {
@@ -305,7 +299,7 @@ InferenceEngine::Blob::Ptr MKLDNNPlugin::MKLDNNInferRequest::GetBlob(const std::
                                                                                                          desc.getShape().getRank()))
                                                                    : MemoryDescUtils::convertToTensorDesc(desc);
                         const auto &tensorDesc = data->getTensorDesc();
-                        if (expectedTensorDesc.getPrecision() != tensorDesc.getPrecision()) {
+                        if (expectedTensorDesc.getPrecision() != normalizeToSupportedPrecision(tensorDesc.getPrecision())) {
                             IE_THROW(ParameterMismatch)
                                     << "Network input and output use the same name: " << name << " but expect blobs with different precision: "
                                     << tensorDesc.getPrecision() << " for input and " << expectedTensorDesc.getPrecision()
@@ -431,10 +425,18 @@ void MKLDNNPlugin::MKLDNNInferRequest::SetBlob(const std::string& name, const In
                     IE_THROW(ParameterMismatch) << "Failed to set input blob. Blocking descriptor mismatch.";
             }
 
-            const auto &actualDesc = graph->getInputNodeByName(name)->getChildEdgesAtPort(0)[0]->getMemory().getDesc();
-            if (blobDesc.getLayout() != InferenceEngine::Layout::ANY &&
-                actualDesc.isCompatible(MemoryDescUtils::convertToCpuBlockedMemoryDesc(blobDesc)) &&
-                graph->_normalizePreprocMap.find(name) == graph->_normalizePreprocMap.end() && !graph->getProperty().batchLimit) {
+            MemoryDescPtr actualDesc = graph->getInputNodeByName(name)->getBaseMemDescAtOutputPort(0);
+            bool blobHasAnyLayout = blobDesc.getLayout() == InferenceEngine::Layout::ANY;
+            if (!blobHasAnyLayout && !actualDesc->isDefined()) {
+                // we must define desc for dynamic case
+                // otherwise we got incorrect check on shape compatibility inside isCompatible
+                // because lower and upper bound will be compared
+                actualDesc = actualDesc->cloneWithNewDims(blobDesc.getLayout() == InferenceEngine::Layout::SCALAR ? InferenceEngine::SizeVector{1} :
+                                                                                                                    blobDesc.getDims());
+            }
+            if (!blobHasAnyLayout &&
+                actualDesc->isCompatible(MemoryDescUtils::convertToCpuBlockedMemoryDesc(blobDesc)) &&
+                    graph->_normalizePreprocMap.find(name) == graph->_normalizePreprocMap.end() && !graph->getProperty().batchLimit) {
                 externalPtr[name] = data->buffer();
             } else if (externalPtr.find(name) != externalPtr.end()) {
                 externalPtr.erase(name);
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.h b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
index 65e175b32a8939..7aad502bd092b7 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
@@ -725,7 +725,6 @@ class MKLDNNNode {
     }
 
     std::vector<VectorDims> lastInputDims = {};
-
     std::shared_ptr<ngraph::Node> opToShapeInfer;
 
 private:
@@ -782,8 +781,7 @@ class MKLDNNNode::NodesFactory : public openvino::cc::Factory<Type,
                                                         const mkldnn::engine &,
                                                         MKLDNNWeightsSharing::Ptr &)> {
 public:
-    NodesFactory()
-        : Factory("NodesFactory") {}
+    NodesFactory();
 
     MKLDNNNode* create(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng,
                        const MKLDNNExtensionManager::Ptr& extMgr, MKLDNNWeightsSharing::Ptr &w_cache);
@@ -797,15 +795,6 @@ struct MKLDNNNodeImpl : public MKLDNNNodeType {
     }
 };
 
-#define REG_MKLDNN_CONCAT3_(X, Y, Z) X ## Y ## Z
-#define REG_MKLDNN_CONCAT3(X, Y, Z) REG_MKLDNN_CONCAT3_(X, Y, Z)
-
-#define REG_MKLDNN_PRIM_FOR(__prim, __type)                                                 \
-static struct REG_MKLDNN_CONCAT3(Registrar4, __prim, __LINE__) {                            \
-    REG_MKLDNN_CONCAT3(Registrar4, __prim, __LINE__)() {                                    \
-        MKLDNNNode::factory()                                                               \
-            .registerNodeIfRequired(MKLDNNPlugin, __prim, __type, MKLDNNNodeImpl<__prim>);  \
-    }                                                                                       \
-} REG_MKLDNN_CONCAT3(_reg_, __prim, __LINE__);
-
 }  // namespace MKLDNNPlugin
+
+#define REG_MKLDNN_PRIM_FOR(__prim, __type)
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
new file mode 100644
index 00000000000000..f7f8aa2c7c7b3d
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_nodes_factory.cpp
@@ -0,0 +1,171 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "nodes/mkldnn_reference_node.h"
+#include "nodes/mkldnn_shapeof.h"
+#include "nodes/mkldnn_batch_to_space_node.h"
+#include "nodes/mkldnn_multiclass_nms.hpp"
+#include "nodes/mkldnn_adaptive_pooling.h"
+#include "nodes/mkldnn_conv_node.h"
+#include "nodes/mkldnn_roi_align_node.h"
+#include "nodes/mkldnn_lrn_node.h"
+#include "nodes/mkldnn_generic_node.h"
+#include "nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h"
+#include "nodes/mkldnn_eltwise_node.h"
+#include "nodes/mkldnn_reorg_yolo_node.h"
+#include "nodes/mkldnn_pooling_node.h"
+#include "nodes/mkldnn_transpose_node.h"
+#include "nodes/mkldnn_grn_node.h"
+#include "nodes/mkldnn_interpolate_node.h"
+#include "nodes/mkldnn_experimental_detectron_detection_output_node.h"
+#include "nodes/mkldnn_roll_node.h"
+#include "nodes/mkldnn_fake_quantize_node.h"
+#include "nodes/mkldnn_embedding_segments_sum_node.h"
+#include "nodes/mkldnn_region_yolo_node.h"
+#include "nodes/mkldnn_matmul_node.h"
+#include "nodes/mkldnn_detection_output_node.h"
+#include "nodes/mkldnn_reverse_sequence_node.h"
+#include "nodes/mkldnn_pad_node.h"
+#include "nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h"
+#include "nodes/mkldnn_reshape_node.h"
+#include "nodes/mkldnn_psroi_pooling_node.h"
+#include "nodes/mkldnn_memory_node.hpp"
+#include "nodes/mkldnn_bin_conv_node.h"
+#include "nodes/mkldnn_gather_elements_node.h"
+#include "nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h"
+#include "nodes/mkldnn_tile_node.h"
+#include "nodes/mkldnn_math_node.h"
+#include "nodes/mkldnn_normalize_node.h"
+#include "nodes/mkldnn_proposal_node.h"
+#include "nodes/mkldnn_tensoriterator_node.h"
+#include "nodes/mkldnn_fullyconnected_node.h"
+#include "nodes/mkldnn_extract_image_patches_node.h"
+#include "nodes/mkldnn_ctc_loss_node.h"
+#include "nodes/mkldnn_reorder_node.h"
+#include "nodes/mkldnn_gather_nd_node.h"
+#include "nodes/mkldnn_shuffle_channels_node.h"
+#include "nodes/mkldnn_bucketize_node.h"
+#include "nodes/mkldnn_space_to_depth_node.h"
+#include "nodes/mkldnn_concat_node.h"
+#include "nodes/mkldnn_softmax_node.h"
+#include "nodes/mkldnn_space_to_batch_node.h"
+#include "nodes/mkldnn_select_node.h"
+#include "nodes/mkldnn_topk_node.h"
+#include "nodes/mkldnn_broadcast_node.h"
+#include "nodes/mkldnn_matrix_nms_node.h"
+#include "nodes/mkldnn_mvn_node.h"
+#include "nodes/mkldnn_gather_node.h"
+#include "nodes/mkldnn_scatter_update_node.h"
+#include "nodes/mkldnn_gather_tree_node.h"
+#include "nodes/mkldnn_def_conv_node.h"
+#include "nodes/mkldnn_embedding_bag_offset_sum_node.h"
+#include "nodes/mkldnn_deconv_node.h"
+#include "nodes/mkldnn_roi_pooling_node.h"
+#include "nodes/mkldnn_range_node.h"
+#include "nodes/mkldnn_split_node.h"
+#include "nodes/mkldnn_one_hot_node.h"
+#include "nodes/mkldnn_log_softmax_node.h"
+#include "nodes/mkldnn_strided_slice_node.h"
+#include "nodes/mkldnn_dft_node.h"
+#include "nodes/mkldnn_non_max_suppression_node.h"
+#include "nodes/mkldnn_convert_node.h"
+#include "nodes/mkldnn_rnn.h"
+#include "nodes/mkldnn_experimental_detectron_topkrois_node.h"
+#include "nodes/mkldnn_cum_sum_node.h"
+#include "nodes/mkldnn_depth_to_space_node.h"
+#include "nodes/mkldnn_input_node.h"
+#include "nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h"
+#include "nodes/mkldnn_embedding_bag_packed_sum_node.h"
+#include "nodes/mkldnn_reduce_node.h"
+#include "nodes/mkldnn_if_node.h"
+#include "nodes/mkldnn_ctc_greedy_decoder_node.h"
+
+#define MKLDNN_NODE(__prim, __type) \
+    registerNodeIfRequired(MKLDNNPlugin, __prim, __type, MKLDNNNodeImpl<__prim>)
+
+MKLDNNPlugin::MKLDNNNode::NodesFactory::NodesFactory()
+    : Factory("NodesFactory") {
+    MKLDNN_NODE(MKLDNNGenericNode, Generic);
+    MKLDNN_NODE(MKLDNNCumSumNode, CumSum);
+    MKLDNN_NODE(MKLDNNConvolutionNode, Convolution);
+    MKLDNN_NODE(MKLDNNSpaceToBatchNode, SpaceToBatch);
+    MKLDNN_NODE(MKLDNNLrnNode, Lrn);
+    MKLDNN_NODE(MKLDNNBatchToSpaceNode, BatchToSpace);
+    MKLDNN_NODE(MKLDNNNormalizeL2Node, NormalizeL2);
+    MKLDNN_NODE(MKLDNNIfNode, If);
+    MKLDNN_NODE(MKLDNNProposalNode, Proposal);
+    MKLDNN_NODE(MKLDNNBroadcastNode, Broadcast);
+    MKLDNN_NODE(MKLDNNExperimentalDetectronTopKROIsNode, ExperimentalDetectronTopKROIs);
+    MKLDNN_NODE(MKLDNNReorderNode, Reorder);
+    MKLDNN_NODE(MKLDNNBinaryConvolutionNode, BinaryConvolution);
+    MKLDNN_NODE(MKLDNNMatrixNmsNode, MatrixNms);
+    MKLDNN_NODE(MKLDNNAdaptivePoolingNode, AdaptivePooling);
+    MKLDNN_NODE(MKLDNNPoolingNode, Pooling);
+    MKLDNN_NODE(MKLDNNReduceNode, Reduce);
+    MKLDNN_NODE(MKLDNNEltwiseNode, Eltwise);
+    MKLDNN_NODE(MKLDNNSoftMaxNode, Softmax);
+    MKLDNN_NODE(MKLDNNEmbeddingBagPackedSumNode, EmbeddingBagPackedSum);
+    MKLDNN_NODE(MKLDNNInputNode, Input);
+    MKLDNN_NODE(MKLDNNInputNode, Output);
+    MKLDNN_NODE(MKLDNNMemoryInputNode, MemoryInput);
+    MKLDNN_NODE(MKLDNNMemoryOutputNode, MemoryOutput);
+    MKLDNN_NODE(MKLDNNTileNode, Tile);
+    MKLDNN_NODE(MKLDNNDFTNode, DFT);
+    MKLDNN_NODE(MKLDNNGatherTreeNode, GatherTree);
+    MKLDNN_NODE(MKLDNNSpaceToDepthNode, SpaceToDepth);
+    MKLDNN_NODE(MKLDNNFullyConnectedNode, FullyConnected);
+    MKLDNN_NODE(MKLDNNCTCGreedyDecoderNode, CTCGreedyDecoder);
+    MKLDNN_NODE(MKLDNNTransposeNode, Transpose);
+    MKLDNN_NODE(MKLDNNDeformableConvolutionNode, DeformableConvolution);
+    MKLDNN_NODE(MKLDNNReorgYoloNode, ReorgYolo);
+    MKLDNN_NODE(MKLDNNEmbeddingSegmentsSumNode, EmbeddingSegmentsSum);
+    MKLDNN_NODE(MKLDNNSelectNode, Select);
+    MKLDNN_NODE(MKLDNNShapeOfNode, ShapeOf);
+    MKLDNN_NODE(MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode, ExperimentalDetectronGenerateProposalsSingleImage);
+    MKLDNN_NODE(MKLDNNReverseSequenceNode, ReverseSequence);
+    MKLDNN_NODE(MKLDNNFakeQuantizeNode, FakeQuantize);
+    MKLDNN_NODE(MKLDNNNonMaxSuppressionNode, NonMaxSuppression);
+    MKLDNN_NODE(MKLDNNExperimentalDetectronPriorGridGeneratorNode, ExperimentalDetectronPriorGridGenerator);
+    MKLDNN_NODE(MKLDNNGatherNDNode, GatherND);
+    MKLDNN_NODE(MKLDNNLogSoftmaxNode, LogSoftmax);
+    MKLDNN_NODE(MKLDNNPSROIPoolingNode, PSROIPooling);
+    MKLDNN_NODE(MKLDNNRNN, RNNCell);
+    MKLDNN_NODE(MKLDNNRNN, RNNSeq);
+    MKLDNN_NODE(MKLDNNCTCLossNode, CTCLoss);
+    MKLDNN_NODE(MKLDNNSplitNode, Split);
+    MKLDNN_NODE(MKLDNNDetectionOutputNode, DetectionOutput);
+    MKLDNN_NODE(MKLDNNGatherElementsNode, GatherElements);
+    MKLDNN_NODE(MKLDNNCTCGreedyDecoderSeqLenNode, CTCGreedyDecoderSeqLen);
+    MKLDNN_NODE(MKLDNNBucketizeNode, Bucketize);
+    MKLDNN_NODE(MKLDNNExperimentalDetectronROIFeatureExtractorNode, ExperimentalDetectronROIFeatureExtractor);
+    MKLDNN_NODE(MKLDNNMathNode, Math);
+    MKLDNN_NODE(MKLDNNMultiClassNmsNode, MulticlassNms);
+    MKLDNN_NODE(MKLDNNConvertNode, Convert);
+    MKLDNN_NODE(MKLDNNEmbeddingBagOffsetSumNode, EmbeddingBagOffsetsSum);
+    MKLDNN_NODE(MKLDNNRollNode, Roll);
+    MKLDNN_NODE(MKLDNNPadNode, Pad);
+    MKLDNN_NODE(MKLDNNReshapeNode, Reshape);
+    MKLDNN_NODE(MKLDNNMVNNode, MVN);
+    MKLDNN_NODE(MKLDNNMatMulNode, MatMul);
+    MKLDNN_NODE(MKLDNNScatterUpdateNode, ScatterUpdate);
+    MKLDNN_NODE(MKLDNNScatterUpdateNode, ScatterElementsUpdate);
+    MKLDNN_NODE(MKLDNNScatterUpdateNode, ScatterNDUpdate);
+    MKLDNN_NODE(MKLDNNInterpolateNode, Interpolate);
+    MKLDNN_NODE(MKLDNNROIPoolingNode, ROIPooling);
+    MKLDNN_NODE(MKLDNNTensorIteratorNode, TensorIterator);
+    MKLDNN_NODE(MKLDNNConcatNode, Concatenation);
+    MKLDNN_NODE(MKLDNNExtractImagePatchesNode, ExtractImagePatches);
+    MKLDNN_NODE(MKLDNNOneHotNode, OneHot);
+    MKLDNN_NODE(MKLDNNExperimentalDetectronDetectionOutputNode, ExperimentalDetectronDetectionOutput);
+    MKLDNN_NODE(MKLDNNROIAlignNode, ROIAlign);
+    MKLDNN_NODE(MKLDNNShuffleChannelsNode, ShuffleChannels);
+    MKLDNN_NODE(MKLDNNDepthToSpaceNode, DepthToSpace);
+    MKLDNN_NODE(MKLDNNDeconvolutionNode, Deconvolution);
+    MKLDNN_NODE(MKLDNNGatherNode, Gather);
+    MKLDNN_NODE(MKLDNNRegionYoloNode, RegionYolo);
+    MKLDNN_NODE(MKLDNNRangeNode, Range);
+    MKLDNN_NODE(MKLDNNTopKNode, TopK);
+    MKLDNN_NODE(MKLDNNStridedSliceNode, StridedSlice);
+    MKLDNN_NODE(MKLDNNGRNNode, GRN);
+}
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index e78257b8b089b4..a9005f628f64a8 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -6,6 +6,7 @@
 #include "mkldnn_plugin.h"
 #include "mkldnn_extension_mngr.h"
 #include "mkldnn_weights_cache.hpp"
+#include "mkldnn_extension.h"
 #include "mkldnn_itt.h"
 #include "mkldnn_serialize.h"
 
@@ -29,6 +30,8 @@
 #include <transformations/common_optimizations/weights_dequantize_to_fake_quantize.hpp>
 #include "transformations/common_optimizations/convert_quantize_dequantize.hpp"
 #include <transformations/common_optimizations/nop_elimination.hpp>
+#include <transformations/common_optimizations/wrap_interpolate_into_transposes.hpp>
+#include <transformations/common_optimizations/transpose_sinking.hpp>
 #include <transformations/op_conversions/convert_depth_to_space.hpp>
 #include <transformations/op_conversions/convert_shuffle_channels3.hpp>
 #include <transformations/op_conversions/convert_space_to_depth.hpp>
@@ -115,7 +118,7 @@ using namespace InferenceEngine;
 
 Engine::Engine() {
     _pluginName = "CPU";
-    extensionManager->AddExtension(std::make_shared<Extensions::Cpu::MKLDNNExtensions>());
+    extensionManager->AddExtension(std::make_shared<MKLDNNPlugin::MKLDNNExtension>());
 }
 
 Engine::~Engine() {
@@ -135,7 +138,6 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
         manager.register_pass<ngraph::pass::DisableConvertConstantFoldingOnConstPath>(
             std::vector<ngraph::element::Type>{ ngraph::element::i8, ngraph::element::u8, ngraph::element::i4, ngraph::element::u4 });
     }
-
     auto get_convert_precisions = []() {
         precisions_array array = {
             {ngraph::element::i64,     ngraph::element::i32},
@@ -159,6 +161,8 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
     static const auto precisions = get_convert_precisions();
 
     manager.register_pass<ngraph::pass::CommonOptimizations>();
+    manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    manager.register_pass<ngraph::pass::TransposeSinking>();
     manager.register_pass<ngraph::pass::ConvertRNNSequenceToTensorIterator>();
     manager.register_pass<ngraph::pass::ConvertGRUSequenceToTensorIterator>();
     manager.register_pass<ngraph::pass::ConvertLSTMSequenceToTensorIterator>();
@@ -298,6 +302,7 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
                 return node->input_value(0).get_partial_shape().rank().get_length() <= 5;
             });
 
+    // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
     pass_config->set_callback<ngraph::pass::ConvertNMSToNMSIEInternal>(
             [](const_node_ptr &node) -> bool {
                 for (size_t i = 0; i < node->get_output_size(); i++) {
@@ -441,8 +446,10 @@ Engine::LoadExeNetworkImpl(const InferenceEngine::CNNNetwork &network, const std
     InferenceEngine::InputsDataMap _networkInputs = network.getInputsInfo();
     for (const auto &ii : _networkInputs) {
         auto input_precision = ii.second->getPrecision();
-        if (input_precision != InferenceEngine::Precision::FP32 &&
+        if (input_precision != InferenceEngine::Precision::FP64 &&
+            input_precision != InferenceEngine::Precision::FP32 &&
             input_precision != InferenceEngine::Precision::I32 &&
+            input_precision != InferenceEngine::Precision::U32 &&
             input_precision != InferenceEngine::Precision::U16 &&
             input_precision != InferenceEngine::Precision::I16 &&
             input_precision != InferenceEngine::Precision::I8 &&
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_serialize.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_serialize.cpp
index ae4044279a3732..36729dfb17d8a6 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_serialize.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_serialize.cpp
@@ -118,7 +118,9 @@ void CNNNetworkSerializer::operator << (const CNNNetwork & network) {
     };
 
     // Serialize to old representation in case of old API
+    OPENVINO_SUPPRESS_DEPRECATED_START
     ov::pass::StreamSerialize serializer(_ostream, getCustomOpSets(), serializeInputsAndOutputs);
+    OPENVINO_SUPPRESS_DEPRECATED_END
     serializer.run_on_function(std::const_pointer_cast<ngraph::Function>(network.getFunction()));
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.cpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.cpp
index 3affd3ab47e7a2..7467f9103d63db 100644
--- a/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.cpp
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/op/fully_connected.cpp
@@ -9,7 +9,7 @@ MKLDNNPlugin::FullyConnectedNode::FullyConnectedNode(const ngraph::Output<Node>&
                                                      const ngraph::Rank& output_rank,
                                                      const ngraph::element::Type output_type)
     : Op({A, B}), m_output_rank(output_rank), m_output_type(output_type) {
-    constructor_validate_and_infer_types();
+    validate_and_infer_types();
 }
 
 MKLDNNPlugin::FullyConnectedNode::FullyConnectedNode(const ngraph::Output<Node>& A,
@@ -18,7 +18,7 @@ MKLDNNPlugin::FullyConnectedNode::FullyConnectedNode(const ngraph::Output<Node>&
                                                      const ngraph::Rank& output_rank,
                                                      const ngraph::element::Type output_type)
     : Op({A, B, C}), m_output_rank(output_rank), m_output_type(output_type) {
-    constructor_validate_and_infer_types();
+    validate_and_infer_types();
 }
 
 std::shared_ptr<ngraph::Node> MKLDNNPlugin::FullyConnectedNode::clone_with_new_inputs(const ngraph::OutputVector& new_args) const {
@@ -40,13 +40,6 @@ void MKLDNNPlugin::FullyConnectedNode::validate_and_infer_types() {
         input_size,
         ", expected: 2 or 3.");
 
-    const auto output_size = get_output_size();
-    NODE_VALIDATION_CHECK(this,
-        output_size == 1,
-        "Number of outputs is incorrect. Current value is: ",
-        output_size,
-        ", expected: 1.");
-
     // Weights shape: [O, I1, ..., Im];
     // O - output channels dimensions, Ik - input channels dimensions
     const auto weights_pshape = get_input_partial_shape(1);
@@ -101,10 +94,7 @@ void MKLDNNPlugin::FullyConnectedNode::validate_and_infer_types() {
 }
 
 bool MKLDNNPlugin::FullyConnectedNode::visit_attributes(ngraph::AttributeVisitor &visitor) {
-    if (m_output_rank.is_static()) {
-        std::int64_t value = m_output_rank.get_length();
-        visitor.on_attribute("out-rank", value);
-    }
+    visitor.on_attribute("out-rank", m_output_rank);
     visitor.on_attribute("out-type", m_output_type);
     return true;
 }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp b/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp
index 5b47b476b0a565..8763b551af9597 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/common/cpu_convert.cpp
@@ -103,7 +103,13 @@ void cpu_convert(const void *srcPtr, void *dstPtr, Precision srcPrc, Precision d
     MKLDNN_CVT(BF16, I64), MKLDNN_CVT(BF16, FP32), MKLDNN_CVT(BF16, BOOL),
     MKLDNN_CVT(BOOL, U8),  MKLDNN_CVT(BOOL, I8),   MKLDNN_CVT(BOOL, U16),
     MKLDNN_CVT(BOOL, I16), MKLDNN_CVT(BOOL, I32),  MKLDNN_CVT(BOOL, U64),
-    MKLDNN_CVT(BOOL, I64), MKLDNN_CVT(BOOL, FP32), MKLDNN_CVT(BOOL, BF16));
+    MKLDNN_CVT(BOOL, I64), MKLDNN_CVT(BOOL, FP32), MKLDNN_CVT(BOOL, BF16),
+    MKLDNN_CVT(FP64, U8),  MKLDNN_CVT(FP64, I8),   MKLDNN_CVT(FP64, U16),
+    MKLDNN_CVT(FP64, I16), MKLDNN_CVT(FP64, I32),  MKLDNN_CVT(FP64, U64),
+    MKLDNN_CVT(FP64, I64), MKLDNN_CVT(FP64, FP32), MKLDNN_CVT(FP64, BF16), MKLDNN_CVT(FP64, BOOL),
+    MKLDNN_CVT(U32, U8),  MKLDNN_CVT(U32, I8),   MKLDNN_CVT(U32, U16),
+    MKLDNN_CVT(U32, I16), MKLDNN_CVT(U32, I32),  MKLDNN_CVT(U32, U64),
+    MKLDNN_CVT(U32, I64), MKLDNN_CVT(U32, FP32), MKLDNN_CVT(U32, BF16), MKLDNN_CVT(U32, BOOL));
 
     if (!ctx.converted)
         IE_THROW() << "cpu_convert can't convert from: " << srcPrc << " precision to: " << dstPrc;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/list.cpp b/inference-engine/src/mkldnn_plugin/nodes/list.cpp
deleted file mode 100644
index c4d7ce8970e01a..00000000000000
--- a/inference-engine/src/mkldnn_plugin/nodes/list.cpp
+++ /dev/null
@@ -1,30 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "nodes/list.hpp"
-
-namespace InferenceEngine {
-namespace Extensions {
-namespace Cpu {
-
-#define FACTORY_DECLARATION(__prim, __type) \
-    void __prim ## __type(MKLDNNExtensions * extInstance)
-
-#define FACTORY_CALL(__prim, __type) \
-    __prim ## __type(this)
-
-#define MKLDNN_EXTENSION_NODE(__prim, __type) FACTORY_DECLARATION(__prim, __type)
-# include "list_tbl.hpp"
-#undef MKLDNN_EXTENSION_NODE
-
-MKLDNNExtensions::MKLDNNExtensions()
-    : layersFactory("LayersFactory") {
-    #define MKLDNN_EXTENSION_NODE(__prim, __type) FACTORY_CALL(__prim, __type)
-    # include "list_tbl.hpp"
-    #undef MKLDNN_EXTENSION_NODE
-}
-
-}  // namespace Cpu
-}  // namespace Extensions
-}  // namespace InferenceEngine
diff --git a/inference-engine/src/mkldnn_plugin/nodes/list.hpp b/inference-engine/src/mkldnn_plugin/nodes/list.hpp
index 8cbe1db6ab38af..ffb0743e0a3cf2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/list.hpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/list.hpp
@@ -43,32 +43,14 @@ class ILayerImplFactory {
 namespace Extensions {
 namespace Cpu {
 
-using ext_factory = std::function<InferenceEngine::ILayerImplFactory*(const std::shared_ptr<ngraph::Node>& op)>;
-
-struct ExtensionsHolder {
-    std::map<std::string, ext_factory> list;
-};
-
+// TODO: remove this
 class MKLDNNExtensions : public IExtension {
 public:
     MKLDNNExtensions();
 
-    virtual StatusCode
-    getPrimitiveTypes(char**& types, unsigned int& size, ResponseDesc* resp) noexcept {
-        collectTypes(types, size);
-        return OK;
-    }
-
     virtual StatusCode
     getFactoryFor(ILayerImplFactory*& factory, const std::shared_ptr<ngraph::Node>& op, ResponseDesc* resp) noexcept {
-        using namespace MKLDNNPlugin;
-        factory = layersFactory.createNodeIfRegistered(MKLDNNPlugin, op->get_type_name(), op);
-        if (!factory) {
-            std::string errorMsg = std::string("Factory for ") + op->get_type_name() + " wasn't found!";
-            errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
-            return NOT_FOUND;
-        }
-        return OK;
+        return NOT_FOUND;
     }
 
     void GetVersion(const InferenceEngine::Version*& versionInfo) const noexcept override {
@@ -82,24 +64,6 @@ class MKLDNNExtensions : public IExtension {
     }
 
     void Unload() noexcept override {}
-
-    using LayersFactory = openvino::cc::Factory<
-                                std::string,
-                                InferenceEngine::ILayerImplFactory*(const std::shared_ptr<ngraph::Node>& op)>;
-
-    LayersFactory layersFactory;
-
-private:
-    void collectTypes(char**& types, unsigned int& size) const {
-        types = new char *[layersFactory.size()];
-        unsigned count = 0;
-        layersFactory.foreach([&](std::pair<std::string, LayersFactory::builder_t> const &builder) {
-            types[count] = new char[builder.first.size() + 1];
-            std::copy(builder.first.begin(), builder.first.end(), types[count]);
-            types[count][builder.first.size() ] = '\0';
-        });
-        size = count;
-    }
 };
 
 }  // namespace Cpu
diff --git a/inference-engine/src/mkldnn_plugin/nodes/list_tbl.hpp b/inference-engine/src/mkldnn_plugin/nodes/list_tbl.hpp
deleted file mode 100644
index d005c1e16b630d..00000000000000
--- a/inference-engine/src/mkldnn_plugin/nodes/list_tbl.hpp
+++ /dev/null
@@ -1,9 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#ifndef MKLDNN_EXTENSION_NODE
-# warning "MKLDNN_EXTENSION_NODE is not defined"
-# define MKLDNN_EXTENSION_NODE(__prim, __type)
-#endif
-
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp
index 43b8fa708ad589..a4811c30affdf4 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.cpp
@@ -2,12 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <cmath>
 #include <vector>
 #include <string>
 #include <mkldnn_types.h>
 #include "ie_parallel.hpp"
-#include "utils/bfloat16.hpp"
 #include <mkldnn_selective_build.h>
 #include "mkldnn_batch_to_space_node.h"
 #include <nodes/common/blocked_desc_creator.h>
@@ -18,10 +16,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNBatchToSpaceNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto batchToSpace = std::dynamic_pointer_cast<const ngraph::opset2::BatchToSpace>(op);
         if (!batchToSpace) {
             errorMessage = "Only opset2 BatchToSpace operation is supported";
@@ -48,11 +42,11 @@ MKLDNNBatchToSpaceNode::MKLDNNBatchToSpaceNode(const std::shared_ptr<ngraph::Nod
 
     errorPrefix = "BatchToSpace layer with name '" + op->get_friendly_name() + "'";
 
-    if (op->get_input_size() != 4 || op->get_output_size() != 1)
+    if (inputShapes.size() != 4 || outputShapes.size() != 1)
         IE_THROW() << errorPrefix << " has incorrect number of input or output edges!";
 
-    inDims = getInputShapeAtPort(0).getStaticDims();
-    outDims = getOutputShapeAtPort(0).getStaticDims();
+    const auto &inDims = getInputShapeAtPort(0).getDims();
+    const auto &outDims = getOutputShapeAtPort(0).getDims();
     if (inDims.size() < 4 || inDims.size() > 5)
         IE_THROW() << errorPrefix << " has unsupported 'data' input rank: " << inDims.size();
     if (inDims.size() != outDims.size())
@@ -66,6 +60,7 @@ void MKLDNNBatchToSpaceNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
+    const auto &inDims = getInputShapeAtPort(0).getDims();
     const auto precision = getOriginalInputPrecisionAtPort(0);
     const std::set<size_t> supported_precision_sizes = {1, 2, 4, 8};
     if (supported_precision_sizes.find(precision.size()) == supported_precision_sizes.end())
@@ -83,7 +78,7 @@ void MKLDNNBatchToSpaceNode::initSupportedPrimitiveDescriptors() {
                           {LayoutType::ncsp}},
                          {{LayoutType::ncsp, precision}},
                          impl_desc_type::ref_any);
-    if (inDims[1] % 8 == 0) {
+    if (inDims[1] != Shape::UNDEFINED_DIM && inDims[1] % 8 == 0) {
         addSupportedPrimDesc({{LayoutType::nCsp8c, precision},
                               {LayoutType::ncsp},
                               {LayoutType::ncsp},
@@ -91,7 +86,7 @@ void MKLDNNBatchToSpaceNode::initSupportedPrimitiveDescriptors() {
                              {{LayoutType::nCsp8c, precision}},
                              impl_desc_type::ref_any);
     }
-    if (inDims[1] % 16 == 0) {
+    if (inDims[1] != Shape::UNDEFINED_DIM && inDims[1] % 16 == 0) {
         addSupportedPrimDesc({{LayoutType::nCsp16c, precision},
                               {LayoutType::ncsp},
                               {LayoutType::ncsp},
@@ -116,6 +111,9 @@ void MKLDNNBatchToSpaceNode::batchToSpaceKernel() {
     const auto *srcData = reinterpret_cast<const T *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
     auto *dstData = reinterpret_cast<T *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
 
+    const auto &inDims = getParentEdgesAtPort(0)[0]->getMemory().getStaticDims();
+    const auto &outDims = getChildEdgesAtPort(0)[0]->getMemory().getStaticDims();
+
     auto srcDesc = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>();
 
     const bool blocked = srcDesc->hasLayoutType(LayoutType::nCsp8c) || srcDesc->hasLayoutType(LayoutType::nCsp16c);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h
index e262108ded04d7..0c0ef5ee06c486 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_batch_to_space_node.h
@@ -22,6 +22,9 @@ class MKLDNNBatchToSpaceNode : public MKLDNNNode {
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needPrepareParams() const override { return false; };
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
@@ -29,8 +32,6 @@ class MKLDNNBatchToSpaceNode : public MKLDNNNode {
     void batchToSpaceKernel();
 
 private:
-    InferenceEngine::SizeVector inDims;
-    InferenceEngine::SizeVector outDims;
     std::vector<size_t> blockShapeIn;
     std::vector<size_t> cropsBeginIn;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
index e03c006fe63587..3b0e1e481c8282 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
@@ -33,11 +33,6 @@ namespace {
 
 bool MKLDNNConcatNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-
         const auto concatOp = ngraph::as_type_ptr<const ngraph::op::v0::Concat>(op);
         if (!concatOp) {
             errorMessage = "Node is not an instance of the Concat operation.";
@@ -66,14 +61,14 @@ MKLDNNConcatNode::MKLDNNConcatNode(const std::shared_ptr<ngraph::Node>& op, cons
 }
 
 void MKLDNNConcatNode::getSupportedDescriptors() {
-    auto& firstParentDims = getInputShapeAtPort(0).getStaticDims();
+    const auto& firstParentDims = getInputShapeAtPort(0).getDims();
     for (size_t i = 1; i < getParentEdges().size(); i++) {
-        auto& dims = getInputShapeAtPort(i).getStaticDims();
+        const auto& dims = getInputShapeAtPort(i).getDims();
         bool incorrectDims = false;
         for (size_t j = 0; j < firstParentDims.size(); j++) {
             if (j == axis)
                 continue;
-            if (dims.size() != firstParentDims.size() || firstParentDims[j] != dims[j]) {
+            if (dims.size() != firstParentDims.size() || !dimsEqualWeak(firstParentDims[j], dims[j])) {
                 incorrectDims = true;
                 break;
             }
@@ -84,9 +79,12 @@ void MKLDNNConcatNode::getSupportedDescriptors() {
     }
 
     // we need the first dims before axis to be 1 to avoid the reorder in the edge between the first parent and this concat
-    const auto& childDims = outputShapes[0].getStaticDims();
-    if (std::all_of(childDims.begin(), childDims.begin() + axis, [](size_t dim) { return  dim == 1; }))
-        canBeInPlace = true;
+    // TODO [DS]: inplace
+    if (!isDynamicNode()) {
+        const auto& childDims = outputShapes[0].getStaticDims();
+        if (std::all_of(childDims.begin(), childDims.begin() + axis, [](size_t dim) { return  dim == 1; }))
+            canBeInPlace = true;
+    }
 }
 
 void MKLDNNConcatNode::initSupportedPrimitiveDescriptors() {
@@ -116,14 +114,14 @@ void MKLDNNConcatNode::initSupportedPrimitiveDescriptors() {
     // check if blocked layouts are available the channels size should be evenly divided by the block size to avoid slow oneDNN ref implementation
     if (dstShape.getRank() > channelAxis) {
         for (auto item : { std::make_pair(8lu, LayoutType::nCsp8c), std::make_pair(16lu, LayoutType::nCsp16c)}) {
-            const VectorDims &blkDims = dstShape.getStaticDims();
-            if (blkDims[channelAxis] % item.first)
+            const VectorDims &blkDims = dstShape.getDims();
+            if (blkDims[channelAxis] == Shape::UNDEFINED_DIM || blkDims[channelAxis] % item.first != 0)
                 continue;
 
             bool blocked = true;
             for (size_t i = 0; i < getParentEdges().size(); i++) {
-                auto& srcDims = getInputShapeAtPort(i).getStaticDims();
-                if (srcDims[channelAxis] % item.first) {
+                auto& srcDims = getInputShapeAtPort(i).getDims();
+                if (srcDims[channelAxis] == Shape::UNDEFINED_DIM || srcDims[channelAxis] % item.first != 0) {
                     blocked = false;
                     break;
                 }
@@ -153,7 +151,13 @@ void MKLDNNConcatNode::initSupportedPrimitiveDescriptors() {
         for (size_t i = 0; i < getParentEdges().size(); ++i) {
             config.inConfs[i].inPlace = -1;
             config.inConfs[i].constant = false;
-            config.inConfs[i].desc = itr->second->createDesc(inputPrecision, getInputShapeAtPort(i)).cloneWithUndefStridesAndOffset();
+            auto desc = itr->second->createSharedDesc(inputPrecision, getInputShapeAtPort(i));
+            // TODO [DS]: inplace
+            if (isDynamicNode()) {
+                config.inConfs[i].desc = desc;
+            } else {
+                config.inConfs[i].desc = desc->cloneWithUndefStridesAndOffset();
+            }
         }
         supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::ref);
         if (itr->first != LayoutType::nspc) {
@@ -167,11 +171,12 @@ void MKLDNNConcatNode::initSupportedPrimitiveDescriptors() {
             return;
         }
     }
+
+    // TODO [DS]: inplace
     if (!canBeInPlace)
         return;
 
     // Optimized inplace case
-
     for (auto refPdIndex : pdIndexesToReuse) {
         const auto& refConfig = supportedPrimitiveDescriptors[refPdIndex].getConfig();
         auto config = refConfig;
@@ -259,7 +264,7 @@ void MKLDNNConcatNode::selectOptimalPrimitiveDescriptor() {
     }
 
     size_t maxCount = 0;
-    auto outDims = getOutputShapeAtPort(0).getStaticDims();
+    const auto &outDims = getOutputShapeAtPort(0).getDims();
     LayoutType convertTo = LayoutType::ncsp;
     for (auto &it : formatFrequency) {
         if (it.second > maxCount) {
@@ -276,13 +281,13 @@ void MKLDNNConcatNode::selectOptimalPrimitiveDescriptor() {
 
     for (auto& item : { std::make_pair(8lu, LayoutType::nCsp8c), std::make_pair(16lu, LayoutType::nCsp16c) }) {
         if (convertTo == item.second) {
-            if (outDims[1] % item.first != 0) {
+            if (outDims[channelAxis] == Shape::UNDEFINED_DIM || outDims[1] % item.first != 0) {
                 convertTo = LayoutType::ncsp;
                 break;
             }
             for (size_t i = 0; i < getParentEdges().size(); i++) {
-                auto& inpDims = getInputShapeAtPort(i).getStaticDims();
-                if (inpDims[1] % item.first != 0) {
+                const auto& inpDims = getInputShapeAtPort(i).getDims();
+                if (inpDims[channelAxis] == Shape::UNDEFINED_DIM || inpDims[1] % item.first != 0) {
                     convertTo = LayoutType::ncsp;
                     break;
                 }
@@ -330,26 +335,27 @@ bool MKLDNNConcatNode::isOptimized() const {
     return getSelectedPrimitiveDescriptor() && getSelectedPrimitiveDescriptor()->getConfig().inConfs[0].inPlace >= 0;
 }
 
-void MKLDNNConcatNode::createPrimitive() {
-    if (prim || isOptimized())
+bool MKLDNNConcatNode::needPrepareParams() const {
+    if (canOptimizeNspc) {
+        return false;
+    }
+    return inputShapesModified();
+}
+
+void MKLDNNConcatNode::prepareParams() {
+    if (canOptimizeNspc || isOptimized())
         return;
 
-    auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    const auto& dstMemPtr = getChildEdgesAtPort(0)[0]->getMemoryPtr();
     if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
         IE_THROW() << "Destination memory didn't allocate.";
     if (getSelectedPrimitiveDescriptor() == nullptr)
         IE_THROW() << "Preferable primitive descriptor is not set.";
 
-    //check if selected Tensor descriptor has nspc layout and concat axis is C
-    if (axis == channelAxis && getChildEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nspc)) {
-        canOptimizeNspc = true;
-        return;
-    }
-
     std::vector<memory::desc> srcs_d;
 
     for (size_t i = 0; i < getParentEdges().size(); i++) {
-        auto& srcMemPtr = getParentEdgeAt(i)->getMemoryPtr();
+        const auto& srcMemPtr = getParentEdgesAtPort(i)[0]->getMemoryPtr();
         if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr()) {
             auto parent = getParentEdgeAt(i)->getParent();
             IE_THROW() << "Source memory from " << parent->getName() << " didn't allocate for node "
@@ -357,7 +363,7 @@ void MKLDNNConcatNode::createPrimitive() {
         }
 
         auto desc = srcMemPtr->GetDescWithType<DnnlMemoryDesc>()->getDnnlDesc();
-        auto& dims = getInputShapeAtPort(i).getStaticDims();
+        const auto& dims = srcMemPtr->getStaticDims();
         for (size_t j = 0; j < dims.size(); j++) {
             desc.data.dims[j] = dims[j];
         }
@@ -365,8 +371,8 @@ void MKLDNNConcatNode::createPrimitive() {
         srcs_d.emplace_back(desc);
     }
 
-    auto desc = getChildEdgeAt(0)->getMemory().GetDescWithType<DnnlMemoryDesc>()->getDnnlDesc();
-    auto& dims = getOutputShapeAtPort(0).getStaticDims();
+    auto desc = dstMemPtr->GetDescWithType<DnnlMemoryDesc>()->getDnnlDesc();
+    const auto& dims = dstMemPtr->getStaticDims();
     for (size_t i = 0; i < dims.size(); i++) {
         desc.data.dims[i] = dims[i];
         desc.data.padded_dims[i] = dims[i];
@@ -376,6 +382,14 @@ void MKLDNNConcatNode::createPrimitive() {
     prim.reset(new concat(primitive_desc));
 }
 
+void MKLDNNConcatNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
 size_t MKLDNNConcatNode::inverseOrder(const SizeVector& order, size_t axis) {
     for (size_t i = 0; i < order.size(); i++) {
         if (axis == order[i]) {
@@ -408,64 +422,66 @@ void MKLDNNConcatNode::initOptimalPrimitiveDescriptor() {
     }
 
     auto config = selected_pd->getConfig();
-    if (isConfigDefined(config))
-        return;
-
-    for (size_t i = 0; i < config.outConfs.size(); i++) {
-        if (config.outConfs[i].desc->isDefined())
-            continue;
+    if (!isDynamicNode() && !isConfigDefined(config)) {
+        for (size_t i = 0; i < config.outConfs.size(); i++) {
+            if (config.outConfs[i].desc->isDefined())
+                continue;
 
-        int num = getChildEdgeAt(i)->getOutputNum();
-        if (num >= 0) {
-            auto childConf = getChildEdgeAt(i)->getChild()->getSelectedPrimitiveDescriptor()->getConfig().inConfs[num];
-            childConf.desc = childConf.desc->cloneWithNewPrecision(config.outConfs[i].desc->getPrecision());
+            int num = getChildEdgeAt(i)->getOutputNum();
+            if (num >= 0) {
+                auto childConf = getChildEdgeAt(i)->getChild()->getSelectedPrimitiveDescriptor()->getConfig().inConfs[num];
+                childConf.desc = childConf.desc->cloneWithNewPrecision(config.outConfs[i].desc->getPrecision());
 
-            if (getChildEdgeAt(i)->getChild()->getSelectedPrimitiveDescriptor()) {
-                if (!childConf.desc->isDefined() && childConf.inPlace >= 0)
-                    getChildEdgeAt(i)->getChild()->initOptimalPrimitiveDescriptor();
+                if (getChildEdgeAt(i)->getChild()->getSelectedPrimitiveDescriptor()) {
+                    if (!childConf.desc->isDefined() && childConf.inPlace >= 0)
+                        getChildEdgeAt(i)->getChild()->initOptimalPrimitiveDescriptor();
 
-                if (childConf.desc->isDefined() && childConf.desc->isCompatible(*config.outConfs[i].desc)) {
-                    config.outConfs[i].desc = childConf.desc;
-                    continue;
+                    if (childConf.desc->isDefined() && childConf.desc->isCompatible(*config.outConfs[i].desc)) {
+                        config.outConfs[i].desc = childConf.desc;
+                        continue;
+                    }
                 }
             }
-        }
 
-        // reset undefined offsets
-        config.outConfs[i].desc = config.outConfs[i].desc->as<BlockedMemoryDesc>()->cloneWithDefaultStridesAndOffset();
-    }
-    auto firstOutBlockingDesc = config.outConfs[0].desc->as<BlockedMemoryDesc>();
-    size_t offset = 0;
-    for (size_t i = 0; i < config.inConfs.size(); i++) {
-        auto oldDesc = config.inConfs[i].desc;
-        auto inpBlockingDesc = oldDesc->as<BlockedMemoryDesc>();
-
-        config.inConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(inpBlockingDesc->getPrecision(),
-                                                                                 inpBlockingDesc->getShape(),
-                                                                                 inpBlockingDesc->getBlockDims(),
-                                                                                 inpBlockingDesc->getOrder(),
-                                                                                 firstOutBlockingDesc->getOffsetPadding() + offset,
-                                                                                 firstOutBlockingDesc->getOffsetPaddingToData(),
-                                                                                 firstOutBlockingDesc->getStrides());
-        size_t axisSize = 1;
-
-        auto firstInpBlockingDesc = config.inConfs[0].desc->as<BlockedMemoryDesc>();
-        if (firstInpBlockingDesc->hasLayoutType(LayoutType::nspc)) {
-            // This is more general and works for any "direct" Layout (such as nchw or nhwc), but it doesn't work for blocked
-            size_t realAxis = inverseOrder(firstInpBlockingDesc->getOrder(), axis);
-            for (size_t j = realAxis; j < inpBlockingDesc->getBlockDims().size(); j++) {
-                size_t jj = firstInpBlockingDesc->getOrder()[j];
-                axisSize *= inpBlockingDesc->getBlockDims()[jj];
-            }
-        } else {
-            // This works for nchw and nchw8c/nchw16c
-            for (size_t j = axis; j < inpBlockingDesc->getBlockDims().size(); j++) {
-                axisSize *= inpBlockingDesc->getBlockDims()[j];
+            // reset undefined offsets
+            config.outConfs[i].desc = config.outConfs[i].desc->as<BlockedMemoryDesc>()->cloneWithDefaultStridesAndOffset();
+        }
+        auto firstOutBlockingDesc = config.outConfs[0].desc->as<BlockedMemoryDesc>();
+        size_t offset = 0;
+        for (size_t i = 0; i < config.inConfs.size(); i++) {
+            auto oldDesc = config.inConfs[i].desc;
+            auto inpBlockingDesc = oldDesc->as<BlockedMemoryDesc>();
+
+            config.inConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(inpBlockingDesc->getPrecision(),
+                                                                            inpBlockingDesc->getShape(),
+                                                                            inpBlockingDesc->getBlockDims(),
+                                                                            inpBlockingDesc->getOrder(),
+                                                                            firstOutBlockingDesc->getOffsetPadding() + offset,
+                                                                            firstOutBlockingDesc->getOffsetPaddingToData(),
+                                                                            firstOutBlockingDesc->getStrides());
+            size_t axisSize = 1;
+
+            auto firstInpBlockingDesc = config.inConfs[0].desc->as<BlockedMemoryDesc>();
+            if (firstInpBlockingDesc->hasLayoutType(LayoutType::nspc)) {
+                // This is more general and works for any "direct" Layout (such as nchw or nhwc), but it doesn't work for blocked
+                size_t realAxis = inverseOrder(firstInpBlockingDesc->getOrder(), axis);
+                for (size_t j = realAxis; j < inpBlockingDesc->getBlockDims().size(); j++) {
+                    size_t jj = firstInpBlockingDesc->getOrder()[j];
+                    axisSize *= inpBlockingDesc->getBlockDims()[jj];
+                }
+            } else {
+                // This works for nchw and nchw8c/nchw16c
+                for (size_t j = axis; j < inpBlockingDesc->getBlockDims().size(); j++) {
+                    axisSize *= inpBlockingDesc->getBlockDims()[j];
+                }
             }
+            offset += axisSize;
         }
-        offset += axisSize;
+        initDescriptor(config);
     }
-    initDescriptor(config);
+
+    // check if selected Tensor descriptor has nspc layout and concat axis is C
+    canOptimizeNspc = axis == channelAxis && getSelectedPrimitiveDescriptor()->getConfig().outConfs.front().desc->hasLayoutType(LayoutType::nspc);
 }
 
 void MKLDNNConcatNode::execute(mkldnn::stream strm) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h
index e9f60b4dec2654..09686ca5f24917 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.h
@@ -23,6 +23,7 @@ class MKLDNNConcatNode : public MKLDNNNode {
     void selectOptimalPrimitiveDescriptor() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
 
     bool isOptimized() const;
 
@@ -31,6 +32,9 @@ class MKLDNNConcatNode : public MKLDNNNode {
         return !isOptimized();
     }
 
+    bool needPrepareParams() const override;
+    void prepareParams() override;
+
 private:
     size_t axis = 0;
     bool canBeInPlace = false;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp
index 5b764d6ce0cc30..68fd40d9a56bcb 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.cpp
@@ -18,10 +18,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNCumSumNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto cumsum = std::dynamic_pointer_cast<const ngraph::opset3::CumSum>(op);
         if (!cumsum) {
             errorMessage = "Only opset3 CumSum operation is supported";
@@ -45,11 +41,11 @@ MKLDNNCumSumNode::MKLDNNCumSumNode(const std::shared_ptr<ngraph::Node>& op, cons
     if ((getOriginalInputsNumber() != numOfInputs && getOriginalInputsNumber() != (numOfInputs - 1)) || getOriginalOutputsNumber() != 1)
         IE_THROW() << errorPrefix << " has incorrect number of input/output edges!";
 
-    const auto &dataShape = op->get_input_shape(CUM_SUM_DATA);
-    if (dataShape.size() < 1) {
-        IE_THROW() << errorPrefix << " doesn't support 'data' input tensor with rank: " << dataShape.size();
+    const auto &dataShape = getInputShapeAtPort(CUM_SUM_DATA);
+    numOfDims = dataShape.getRank();
+    if (numOfDims < 1) {
+        IE_THROW() << errorPrefix << " doesn't support 'data' input tensor with rank: " << numOfDims;
     }
-    numOfDims = dataShape.size();
 
     const auto cumsum = std::dynamic_pointer_cast<const ngraph::opset3::CumSum>(op);
     if (cumsum == nullptr)
@@ -60,14 +56,13 @@ MKLDNNCumSumNode::MKLDNNCumSumNode(const std::shared_ptr<ngraph::Node>& op, cons
     reverse = cumsum->is_reverse();
 
     if (getOriginalInputsNumber() == numOfInputs) {
-        if (!ngraph::is_scalar(cumsum->get_input_shape(AXIS)))
+        const auto axis_shape = cumsum->get_input_partial_shape(AXIS);
+        if (axis_shape.is_dynamic() || !ngraph::is_scalar(axis_shape.to_shape()))
             IE_THROW() << errorPrefix << " doesn't support 'axis' input tensor with non scalar rank";
     }
 
-    if (dataShape != cumsum->get_output_shape(0))
+    if (dataShape != getOutputShapeAtPort(0))
         IE_THROW() << errorPrefix << " has different 'data' input and output dimensions";
-
-    shape = dataShape;
 }
 
 void MKLDNNCumSumNode::initSupportedPrimitiveDescriptors() {
@@ -162,6 +157,7 @@ template <bool reverse, bool exclusive, typename dataType>
 void MKLDNNCumSumNode::cumSum(const dataType *input, dataType *output, const VectorDims &strides) {
     SizeVector iterationRange(numOfDims - 1);
     size_t j = 0;
+    const auto &shape = getParentEdgesAtPort(CUM_SUM_DATA)[0]->getMemory().getStaticDims();
     for (size_t i = 0; i < shape.size(); i++) {
         if (i == axis)
             continue;
@@ -283,4 +279,18 @@ bool MKLDNNCumSumNode::created() const {
     return getType() == CumSum;
 }
 
+bool MKLDNNCumSumNode::needPrepareParams() const {
+    return false;
+}
+
+void MKLDNNCumSumNode::executeDynamicImpl(mkldnn::stream strm) {
+    return execute(strm);
+}
+
+void MKLDNNCumSumNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
 REG_MKLDNN_PRIM_FOR(MKLDNNCumSumNode, CumSum)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h
index 2836af15660519..2e5ebfaf7d866d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_cum_sum_node.h
@@ -15,10 +15,13 @@ class MKLDNNCumSumNode : public MKLDNNNode {
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
@@ -41,7 +44,6 @@ class MKLDNNCumSumNode : public MKLDNNNode {
     bool reverse;
     size_t numOfDims;
     size_t axis = 0;
-    std::vector<size_t> shape;
 
     InferenceEngine::Precision dataPrecision;
     std::string errorPrefix;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h
index a01941b9ef7f64..3d054d5e8b25d9 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.h
@@ -39,6 +39,9 @@ class MKLDNNDeconvolutionNode : public MKLDNNNode {
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
     bool canFuse(const MKLDNNNodePtr& node) const override;
 
+    const InferenceEngine::SizeVector& getWeightDims() { return weightDims; }
+    const std::vector<ptrdiff_t>& getStride() { return stride; }
+
 private:
     bool withGroups = false;
     bool isDW = false;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.cpp
index c341e98a78bcf4..41b8983d362b5d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.cpp
@@ -17,17 +17,11 @@
 
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
-using namespace mkldnn;
 using namespace mkldnn::impl;
-using namespace mkldnn::impl::cpu::x64;
 
 bool MKLDNNDepthToSpaceNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-        const auto depthToSpace = std::dynamic_pointer_cast<const ngraph::opset1::DepthToSpace>(op);
+        auto depthToSpace = ov::as_type_ptr<const ngraph::opset1::DepthToSpace>(op);
         if (!depthToSpace) {
             errorMessage = "Only opset1 DepthToSpace operation is supported";
             return false;
@@ -46,75 +40,58 @@ bool MKLDNNDepthToSpaceNode::isSupportedOperation(const std::shared_ptr<const ng
 MKLDNNDepthToSpaceNode::MKLDNNDepthToSpaceNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
         : MKLDNNNode(op, eng, cache) {
     std::string errorMessage;
-    if (isSupportedOperation(op, errorMessage)) {
-        const auto depthToSpace = std::dynamic_pointer_cast<const ngraph::opset1::DepthToSpace>(op);
-
-        const auto modeNgraph = depthToSpace->get_mode();
-        if (modeNgraph == ngraph::op::v0::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST) {
-            mode = Mode::BLOCKS_FIRST;
-        } else if (modeNgraph == ngraph::op::v0::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST) {
-            mode = Mode::DEPTH_FIRST;
-        } else {
-            THROW_ERROR << "doesn't support mode: " << ngraph::as_string(modeNgraph);
-        }
-
-        blockSize = depthToSpace->get_block_size();
-        if (blockSize == 0)
-            THROW_ERROR << "has incorrect block_size parameter is zero!";
-
-        size_t nSpatialDims = inputShapes[0].getRank() - 2;
-        blockStep = static_cast<size_t>(std::pow(blockSize, nSpatialDims));
-    } else {
+    if (!isSupportedOperation(op, errorMessage)) {
         IE_THROW(NotImplemented) << errorMessage;
     }
-}
+    if (inputShapes.size() != 1 || outputShapes.size() != 1)
+        THROW_ERROR << "has incorrect number of input/output edges!";
+
+    auto depthToSpace = ov::as_type_ptr<const ngraph::opset1::DepthToSpace>(op);
+    if (!depthToSpace)
+        THROW_ERROR << "supports only opset1";
+
+    const auto modeNgraph = depthToSpace->get_mode();
+    if (modeNgraph == ngraph::op::v0::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST) {
+        attrs.mode = Mode::BLOCKS_FIRST;
+    } else if (modeNgraph == ngraph::op::v0::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST) {
+        attrs.mode = Mode::DEPTH_FIRST;
+    } else {
+        THROW_ERROR << "doesn't support mode: " << ngraph::as_string(modeNgraph);
+    }
+
+    attrs.blockSize = depthToSpace->get_block_size();
+    if (attrs.blockSize == 0)
+        THROW_ERROR << "has incorrect block_size parameter is zero!";
 
-void MKLDNNDepthToSpaceNode::getSupportedDescriptors() {
-    SizeVector srcDims = inputShapes[0].getStaticDims();
-    if (srcDims.size() < 3)
+    const size_t srcRank = getInputShapeAtPort(0).getRank();
+    const size_t dstRank = getOutputShapeAtPort(0).getRank();
+
+    if (srcRank < 3)
         THROW_ERROR << "has incorrect number of input dimensions";
-    if (srcDims.size() > 5)
+    if (srcRank > 5)
         THROW_ERROR << "doesn't support dimensions with rank greater than 5";
-
-    SizeVector dstDims = outputShapes[0].getStaticDims();
-    if (srcDims.size() != dstDims.size())
+    if (srcRank != dstRank)
         THROW_ERROR << "has incorrect number of input/output dimensions";
 
-    if (srcDims[1] % blockStep)
-        THROW_ERROR << "has block_size parameter which is incompatible with input tensor channels dimension size";
-
-    if (srcDims[1] / blockStep != dstDims[1])
-        THROW_ERROR << "has incompatible input/output channels";
-
-    size_t nSpatialDims = srcDims.size() - 2;
-    for (size_t i = 0; i < nSpatialDims; ++i) {
-        if (srcDims[i + 2] * blockSize != dstDims[i + 2])
-            THROW_ERROR << "has incompatible spatial dims";
-    }
-
-    if (getParentEdges().size() != 1)
-        THROW_ERROR << "has incorrect number of input edges";
-    if (getChildEdges().empty())
-        THROW_ERROR << "has incorrect number of output edges";
+    const size_t nSpatialDims = srcRank - 2;
+    attrs.blockStep = static_cast<size_t>(std::pow(attrs.blockSize, nSpatialDims));
 }
 
+void MKLDNNDepthToSpaceNode::getSupportedDescriptors() {}
+
 void MKLDNNDepthToSpaceNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
     InferenceEngine::Precision precision = getOriginalInputPrecisionAtPort(0);
-    auto srcDims = getInputShapeAtPort(0).getStaticDims();
-    const size_t nDims = srcDims.size();
 
-    impl_desc_type impl_type;
-    if (mayiuse(impl::cpu::x64::avx512_common)) {
+    impl_desc_type impl_type = impl_desc_type::ref;
+    if (cpu::x64::mayiuse(cpu::x64::avx512_common)) {
         impl_type = impl_desc_type::jit_avx512;
-    } else if (mayiuse(cpu::x64::avx2)) {
+    } else if (cpu::x64::mayiuse(cpu::x64::avx2)) {
         impl_type = impl_desc_type::jit_avx2;
-    } else if (mayiuse(cpu::x64::sse41)) {
+    } else if (cpu::x64::mayiuse(cpu::x64::sse41)) {
         impl_type = impl_desc_type::jit_sse42;
-    } else {
-        impl_type = impl_desc_type::ref;
     }
 
     NodeConfig config;
@@ -126,11 +103,15 @@ void MKLDNNDepthToSpaceNode::initSupportedPrimitiveDescriptors() {
     config.outConfs[0].inPlace = -1;
     config.outConfs[0].constant = false;
 
+    const auto& inputDataShape = getInputShapeAtPort(0);
+    const auto& outputDataShape = getOutputShapeAtPort(0);
+
     std::vector<LayoutType> supportedTypes;
-    if (nDims > 2) {
+    if (inputDataShape.getRank() > 2) {
+        const auto& srcDims = inputDataShape.getDims();
         auto canUseBlocked = [=](const size_t block) {
-            return srcDims[1] % block == 0 && (srcDims[1] / block) % blockStep == 0 &&
-                   (mode == Mode::DEPTH_FIRST ? block % blockStep == 0 : true);
+            return srcDims[1] != Shape::UNDEFINED_DIM && srcDims[1] % block == 0 && (srcDims[1] / block) % attrs.blockStep == 0 &&
+                   (attrs.mode == Mode::DEPTH_FIRST ? block % attrs.blockStep == 0 : true);
         };
 
         supportedTypes.push_back(LayoutType::nspc);
@@ -141,18 +122,18 @@ void MKLDNNDepthToSpaceNode::initSupportedPrimitiveDescriptors() {
     }
     supportedTypes.push_back(LayoutType::ncsp);
     auto creators = BlockedDescCreator::getCommonCreators();
-    auto range = BlockedDescCreator::makeFilteredRange(creators, nDims, supportedTypes);
+    auto range = BlockedDescCreator::makeFilteredRange(creators, inputDataShape.getRank(), supportedTypes);
 
     for (auto itr = range.first; itr != range.second; ++itr) {
-        config.inConfs[0].desc = itr->second->createSharedDesc(precision, getInputShapeAtPort(0));
-        config.outConfs[0].desc = itr->second->createSharedDesc(precision, getOutputShapeAtPort(0));
+        config.inConfs[0].desc = itr->second->createSharedDesc(precision, inputDataShape);
+        config.outConfs[0].desc = itr->second->createSharedDesc(precision, outputDataShape);
         supportedPrimitiveDescriptors.emplace_back(config, impl_type);
     }
 }
 
 void MKLDNNDepthToSpaceNode::createPrimitive() {
-    auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
-    auto &srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
+    auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
     if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
         THROW_ERROR << "has not allocated destination memory";
     if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
@@ -160,24 +141,44 @@ void MKLDNNDepthToSpaceNode::createPrimitive() {
     if (getSelectedPrimitiveDescriptor() == nullptr)
         THROW_ERROR << "has unidentified preferable primitive descriptor";
 
-    VectorDims srcDims = srcMemPtr->getStaticDims();
+    const auto& memoryDesc = srcMemPtr->getDesc();
+    attrs.dataSize = memoryDesc.getPrecision().size();
+    attrs.nSpatialDims = memoryDesc.getShape().getRank() - 2;
+    attrs.layoutType = memoryDesc.hasLayoutType(LayoutType::nCsp16c) ? LayoutType::nCsp16c :
+                       memoryDesc.hasLayoutType(LayoutType::nCsp8c) ? LayoutType::nCsp8c :
+                       memoryDesc.hasLayoutType(LayoutType::nspc) ? LayoutType::nspc : LayoutType::ncsp;
+
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNDepthToSpaceNode::prepareParams() {
+    attrs.srcBlockedDims = getParentEdgeAt(0)->getMemoryPtr()->GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
+    execPtr = std::make_shared<DepthToSpaceExecutor>(attrs);
+}
+
+MKLDNNDepthToSpaceNode::DepthToSpaceExecutor::DepthToSpaceExecutor(const DepthToSpaceAttrs& attrs) {
+    if (!MKLDNNPlugin::one_of(attrs.layoutType, LayoutType::nCsp16c, LayoutType::nCsp8c, LayoutType::nspc, LayoutType::ncsp))
+        IE_THROW() << "DepthToSpace executor supports only 'nCsp16c', 'nCsp8c', 'nspc' or 'ncsp' layouts.";
 
-    size_t nDims = srcDims.size();
-    const size_t nSpatialDims = nDims - 2;
-    const bool isBlocked = getParentEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp8c) ||
-                           getParentEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp16c);
-    const size_t reshapedRank = nDims + nSpatialDims + static_cast<int>(isBlocked) + static_cast<int>(isBlocked && mode == Mode::DEPTH_FIRST);
+    const bool isBlocked = MKLDNNPlugin::one_of(attrs.layoutType, LayoutType::nCsp16c, LayoutType::nCsp8c);
+    const bool isChannelsFirst = attrs.layoutType == LayoutType::nspc;
+    const size_t nDims = attrs.srcBlockedDims.size();
+    const size_t reshapedRank = nDims + attrs.nSpatialDims + static_cast<int>(isBlocked && attrs.mode == Mode::DEPTH_FIRST);
     const size_t lastIdx = reshapedRank - 1;
     size_t firstSpatialOrder = 2;
 
     PermuteParams params;
-    params.data_size = getSelectedPrimitiveDescriptor()->getConfig().inConfs[0].desc->getPrecision().size();
+    params.data_size = attrs.dataSize;
     params.order.resize(reshapedRank, 0);
     params.src_block_order.resize(reshapedRank);
     params.dst_block_order.resize(reshapedRank);
     params.dst_block_dims.resize(reshapedRank);
     params.src_block_dims.resize(reshapedRank);
-    params.src_block_dims[0] = srcDims[0];
+    params.src_block_dims[0] = attrs.srcBlockedDims[0];
 
     // reshaping of src dimensions and creating the permutation order for each layout:
     // new shape: mode = blocks_first [N, block_size, block_size, ..., block_size, C / (block_size ^ K), D1, D2, ..., DK]
@@ -186,62 +187,58 @@ void MKLDNNDepthToSpaceNode::createPrimitive() {
     //            mode = depth_first  : [0,  1,  K + 2, 2, K + 3, 3, K + 4, 4, ..., K + (K + 1), K + 1]
     // where `k` is number of spatial dimensions
 
-    auto reshapeAndSetPermOrder = [&](const size_t idx1, const size_t idx2, const size_t shift, const SizeVector& dims) {
-        for (size_t i = 0; i < nSpatialDims; i++) {
+    auto reshapeAndSetPermOrder = [&](const size_t idx1, const size_t idx2, const size_t shift, const VectorDims& dims) {
+        for (size_t i = 0; i < attrs.nSpatialDims; i++) {
             params.order[i * 2 + shift] = i + idx1;
             params.order[i * 2 + shift + 1] = i + idx2;
 
             params.src_block_dims[params.order[i * 2 + shift]] = dims[i + shift];
-            params.src_block_dims[params.order[i * 2 + shift + 1]] = blockSize;
+            params.src_block_dims[params.order[i * 2 + shift + 1]] = attrs.blockSize;
         }
     };
 
     if (isBlocked) {
-        VectorDims srcBlockedDims = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
-
         size_t orderShiftForBlocks, orderShiftForDims;
-        if (mode == Mode::BLOCKS_FIRST) {
+        if (attrs.mode == Mode::BLOCKS_FIRST) {
             orderShiftForBlocks = 1;
-            orderShiftForDims = nSpatialDims + 2;
+            orderShiftForDims = attrs.nSpatialDims + 2;
 
-            params.src_block_dims[nSpatialDims + 1] = srcBlockedDims[1] / blockStep;
-            params.src_block_dims[lastIdx] = srcBlockedDims.back();
+            params.src_block_dims[attrs.nSpatialDims + 1] = attrs.srcBlockedDims[1] / attrs.blockStep;
+            params.src_block_dims[lastIdx] = attrs.srcBlockedDims.back();
 
-            params.order[1] = nSpatialDims + 1;
+            params.order[1] = attrs.nSpatialDims + 1;
             params.order[lastIdx] = lastIdx;
         } else {
-            orderShiftForBlocks = nSpatialDims + 4;
+            orderShiftForBlocks = attrs.nSpatialDims + 4;
             orderShiftForDims = 3;
 
-            size_t newBlockSize = srcBlockedDims.back() / blockStep;
-            size_t newBlocksCount = srcBlockedDims[1] / blockStep;
+            size_t newBlockSize = attrs.srcBlockedDims.back() / attrs.blockStep;
+            size_t newBlocksCount = attrs.srcBlockedDims[1] / attrs.blockStep;
             params.src_block_dims[1] = newBlocksCount;
-            params.src_block_dims[2] = srcBlockedDims[1] / newBlocksCount;
-            params.src_block_dims[lastIdx - nSpatialDims] = newBlockSize;
+            params.src_block_dims[2] = attrs.srcBlockedDims[1] / newBlocksCount;
+            params.src_block_dims[lastIdx - attrs.nSpatialDims] = newBlockSize;
 
             params.order[1] = 1;
             params.order[2] = 3;
             params.order[lastIdx - 1] = 2;
-            params.order[lastIdx] = lastIdx - nSpatialDims;
+            params.order[lastIdx] = lastIdx - attrs.nSpatialDims;
         }
 
-        reshapeAndSetPermOrder(orderShiftForDims, orderShiftForBlocks, firstSpatialOrder, srcBlockedDims);
-    } else if (getParentEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nspc)) {
-        srcDims.push_back(srcDims[1]);
-        srcDims.erase(srcDims.begin() + 1);
+        reshapeAndSetPermOrder(orderShiftForDims, orderShiftForBlocks, firstSpatialOrder, attrs.srcBlockedDims);
+    } else if (isChannelsFirst) {
         firstSpatialOrder = 1;
 
-        size_t shift = static_cast<size_t>(mode == DEPTH_FIRST) + nSpatialDims + 1;
-        params.order[lastIdx] = mode == Mode::DEPTH_FIRST ? nSpatialDims + 1 : lastIdx;
-        params.src_block_dims[params.order[lastIdx]] = srcDims.back() / blockStep;
+        size_t shift = static_cast<size_t>(attrs.mode == DEPTH_FIRST) + attrs.nSpatialDims + 1;
+        params.order[lastIdx] = attrs.mode == Mode::DEPTH_FIRST ? attrs.nSpatialDims + 1 : lastIdx;
+        params.src_block_dims[params.order[lastIdx]] = attrs.srcBlockedDims.back() / attrs.blockStep;
 
-        reshapeAndSetPermOrder(firstSpatialOrder, shift, firstSpatialOrder, srcDims);
+        reshapeAndSetPermOrder(firstSpatialOrder, shift, firstSpatialOrder, attrs.srcBlockedDims);
     } else {
-        size_t shift = static_cast<size_t>(mode == DEPTH_FIRST) + 1;
-        params.order[1] = mode == DEPTH_FIRST ? 1 : nSpatialDims + 1;
-        params.src_block_dims[params.order[1]] = srcDims[1] / blockStep;
+        size_t shift = static_cast<size_t>(attrs.mode == DEPTH_FIRST) + 1;
+        params.order[1] = attrs.mode == DEPTH_FIRST ? 1 : attrs.nSpatialDims + 1;
+        params.src_block_dims[params.order[1]] = attrs.srcBlockedDims[1] / attrs.blockStep;
 
-        reshapeAndSetPermOrder(nSpatialDims + firstSpatialOrder, shift, firstSpatialOrder, srcDims);
+        reshapeAndSetPermOrder(attrs.nSpatialDims + firstSpatialOrder, shift, firstSpatialOrder, attrs.srcBlockedDims);
     }
 
     std::iota(params.src_block_order.begin(), params.src_block_order.end(), 0);
@@ -252,11 +249,27 @@ void MKLDNNDepthToSpaceNode::createPrimitive() {
     permuteKernel = std::unique_ptr<PermuteKernel>(new PermuteKernel(params));
 }
 
+void MKLDNNDepthToSpaceNode::DepthToSpaceExecutor::exec(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr, const int MB) {
+    if (!permuteKernel)
+        IE_THROW() << "Could not execute. Kernel for Transpose node was not compiled.";
+
+    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(srcMemPtr->GetPtr());
+    uint8_t* dstData = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
+
+    permuteKernel->execute(srcData, dstData, MB);
+}
+
 void MKLDNNDepthToSpaceNode::execute(mkldnn::stream strm) {
-    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    uint8_t* dstData = reinterpret_cast<uint8_t*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
+    if (!execPtr) {
+        THROW_ERROR << "doesn't have a compiled executor.";
+    }
+
+    int MB = isDynamicNode() ? getParentEdgeAt(0)->getMemoryPtr()->getStaticDims()[0] : batchToProcess();
+    execPtr->exec(getParentEdgeAt(0)->getMemoryPtr(), getChildEdgeAt(0)->getMemoryPtr(), MB);
+}
 
-    permuteKernel->execute(srcData, dstData, batchToProcess());
+void MKLDNNDepthToSpaceNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
 }
 
 bool MKLDNNDepthToSpaceNode::created() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.h
index 0ab0b3bd940b06..f32a540cdc9ca3 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_depth_to_space_node.h
@@ -15,13 +15,17 @@ class MKLDNNDepthToSpaceNode : public MKLDNNNode {
 public:
     MKLDNNDepthToSpaceNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
 
+    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
     void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
-    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+    void prepareParams() override;
+
+protected:
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
 private:
     enum Mode {
@@ -29,11 +33,26 @@ class MKLDNNDepthToSpaceNode : public MKLDNNNode {
         DEPTH_FIRST = 1
     };
 
-    Mode mode;
-    size_t blockSize;
-    size_t blockStep;
-
-    std::unique_ptr<PermuteKernel> permuteKernel;
+    struct DepthToSpaceAttrs {
+        LayoutType layoutType;
+        Mode mode;
+        size_t blockSize = 0lu;
+        size_t blockStep = 0lu;
+        size_t dataSize = 1lu;
+        size_t nSpatialDims = 0lu;
+        VectorDims srcBlockedDims;
+    } attrs;
+
+    struct DepthToSpaceExecutor {
+        DepthToSpaceExecutor(const DepthToSpaceAttrs& attrs);
+        void exec(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr, const int MB);
+        ~DepthToSpaceExecutor() = default;
+
+    private:
+        std::unique_ptr<PermuteKernel> permuteKernel;
+    };
+    using executorPtr = std::shared_ptr<DepthToSpaceExecutor>;
+    executorPtr execPtr = nullptr;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp
index a2822931efd6ac..24b0269e8cff25 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_detection_output_node.cpp
@@ -15,6 +15,8 @@ using namespace mkldnn;
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
+namespace {
+
 template <typename T>
 bool SortScorePairDescend(const std::pair<float, T>& pair1,
                           const std::pair<float, T>& pair2) {
@@ -27,6 +29,8 @@ bool SortScorePairDescend<std::pair<int, int>>(const std::pair<float, std::pair<
     return (pair1.first > pair2.first) || (pair1.first == pair2.first && pair1.second.second < pair2.second.second);
 }
 
+} // namespace
+
 bool MKLDNNDetectionOutputNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
         if (isDynamicNgraphNode(op)) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
index cccf7afa988dca..bb9f21f1d4bfa4 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_eltwise_node.cpp
@@ -1181,7 +1181,20 @@ void MKLDNNEltwiseNode::initSupportedPrimitiveDescriptors() {
                 portConfig.inPlace = (!i && canBeInPlace() && inputPrecisions[i] == outputPrecision) ? 0 : -1;
             portConfig.constant = false;
 
-            portConfig.desc = createMemoryDesc(getInputShapeAtPort(i), inputPrecisions[i], offset);
+            const auto &srcShape = getInputShapeAtPort(i);
+            portConfig.desc = createMemoryDesc(srcShape, inputPrecisions[i], offset);
+            if (!isDynamicNode() && srcShape.getDims()[0] == 1) {
+                const auto denseDesc = portConfig.desc->as<BlockedMemoryDesc>();
+                auto strides = denseDesc->getStrides();
+                strides[0] = Shape::UNDEFINED_DIM;
+                portConfig.desc = std::make_shared<CpuBlockedMemoryDesc>(denseDesc->getPrecision(),
+                                                                         denseDesc->getShape(),
+                                                                         denseDesc->getBlockDims(),
+                                                                         denseDesc->getOrder(),
+                                                                         denseDesc->getOffsetPadding(),
+                                                                         denseDesc->getOffsetPaddingToData(),
+                                                                         strides);
+            }
 
             config.inConfs.push_back(portConfig);
         }
@@ -1190,7 +1203,20 @@ void MKLDNNEltwiseNode::initSupportedPrimitiveDescriptors() {
         portConfig.inPlace = -1;
         portConfig.constant = false;
 
-        portConfig.desc = createMemoryDesc(getOutputShapeAtPort(0), outputPrecision, offset);
+        const auto &dstShape = getOutputShapeAtPort(0);
+        portConfig.desc = createMemoryDesc(dstShape, outputPrecision, offset);
+        if (!isDynamicNode() && dstShape.getDims()[0] == 1) {
+            const auto denseDesc = portConfig.desc->as<BlockedMemoryDesc>();
+            auto strides = denseDesc->getStrides();
+            strides[0] = Shape::UNDEFINED_DIM;
+            portConfig.desc = std::make_shared<CpuBlockedMemoryDesc>(denseDesc->getPrecision(),
+                                                                     denseDesc->getShape(),
+                                                                     denseDesc->getBlockDims(),
+                                                                     denseDesc->getOrder(),
+                                                                     denseDesc->getOffsetPadding(),
+                                                                     denseDesc->getOffsetPaddingToData(),
+                                                                     strides);
+        }
 
         config.outConfs.push_back(portConfig);
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp
index 2d841ef9d085b5..4a1c30e16b163e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.cpp
@@ -311,10 +311,6 @@ void reorder_rois(const float *rois, const int* ids, int* mapping, const int roi
 bool MKLDNNExperimentalDetectronROIFeatureExtractorNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op,
                                                                               std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto roiFeatureExtractor = std::dynamic_pointer_cast<const ngraph::opset6::ExperimentalDetectronROIFeatureExtractor>(op);
         if (!roiFeatureExtractor) {
             errorMessage = "Only opset6 ExperimentalDetectronROIFeatureExtractor operation is supported";
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
index cb17ee2cadd621..aceba74e694f81 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_roifeatureextractor_node.h
@@ -19,6 +19,9 @@ class MKLDNNExperimentalDetectronROIFeatureExtractorNode : public MKLDNNNode {
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needPrepareParams() const override { return false; };
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
index 66e6877034f465..6226ef03bfbf36 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
@@ -296,13 +296,16 @@ void MKLDNNFullyConnectedNode::createDescriptorInternal(const mkldnn::memory::de
 
     if (in_candidate.dims().size() == 3) {
         auto inDims = in_candidate.dims();
-        auto outDims = out_candidate.dims();
         auto normalizedInDims = {inDims[0] * inDims[1], inDims[2]};
-        auto normalizedOutDims = {outDims[0] * outDims[1], outDims[2]};
         in_candidate = mkldnn::memory::desc(normalizedInDims, in_candidate.data_type(),
                                          MKLDNNExtensionUtils::GetPlainFormatByRank(normalizedInDims.size()));
+    }
+
+    if (out_candidate.dims().size() == 3) {
+        auto outDims = out_candidate.dims();
+        auto normalizedOutDims = { outDims[0] * outDims[1], outDims[2] };
         out_candidate = mkldnn::memory::desc(normalizedOutDims, out_candidate.data_type(),
-                                             MKLDNNExtensionUtils::GetPlainFormatByRank(normalizedOutDims.size()));
+                                         MKLDNNExtensionUtils::GetPlainFormatByRank(normalizedOutDims.size()));
     }
 
     mkldnn::memory::desc wgh_candidate(MKLDNNExtensionUtils::convertToDnnlDims(weightsDims), wdt, mkldnn::memory::format_tag::any);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp
index 4ca5fe84ecfb3f..a4045e233e6b82 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.cpp
@@ -8,7 +8,7 @@
 #include <mkldnn_types.h>
 #include "ie_parallel.hpp"
 #include "mkldnn_gather_nd_node.h"
-#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset8.hpp>
 #include <precision_utils.h>
 #include <utils/general_utils.h>
 #include "common/cpu_memcpy.h"
@@ -16,15 +16,12 @@
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
+#define THROW_ERROR IE_THROW() << "GatherND layer with name '" << getName() << "' "
+
 bool MKLDNNGatherNDNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-        const auto gatherElementsOp = ngraph::as_type_ptr<const ngraph::op::v5::GatherND>(op);
-        if (!gatherElementsOp) {
-            errorMessage = "Node is not an instance of the GatherND operation from operation set v5.";
+        if (!MKLDNNPlugin::one_of(op->get_type_info(), ngraph::op::v5::GatherND::get_type_info_static(), ngraph::op::v8::GatherND::get_type_info_static())) {
+            errorMessage = "Node is not an instance of the GatherND operation from operation set v5 and v8.";
             return false;
         }
     } catch (...) {
@@ -40,58 +37,36 @@ MKLDNNGatherNDNode::MKLDNNGatherNDNode(const std::shared_ptr<ngraph::Node>& op,
     if (!isSupportedOperation(op, errorMessage)) {
         IE_THROW(NotImplemented) << errorMessage;
     }
-    _errorPrefix = std::string("Layer GatherND with name '") + op->get_friendly_name() + "'";
-
-    if (op->get_input_size() != 2 || op->get_output_size() != 1)
-        IE_THROW() << _errorPrefix << " has invalid number of input/output edges.";
 
-    const auto& dataDims = op->get_input_shape(_dataIndex);
-    const auto& indicesDims = op->get_input_shape(_indicesIndex);
+    if (inputShapes.size() != 2 && outputShapes.size() != 1)
+        THROW_ERROR << "has invalid number of input/output edges.";
 
-    auto gatherNdOp = ngraph::as_type_ptr<const ngraph::op::v5::GatherND>(op);
-    _batchDims = gatherNdOp->get_batch_dims();
-    if (_batchDims >= std::min(dataDims.size(), indicesDims.size()))
-        IE_THROW() << _errorPrefix << " has invalid batch_dims attribute: " << _batchDims;
+    const size_t inputDataRank = getInputShapeAtPort(GATHERND_DATA).getRank();
+    const size_t indicesDimsRank = getInputShapeAtPort(GATHERND_INDEXES).getRank();
 
-    _batchNum = 1lu;
-    for (size_t i = 0; i < _batchDims; i++) {
-        _batchNum *= indicesDims[i];
+    if (auto gatherNdOp = ngraph::as_type_ptr<const ngraph::op::v8::GatherND>(op)) {
+        attrs.batchDims = gatherNdOp->get_batch_dims();
+    } else if (auto gatherNdOp = ngraph::as_type_ptr<const ngraph::op::v5::GatherND>(op)) {
+        attrs.batchDims = gatherNdOp->get_batch_dims();
+    } else {
+        THROW_ERROR << "has support only opset5.";
     }
 
-    _sliceRank = indicesDims[indicesDims.size() - 1];
-    _dataRank = dataDims.size() - _batchDims;
-    if (_sliceRank > _dataRank)
-        IE_THROW() << _errorPrefix << " has invalid inputs shapes.";
-
-    _blockSize = 1;
-    for (size_t i = _sliceRank + _batchDims; i < dataDims.size(); i++) {
-        _blockSize *= dataDims[i];
-    }
-    _batchStep = 1;
-    for (size_t i = _batchDims; i < dataDims.size(); i++) {
-        _batchStep *= dataDims[i];
-    }
+    if (attrs.batchDims >= std::min(inputDataRank, indicesDimsRank))
+        THROW_ERROR << "has invalid batch_dims attribute: " << attrs.batchDims;
 }
 
 void MKLDNNGatherNDNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    Precision inDataPrecision = getOriginalInputPrecisionAtPort(_dataIndex);
-    if (!MKLDNNPlugin::one_of(inDataPrecision.size(),
-                              sizeof(PrecisionTrait<Precision::I32>::value_type),
-                              sizeof(PrecisionTrait<Precision::I16>::value_type),
-                              sizeof(PrecisionTrait<Precision::I8>::value_type))) {
-        IE_THROW() << _errorPrefix << " has unsupported 'data' input precision: " << inDataPrecision;
-    }
-
-    Precision indicesPrecision = getOriginalInputPrecisionAtPort(_indicesIndex);
+    Precision inDataPrecision = getOriginalInputPrecisionAtPort(GATHERND_DATA);
+    Precision indicesPrecision = getOriginalInputPrecisionAtPort(GATHERND_INDEXES);
     if (!MKLDNNPlugin::one_of(indicesPrecision,
                               Precision::I32, Precision::I64, Precision::I16, Precision::U16, Precision::I8, Precision::U8)) {
-        IE_THROW() << _errorPrefix << " has unsupported 'indices' input precision: " << indicesPrecision;
+        THROW_ERROR << "has unsupported 'indices' input precision: " << indicesPrecision;
     }
-
-    _dataTypeSize = inDataPrecision.size();
+    attrs.dataSize = inDataPrecision.size();
 
     addSupportedPrimDesc({{LayoutType::ncsp, inDataPrecision},
                           {LayoutType::ncsp, Precision::I32}},
@@ -99,121 +74,77 @@ void MKLDNNGatherNDNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
-template <typename dataType>
-void MKLDNNGatherNDNode::gatherElementwise() {
-    const auto *srcData = reinterpret_cast<const dataType *>(getParentEdgeAt(_dataIndex)->getMemoryPtr()->GetPtr());
-    const auto *indices = reinterpret_cast<const int *>(getParentEdgeAt(_indicesIndex)->getMemoryPtr()->GetPtr());
-    auto *dstData = reinterpret_cast<dataType *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
-
-    auto strides = getParentEdgeAt(_dataIndex)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
-    const size_t* srcMultipliers = strides.data() + _batchDims;
-
-    const size_t cycles = getChildEdgeAt(0)->getMemory().GetShape().getElementsCount() *
-                          getChildEdgeAt(0)->getMemory().getDesc().getPrecision().size() / (sizeof(dataType) * _batchNum);
-    const size_t CS = cycles * _sliceRank;
-    const size_t CB = cycles * _blockSize;
-    const size_t workAmount = _batchNum * cycles;
-
-    auto threadBody = [&](const int ithr, const int nthr) {
-        size_t start(0lu), end(0lu);
-        splitter(workAmount, nthr, ithr, start, end);
-        if (start >= end)
-            return;
-        size_t bStart = start / cycles;
-        size_t cStart = start % cycles;
-        size_t workCounter = start;
-
-        const dataType* shiftedSrcData = srcData + bStart * _batchStep;
-        const int* shiftedIndices = indices + bStart * CS + cStart * _sliceRank;
-        dataType* shiftedDstData = dstData + bStart * CB + cStart * _blockSize;
-
-        for (size_t b = bStart; b < _batchNum; b++) {
-            for (size_t j = cStart; j < cycles; j++) {
-                size_t dataIdx = 0lu;
-                for (size_t i = 0lu; i < _sliceRank; i++)
-                    dataIdx += srcMultipliers[i] * shiftedIndices[i];
-                shiftedDstData[0] = shiftedSrcData[dataIdx];
-                shiftedDstData++;
-                shiftedIndices += _sliceRank;
-                if (++workCounter == end) {
-                    return;
-                }
-            }
-            cStart = 0lu;
-            shiftedSrcData += _batchStep;
-        }
-    };
+void MKLDNNGatherNDNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
 
-    parallel_nt(0, threadBody);
+void MKLDNNGatherNDNode::prepareParams() {
+    auto& srcMemPtr = getParentEdgeAt(GATHERND_DATA)->getMemoryPtr();
+    auto& idxMemPtr = getParentEdgeAt(GATHERND_INDEXES)->getMemoryPtr();
+    auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
+        THROW_ERROR << " has not allocated input memory of 'data'.";
+    if (!idxMemPtr || !idxMemPtr->GetPrimitivePtr())
+        THROW_ERROR << " has not allocated input memory of 'indices'.";
+    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
+        THROW_ERROR << " has not allocated output memory.";
+    if (getSelectedPrimitiveDescriptor() == nullptr)
+        THROW_ERROR << " has unidentified preferable primitive descriptor.";
+
+    attrs.srcDims = srcMemPtr->getStaticDims();
+    attrs.srcStrides = srcMemPtr->GetDescWithType<BlockedMemoryDesc>()->getStrides();
+    attrs.dstSize = dstMemPtr->GetSize();
+    attrs.sliceRank =  idxMemPtr->getStaticDims().back();
+    execPtr = std::make_shared<GatherNDExecutor>(attrs);
 }
 
-void MKLDNNGatherNDNode::gatherBlocks() {
-    const uint8_t* srcData = reinterpret_cast<const uint8_t *>(getParentEdgeAt(_dataIndex)->getMemoryPtr()->GetPtr());
-    const int* indices = reinterpret_cast<const int *>(getParentEdgeAt(_indicesIndex)->getMemoryPtr()->GetPtr());
-    uint8_t* dstData = reinterpret_cast<uint8_t *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
-
-    std::vector<size_t> srcMultipliers(_sliceRank);
-    for (size_t i = 0; i < _sliceRank ; i++)
-        srcMultipliers[i] = _dataTypeSize * getParentEdgeAt(_dataIndex)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides()[i + _batchDims];
-
-    const size_t batchStep = _batchStep * _dataTypeSize;
-    const size_t dataStep = _blockSize * _dataTypeSize;
-    const size_t cycles = getChildEdgeAt(0)->getMemory().GetSize() / (dataStep * _batchNum);
-    const size_t CS = cycles * _sliceRank;
-    const size_t CB = cycles * dataStep;
-    const size_t workAmount = _batchNum * cycles;
-
-    auto threadBody = [&](const int ithr, const int nthr) {
-        size_t start(0lu), end(0lu);
-        splitter(workAmount, nthr, ithr, start, end);
-        if (start >= end)
-            return;
-        size_t bStart = start / cycles;
-        size_t cStart = start % cycles;
-        size_t workCounter = start;
-
-        const uint8_t* shiftedSrcData = srcData + bStart * batchStep;
-        const int* shiftedIndices = indices + bStart * CS + cStart * _sliceRank;
-        uint8_t* shiftedDstData = dstData + bStart * CB + cStart * dataStep;
-
-        for (size_t b = bStart; b < _batchNum; b++) {
-            for (size_t j = cStart; j < cycles; j++) {
-                size_t dataIdx = 0lu;
-                for (size_t i = 0; i < _sliceRank ; i++)
-                    dataIdx += srcMultipliers[i] * shiftedIndices[i];
-                cpu_memcpy(shiftedDstData, &(shiftedSrcData[dataIdx]), dataStep);
-                shiftedDstData += dataStep;
-                shiftedIndices += _sliceRank;
-                if (++workCounter == end) {
-                    return;
-                }
-            }
-            cStart = 0;
-            shiftedSrcData += batchStep;
-        }
-    };
+MKLDNNGatherNDNode::GatherNDExecutor::GatherNDExecutor(const GatherNDAttributes& attrs) : attrs(attrs) {
+    batchSize = std::accumulate(attrs.srcDims.begin(), attrs.srcDims.begin() + attrs.batchDims, 1lu, std::multiplies<size_t>());
+    dataLength = std::accumulate(attrs.srcDims.begin() + attrs.sliceRank + attrs.batchDims, attrs.srcDims.end(), 1lu,
+                                 std::multiplies<size_t>()) * attrs.dataSize;
+    cycles = attrs.dstSize / (dataLength * batchSize);
+
+    srcBatchStride = std::accumulate(attrs.srcDims.begin() + attrs.batchDims, attrs.srcDims.end(), 1lu,
+                                     std::multiplies<size_t>()) * attrs.dataSize;
+    idxBatchStride = cycles * attrs.sliceRank;
+    dstBatchStride = cycles * dataLength;
 
-    parallel_nt(0, threadBody);
+    srcShifts.resize(attrs.sliceRank, 0);
+    for (size_t i = 0; i < attrs.sliceRank ; i++)
+        srcShifts[i] = attrs.srcStrides[i + attrs.batchDims] * attrs.dataSize;
+}
+
+void MKLDNNGatherNDNode::GatherNDExecutor::exec(const uint8_t* srcData, const int32_t* indices, uint8_t* dstData) {
+    parallel_for2d(batchSize, cycles, [&](const size_t b, const size_t j) {
+        const size_t srcStride = b * srcBatchStride;
+        const size_t idxStride = b * idxBatchStride + j * attrs.sliceRank;
+        const size_t dstStride = b * dstBatchStride + j * dataLength;
+
+        size_t dataIdx = 0lu;
+        for (size_t i = 0; i < attrs.sliceRank ; ++i)
+            dataIdx += srcShifts[i] * indices[idxStride + i];
+
+        cpu_memcpy(&dstData[dstStride], &srcData[srcStride + dataIdx], dataLength);
+    });
 }
 
 void MKLDNNGatherNDNode::execute(mkldnn::stream strm) {
-    if (_blockSize > 1) {
-        gatherBlocks();
-    } else {
-        switch (_dataTypeSize) {
-            case sizeof(PrecisionTrait<Precision::I32>::value_type):
-                gatherElementwise<PrecisionTrait<Precision::I32>::value_type>();
-                break;
-            case sizeof(PrecisionTrait<Precision::I16>::value_type):
-                gatherElementwise<PrecisionTrait<Precision::I16>::value_type>();
-                break;
-            case sizeof(PrecisionTrait<Precision::I8>::value_type):
-                gatherElementwise<PrecisionTrait<Precision::I8>::value_type>();
-                break;
-            default:
-                IE_THROW() << _errorPrefix + " has data input with unsupported precision: " + getOriginalInputPrecisionAtPort(_dataIndex).name();
-        }
-    }
+    if (!execPtr)
+        THROW_ERROR << "has not compiled executor.";
+
+    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(getParentEdgeAt(GATHERND_DATA)->getMemoryPtr()->GetPtr());
+    const int32_t* indices = reinterpret_cast<const int32_t*>(getParentEdgeAt(GATHERND_INDEXES)->getMemoryPtr()->GetPtr());
+    uint8_t* dstData = reinterpret_cast<uint8_t*>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
+
+    execPtr->exec(srcData, indices, dstData);
+}
+
+void MKLDNNGatherNDNode::executeDynamicImpl(dnnl::stream strm) {
+    execute(strm);
 }
 
 bool MKLDNNGatherNDNode::created() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h
index 3d826621c9d0af..fefce4415906c8 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_gather_nd_node.h
@@ -18,27 +18,50 @@ class MKLDNNGatherNDNode : public MKLDNNNode {
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+protected:
+    void executeDynamicImpl(mkldnn::stream strm) override;
+    void prepareParams() override;
+
 private:
-    size_t _dataRank;
-    size_t _sliceRank;
-    size_t _blockSize;
-    size_t _batchDims;
-    size_t _batchNum;
-    size_t _batchStep;
-    size_t _dataTypeSize;
-    const size_t _dataIndex = 0;
-    const size_t _indicesIndex = 1;
-    std::string _errorPrefix;
-
-    template <typename dataType>
-    void gatherElementwise();
-    void gatherBlocks();
+    struct GatherNDAttributes {
+        size_t batchDims = 0lu;
+        size_t dataSize = 1lu;
+        size_t dstSize = 0lu;
+        size_t sliceRank = 0lu;
+
+        VectorDims srcDims;
+        VectorDims srcStrides;
+    } attrs;
+
+    struct GatherNDExecutor {
+        GatherNDExecutor(const GatherNDAttributes& attrs);
+        ~GatherNDExecutor() = default;
+        void exec(const uint8_t* srcData, const int32_t* indices, uint8_t* dstData);
+
+    private:
+        size_t batchSize = 1lu;
+        size_t cycles = 1lu;
+        size_t dataLength = 1lu;
+
+        size_t srcBatchStride = 1lu;
+        size_t idxBatchStride = 1lu;
+        size_t dstBatchStride = 1lu;
+        VectorDims srcShifts;
+
+        GatherNDAttributes attrs;
+    };
+
+    static constexpr size_t GATHERND_DATA = 0lu;
+    static constexpr size_t GATHERND_INDEXES = 1lu;
+
+    using executorPtr = std::shared_ptr<GatherNDExecutor>;
+    executorPtr execPtr = nullptr;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp
index 3efe3f69cbe456..9f537b490fcac7 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.cpp
@@ -13,10 +13,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNLogSoftmaxNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto logSoftMax = std::dynamic_pointer_cast<const ngraph::opset5::LogSoftmax>(op);
         if (!logSoftMax) {
             errorMessage = "Only opset5 LogSoftmax operation is supported";
@@ -41,21 +37,45 @@ MKLDNNLogSoftmaxNode::MKLDNNLogSoftmaxNode(const std::shared_ptr<ngraph::Node>&
         IE_THROW() << "Operation with name '" << op->get_friendly_name() <<
             "' is not an instance of LogSoftmax from opset5.";
 
-    if (getOriginalInputsNumber() != 1 || getOriginalOutputsNumber() != 1)
+    if (inputShapes.size() != 1 || outputShapes.size() != 1)
         IE_THROW() << errorPrefix << " has incorrect number of input/output edges!";
 
-    SizeVector dims = op->get_input_shape(0);
-    if (!dims.size())
-        dims = SizeVector(1, 1);
-    int axis = logSoftMax->get_axis();
+    auto dimsSize = getInputShapeAtPort(0).getDims().size();
+    if (dimsSize == 0)
+        dimsSize += 1;
+    axis = logSoftMax->get_axis();
     if (axis < 0)
-        axis += dims.size();
+        axis += dimsSize;
 
-    if (dims.size() < static_cast<size_t>((size_t)(1) + axis))
+    if (dimsSize < static_cast<size_t>((size_t)(1) + axis))
         IE_THROW() << errorPrefix << " has incorrect input parameters dimensions and axis number!";
+}
+
+void MKLDNNLogSoftmaxNode::initSupportedPrimitiveDescriptors() {
+    if (!supportedPrimitiveDescriptors.empty())
+        return;
 
-    int j;
-    for (j = dims.size() - 1; j >= 0; j--) {
+    addSupportedPrimDesc({{LayoutType::ncsp, Precision::FP32}},
+                         {{LayoutType::ncsp, Precision::FP32}},
+                         impl_desc_type::ref_any);
+}
+
+void MKLDNNLogSoftmaxNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNLogSoftmaxNode::prepareParams() {
+    const auto &dims = getParentEdgesAtPort(0)[0]->getMemory().getStaticDims();
+    reducedAxisStride = 1;
+    axisStep = 1;
+    isLastDim = false;
+
+    int j = static_cast<int>(dims.size()) - 1;
+    for (; j >= 0; j--) {
         if (dims[j] != 1) break;
     }
     if (j == axis) isLastDim = true;
@@ -67,15 +87,6 @@ MKLDNNLogSoftmaxNode::MKLDNNLogSoftmaxNode(const std::shared_ptr<ngraph::Node>&
         reducedAxisStride *= dims[i];
 }
 
-void MKLDNNLogSoftmaxNode::initSupportedPrimitiveDescriptors() {
-    if (!supportedPrimitiveDescriptors.empty())
-        return;
-
-    addSupportedPrimDesc({{LayoutType::ncsp, Precision::FP32}},
-                         {{LayoutType::ncsp, Precision::FP32}},
-                         impl_desc_type::ref_any);
-}
-
 void MKLDNNLogSoftmaxNode::execute(mkldnn::stream strm) {
     const float *srcData = reinterpret_cast<const float *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
     float* dstData = reinterpret_cast<float *>(getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h
index 15479cb81ffff5..f74b4dbae9af73 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_log_softmax_node.h
@@ -16,13 +16,17 @@ class MKLDNNLogSoftmaxNode : public MKLDNNNode {
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    void prepareParams() override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
+    int axis;
     size_t reducedAxisSize;
     size_t reducedAxisStride = 1;
     size_t axisStep = 1;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp
index 4f8d4fba1e11aa..163e76ca483a1a 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.cpp
@@ -16,10 +16,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNMathNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         if (initializers.find(op->get_type_info()) == initializers.end()) {
             errorMessage = "Unsupported Math layer type.";
             return false;
@@ -63,6 +59,10 @@ void MKLDNNMathNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
+std::vector<VectorDims> MKLDNNMathNode::shapeInfer() const {
+    return std::vector<VectorDims>{getParentEdgesAtPort(0)[0]->getMemory().getStaticDims()};
+}
+
 void MKLDNNMathNode::execute(mkldnn::stream strm) {
     size_t dataSize = getChildEdgesAtPort(0)[0]->getMemory().GetShape().getElementsCount();
     const float *src_data = reinterpret_cast<const float *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h
index 5c5de72a750a70..ee2ce5d75b1475 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_math_node.h
@@ -19,6 +19,10 @@ class MKLDNNMathNode : public MKLDNNNode {
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    std::vector<VectorDims> shapeInfer() const override;
+    bool needPrepareParams() const override { return false; };
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
index e0b77e3f08062f..d87cc68d867370 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
@@ -13,7 +13,7 @@
 
 namespace MKLDNNPlugin {
 
-enum MatrixNmsSortResultType {
+enum class MatrixNmsSortResultType {
     CLASSID,  // sort selected boxes by class id (ascending) in each batch element
     SCORE,    // sort selected boxes by score (descending) in each batch element
     NONE      // do not guarantee the order in each batch element
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
index 64d08e0fab3255..15f26d87145aef 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
@@ -214,20 +214,20 @@ void MKLDNNMultiClassNmsNode::execute(mkldnn::stream strm) {
     }
 
     if (sort_result_across_batch) {
-        if (sort_result_type == SCORE) {
+        if (sort_result_type == MulticlassNmsSortResultType::SCORE) {
             parallel_sort(filtBoxes.begin(), filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
                 return (l.score > r.score) || (l.score == r.score && l.batch_index < r.batch_index) ||
                        (l.score == r.score && l.batch_index == r.batch_index && l.class_index < r.class_index) ||
                        (l.score == r.score && l.batch_index == r.batch_index && l.class_index == r.class_index && l.box_index < r.box_index);
             });
-        } else if (sort_result_type == CLASSID) {
+        } else if (sort_result_type == MulticlassNmsSortResultType::CLASSID) {
             parallel_sort(filtBoxes.begin(), filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
                 return (l.class_index < r.class_index) || (l.class_index == r.class_index && l.batch_index < r.batch_index) ||
                        (l.class_index == r.class_index && l.batch_index == r.batch_index && l.score > r.score) ||
                        (l.class_index == r.class_index && l.batch_index == r.batch_index && l.score == r.score && l.box_index < r.box_index);
             });
         }
-    } else if (sort_result_type == CLASSID) {
+    } else if (sort_result_type == MulticlassNmsSortResultType::CLASSID) {
         parallel_sort(filtBoxes.begin(), filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
             return ((l.batch_index < r.batch_index) ||
                     ((l.batch_index == r.batch_index) &&
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
index ac4a350bc564cd..2fbdc2a9712a61 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
@@ -11,7 +11,7 @@
 
 namespace MKLDNNPlugin {
 
-enum MulticlassNmsSortResultType {
+enum class MulticlassNmsSortResultType {
     CLASSID,  // sort selected boxes by class id (ascending) in each batch element
     SCORE,    // sort selected boxes by score (descending) in each batch element
     NONE      // do not guarantee the order in each batch element
@@ -40,7 +40,7 @@ class MKLDNNMultiClassNmsNode : public MKLDNNNode {
     const size_t NMS_SELECTEDNUM = 2;
 
     bool sort_result_across_batch = false;
-    MulticlassNmsSortResultType sort_result_type = NONE;
+    MulticlassNmsSortResultType sort_result_type = MulticlassNmsSortResultType::NONE;
 
     size_t num_batches;
     size_t num_boxes;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp
index aec7819ce95020..6bd0e44ae8c953 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.cpp
@@ -20,21 +20,13 @@ using namespace InferenceEngine;
 
 bool MKLDNNNonMaxSuppressionNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (op->is_dynamic()) {
-            errorMessage = "Doesn't support op with dynamic input shapes";
-            return false;
-        }
-
+        // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
         using NonMaxSuppressionV5 = ngraph::op::v5::NonMaxSuppression;
         if (!one_of(op->get_type_info(), NonMaxSuppressionV5::get_type_info_static(),
                     ngraph::op::internal::NonMaxSuppressionIEInternal::get_type_info_static())) {
             errorMessage = "Only NonMaxSuppression v5 and NonMaxSuppressionIEInternal are supported";
             return false;
         }
-        if (op->get_input_size() > 2 && !dynamic_cast<ngraph::op::v0::Constant *>(op->get_input_node_ptr(2))) {
-            errorMessage = "Doesn't support NonMaxSuppression with undefined max_output_boxes_per_class";
-            return false;
-        }
 
         if (const auto nms5 = std::dynamic_pointer_cast<const NonMaxSuppressionV5>(op)) {
             const auto boxEncoding = nms5->get_box_encoding();
@@ -67,6 +59,7 @@ MKLDNNNonMaxSuppressionNode::MKLDNNNonMaxSuppressionNode(const std::shared_ptr<n
         if (const auto nms5 = std::dynamic_pointer_cast<const ngraph::op::v5::NonMaxSuppression>(op)) {
             boxEncodingType = static_cast<boxEncoding>(nms5->get_box_encoding());
             sort_result_descending = nms5->get_sort_result_descending();
+        // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
         } else if (const auto nmsIe = std::dynamic_pointer_cast<const ngraph::op::internal::NonMaxSuppressionIEInternal>(op)) {
             boxEncodingType = nmsIe->m_center_point_box ? boxEncoding::CENTER : boxEncoding::CORNER;
             sort_result_descending = nmsIe->m_sort_result_descending;
@@ -75,28 +68,16 @@ MKLDNNNonMaxSuppressionNode::MKLDNNNonMaxSuppressionNode(const std::shared_ptr<n
             IE_THROW() << errorPrefix << " doesn't support NMS: " << typeInfo.name << " v" << typeInfo.version;
         }
 
-        const auto &boxes_dims = getInputShapeAtPort(NMS_BOXES).getStaticDims();
-        num_batches = boxes_dims[0];
-        num_boxes = boxes_dims[1];
+        const auto &boxes_dims = getInputShapeAtPort(NMS_BOXES).getDims();
         if (boxes_dims.size() != 3)
             IE_THROW() << errorPrefix << "has unsupported 'boxes' input rank: " << boxes_dims.size();
         if (boxes_dims[2] != 4)
             IE_THROW() << errorPrefix << "has unsupported 'boxes' input 3rd dimension size: " << boxes_dims[2];
 
-        const auto &scores_dims = getInputShapeAtPort(NMS_SCORES).getStaticDims();
-        num_classes = scores_dims[1];
+        const auto &scores_dims = getInputShapeAtPort(NMS_SCORES).getDims();
         if (scores_dims.size() != 3)
             IE_THROW() << errorPrefix << "has unsupported 'scores' input rank: " << scores_dims.size();
 
-        if (num_batches != scores_dims[0])
-            IE_THROW() << errorPrefix << " num_batches is different in 'boxes' and 'scores' inputs";
-        if (num_boxes != scores_dims[2])
-            IE_THROW() << errorPrefix << " num_boxes is different in 'boxes' and 'scores' inputs";
-
-        numFiltBox.resize(num_batches);
-        for (auto & i : numFiltBox)
-            i.resize(num_classes);
-
         const Shape valid_outputs_shape = getOutputShapeAtPort(NMS_VALIDOUTPUTS);
         if (valid_outputs_shape.getRank() != 1)
             IE_THROW() << errorPrefix << "has unsupported 'valid_outputs' output rank: " << valid_outputs_shape.getRank();
@@ -147,6 +128,36 @@ void MKLDNNNonMaxSuppressionNode::initSupportedPrimitiveDescriptors() {
     addSupportedPrimDesc(inDataConf, outDataConf, impl_desc_type::ref_any);
 }
 
+void MKLDNNNonMaxSuppressionNode::prepareParams() {
+    const auto& boxes_dims = isDynamicNode() ? getParentEdgesAtPort(NMS_BOXES)[0]->getMemory().getStaticDims() :
+                                               getInputShapeAtPort(NMS_BOXES).getStaticDims();
+    const auto& scores_dims = isDynamicNode() ? getParentEdgesAtPort(NMS_SCORES)[0]->getMemory().getStaticDims() :
+                                                getInputShapeAtPort(NMS_SCORES).getStaticDims();
+
+    num_batches = boxes_dims[0];
+    num_boxes = boxes_dims[1];
+    num_classes = scores_dims[1];
+    if (num_batches != scores_dims[0])
+        IE_THROW() << errorPrefix << " num_batches is different in 'boxes' and 'scores' inputs";
+    if (num_boxes != scores_dims[2])
+        IE_THROW() << errorPrefix << " num_boxes is different in 'boxes' and 'scores' inputs";
+
+    numFiltBox.resize(num_batches);
+    for (auto & i : numFiltBox)
+        i.resize(num_classes);
+}
+
+void MKLDNNNonMaxSuppressionNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNNonMaxSuppressionNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
 void MKLDNNNonMaxSuppressionNode::execute(mkldnn::stream strm) {
     const float *boxes = reinterpret_cast<const float *>(getParentEdgeAt(NMS_BOXES)->getMemoryPtr()->GetPtr());
     const float *scores = reinterpret_cast<const float *>(getParentEdgeAt(NMS_SCORES)->getMemoryPtr()->GetPtr());
@@ -155,9 +166,7 @@ void MKLDNNNonMaxSuppressionNode::execute(mkldnn::stream strm) {
         max_output_boxes_per_class = reinterpret_cast<int *>(getParentEdgeAt(NMS_MAXOUTPUTBOXESPERCLASS)->getMemoryPtr()->GetPtr())[0];
     }
 
-    if (!isDynamicNode()) {
-        max_output_boxes_per_class = std::min(max_output_boxes_per_class, num_boxes);
-    }
+    max_output_boxes_per_class = std::min(max_output_boxes_per_class, num_boxes);
 
     if (max_output_boxes_per_class == 0)
         return;
@@ -216,6 +225,7 @@ void MKLDNNNonMaxSuppressionNode::execute(mkldnn::stream strm) {
     auto scoresMemPtr =  getChildEdgesAtPort(NMS_SELECTEDSCORES)[0]->getMemoryPtr();
     const size_t validOutputs = std::min(filtBoxes.size(), maxNumberOfBoxes);
 
+    // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
     if (isDynamicNode()) {
         VectorDims newDims{validOutputs, 3};
         indicesMemPtr->redefineDesc(getBaseMemDescAtOutputPort(NMS_SELECTEDINDICES)->cloneWithNewDims(newDims));
@@ -240,6 +250,7 @@ void MKLDNNNonMaxSuppressionNode::execute(mkldnn::stream strm) {
         selectedScoresPtr += selectedIndicesStride;
     }
 
+    // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
     if (!isDynamicNode()) {
         std::fill(selectedIndicesPtr, selectedIndicesPtr + (maxNumberOfBoxes - idx) * selectedIndicesStride, -1);
         std::fill(selectedScoresPtr, selectedScoresPtr + (maxNumberOfBoxes - idx) * selectedIndicesStride, -1.f);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h
index 1578e6f4a32a8a..bbd6e8cf0814bf 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_max_suppression_node.h
@@ -20,7 +20,7 @@ class MKLDNNNonMaxSuppressionNode : public MKLDNNNode {
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
@@ -50,10 +50,10 @@ class MKLDNNNonMaxSuppressionNode : public MKLDNNNode {
     void nmsWithoutSoftSigma(const float *boxes, const float *scores, const SizeVector &boxesStrides,
                              const SizeVector &scoresStrides, std::vector<filteredBoxes> &filtBoxes);
 
-    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
     bool needShapeInfer() const override { return false; }
-    bool needPrepareParams() const override { return false; }
+    void prepareParams() override;
 
 private:
     // input
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp
new file mode 100644
index 00000000000000..f6e0624a4ba0c9
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.cpp
@@ -0,0 +1,138 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "mkldnn_non_zero.h"
+#include <ngraph/opsets/opset3.hpp>
+#include <utils/bfloat16.hpp>
+
+using namespace MKLDNNPlugin;
+using namespace InferenceEngine;
+
+bool MKLDNNNonZeroNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
+    try {
+        if (op->get_type_info() != ngraph::op::v3::NonZero::get_type_info_static()) {
+            errorMessage = "Node is not an instance of NonZero from the operation set v3.";
+            return false;
+        }
+    } catch (...) {
+        return false;
+    }
+    return true;
+}
+
+MKLDNNNonZeroNode::MKLDNNNonZeroNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng,
+                                     MKLDNNWeightsSharing::Ptr &cache) : MKLDNNNode(op, eng, cache) {
+    std::string errorMessage;
+    if (isSupportedOperation(op, errorMessage)) {
+        errorPrefix = "NonZero layer with name '" + getName() + "' ";
+    } else {
+        IE_THROW(NotImplemented) << errorMessage;
+    }
+    if (op->get_output_element_type(0) != ngraph::element::i32) {
+        IE_THROW() << errorPrefix << "doesn't support demanded output precision";
+    }
+}
+
+void MKLDNNNonZeroNode::getSupportedDescriptors() {
+    if (!descs.empty())
+        return;
+    if (getParentEdges().size() != 1)
+        IE_THROW() << errorPrefix << "has incorrect number of input edges: " << getParentEdges().size();
+    if (getChildEdges().size() != 1)
+        IE_THROW() << errorPrefix << "has incorrect number of output edges: " << getChildEdges().size();
+}
+
+void MKLDNNNonZeroNode::initSupportedPrimitiveDescriptors() {
+    if (!supportedPrimitiveDescriptors.empty())
+        return;
+
+    const auto &inPrc = getOriginalInputPrecisionAtPort(0);
+    if (!one_of(inPrc, Precision::FP32, Precision::BF16, Precision::I32, Precision::U32, Precision::I8,  Precision::U8)) {
+        IE_THROW() << "Can't create primitive descriptor for NonZero layer with name: " << getName() << " doesn't support "
+                   << inPrc.name() << " precision on 0 port";
+    }
+
+    addSupportedPrimDesc({{LayoutType::ncsp}},
+                         {{LayoutType::ncsp, Precision::I32}},
+                         impl_desc_type::ref);
+}
+
+template <typename T>
+size_t MKLDNNNonZeroNode::getNonZeroElementsCount(const T* src, const Shape& inShape) {
+    T zero = 0;
+    size_t count = 0;
+    size_t inSize = inShape.getElementsCount();
+    if (inShape.getRank() == 0) {
+        if (src[0] != zero)
+            count = 1;
+    } else {
+        for (size_t i = 0; i < inSize; i++) {
+            if (src[i] != zero)
+                count++;
+        }
+    }
+    return count;
+}
+namespace {
+struct NonZeroContext {
+    MKLDNNNonZeroNode &node;
+};
+}
+template<typename T>
+struct MKLDNNNonZeroNode::NonZeroExecute {
+    void operator()(NonZeroContext & ctx) {
+        ctx.node.executeSpecified<T>();
+    }
+};
+void MKLDNNNonZeroNode::execute(mkldnn::stream strm) {
+    auto inputPrec = getParentEdgesAtPort(0)[0]->getMemory().getDesc().getPrecision();
+    NonZeroContext ctx = {*this };
+    OV_SWITCH(MKLDNNPlugin, NonZeroExecute, ctx, inputPrec,
+              OV_CASE(Precision::FP32, float),
+              OV_CASE(Precision::BF16, bfloat16_t),
+              OV_CASE(Precision::I32, int),
+              OV_CASE(Precision::U32, uint32_t),
+              OV_CASE(Precision::I8, int8_t),
+              OV_CASE(Precision::U8, uint8_t))
+}
+template <typename T>
+void MKLDNNNonZeroNode::executeSpecified() {
+    T zero = 0;
+    T *src = reinterpret_cast<T *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
+    auto dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    Shape inShape = getParentEdgeAt(0)->getMemory().GetShape();
+    size_t inRank = inShape.getRank();
+    size_t nonZeroCount = getNonZeroElementsCount(src, inShape);
+    if (isDynamicNode()) {
+        VectorDims newDims{inRank, nonZeroCount};
+        dstMemPtr->redefineDesc(getBaseMemDescAtOutputPort(0)->cloneWithNewDims(newDims));
+    }
+    int *dst = reinterpret_cast<int *>(dstMemPtr->GetPtr());
+    size_t inSize = inShape.getElementsCount();
+    auto srcStrides = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
+    if (nonZeroCount == 0)
+        return;
+    if (inShape.getRank() == 0) {
+        dst[0] = 0;
+    } else {
+        size_t colIndex = 0, outIndex = 0;
+        for (size_t i = 0; i < inSize; i++) {
+            if (src[i] != zero) {
+                size_t temp = i;
+                for (size_t j = 0; j < inRank; j++) {
+                    outIndex = j * nonZeroCount + colIndex;
+                    dst[outIndex] = temp / srcStrides[j];
+                    temp = temp % srcStrides[j];
+                }
+                colIndex++;
+            }
+        }
+    }
+}
+
+bool MKLDNNNonZeroNode::created() const {
+    return getType() == NonZero;
+}
+
+REG_MKLDNN_PRIM_FOR(MKLDNNNonZeroNode, NonZero)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h
new file mode 100644
index 00000000000000..2f46042a331d5e
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_non_zero.h
@@ -0,0 +1,42 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <mkldnn_node.h>
+#include <string>
+#include <memory>
+#include <vector>
+#include <mkldnn_extension_utils.h>
+
+namespace MKLDNNPlugin {
+
+class MKLDNNNonZeroNode : public MKLDNNNode {
+public:
+  MKLDNNNonZeroNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
+
+    void getSupportedDescriptors() override;
+    void initSupportedPrimitiveDescriptors() override;
+    void createPrimitive() override {
+        if (inputShapesDefined())
+            updateLastInputDims();
+    };
+    void execute(mkldnn::stream strm) override;
+    bool created() const override;
+    bool needShapeInfer() const override {return false;};
+    bool needPrepareParams() const override {return false;};
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+
+private:
+    std::string errorPrefix;
+    template <typename inputType>
+    void executeSpecified();
+    template<typename T>
+    struct NonZeroExecute;
+    template <typename T>
+    size_t getNonZeroElementsCount(const T* arg, const Shape& arg_shape);
+};
+
+}  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
index 626d66921d1d59..db4eb8629219c1 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
@@ -18,36 +18,37 @@ using namespace mkldnn;
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
+#define THROW_ERROR IE_THROW() << "Pad layer with name '" << getName() << "' "
+
 bool MKLDNNPadNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-
-        const auto pad = std::dynamic_pointer_cast<const ngraph::opset1::Pad>(op);
+        auto pad = ov::as_type_ptr<const ngraph::opset1::Pad>(op);
         if (!pad) {
             errorMessage = "Only opset1 Pad operation is supported";
             return false;
         }
-        if (std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PADS_BEGIN_ID)) == nullptr ||
-            std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PADS_END_ID)) == nullptr ||
-            (pad->get_pad_mode() == ngraph::op::PadMode::CONSTANT && pad->get_input_size() == 4 &&
-                std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PAD_VALUE_ID)) == nullptr)) {
-            errorMessage = "Only Constant operation on 'pads_begin', 'pads_end', 'pad_value' inpus is supported";
-            return false;
-        }
+
         const auto pad_mode = pad->get_pad_mode();
-        if (pad_mode != ngraph::op::PadMode::CONSTANT && pad_mode != ngraph::op::PadMode::EDGE && pad_mode != ngraph::op::PadMode::REFLECT &&
-                pad_mode != ngraph::op::PadMode::SYMMETRIC) {
+        if (!MKLDNNPlugin::one_of(pad_mode, ngraph::op::PadMode::CONSTANT, ngraph::op::PadMode::EDGE, ngraph::op::PadMode::REFLECT,
+            ngraph::op::PadMode::SYMMETRIC)) {
             errorMessage = "Has unsupported pad_mode: " + ngraph::as_string(pad_mode);
             return false;
         }
+
+        if (op->get_input_node_shared_ptr(PADS_BEGIN_ID)->get_type_info() != ov::op::v0::Constant::get_type_info_static() ||
+            op->get_input_node_shared_ptr(PADS_END_ID)->get_type_info() != ov::op::v0::Constant::get_type_info_static() ||
+            (pad->get_input_size() == 4 && pad->get_pad_mode() == ngraph::op::PadMode::CONSTANT &&
+            op->get_input_node_shared_ptr(PAD_VALUE_ID)->get_type_info() != ov::op::v0::Constant::get_type_info_static())) {
+            // TODO: Support pads_begin, pads_end, pad_value inputs for dynamic shapes.
+            errorMessage = "Only Constant 'pads_begin', 'pads_end' and 'pad_value' inputs are supported.";
+            return false;
+        }
+
         const auto pb = pad->get_pads_begin();
         const auto pe = pad->get_pads_end();
-        if (std::count_if(pb.begin(), pb.end(), [](ptrdiff_t x) { return x < 0; }) != 0 ||
-                std::count_if(pe.begin(), pe.end(), [](ptrdiff_t x) { return x < 0; }) != 0) {
-            errorMessage = "Doesn't support 'pads_begin' or 'pads_end' negative value";
+        if (std::any_of(pb.begin(), pb.end(), [](ptrdiff_t x) { return x < 0; }) ||
+            std::any_of(pe.begin(), pe.end(), [](ptrdiff_t x) { return x < 0; })) {
+            errorMessage =  "Doesn't support 'pads_begin' or 'pads_end' with negative values";
             return false;
         }
     } catch (...) {
@@ -59,62 +60,61 @@ bool MKLDNNPadNode::isSupportedOperation(const std::shared_ptr<const ngraph::Nod
 MKLDNNPadNode::MKLDNNPadNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
         : MKLDNNNode(op, eng, cache) {
     std::string errorMessage;
-    if (isSupportedOperation(op, errorMessage)) {
-        errorPrefix = "Pad node with name '" + op->get_friendly_name() + "'";
-        const auto pad = std::dynamic_pointer_cast<const ngraph::opset1::Pad>(op);
+    if (!isSupportedOperation(op, errorMessage)) {
+        IE_THROW(NotImplemented) << errorMessage;
+    }
+
+    if (inputShapes.size() != 3 && inputShapes.size() != 4)
+        THROW_ERROR << " has incorrect number of input edges";
+    if (outputShapes.size() != 1)
+        THROW_ERROR << "Incorrect number of output edges";
 
+    const size_t srcDimsRank = inputShapes[DATA_ID].getRank();
+    const size_t dstDimsRank = outputShapes[DATA_ID].getRank();
+    if (srcDimsRank != dstDimsRank)
+        THROW_ERROR << "has incorrect number of input/output dimensions!";
+
+    auto pad = ov::as_type_ptr<const ngraph::opset1::Pad>(op);
+    if (!pad) {
+        THROW_ERROR << "couldn't be casted to op of opset1";
+    }
+
+    if (op->get_input_node_shared_ptr(PADS_BEGIN_ID)->get_type_info() == ov::op::v0::Constant::get_type_info_static() &&
+        op->get_input_node_shared_ptr(PADS_END_ID)->get_type_info() == ov::op::v0::Constant::get_type_info_static()) {
         const auto pb = pad->get_pads_begin();
         const auto pe = pad->get_pads_end();
+
         for (size_t i = 0; i < pb.size(); i++)
-            padsBegin.push_back(static_cast<unsigned int>(pb[i]));
-         for (size_t i = 0; i < pe.size(); i++)
-            padsEnd.push_back(static_cast<unsigned int>(pe[i]));
+            attrs.padsBegin.push_back(static_cast<unsigned int>(pb[i]));
+        for (size_t i = 0; i < pe.size(); i++)
+            attrs.padsEnd.push_back(static_cast<unsigned int>(pe[i]));
 
-        const auto pad_mode = pad->get_pad_mode();
-        isPadValueSpecified = pad->get_input_size() == 4;
-        if (pad_mode == ngraph::op::PadMode::CONSTANT) {
-            padMode = CONSTANT;
-            if (isPadValueSpecified) {
-                if (!ngraph::is_scalar(pad->get_input_shape(PAD_VALUE_ID)))
-                    IE_THROW() << errorPrefix << " has non scalar 'pad_value' input";
-                padValue = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PAD_VALUE_ID))->cast_vector<float>()[0];
-            }
-        } else if (pad_mode == ngraph::op::PadMode::EDGE) {
-            padMode = EDGE;
-        } else if (pad_mode == ngraph::op::PadMode::REFLECT) {
-            padMode = REFLECT;
-        } else if (pad_mode == ngraph::op::PadMode::SYMMETRIC) {
-            padMode = SYMMETRIC;
-        }
-    } else {
-        IE_THROW(NotImplemented) << errorMessage;
+        if (attrs.padsBegin.size() != srcDimsRank || attrs.padsEnd.size() != srcDimsRank)
+            THROW_ERROR << "has incorrect number of input/output dimensions!";
     }
-}
 
-void MKLDNNPadNode::getSupportedDescriptors() {
-    if (getParentEdges().size() != 3 && getParentEdges().size() != 4)
-        IE_THROW() << errorPrefix << " has incorrect number of input edges";
-    if (getChildEdges().empty())
-        IE_THROW() << errorPrefix << "Incorrect number of output edges";
-
-    const auto srcDims = getInputShapeAtPort(DATA_ID).getStaticDims();
-    const auto dstDims = getOutputShapeAtPort(DATA_ID).getStaticDims();
-    if (srcDims.size() != dstDims.size() || padsBegin.size() != srcDims.size() || padsEnd.size() != srcDims.size())
-        IE_THROW() << errorPrefix << " has incorrect number of input/output dimensions!";
-
-    if (padMode == REFLECT) {
-        for (size_t i = 0; i < srcDims.size(); i++) {
-            if ((srcDims[i] - 1) < padsBegin[i] || (srcDims[i] - 1) < padsEnd[i])
-                IE_THROW() << errorPrefix << " has incorrect padsBegin or padsEnd for 'reflect' pad mode";
-        }
-    } else if (padMode == SYMMETRIC) {
-        for (size_t i = 0; i < srcDims.size(); i++) {
-            if (srcDims[i] < padsBegin[i] || srcDims[i] < padsEnd[i])
-                IE_THROW() << errorPrefix << " has incorrect padsBegin or padsEnd for 'symmetric' pad mode";
+    const auto pad_mode = pad->get_pad_mode();
+    isPadValueSpecified = pad->get_input_size() == 4;
+    if (pad_mode == ngraph::op::PadMode::CONSTANT) {
+        attrs.padMode = CONSTANT;
+        if (isPadValueSpecified && op->get_input_node_shared_ptr(PAD_VALUE_ID)->get_type_info() == ov::op::v0::Constant::get_type_info_static()) {
+            if (!ngraph::is_scalar(pad->get_input_shape(PAD_VALUE_ID)))
+                THROW_ERROR << "has non scalar 'pad_value' input";
+            attrs.padValue = ov::as_type_ptr<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PAD_VALUE_ID))->cast_vector<float>()[0];
         }
+    } else if (pad_mode == ngraph::op::PadMode::EDGE) {
+        attrs.padMode = EDGE;
+    } else if (pad_mode == ngraph::op::PadMode::REFLECT) {
+        attrs.padMode = REFLECT;
+    } else if (pad_mode == ngraph::op::PadMode::SYMMETRIC) {
+        attrs.padMode = SYMMETRIC;
+    } else {
+        THROW_ERROR << "has unsupported pad_mode: " + ngraph::as_string(pad_mode);
     }
 }
 
+void MKLDNNPadNode::getSupportedDescriptors() {}
+
 void MKLDNNPadNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
@@ -126,8 +126,8 @@ void MKLDNNPadNode::initSupportedPrimitiveDescriptors() {
     if (std::find(supportedPrecisions.begin(), supportedPrecisions.end(), precision) == supportedPrecisions.end())
         precision = precision.is_float() ? InferenceEngine::Precision::FP32 : InferenceEngine::Precision::I32;
 
-    auto srcDims = getInputShapeAtPort(DATA_ID).getStaticDims();
-    int numOfDims = srcDims.size();
+    const auto& inputDataShape = getInputShapeAtPort(DATA_ID);
+    const size_t numOfDims = inputDataShape.getRank();
 
     NodeConfig config;
     config.dynBatchSupport = false;
@@ -151,15 +151,17 @@ void MKLDNNPadNode::initSupportedPrimitiveDescriptors() {
 
     pushSupportedPrimitiveDescriptor(LayoutType::ncsp);
 
-    auto canUseBlocked = [=](const size_t blockSize) {
-        return (padMode == CONSTANT && padsBegin[1] % blockSize == 0 && padsEnd[1] % blockSize == 0) ||
-               (padMode != CONSTANT && padsBegin[1] == 0 && padsEnd[1] == 0);
+    auto canUseBlocked = [&](const size_t blockSize) {
+        const auto& srcDims = inputDataShape.getDims();
+        return srcDims[1] != Shape::UNDEFINED_DIM && srcDims[1] % blockSize == 0 &&
+               ((attrs.padMode == CONSTANT && attrs.padsBegin[1] % blockSize == 0 && attrs.padsEnd[1] % blockSize == 0) ||
+               (attrs.padMode != CONSTANT && attrs.padsBegin[1] == 0 && attrs.padsEnd[1] == 0));
     };
 
     if (numOfDims == 4 || numOfDims == 5) {
-        if (srcDims[1] % 8 == 0 && canUseBlocked(8))
+        if (canUseBlocked(8))
             pushSupportedPrimitiveDescriptor(LayoutType::nCsp8c);
-        if (srcDims[1] % 16 == 0 && canUseBlocked(16))
+        if (canUseBlocked(16))
             pushSupportedPrimitiveDescriptor(LayoutType::nCsp16c);
     }
 }
@@ -168,65 +170,90 @@ void MKLDNNPadNode::createPrimitive() {
     auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
     if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
-        IE_THROW() << "Destination memory for Pad " << getName() << " didn't allocate.";
+        THROW_ERROR << "has not allocated source memory.";
     if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
-        IE_THROW() << "Input memory for Pad " << getName() << " didn't allocate.";
+        THROW_ERROR << "has not allocated destination memory.";
     if (getSelectedPrimitiveDescriptor() == nullptr)
-        IE_THROW() << "Preferable primitive descriptor for Pad " << getName() << " is not set.";
-
-    params.sizeData = this->getSelectedPrimitiveDescriptor()->getConfig().inConfs[0].desc->getPrecision().size();
-
-    const auto inBlkDesc = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>();
-    params.srcDims = inBlkDesc->getBlockDims();
-    params.dstDims = getChildEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
-
-    size_t nDims = params.srcDims.size();
-    params.srcStrides.resize(nDims, 1);
-    params.dstStrides.resize(nDims, 1);
-    for (int i = nDims - 2; i >= 0; i--) {
-        params.srcStrides[i] = params.srcStrides[i + 1] * params.srcDims[i + 1];
-        params.dstStrides[i] = params.dstStrides[i + 1] * params.dstDims[i + 1];
-    }
-
-    if (getParentEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp16c) ||
-            getParentEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp8c)) {
-        padsBegin[1] /= params.srcDims[params.srcDims.size() - 1];
-        padsEnd[1] /= params.srcDims[params.srcDims.size() - 1];
-        padsBegin.push_back(0);
-        padsEnd.push_back(0);
+        THROW_ERROR << "has unidentified preferable primitive descriptor";
+
+    // pads are constant, so we can calculate new collapsing pads for first target dimensions and use it for the next dimensions
+    // to avoid permanent identical pad calculations
+    const size_t blockSize = srcMemPtr->getDesc().hasLayoutType(LayoutType::nCsp16c) ? 16 :
+                             (srcMemPtr->getDesc().hasLayoutType(LayoutType::nCsp8c) ? 8 : 1);
+    if (blockSize > 1) {
+        attrs.padsBegin[1] /= blockSize;
+        attrs.padsEnd[1] /= blockSize;
+        attrs.padsBegin.push_back(0);
+        attrs.padsEnd.push_back(0);
     } else {
-        auto order = inBlkDesc->getOrder();
-        std::vector<unsigned int> newPadsBegin(padsBegin.size(), 0), newPadsEnd(padsEnd.size(), 0);
-        for (size_t i = 0; i < padsBegin.size(); ++i) {
-            newPadsBegin[i] = padsBegin[order[i]];
-            newPadsEnd[i] = padsEnd[order[i]];
+        auto order = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getOrder();
+        std::vector<unsigned int> newPadsBegin(attrs.padsBegin.size(), 0), newPadsEnd(attrs.padsEnd.size(), 0);
+        for (size_t i = 0; i < attrs.padsBegin.size(); ++i) {
+            newPadsBegin[i] = attrs.padsBegin[order[i]];
+            newPadsEnd[i] = attrs.padsEnd[order[i]];
         }
-        padsBegin = newPadsBegin;
-        padsEnd = newPadsEnd;
+        attrs.padsBegin = newPadsBegin;
+        attrs.padsEnd = newPadsEnd;
     }
 
-    int beginIdx = 0;
-    int endIdx = padsBegin.size() - 1;
+    // collapse dimensions
+    attrs.beginPadIdx = 0;
+    attrs.endPadIdx = attrs.padsBegin.size() - 1;
 
-    for (int i = 0; i < padsBegin.size(); ++i) {
-        if (padsBegin[i] != 0 || padsEnd[i] != 0) {
-            beginIdx = i - 1;
+    for (int i = 0; i < attrs.padsBegin.size(); ++i) {
+        if (attrs.padsBegin[i] != 0 || attrs.padsEnd[i] != 0) {
+            attrs.beginPadIdx = i - 1;
             break;
         }
     }
 
-    for (int i = padsBegin.size() - 1; i >= 0; --i) {
-        if (padsBegin[i] != 0 || padsEnd[i] != 0) {
-            endIdx = i;
+    for (int i = attrs.padsBegin.size() - 1; i >= 0; --i) {
+        if (attrs.padsBegin[i] != 0 || attrs.padsEnd[i] != 0) {
+            attrs.endPadIdx = i;
             break;
         }
     }
 
-    params.lastDstDim = params.dstStrides[std::max(endIdx - 1, 0)];
-    params.nDimsForWork = endIdx - std::max(beginIdx, 0);
+    if (attrs.beginPadIdx > 0) {
+        attrs.padsBegin.erase(attrs.padsBegin.begin() + 1, attrs.padsBegin.begin() + attrs.beginPadIdx + 1);
+        attrs.padsEnd.erase(attrs.padsEnd.begin() + 1, attrs.padsEnd.begin() + attrs.beginPadIdx + 1);
+    }
+
+    attrs.prc = srcMemPtr->getDesc().getPrecision();
+
+    if (inputShapesDefined()) {
+        prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNPadNode::prepareParams() {
+    execPtr = std::make_shared<PadExecutor>(attrs,
+                                            getParentEdgeAt(0)->getMemoryPtr()->GetDescWithType<BlockedMemoryDesc>()->getBlockDims(),
+                                            getChildEdgeAt(0)->getMemoryPtr()->GetDescWithType<BlockedMemoryDesc>()->getBlockDims());
+}
+
+MKLDNNPadNode::PadExecutor::PadExecutor(const PadAttrs& attrs,
+                                        const VectorDims& srcDims,
+                                        const VectorDims& dstDims) {
+    params.attrs = attrs;
+    params.srcDims = srcDims;
+    params.dstDims = dstDims;
+    params.dataSize = attrs.prc.size();
+
+    size_t nDims = params.srcDims.size();
+    params.srcStrides.resize(nDims, 1);
+    params.dstStrides.resize(nDims, 1);
+    for (int i = nDims - 2; i >= 0; i--) {
+        params.srcStrides[i] = params.srcStrides[i + 1] * params.srcDims[i + 1];
+        params.dstStrides[i] = params.dstStrides[i + 1] * params.dstDims[i + 1];
+    }
+
+    params.lastDstDim = params.dstStrides[std::max(params.attrs.endPadIdx - 1, 0)];
+    params.nDimsForWork = params.attrs.endPadIdx - std::max(params.attrs.beginPadIdx, 0);
     params.nThreads = params.nDimsForWork > 0 ? 0 : 1;
     params.workAmount = params.nDimsForWork > 0 ? params.dstDims[0] : 1lu;
-    for (int i = 1; i <= beginIdx; ++i) {
+    for (int i = 1; i <= params.attrs.beginPadIdx; ++i) {
         params.workAmount *= params.dstDims[i];
         params.dstDims[0] *= params.dstDims[i];
         params.srcDims[0] *= params.srcDims[i];
@@ -234,51 +261,62 @@ void MKLDNNPadNode::createPrimitive() {
         params.srcStrides[0] /= params.srcDims[i];
     }
 
-    if (beginIdx > 0) {
-        beginIdx++;
-        params.dstDims.erase(params.dstDims.begin() + 1, params.dstDims.begin() + beginIdx);
-        params.srcDims.erase(params.srcDims.begin() + 1, params.srcDims.begin() + beginIdx);
-        params.dstStrides.erase(params.dstStrides.begin() + 1, params.dstStrides.begin() + beginIdx);
-        params.srcStrides.erase(params.srcStrides.begin() + 1, params.srcStrides.begin() + beginIdx);
-        padsBegin.erase(padsBegin.begin() + 1, padsBegin.begin() + beginIdx);
-        padsEnd.erase(padsEnd.begin() + 1, padsEnd.begin() + beginIdx);
+    if (params.attrs.beginPadIdx > 0) {
+        params.attrs.beginPadIdx++;
+        params.dstDims.erase(params.dstDims.begin() + 1, params.dstDims.begin() + params.attrs.beginPadIdx);
+        params.srcDims.erase(params.srcDims.begin() + 1, params.srcDims.begin() + params.attrs.beginPadIdx);
+        params.dstStrides.erase(params.dstStrides.begin() + 1, params.dstStrides.begin() + params.attrs.beginPadIdx);
+        params.srcStrides.erase(params.srcStrides.begin() + 1, params.srcStrides.begin() + params.attrs.beginPadIdx);
     }
 
     params.workAmount = params.workAmount * params.dstStrides[0] / params.lastDstDim;
     params.shift = params.dstStrides[params.nDimsForWork];
-    if (padMode != CONSTANT || (padMode == CONSTANT && padValue == 0)) {
-        params.lastDstDim *= params.sizeData;
-        params.shift *= params.sizeData;
+    if (params.attrs.padMode != CONSTANT || (params.attrs.padMode == CONSTANT && params.attrs.padValue == 0)) {
+        params.lastDstDim *= params.dataSize;
+        params.shift *= params.dataSize;
     }
 
+    params.srcODims.clear();
     for (size_t i = 0; i < params.srcDims.size(); ++i)
-        params.srcODims.push_back(padsBegin[i] + params.srcDims[i]);
+        params.srcODims.push_back(params.attrs.padsBegin[i] + params.srcDims[i]);
 
-    if (padMode == REFLECT || padMode == SYMMETRIC) {
-        int shift = padMode == SYMMETRIC ? 1 : 0;
+    params.srcDimsForReflectOrSymmetric.clear();
+    if (params.attrs.padMode == REFLECT || params.attrs.padMode == SYMMETRIC) {
+        int shift = params.attrs.padMode == SYMMETRIC ? 1 : 0;
         for (size_t i = 0; i < params.srcDims.size(); ++i)
             params.srcDimsForReflectOrSymmetric.push_back(params.srcDims[i] + params.srcODims[i] - 2 + shift);
     }
 }
 
-void MKLDNNPadNode::execute(mkldnn::stream strm) {
-    switch (padMode) {
+void MKLDNNPadNode::PadExecutor::exec(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
+    switch (params.attrs.padMode) {
         case CONSTANT:
-            padConstant();
+            padConstant(srcMemPtr, dstMemPtr);
             break;
         case EDGE:
-            padEdge();
+            padEdge(srcMemPtr, dstMemPtr);
             break;
         case REFLECT:
-            padReflectOrSymmetric();
+            padReflectOrSymmetric(srcMemPtr, dstMemPtr);
             break;
         case SYMMETRIC:
-            padReflectOrSymmetric(true);
+            padReflectOrSymmetric(srcMemPtr, dstMemPtr, true);
             break;
     }
 }
 
-static inline size_t parallel_init(size_t start, size_t nDims, const SizeVector& dims, SizeVector& indexes) {
+void MKLDNNPadNode::execute(mkldnn::stream strm) {
+    if (!execPtr)
+        THROW_ERROR << "has not compiled executor.";
+
+    execPtr->exec(getParentEdgeAt(0)->getMemoryPtr(), getChildEdgeAt(0)->getMemoryPtr());
+}
+
+void MKLDNNPadNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+}
+
+static inline size_t parallel_init(size_t start, size_t nDims, const VectorDims& dims, VectorDims& indexes) {
     for (int j = nDims - 1; j >= 0; j--) {
         indexes[j] = start % dims[j];
         start = start / dims[j];
@@ -286,7 +324,7 @@ static inline size_t parallel_init(size_t start, size_t nDims, const SizeVector&
     return start;
 }
 
-static inline void parallel_step(size_t nDims, const SizeVector& dims, SizeVector& indexes) {
+static inline void parallel_step(size_t nDims, const VectorDims& dims, VectorDims& indexes) {
     for (int j = nDims - 1; j >= 0; --j) {
         ++indexes[j];
         if (indexes[j] < dims[j])
@@ -296,17 +334,14 @@ static inline void parallel_step(size_t nDims, const SizeVector& dims, SizeVecto
     }
 }
 
-void MKLDNNPadNode::padConstant() {
-    if (padValue == 0) {
-        padConstantZero();
+void MKLDNNPadNode::PadExecutor::padConstant(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
+    if (params.attrs.padValue == 0) {
+        padConstantZero(srcMemPtr, dstMemPtr);
         return;
     }
 
-    auto selectedPrimitiveDescriptor = getSelectedPrimitiveDescriptor();
-    if (!selectedPrimitiveDescriptor)
-        IE_THROW() << "CPU Pad node with name '" << getName() << "' doesn't have primitive descriptors.";
-    InferenceEngine::Precision precision = selectedPrimitiveDescriptor->getConfig().inConfs[0].desc->getPrecision();
-    OV_SWITCH(MKLDNNPlugin, PadConstantEmitter, this, precision,
+    PadContext ctx { this, srcMemPtr, dstMemPtr };
+    OV_SWITCH(MKLDNNPlugin, PadConstantEmitter, ctx, params.attrs.prc,
               OV_CASE(InferenceEngine::Precision::FP32, float),
               OV_CASE(InferenceEngine::Precision::I32, int32_t),
               OV_CASE(InferenceEngine::Precision::BF16, bfloat16_t),
@@ -315,18 +350,18 @@ void MKLDNNPadNode::padConstant() {
 }
 
 template<typename T>
-void MKLDNNPadNode::padConstantCommon() {
-    const T* srcData = reinterpret_cast<const T*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    T* dstData = reinterpret_cast<T*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
-    const T value = static_cast<T>(padValue);
+void MKLDNNPadNode::PadExecutor::padConstantCommon(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
+    const T* srcData = reinterpret_cast<const T*>(srcMemPtr->GetPtr());
+    T* dstData = reinterpret_cast<T*>(dstMemPtr->GetPtr());
+    const T value = static_cast<T>(params.attrs.padValue);
 
-    const size_t beginShift = padsBegin[params.nDimsForWork] * params.shift;
+    const size_t beginShift = params.attrs.padsBegin[params.nDimsForWork] * params.shift;
     const size_t copySize = params.srcDims[params.nDimsForWork] * params.shift;
-    const size_t endShift = padsEnd[params.nDimsForWork] * params.shift;
+    const size_t endShift = params.attrs.padsEnd[params.nDimsForWork] * params.shift;
 
     parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
         size_t start = 0, end = 0;
-        SizeVector indexes(params.nDimsForWork, 0);
+        VectorDims indexes(params.nDimsForWork, 0);
         splitter(params.workAmount, nthr, ithr, start, end);
 
         parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
@@ -336,7 +371,7 @@ void MKLDNNPadNode::padConstantCommon() {
         for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
             size_t j = 0;
             for (; j < params.nDimsForWork; ++j) {
-                if (indexes[j] < padsBegin[j] || indexes[j] >= params.srcODims[j])
+                if (indexes[j] < params.attrs.padsBegin[j] || indexes[j] >= params.srcODims[j])
                     break;
             }
 
@@ -348,10 +383,10 @@ void MKLDNNPadNode::padConstantCommon() {
 
             size_t srcIdx = 0;
             for (size_t idx = 0; idx < params.nDimsForWork; ++idx)
-                srcIdx += (indexes[idx] - padsBegin[idx]) * params.srcStrides[idx];
+                srcIdx += (indexes[idx] - params.attrs.padsBegin[idx]) * params.srcStrides[idx];
 
             std::fill_n(&dstData[dstIdx], beginShift, value);
-            cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize * params.sizeData);
+            cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize * params.dataSize);
             std::fill_n(&dstData[dstIdx + beginShift + copySize], endShift, value);
 
             parallel_step(params.nDimsForWork, params.dstDims, indexes);
@@ -359,27 +394,28 @@ void MKLDNNPadNode::padConstantCommon() {
     });
 }
 
-void MKLDNNPadNode::padConstantZero() {
-    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    uint8_t* dstData = reinterpret_cast<uint8_t*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
+void MKLDNNPadNode::PadExecutor::padConstantZero(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
+    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(srcMemPtr->GetPtr());
+    uint8_t* dstData = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
 
-    const size_t beginShift = padsBegin[params.nDimsForWork] * params.shift;
+    const size_t beginShift = params.attrs.padsBegin[params.nDimsForWork] * params.shift;
     const size_t copySize = params.srcDims[params.nDimsForWork] * params.shift;
-    const size_t endShift = padsEnd[params.nDimsForWork] * params.shift;
+    const size_t endShift = params.attrs.padsEnd[params.nDimsForWork] * params.shift;
 
     parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
         size_t start = 0, end = 0;
-        SizeVector indexes(params.nDimsForWork, 0);
+        VectorDims indexes(params.nDimsForWork, 0);
         splitter(params.workAmount, nthr, ithr, start, end);
 
         parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
         size_t dstIdx = 0;
         getDstIdx(indexes, dstIdx);
+        dstIdx *= params.dataSize;
 
         for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
             size_t j = 0;
             for (; j < params.nDimsForWork; ++j) {
-                if (indexes[j] < padsBegin[j] || indexes[j] >= params.srcODims[j])
+                if (indexes[j] < params.attrs.padsBegin[j] || indexes[j] >= params.srcODims[j])
                     break;
             }
 
@@ -391,8 +427,8 @@ void MKLDNNPadNode::padConstantZero() {
 
             size_t srcIdx = 0;
             for (size_t idx = 0; idx < params.nDimsForWork; ++idx)
-                srcIdx += (indexes[idx] - padsBegin[idx]) * params.srcStrides[idx];
-            srcIdx *= params.sizeData;
+                srcIdx += (indexes[idx] - params.attrs.padsBegin[idx]) * params.srcStrides[idx];
+            srcIdx *= params.dataSize;
 
             memset(&dstData[dstIdx], 0, beginShift);
             cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize);
@@ -403,37 +439,38 @@ void MKLDNNPadNode::padConstantZero() {
     });
 }
 
-void MKLDNNPadNode::padEdge() {
-    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    uint8_t* dstData = reinterpret_cast<uint8_t*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
+void MKLDNNPadNode::PadExecutor::padEdge(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
+    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(srcMemPtr->GetPtr());
+    uint8_t* dstData = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
 
-    const size_t beginShift = padsBegin[params.nDimsForWork] * params.shift;
+    const size_t beginShift = params.attrs.padsBegin[params.nDimsForWork] * params.shift;
     const size_t copySize = params.srcDims[params.nDimsForWork] * params.shift;
 
     parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
         size_t start = 0, end = 0;
-        SizeVector indexes(params.nDimsForWork, 0);
+        VectorDims indexes(params.nDimsForWork, 0);
         splitter(params.workAmount, nthr, ithr, start, end);
 
         parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
         size_t dstIdx = 0;
         getDstIdx(indexes, dstIdx);
+        dstIdx *= params.dataSize;
 
         for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
             size_t srcIdx = 0;
             for (size_t idx = 0; idx < params.nDimsForWork; ++idx) {
-                size_t shift = (indexes[idx] < padsBegin[idx]) ? 0 :
-                               ((indexes[idx] >= params.srcODims[idx]) ? (params.srcDims[idx] - 1) : (indexes[idx] - padsBegin[idx]));
+                size_t shift = (indexes[idx] < params.attrs.padsBegin[idx]) ? 0 :
+                               ((indexes[idx] >= params.srcODims[idx]) ? (params.srcDims[idx] - 1) : (indexes[idx] - params.attrs.padsBegin[idx]));
                 srcIdx += shift * params.srcStrides[idx];
             }
-            srcIdx *= params.sizeData;
+            srcIdx *= params.dataSize;
 
-            for (size_t i = 0; i < padsBegin[params.nDimsForWork]; ++i)
+            for (size_t i = 0; i < params.attrs.padsBegin[params.nDimsForWork]; ++i)
                 cpu_memcpy(&dstData[dstIdx + i * params.shift], &srcData[srcIdx], params.shift);
 
             cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize);
 
-            for (size_t i = 0; i < padsEnd[params.nDimsForWork]; ++i)
+            for (size_t i = 0; i < params.attrs.padsEnd[params.nDimsForWork]; ++i)
                 cpu_memcpy(&dstData[dstIdx + beginShift + copySize + i * params.shift],
                            &srcData[srcIdx + (params.srcDims[params.nDimsForWork] - 1) * params.shift], params.shift);
 
@@ -442,38 +479,40 @@ void MKLDNNPadNode::padEdge() {
     });
 }
 
-void MKLDNNPadNode::padReflectOrSymmetric(const bool isSymmetric) {
-    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    uint8_t* dstData = reinterpret_cast<uint8_t*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
+void MKLDNNPadNode::PadExecutor::padReflectOrSymmetric(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr, const bool isSymmetric) {
+    const uint8_t* srcData = reinterpret_cast<const uint8_t*>(srcMemPtr->GetPtr());
+    uint8_t* dstData = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
     size_t shift = isSymmetric ? 1 : 0;
 
     parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
         size_t start = 0, end = 0;
-        SizeVector indexes(params.nDimsForWork, 0);
+        VectorDims indexes(params.nDimsForWork, 0);
         splitter(params.workAmount, nthr, ithr, start, end);
 
         parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
         size_t dstIdx = 0;
         getDstIdx(indexes, dstIdx);
+        dstIdx *= params.dataSize;
 
         for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
             size_t srcIdx = 0;
             for (size_t i = 0; i < params.nDimsForWork; ++i) {
-                size_t idx = (indexes[i] < padsBegin[i]) ? (padsBegin[i] - indexes[i] - shift) :
-                             ((indexes[i] >= params.srcODims[i]) ? (params.srcDimsForReflectOrSymmetric[i] - indexes[i]) : (indexes[i] - padsBegin[i]));
+                size_t idx = (indexes[i] < params.attrs.padsBegin[i]) ? (params.attrs.padsBegin[i] - indexes[i] - shift) :
+                             ((indexes[i] >= params.srcODims[i]) ? (params.srcDimsForReflectOrSymmetric[i] - indexes[i]) :
+                             (indexes[i] - params.attrs.padsBegin[i]));
                 srcIdx += idx * params.srcStrides[i];
             }
-            srcIdx *= params.sizeData;
+            srcIdx *= params.dataSize;
 
-            for (size_t i = 0; i < padsBegin[params.nDimsForWork]; ++i)
+            for (size_t i = 0; i < params.attrs.padsBegin[params.nDimsForWork]; ++i)
                 cpu_memcpy(&dstData[dstIdx + i * params.shift],
-                           &srcData[srcIdx + (padsBegin[params.nDimsForWork] - shift - i) * params.shift], params.shift);
+                           &srcData[srcIdx + (params.attrs.padsBegin[params.nDimsForWork] - shift - i) * params.shift], params.shift);
 
-            cpu_memcpy(&dstData[dstIdx + padsBegin[params.nDimsForWork] * params.shift], &srcData[srcIdx],
+            cpu_memcpy(&dstData[dstIdx + params.attrs.padsBegin[params.nDimsForWork] * params.shift], &srcData[srcIdx],
                        params.srcDims[params.nDimsForWork] * params.shift);
 
             size_t srcShift = (params.srcDimsForReflectOrSymmetric[params.nDimsForWork] - params.srcODims[params.nDimsForWork]) * params.shift;
-            for (size_t i = 0; i < padsEnd[params.nDimsForWork]; ++i)
+            for (size_t i = 0; i < params.attrs.padsEnd[params.nDimsForWork]; ++i)
                 cpu_memcpy(&dstData[dstIdx + (params.srcODims[params.nDimsForWork] + i) * params.shift],
                            &srcData[srcIdx + srcShift - i * params.shift], params.shift);
 
@@ -482,10 +521,9 @@ void MKLDNNPadNode::padReflectOrSymmetric(const bool isSymmetric) {
     });
 }
 
-inline void MKLDNNPadNode::getDstIdx(const InferenceEngine::SizeVector& indexes, size_t& dstIdx) const {
+inline void MKLDNNPadNode::PadExecutor::getDstIdx(const VectorDims& indexes, size_t& dstIdx) const {
     for (size_t i = 0; i < params.nDimsForWork; ++i)
         dstIdx += indexes[i] * params.dstStrides[i];
-    dstIdx *= (padMode == CONSTANT && padValue != 0) ? 1 : params.sizeData;
 }
 
 bool MKLDNNPadNode::created() const {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
index 68af48dac0ba50..74189292fd80c4 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
@@ -14,13 +14,17 @@ class MKLDNNPadNode : public MKLDNNNode {
 public:
     MKLDNNPadNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
 
+    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
     void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
-    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+    void prepareParams() override;
+
+protected:
+    void executeDynamicImpl(mkldnn::stream strm) override;
 
 private:
     enum PadMode {
@@ -30,48 +34,70 @@ class MKLDNNPadNode : public MKLDNNNode {
         SYMMETRIC = 3
     };
 
-    void padConstant();
-    template<typename T> void padConstantCommon();
-    void padConstantZero();
-    void padEdge();
-    void padReflectOrSymmetric(const bool isSymmetric = false);
-
-    inline void getDstIdx(const InferenceEngine::SizeVector& indexes, size_t& dstIdx) const;
-
-    PadMode padMode = CONSTANT;
-    float padValue = 0.f;
-    std::vector<unsigned int> padsBegin;
-    std::vector<unsigned int> padsEnd;
-
-    struct {
-        InferenceEngine::SizeVector srcDims;
-        InferenceEngine::SizeVector dstDims;
-        InferenceEngine::SizeVector srcODims;
-        InferenceEngine::SizeVector srcStrides;
-        InferenceEngine::SizeVector dstStrides;
-        InferenceEngine::SizeVector srcDimsForReflectOrSymmetric;
-        int nThreads = 0;
-        size_t nDimsForWork = 0lu;
-        size_t workAmount = 0lu;
-        size_t lastDstDim = 1lu;
-        size_t shift = 0lu;
-        uint8_t sizeData = 1;
-    } params;
-
-    template<typename T>
-    struct PadConstantEmitter {
-        void operator()(MKLDNNPadNode* node) {
-            node->padConstantCommon<T>();
-        }
+    struct PadAttrs {
+        PadMode padMode = CONSTANT;
+        float padValue = 0.f;
+        std::vector<unsigned int> padsBegin;
+        std::vector<unsigned int> padsEnd;
+        int beginPadIdx = 0;
+        int endPadIdx = 0;
+        InferenceEngine::Precision prc;
+    } attrs;
+
+    struct PadExecutor {
+        PadExecutor(const PadAttrs& params, const VectorDims& srcDims, const VectorDims& dstDims);
+        void exec(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
+        ~PadExecutor() = default;
+
+    private:
+        void padConstant(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
+        template<typename T> void padConstantCommon(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
+        void padConstantZero(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
+        void padEdge(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
+        void padReflectOrSymmetric(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr, const bool isSymmetric = false);
+
+        inline void getDstIdx(const VectorDims& indexes, size_t& dstIdx) const;
+
+        struct PadContext {
+            PadExecutor* executor;
+            MKLDNNMemoryPtr srcMemPtr;
+            MKLDNNMemoryPtr dstMemPtr;
+        };
+
+        template<typename T>
+        struct PadConstantEmitter {
+            void operator()(PadContext& ctx) {
+                ctx.executor->padConstantCommon<T>(ctx.srcMemPtr, ctx.dstMemPtr);
+            }
+        };
+
+        struct {
+            PadAttrs attrs;
+            VectorDims srcDims;
+            VectorDims dstDims;
+            VectorDims srcODims;
+            VectorDims srcStrides;
+            VectorDims dstStrides;
+            VectorDims srcDimsForReflectOrSymmetric;
+            int nThreads = 0;
+            size_t nDimsForWork = 0lu;
+            size_t workAmount = 0lu;
+            size_t lastDstDim = 1lu;
+            size_t shift = 0lu;
+            size_t dataSize = 1lu;
+            PadMode padMode;
+        } params;
     };
 
-    std::string errorPrefix;
-    static const size_t DATA_ID = 0;
-    static const size_t PADS_BEGIN_ID = 1;
-    static const size_t PADS_END_ID = 2;
-    static const size_t PAD_VALUE_ID = 3;
+    static constexpr size_t DATA_ID = 0lu;
+    static constexpr size_t PADS_BEGIN_ID = 1lu;
+    static constexpr size_t PADS_END_ID = 2lu;
+    static constexpr size_t PAD_VALUE_ID = 3lu;
 
     bool isPadValueSpecified = false;
+
+    using executorPtr = std::shared_ptr<PadExecutor>;
+    executorPtr execPtr = nullptr;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp
index 72f26b5b0b4933..941c043d140d11 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp
@@ -22,10 +22,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNPoolingNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         if (!ngraph::as_type_ptr<const ngraph::op::v1::MaxPool>(op) && !ngraph::as_type_ptr<const ngraph::op::v1::AvgPool>(op)) {
             errorMessage = "Only opset1 MaxPool and AvgPool operations are supported";
             return false;
@@ -61,6 +57,7 @@ MKLDNNPoolingNode::MKLDNNPoolingNode(const std::shared_ptr<ngraph::Node>& op, co
         for (int i = 0; i < maxPoolOp->get_pads_end().size(); i++) {
             data_pad_end.push_back(static_cast<ptrdiff_t>(maxPoolOp->get_pads_end()[i]));
         }
+        auto_pad = (maxPoolOp->get_auto_pad() == ov::op::PadType::SAME_LOWER || maxPoolOp->get_auto_pad() == ov::op::PadType::SAME_UPPER);
     } else if (avgPoolOp) {
         algorithm = PoolingAvg;
         exclude_pad = avgPoolOp->get_exclude_pad();
@@ -77,6 +74,7 @@ MKLDNNPoolingNode::MKLDNNPoolingNode(const std::shared_ptr<ngraph::Node>& op, co
         for (int i = 0; i < avgPoolOp->get_pads_end().size(); i++) {
             data_pad_end.push_back(static_cast<ptrdiff_t>(avgPoolOp->get_pads_end()[i]));
         }
+        auto_pad = (avgPoolOp->get_auto_pad() == ov::op::PadType::SAME_LOWER || avgPoolOp->get_auto_pad() == ov::op::PadType::SAME_UPPER);
     }
 }
 
@@ -96,6 +94,23 @@ std::vector<memory::format_tag> MKLDNNPoolingNode::getAvailableFormatsForDims(co
     return {memory::format_tag::any};
 }
 
+void MKLDNNPoolingNode::initEffectivePad(const Shape &inShape, const Shape &outShape) {
+    effective_pad_begin = data_pad_begin;
+    effective_pad_end.resize(data_pad_end.size());
+
+    const auto &inDims = inShape.getStaticDims();
+    const auto &outDims = outShape.getStaticDims();
+
+    for (int i = 0; i < effective_pad_end.size(); i++) {
+        int krn = kernel[i];
+        int src = inDims[2 + i];
+        int dst = outDims[2 + i];
+
+        int calc_dst = (src - krn + data_pad_begin[i]) / stride[i] + 1;
+        effective_pad_end[i] = (dst - calc_dst) * stride[i];
+    }
+}
+
 void MKLDNNPoolingNode::getSupportedDescriptors() {
     if (!descs.empty())
         return;
@@ -129,24 +144,16 @@ void MKLDNNPoolingNode::getSupportedDescriptors() {
     auto inputDataType = MKLDNNExtensionUtils::IEPrecisionToDataType(inputPrecision);
     auto outputDataType = MKLDNNExtensionUtils::IEPrecisionToDataType(outputPrecision);
 
-    effective_pad_begin = data_pad_begin;
-    effective_pad_end.resize(data_pad_end.size());
-
-    auto parentShape = getInputShapeAtPort(0);
-    auto childShape = getOutputShapeAtPort(0);
+    const auto &parentShape = getInputShapeAtPort(0);
+    const auto &childShape = getOutputShapeAtPort(0);
     const size_t inputRank = getInputShapeAtPort(0).getRank();
 
     if ((inputRank < 4) || (inputRank > 5))
         IE_THROW() << "Pooling layer. Unsupported mode. Only 4D and 5D blobs are supported as input.";
 
-    for (int i = 0; i < effective_pad_end.size(); i++) {
-        int krn = kernel[i];
-        int src = getInputShapeAtPort(0).getStaticDims()[2 + i];
-        int dst = getOutputShapeAtPort(0).getStaticDims()[2 + i];
+    initEffectivePad(MemoryDescUtils::makeDummyShape(parentShape),
+                     MemoryDescUtils::makeDummyShape(childShape));
 
-        int calc_dst = (src - krn + data_pad_begin[i]) / stride[i] + 1;
-        effective_pad_end[i] = (dst - calc_dst) * stride[i];
-    }
     if (inputPrecision == Precision::I8 || inputPrecision == Precision::U8) {
         //  We have to extend i8i8_pooling_fwd_t from oneDNN to support BF16 output data type
         if (outputDataType == memory::data_type::bf16)
@@ -157,7 +164,7 @@ void MKLDNNPoolingNode::getSupportedDescriptors() {
         const auto out_candidate = std::make_shared<DnnlBlockedMemoryDesc>(childShape, outputDataType, inputRank == 5 ?
                                                                                     memory::format_tag::ndhwc : memory::format_tag::nhwc);
         createDescriptor({ in_candidate }, { out_candidate });
-    } else if ((inputRank == 4 || inputRank == 5) && parentShape.getStaticDims()[1] == 1) {
+    } else if ((inputRank == 4 || inputRank == 5) && parentShape.getDims()[1] == 1) {
         // WA. We should force planar layout since it provides better performance
         const auto in_candidate = std::make_shared<DnnlBlockedMemoryDesc>(parentShape, inputDataType, inputRank == 5 ?
                                                                                    memory::format_tag::ncdhw : memory::format_tag::nchw);
@@ -178,14 +185,70 @@ void MKLDNNPoolingNode::getSupportedDescriptors() {
     }
 }
 
-void MKLDNNPoolingNode::createPrimitive() {
-    if (prim)
-        return;
+std::pair<std::vector<ptrdiff_t>, std::vector<ptrdiff_t>> MKLDNNPoolingNode::getPaddingFromNode(std::shared_ptr<ngraph::Node> node) const {
+    const auto convertPadding = [](const VectorDims &newPads) {
+        std::vector<ptrdiff_t> pads(newPads.size());
+        for (int i = 0; i < newPads.size(); i++) {
+            pads[i] = static_cast<ptrdiff_t>(newPads[i]);
+        }
+        return pads;
+    };
 
-    mkldnn::primitive_attr attr;
-    setPostOps(attr, true);
+    VectorDims padsBegin, padsEnd;
+    if (getAlgorithm() == PoolingMax) {
+        const auto pool = ngraph::as_type_ptr<const ngraph::op::v1::MaxPool>(opToShapeInfer);
+        padsBegin = pool->get_pads_begin();
+        padsEnd = pool->get_pads_end();
+    } else if (getAlgorithm() == PoolingAvg) {
+        const auto pool = ngraph::as_type_ptr<const ngraph::op::v1::AvgPool>(opToShapeInfer);
+        padsBegin = pool->get_pads_begin();
+        padsEnd = pool->get_pads_end();
+    }
+    return {convertPadding(padsBegin), convertPadding(padsEnd)};
+}
+
+void MKLDNNPoolingNode::prepareParams() {
+    const NodeDesc *selected_pd = getSelectedPrimitiveDescriptor();
+    if (selected_pd == nullptr)
+        IE_THROW()  << "Pooling node with name '" << getName() << "' did not set preferable primitive descriptor";
+
+    AttrPtr attr;
+    if (isDynamicNode()) {
+        if (!pAttr) {
+            pAttr = initPrimitiveAttr();
+        }
+        attr = pAttr;
+    } else {
+        attr = initPrimitiveAttr();
+    }
+
+    auto inDesc = getParentEdgesAtPort(0)[0]->getMemory().GetDescWithType<DnnlMemoryDesc>();
+    const auto& in_candidate = inDesc->getDnnlDesc();
+    auto outDesc = getChildEdgesAtPort(0)[0]->getMemory().GetDescWithType<DnnlMemoryDesc>();
+    const auto& out_candidate = outDesc->getDnnlDesc();
 
-    auto prim_desc = createPrimitiveDescriptor<pooling_forward::primitive_desc, pooling_forward::desc>(attr);
+    if (isDynamicNode()) {
+        if (auto_pad) {
+            std::tie(data_pad_begin, data_pad_end) = getPaddingFromNode(opToShapeInfer);
+        }
+        initEffectivePad(inDesc->getShape(), outDesc->getShape());
+    }
+
+    mkldnn::algorithm alg = getPoolingAlgorithm();
+    MKLDNNDescriptor desc{createDescriptorInternal(in_candidate, out_candidate, alg)};
+    pooling_forward::primitive_desc prim_desc;
+    primitive_desc_iterator itpd = desc.createPrimitiveDescriptorIterator(getEngine(), *attr);
+
+    while (static_cast<bool>(itpd))  {
+        impl_desc_type impl_type = parse_impl_name(itpd.impl_info_str());
+
+        if (impl_type == selected_pd->getImplementationType()) {
+            prim_desc = itpd.get();
+            break;
+        }
+        if (!itpd.next_impl())
+            IE_THROW() << "Primitive descriptor was not found for node " << getName() << ".";
+    }
 
     prim.reset(new pooling_forward(prim_desc));
 
@@ -194,16 +257,19 @@ void MKLDNNPoolingNode::createPrimitive() {
     primArgs = {{DNNL_ARG_SRC, src}, {DNNL_ARG_DST, dst}};
 }
 
+void MKLDNNPoolingNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
 bool MKLDNNPoolingNode::created() const {
     return getType() == Pooling;
 }
 
-void MKLDNNPoolingNode::createDescriptor(const std::vector<MemoryDescPtr> &inputDesc,
-                                         const std::vector<MemoryDescPtr> &outputDesc) {
-    auto in_candidate =  MemoryDescUtils::convertToDnnlMemoryDesc(inputDesc[0])->getDnnlDesc();
-    auto out_candidate = MemoryDescUtils::convertToDnnlMemoryDesc(outputDesc[0])->getDnnlDesc();
-
-    mkldnn::algorithm alg;
+mkldnn::algorithm MKLDNNPoolingNode::getPoolingAlgorithm() const {
     if (algorithm == PoolingAvg) {
         bool not_zero_l = false;
         for (auto lr : data_pad_begin) {
@@ -220,12 +286,20 @@ void MKLDNNPoolingNode::createDescriptor(const std::vector<MemoryDescPtr> &input
             }
         }
         if (!exclude_pad && (not_zero_l || not_zero_r))
-            alg = mkldnn::algorithm::pooling_avg_include_padding;
+            return mkldnn::algorithm::pooling_avg_include_padding;
         else
-            alg = mkldnn::algorithm::pooling_avg_exclude_padding;
+            return mkldnn::algorithm::pooling_avg_exclude_padding;
     } else if (algorithm == PoolingMax) {
-        alg = mkldnn::algorithm::pooling_max;
+        return mkldnn::algorithm::pooling_max;
     } else {
+        return mkldnn::algorithm::undef;
+    }
+}
+
+std::shared_ptr<pooling_forward::desc> MKLDNNPoolingNode::createDescriptorInternal(const mkldnn::memory::desc& in_candidate,
+                                                                                   const mkldnn::memory::desc& out_candidate,
+                                                                                   const mkldnn::algorithm alg) const {
+    if (alg == mkldnn::algorithm::undef) {
         IE_THROW() << "Unsupported pooling type";
     }
 
@@ -253,6 +327,30 @@ void MKLDNNPoolingNode::createDescriptor(const std::vector<MemoryDescPtr> &input
         }
     }
 
+    return desc_ptr;
+}
+
+void MKLDNNPoolingNode::createDescriptor(const std::vector<MemoryDescPtr> &inputDesc,
+                                         const std::vector<MemoryDescPtr> &outputDesc) {
+    auto inDesc = inputDesc[0]->isDefined() ? inputDesc[0] : MemoryDescUtils::makeDummyDesc(*inputDesc[0]);
+    auto dnnlInDesc = MemoryDescUtils::convertToDnnlMemoryDesc(inDesc);
+    auto in_candidate = dnnlInDesc->getDnnlDesc();
+
+    auto outDesc = outputDesc[0];
+    if (!outDesc->isDefined()) {
+        auto outDims = shapeInferGeneric({Shape(inDesc->getShape().getStaticDims())});
+        outDesc = outDesc->cloneWithNewDims(outDims[0]);
+        if (auto_pad) {
+            std::tie(data_pad_begin, data_pad_end) = getPaddingFromNode(opToShapeInfer);
+        }
+        initEffectivePad(inDesc->getShape(), outDesc->getShape());
+    }
+    auto dnnlOutDesc = MemoryDescUtils::convertToDnnlBlockedMemoryDesc(*outDesc);
+    auto out_candidate = dnnlOutDesc.getDnnlDesc();
+
+    mkldnn::algorithm alg = getPoolingAlgorithm();
+    auto desc_ptr = createDescriptorInternal(in_candidate, out_candidate, alg);
+
     descs.emplace_back(desc_ptr);
 }
 
@@ -272,12 +370,8 @@ void MKLDNNPoolingNode::initSupportedPrimitiveDescriptors() {
                 PortConfig dataConfig;
                 dataConfig.inPlace = -1;
                 dataConfig.constant = false;
-                auto desc = getSrcMemDesc(itpd, i);
-                if (desc->getType() & MemoryDescType::Blocked) {
-                    dataConfig.desc = desc->as<BlockedMemoryDesc>()->cloneWithUndefStridesAndOffset();
-                } else {
-                    dataConfig.desc = std::move(desc);
-                }
+                dataConfig.desc = getSrcMemDesc(itpd, i);
+
                 config.inConfs.push_back(dataConfig);
             }
 
@@ -285,12 +379,8 @@ void MKLDNNPoolingNode::initSupportedPrimitiveDescriptors() {
                 PortConfig dataConfig;
                 dataConfig.inPlace = canBeInPlace() ? 0 : -1;
                 dataConfig.constant = false;
-                auto desc = getDstMemDesc(itpd, i);
-                if (desc->getType() & MemoryDescType::Blocked) {
-                    dataConfig.desc = desc->as<BlockedMemoryDesc>()->cloneWithUndefStridesAndOffset();
-                } else {
-                    dataConfig.desc = std::move(desc);
-                }
+                dataConfig.desc = getDstMemDesc(itpd, i);
+
                 config.outConfs.push_back(dataConfig);
             }
             impl_desc_type impl_type = parse_impl_name(itpd.impl_info_str());
@@ -382,7 +472,15 @@ void MKLDNNPoolingNode::initDescriptor(const NodeConfig& config) {
     selectedPD->setConfig(rightConfig);
 }
 
-void MKLDNNPoolingNode::setPostOps(mkldnn::primitive_attr &attr, bool initWeights) {
+MKLDNNNode::AttrPtr MKLDNNPoolingNode::initPrimitiveAttr() const {
+    auto attr = std::make_shared<mkldnn::primitive_attr>(mkldnn::primitive_attr());
+
+    setPostOps(*attr, true);
+
+    return attr;
+}
+
+void MKLDNNPoolingNode::setPostOps(mkldnn::primitive_attr &attr, bool initWeights) const {
     mkldnn::post_ops ops;
 
     for (auto &node : fusedWith) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
index d131bed7857cad..104b6f65e0bbe7 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
@@ -28,11 +28,27 @@ class MKLDNNPoolingNode : public MKLDNNNode {
         return false;
     }
 
+    void prepareParams() override;;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+protected:
+    AttrPtr initPrimitiveAttr() const override;
+
 private:
-    void setPostOps(mkldnn::primitive_attr &attr, bool initWeights = false);
+    void setPostOps(mkldnn::primitive_attr &attr, bool initWeights = false) const;
+
+    std::pair<std::vector<ptrdiff_t>, std::vector<ptrdiff_t>> getPaddingFromNode(std::shared_ptr<ngraph::Node> node) const;
+    void initEffectivePad(const Shape &inDims, const Shape &outDims);
+    mkldnn::algorithm getPoolingAlgorithm() const;
+    std::shared_ptr<mkldnn::pooling_forward::desc> createDescriptorInternal(const mkldnn::memory::desc& in_candidate,
+                                                                            const mkldnn::memory::desc& out_candidate,
+                                                                            const mkldnn::algorithm alg) const;
+
+    AttrPtr pAttr;
 
+    bool auto_pad = false;
     bool exclude_pad = false;
     std::vector<ptrdiff_t> stride;
     std::vector<ptrdiff_t> kernel;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp
index bcc37aebfe5305..1d3f2ff60d11fe 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.cpp
@@ -73,10 +73,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNProposalNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto proposal0Op = ngraph::as_type_ptr<const ngraph::op::v0::Proposal>(op);
         const auto proposal4Op = ngraph::as_type_ptr<const ngraph::op::v4::Proposal>(op);
         if (!proposal0Op && !proposal4Op) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h
index c2da9756b7fc45..0c0c6d443a86ce 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_proposal_node.h
@@ -22,6 +22,9 @@ class MKLDNNProposalNode : public MKLDNNNode {
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needPrepareParams() const override { return false; };
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
@@ -35,8 +38,6 @@ class MKLDNNProposalNode : public MKLDNNNode {
     std::vector<float> anchors;
     std::vector<int> roi_indices;
     bool store_prob;  // store blob with proposal probabilities
-
-    std::string errorPrefix;
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.cpp
index 5ee2f0f92dd3bd..3d34abdef96d23 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.cpp
@@ -22,6 +22,13 @@ MKLDNNReferenceNode::MKLDNNReferenceNode(const std::shared_ptr<ngraph::Node>& op
     }
     setType(Reference);
     setTypeStr("Reference");
+
+    // RandomUniform should generate new sequence each run even if all inputs are constants. So that method MKLDNNNode::IsConstant()
+    // doesn't return 'True' for RandomUniform with all constant inputs and the node generates new values for each inference,
+    // we set 'NoConst' value for 'ConstantType' in ctor
+    if (ov::is_type<ngraph::op::v8::RandomUniform>(ngraphOp)) {
+        constant = ConstantType::NoConst;
+    }
 }
 
 void MKLDNNReferenceNode::getSupportedDescriptors() {}
@@ -98,3 +105,7 @@ void MKLDNNReferenceNode::executeDynamicImpl(mkldnn::stream strm) {
 bool MKLDNNReferenceNode::created() const {
     return getType() == Reference;
 }
+
+bool MKLDNNReferenceNode::needShapeInfer() const {
+    return true;
+}
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.h
index 41c4b3c7ac83d8..7b22ca646dbd79 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reference_node.h
@@ -19,6 +19,7 @@ class MKLDNNReferenceNode : public MKLDNNNode {
     bool created() const override;
 
     std::vector<VectorDims> shapeInfer() const override;
+    bool needShapeInfer() const override;
     bool needPrepareParams() const override { return false; }
     void executeDynamicImpl(mkldnn::stream strm) override;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.cpp
index d4367bb4837d80..7c3e3eb2c20e46 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.cpp
@@ -794,6 +794,7 @@ void MKLDNNRNN::execute(mkldnn::stream strm) {
     (*prim).execute(strm, args);
 }
 
+}  // namespace MKLDNNPlugin
+
 REG_MKLDNN_PRIM_FOR(MKLDNNRNN, RNNCell);
 REG_MKLDNN_PRIM_FOR(MKLDNNRNN, RNNSeq);
-}  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp
index c6081a04593c61..1412d0d5afeb94 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.cpp
@@ -21,11 +21,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNSplitNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-
         if (!MKLDNNPlugin::one_of(op->get_type_info(), ngraph::op::v1::Split::get_type_info_static(), ngraph::op::v1::VariadicSplit::get_type_info_static())) {
             errorMessage = "Only opset1 Split and VariadicSplit operations are supported";
             return false;
@@ -61,12 +56,13 @@ MKLDNNSplitNode::MKLDNNSplitNode(const std::shared_ptr<ngraph::Node>& op, const
         INPUTS_NUM = 3;
     }
 
+    const auto inRank = getInputShapeAtPort(0).getRank();
     auto axisOp = ngraph::as_type_ptr<ngraph::op::v0::Constant>(op->get_input_node_shared_ptr(1));
     auto axis = axisOp->cast_vector<int64_t>()[0];
     if (axis < 0) {
-        axis += op->get_input_shape(0).size();
+        axis += inRank;
     }
-    if (axis >= op->get_input_shape(0).size()) {
+    if (axis >= inRank) {
         THROW_ERROR << "Split node with name '" << op->get_friendly_name() << "' has invalid value of axis parameter: " << axis;
     }
     this->axis = axis;
@@ -81,26 +77,21 @@ void MKLDNNSplitNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    auto srcShape = getInputShapeAtPort(0);
-    auto axis_size = 0;
-    auto dstFirstDims = getOutputShapeAtPort(0).getStaticDims();
+    const auto &srcShape = getInputShapeAtPort(0);
+    const auto &dstFirstDims = getOutputShapeAtPort(0).getDims();
     for (size_t i = 0; i < outputShapes.size(); i++) {
-        auto o_Dims = outputShapes[i].getStaticDims();
+        const auto &o_Dims = outputShapes[i].getDims();
         if (dstFirstDims.size() != o_Dims.size()) {
             THROW_ERROR << "only supports output blobs with equal number of dimensions";
         }
 
-        axis_size += o_Dims[axis];
         for (size_t j = 0; j < dstFirstDims.size(); j++) {
             if (j == axis)
                 continue;
-            if (o_Dims[j] != dstFirstDims[j])
+            if (!dimsEqualWeak(o_Dims[j], dstFirstDims[j]))
                 THROW_ERROR << "has incorrect output dimensions";
         }
     }
-    dstFirstDims[axis] = axis_size;
-    if (std::accumulate(dstFirstDims.begin(), dstFirstDims.end(), 1, std::multiplies<size_t>()) != srcShape.getElementsCount())
-        THROW_ERROR << "sizes of input blob and sum of output blobs are not equal.";
 
     InferenceEngine::Precision inpPrecision = getOriginalInputPrecisionAtPort(0);
     const auto axisPrecision = getOriginalInputPrecisionAtPort(1);
@@ -111,19 +102,20 @@ void MKLDNNSplitNode::initSupportedPrimitiveDescriptors() {
         dynBatchSupport = false;
     }
 
-    //Set plain and tailC formats
+    // Set plain and tailC formats
     std::vector<LayoutType> tdCreatorTypes{ LayoutType::ncsp, LayoutType::nspc };
 
-    //Support channel blocked format
+    // Support channel blocked format
     if (srcShape.getRank() > 2) {
         for (auto item : { std::make_pair(8lu, LayoutType::nCsp8c), std::make_pair(16lu, LayoutType::nCsp16c) }) {
-            SizeVector blkDims = srcShape.getStaticDims();
-            if (blkDims[channelsPos] % item.first)
+            const auto &blkDims = srcShape.getDims();
+            if (blkDims[channelsPos] == Shape::UNDEFINED_DIM || blkDims[channelsPos] % item.first != 0)
                 continue;
 
             bool blocked = true;
             for (size_t i = 0; i < outputShapes.size(); i++) {
-                if (outputShapes[i].getStaticDims()[channelsPos] % item.first) {
+                const auto &outBlkDims = getOutputShapeAtPort(i).getDims();
+                if (outBlkDims[channelsPos] == Shape::UNDEFINED_DIM || outBlkDims[channelsPos] % item.first != 0) {
                     blocked = false;
                     break;
                 }
@@ -148,9 +140,9 @@ void MKLDNNSplitNode::initSupportedPrimitiveDescriptors() {
         config.inConfs[0].desc = std::make_shared<CpuBlockedMemoryDesc>(itr->second->createDesc(inpPrecision, srcShape));
         config.inConfs[1].inPlace = -1;
         config.inConfs[1].constant = true;
-        config.inConfs[1].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(SizeVector {1}));
+        config.inConfs[1].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(VectorDims{1}));
         if (INPUTS_NUM == 3) {
-            config.inConfs[2].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(SizeVector{outputShapes.size()}));
+            config.inConfs[2].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(VectorDims{outputShapes.size()}));
             config.inConfs[2].constant = true;
         }
 
@@ -174,38 +166,41 @@ void MKLDNNSplitNode::initSupportedPrimitiveDescriptors() {
     }
 
     // Optimized inplace case
-    for (auto refPdIndex : pdIndexesToReuse) {
-        const auto& refConfig = supportedPrimitiveDescriptors[refPdIndex].getConfig();
-        auto config = refConfig;
-        const auto inBlockingDesc = refConfig.inConfs[0].desc->as<CpuBlockedMemoryDesc>();
-        const auto& order = inBlockingDesc->getOrder();
-        const auto& blkDims = inBlockingDesc->getBlockDims();
-        auto numOfDim = blkDims.size();
-
-        SizeVector offsets(numOfDim, 0lu);
-        SizeVector strides(numOfDim);
-        strides.back() = 1lu;
-        size_t offset = (std::numeric_limits<size_t>::max)();
-
-        for (size_t i = 2; i <= numOfDim; i++) {
-            if (numOfDim - i < axis) {
-                strides[numOfDim - i] = (std::numeric_limits<size_t>::max)();
-            } else {
-                strides[numOfDim - i] = strides[numOfDim - i + 1] * blkDims[numOfDim - i + 1];
+    // TODO [DS]: inplace
+    if (!isDynamicNode()) {
+        for (auto refPdIndex : pdIndexesToReuse) {
+            const auto& refConfig = supportedPrimitiveDescriptors[refPdIndex].getConfig();
+            auto config = refConfig;
+            const auto inBlockingDesc = refConfig.inConfs[0].desc->as<CpuBlockedMemoryDesc>();
+            const auto& order = inBlockingDesc->getOrder();
+            const auto& blkDims = inBlockingDesc->getBlockDims();
+            auto numOfDim = blkDims.size();
+
+            SizeVector offsets(numOfDim, 0lu);
+            SizeVector strides(numOfDim);
+            strides.back() = 1lu;
+            size_t offset = (std::numeric_limits<size_t>::max)();
+
+            for (size_t i = 2; i <= numOfDim; i++) {
+                if (numOfDim - i < axis) {
+                    strides[numOfDim - i] = (std::numeric_limits<size_t>::max)();
+                } else {
+                    strides[numOfDim - i] = strides[numOfDim - i + 1] * blkDims[numOfDim - i + 1];
+                }
             }
-        }
 
-        config.inConfs[0].desc = std::make_shared<CpuBlockedMemoryDesc>(inpPrecision, srcShape, blkDims, order, offset, offsets, strides);
+            config.inConfs[0].desc = std::make_shared<CpuBlockedMemoryDesc>(inpPrecision, srcShape, blkDims, order, offset, offsets, strides);
 
-        for (size_t i = 0; i < outputShapes.size(); i++) {
-            auto outBlockingDesc = refConfig.outConfs[i].desc->as<CpuBlockedMemoryDesc>();
-            const auto& outBlkDims = outBlockingDesc->getBlockDims();
-            const auto& dims = outBlockingDesc->getShape().getStaticDims();
+            for (size_t i = 0; i < outputShapes.size(); i++) {
+                auto outBlockingDesc = refConfig.outConfs[i].desc->as<CpuBlockedMemoryDesc>();
+                const auto& outBlkDims = outBlockingDesc->getBlockDims();
+                const auto& dims = outBlockingDesc->getShape().getStaticDims();
 
-            config.outConfs[i].inPlace = 0;
-            config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(outPrecision, Shape(dims), outBlkDims, order, offset, offsets, strides);
+                config.outConfs[i].inPlace = 0;
+                config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(outPrecision, Shape(dims), outBlkDims, order, offset, offsets, strides);
+            }
+            supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
         }
-        supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
     }
 
     // Special nspc -> ncsp case when splitting channels
@@ -219,9 +214,9 @@ void MKLDNNSplitNode::initSupportedPrimitiveDescriptors() {
         config.inConfs[0].desc = creatorsMap.at(LayoutType::nspc)->createSharedDesc(inpPrecision, srcShape);
         config.inConfs[1].inPlace = -1;
         config.inConfs[1].constant = true;
-        config.inConfs[1].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(SizeVector{1}));
+        config.inConfs[1].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(VectorDims{1}));
         if (INPUTS_NUM == 3) {
-            config.inConfs[2].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(SizeVector{outputShapes.size()}));
+            config.inConfs[2].desc = std::make_shared<CpuBlockedMemoryDesc>(axisPrecision, Shape(VectorDims{outputShapes.size()}));
             config.inConfs[2].constant = true;
         }
         config.outConfs.resize(outputShapes.size());
@@ -235,63 +230,75 @@ void MKLDNNSplitNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
-void MKLDNNSplitNode::createPrimitive() {
-    auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
-    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
-        THROW_ERROR << "Input memory has not been allocated.";
-    for (size_t i = 0; i < getChildEdges().size(); i++) {
-        if (!getChildEdgeAt(i)->getMemoryPtr() || !getChildEdgeAt(i)->getMemory().GetPrimitivePtr())
-            THROW_ERROR << "Destination memory has not been allocated.";
+bool MKLDNNSplitNode::needPrepareParams() const {
+    if (isOptimized()) {
+        return false;
     }
-    if (getSelectedPrimitiveDescriptor() == nullptr)
-        THROW_ERROR << "Preferable primitive descriptor is not set.";
+    return MKLDNNNode::inputShapesModified();
+}
 
-    auto& memDesc = getParentEdgeAt(0)->getMemoryPtr()->getDesc();
+void MKLDNNSplitNode::prepareParams() {
+    const auto &srcMemPtr = getParentEdgesAtPort(0)[0]->getMemoryPtr();
+    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr()) {
+        THROW_ERROR << "has not allocated input memory";
+    }
 
-    canUseOptimizedNspc2Ncsp = false;
-    if (axis == 1 && one_of(memDesc.getShape().getRank(), 4, 5) && memDesc.hasLayoutType(LayoutType::nspc)) {
-        canUseOptimizedNspc2Ncsp = true;
-        for (size_t i = 0; i < getChildEdges().size(); i++) {
-            auto& childMemDesc = getChildEdgeAt(i)->getMemoryPtr()->getDesc();
-            if (!childMemDesc.hasLayoutType(LayoutType::ncsp))
-                canUseOptimizedNspc2Ncsp = false;
+    dstMemPtrs.clear();
+    std::vector<BlockedMemoryDescCPtr> outDescs;
+    for (size_t i = 0; i < outputShapes.size(); ++i) {
+        const auto &outMemPtr = this->getChildEdgesAtPort(i)[0]->getMemoryPtr();
+        if (!outMemPtr || !outMemPtr->GetPrimitivePtr()) {
+            THROW_ERROR << "has not allocated destination memory";
+        }
+
+        if (uint8_t* dstData = reinterpret_cast<uint8_t*>(outMemPtr->GetPtr())) {
+            dstMemPtrs.push_back(dstData);
+        } else {
+            THROW_ERROR << "can't get child edge indx " << i << "data.";
+        }
+
+        if (!canUseOptimizedNspc2Ncsp) {
+            outDescs.push_back(outMemPtr->GetDescWithType<BlockedMemoryDesc>());
         }
     }
 
-    if (!isOptimized()) {
-        initializeDstMemPtrs();
-        if (!canUseOptimizedNspc2Ncsp)
-            prepareOptimizedParams();
+    if (!canUseOptimizedNspc2Ncsp) {
+        const auto inDesc = srcMemPtr->GetDescWithType<BlockedMemoryDesc>();
+        execPtr = std::make_shared<SplitOptimizedExecutor>(inDesc, outDescs, axis);
+    }
+}
+
+void MKLDNNSplitNode::createPrimitive() {
+    if (getSelectedPrimitiveDescriptor() == nullptr)
+        THROW_ERROR << "Preferable primitive descriptor is not set.";
+
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
     }
 }
 
 void MKLDNNSplitNode::execute(mkldnn::stream strm) {
-    if (isOptimized())
+    if (isOptimized()) {
         return;
+    }
 
     if (dstMemPtrs.empty())
         THROW_ERROR << "Output data pointers have not been initialized.";
 
-    int MB = batchToProcess();
+    const auto &srcMem = getParentEdgesAtPort(0)[0]->getMemory();
+    size_t batch = srcMem.getStaticDims()[0];
+    Dim MB = isDynamicNode() ? batch : batchToProcess();
 
     if (canUseOptimizedNspc2Ncsp) {
         optimizedNspc2Ncsp(MB);
         return;
     }
 
-    uint8_t* srcData = reinterpret_cast<uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    size_t batch = getParentEdgesAtPort(0)[0]->getMemory().getStaticDims()[0];
-
-    if (batch != MB)
-        optimizedParams.countStrides = optimizedParams.countStrides / batch * MB;
-
-    parallel_for2d(dstMemPtrs.size(), optimizedParams.countStrides, [&](size_t i, size_t j) {
-        uint8_t* dstData = dstMemPtrs[i];
-
-        cpu_memcpy(&dstData[j * optimizedParams.dataSize[i]],
-                   &srcData[optimizedParams.srcDataOffsets[i] + j * optimizedParams.srcDataStride],
-                   optimizedParams.dataSize[i]);
-    });
+    uint8_t* srcData = reinterpret_cast<uint8_t*>(srcMem.GetPtr());
+    IE_ASSERT(execPtr != nullptr);
+    execPtr->exec(srcData, dstMemPtrs, batch, MB);
 }
 
 bool MKLDNNSplitNode::created() const {
@@ -303,61 +310,70 @@ bool MKLDNNSplitNode::isOptimized() const {
 }
 
 void MKLDNNSplitNode::initOptimalPrimitiveDescriptor() {
-    if (!isOptimized()) {
-        MKLDNNNode::initOptimalPrimitiveDescriptor();
-        return;
-    }
-
     auto selected_pd = getSelectedPrimitiveDescriptor();
     if (selected_pd == nullptr)
         THROW_ERROR << "Preferable primitive descriptor is not set.";
     auto config = selected_pd->getConfig();
-    if (isConfigDefined(config))
-        return;
 
-    for (size_t i = 0; i < config.inConfs.size(); i++) {
-        if (config.inConfs[i].desc->isDefined())
-            continue;
-
-        int num = getParentEdgeAt(i)->getOutputNum();
-        if (getParentEdgeAt(i)->getParent()->getSelectedPrimitiveDescriptor()) {
-            if (num >= 0) {
-                const auto& parentConfig = getParentEdgeAt(i)->getParent()->getSelectedPrimitiveDescriptor()->getConfig().outConfs[num];
-                if (!parentConfig.desc->isDefined() && parentConfig.inPlace >= 0)
-                    getParentEdgeAt(i)->getParent()->initOptimalPrimitiveDescriptor();
-                if (parentConfig.desc->isDefined() && parentConfig.desc->isCompatible(*config.inConfs[i].desc)) {
-                    config.inConfs[i].desc = parentConfig.desc;
-                    continue;
+    if (!isOptimized()) {
+        MKLDNNNode::initOptimalPrimitiveDescriptor();
+    } else if (!isConfigDefined(config)) {
+        for (size_t i = 0; i < config.inConfs.size(); i++) {
+            if (config.inConfs[i].desc->isDefined())
+                continue;
+
+            int num = getParentEdgeAt(i)->getOutputNum();
+            if (getParentEdgeAt(i)->getParent()->getSelectedPrimitiveDescriptor()) {
+                if (num >= 0) {
+                    const auto& parentConfig = getParentEdgeAt(i)->getParent()->getSelectedPrimitiveDescriptor()->getConfig().outConfs[num];
+                    if (!parentConfig.desc->isDefined() && parentConfig.inPlace >= 0)
+                        getParentEdgeAt(i)->getParent()->initOptimalPrimitiveDescriptor();
+                    if (parentConfig.desc->isDefined() && parentConfig.desc->isCompatible(*config.inConfs[i].desc)) {
+                        config.inConfs[i].desc = parentConfig.desc;
+                        continue;
+                    }
                 }
             }
+
+            // reset undefined offsets
+            config.inConfs[i].desc = config.inConfs[i].desc->as<BlockedMemoryDesc>()->cloneWithDefaultStridesAndOffset();
         }
+        if (config.outConfs.size() != outputShapes.size())
+            THROW_ERROR << "has invalid config";
 
-        // reset undefined offsets
-        config.inConfs[i].desc = config.inConfs[i].desc->as<BlockedMemoryDesc>()->cloneWithDefaultStridesAndOffset();
+        auto firstInBlockingDesc = config.inConfs[0].desc->as<BlockedMemoryDesc>();
+        size_t offset = 0;
+        for (size_t i = 0; i < outputShapes.size(); i++) {
+            auto oldDesc = config.outConfs[i].desc;
+            auto outBlockingDesc = oldDesc->as<BlockedMemoryDesc>();
+            config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(outBlockingDesc->getPrecision(),
+                                                                             outBlockingDesc->getShape(),
+                                                                             outBlockingDesc->getBlockDims(),
+                                                                             outBlockingDesc->getOrder(),
+                                                                             firstInBlockingDesc->getOffsetPadding() + offset,
+                                                                             firstInBlockingDesc->getOffsetPaddingToData(),
+                                                                             firstInBlockingDesc->getStrides());
+
+            size_t axisSize = 1;
+            for (size_t j = axis; j < outBlockingDesc->getBlockDims().size(); j++) {
+                axisSize *= outBlockingDesc->getBlockDims()[j];
+            }
+            offset += axisSize;
+        }
+        initDescriptor(config);
     }
-    if (config.outConfs.size() != outputShapes.size())
-        THROW_ERROR << "has invalid config";
 
-    auto firstInBlockingDesc = config.inConfs[0].desc->as<BlockedMemoryDesc>();
-    size_t offset = 0;
-    for (size_t i = 0; i < outputShapes.size(); i++) {
-        auto oldDesc = config.outConfs[i].desc;
-        auto outBlockingDesc = oldDesc->as<BlockedMemoryDesc>();
-        config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(outBlockingDesc->getPrecision(),
-                                                                 outBlockingDesc->getShape(),
-                                                                 outBlockingDesc->getBlockDims(),
-                                                                 outBlockingDesc->getOrder(),
-                                                                 firstInBlockingDesc->getOffsetPadding() + offset,
-                                                                 firstInBlockingDesc->getOffsetPaddingToData(),
-                                                                 firstInBlockingDesc->getStrides());
-
-        size_t axisSize = 1;
-        for (size_t j = axis; j < outBlockingDesc->getBlockDims().size(); j++) {
-            axisSize *= outBlockingDesc->getBlockDims()[j];
+    config = selected_pd->getConfig();
+    canUseOptimizedNspc2Ncsp = false;
+    IE_ASSERT(config.inConfs.size() > 0);
+    const auto inConfDesc = config.inConfs[0].desc;
+    if (axis == 1 && one_of(inConfDesc->getShape().getRank(), 4, 5) && inConfDesc->hasLayoutType(LayoutType::nspc)) {
+        canUseOptimizedNspc2Ncsp = true;
+        for (size_t i = 0; i < config.outConfs.size(); i++) {
+            if (!config.outConfs[i].desc->hasLayoutType(LayoutType::ncsp))
+                canUseOptimizedNspc2Ncsp = false;
         }
-        offset += axisSize;
     }
-    initDescriptor(config);
 }
 
 void MKLDNNSplitNode::selectOptimalPrimitiveDescriptor() {
@@ -375,8 +391,7 @@ void MKLDNNSplitNode::selectOptimalPrimitiveDescriptor() {
         }
     }
 
-    //check the descriptors and select the ones that have the same data format as the input
-
+    // check the descriptors and select the ones that have the same data format as the input
     std::vector<size_t> canSelectPrimitive;
     for (size_t i = 0; i < supportedPrimitiveDescriptors.size(); i++) {
         auto parentEdge = getParentEdgeAt(0);
@@ -397,6 +412,7 @@ void MKLDNNSplitNode::selectOptimalPrimitiveDescriptor() {
         selectPrimitiveDescriptorByIndex(static_cast<int>(canSelectPrimitive[0]));
         return;
     }
+
     // if there are more then one PD with similar data layouts - select the optimized one
     for (auto indx : canSelectPrimitive) {
         if (supportedPrimitiveDescriptors[indx].getImplementationType() == impl_desc_type::unknown) {
@@ -463,55 +479,6 @@ void MKLDNNSplitNode::setDynamicBatchLim(int lim) {
     dynBatchLim = lim;
 }
 
-void MKLDNNSplitNode::prepareOptimizedParams() {
-    auto selectedPrimitiveDescriptor = getSelectedPrimitiveDescriptor();
-    if (!selectedPrimitiveDescriptor)
-        IE_THROW() << "CPU Split node with name '" << getName() << "' doesn't have primitive descriptors.";
-    const auto inpTensorDesc = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>();
-    const auto outputPortsCount = outputShapes.size();
-
-    //find axis order position
-    const auto& order = inpTensorDesc->getOrder();
-    unsigned axisOrderPos = std::numeric_limits<unsigned>::max();
-    for (size_t i = 0; i < order.size(); ++i) {
-        if (order[i] == axis) {
-            axisOrderPos = i;
-            break;
-        }
-    }
-    if (std::numeric_limits<unsigned>::max() == axisOrderPos) {
-        THROW_ERROR << "Can't find the axis in the input tensor order list";
-    }
-
-    uint8_t srcDataSize = inpTensorDesc->getPrecision().size();
-    const auto& srcDims = inpTensorDesc->getBlockDims();
-    const auto getRank = srcDims.size();
-
-    optimizedParams.countStrides = 1;
-    for (int i = 0; i < axisOrderPos; i++)
-        optimizedParams.countStrides *= srcDims[i];
-
-    optimizedParams.srcDataStride = 0;
-    optimizedParams.dataSize.resize(outputPortsCount);
-
-    for (size_t i = 0; i < outputPortsCount; i++) {
-        auto outputEdge = this->getChildEdgesAtPort(i).front();
-        optimizedParams.dataSize[i] = srcDataSize;
-
-        auto desc = outputEdge->getMemory().getDesc().as<CpuBlockedMemoryDesc>();
-        for (size_t j = axisOrderPos; j < getRank; j++)
-            optimizedParams.dataSize[i] *= desc->getBlockDims()[j];
-
-        optimizedParams.srcDataStride += optimizedParams.dataSize[i];
-    }
-
-    optimizedParams.srcDataOffsets.resize(outputPortsCount);
-    optimizedParams.srcDataOffsets[0] = 0;
-    for (size_t i = 1; i < outputPortsCount; i++) {
-        optimizedParams.srcDataOffsets[i] = optimizedParams.srcDataOffsets[i - 1] + optimizedParams.dataSize[i - 1];
-    }
-}
-
 void MKLDNNSplitNode::optimizedNspc2Ncsp(size_t MB) {
     auto parentEdge = getParentEdgeAt(0);
     const int rank = parentEdge->getMemory().GetShape().getRank();
@@ -534,7 +501,7 @@ void MKLDNNSplitNode::optimizedNspc2Ncsp(size_t MB) {
         auto dstData = dstMemPtrs[i];
 
         size_t innerSize = 1;
-        auto dims = outputShapes[i].getStaticDims();
+        auto dims = getChildEdgesAtPort(i)[0]->getMemory().getStaticDims();
 
         for (size_t j = axis; j < dims.size(); j++) {
             innerSize *= dims[j];
@@ -558,17 +525,62 @@ void MKLDNNSplitNode::optimizedNspc2Ncsp(size_t MB) {
     }
 }
 
-void MKLDNNSplitNode::initializeDstMemPtrs() {
-    dstMemPtrs.clear();
-
-    for (size_t i = 0; i < outputShapes.size(); ++i) {
-        auto outputEdges = this->getChildEdgesAtPort(i);
-        if (uint8_t* dstData = reinterpret_cast<uint8_t*>(outputEdges.front()->getMemoryPtr()->GetPtr())) {
-            dstMemPtrs.push_back(dstData);
-        } else {
-            THROW_ERROR << "can't get child edge indx " << i << "data.";
+MKLDNNSplitNode::SplitOptimizedExecutor::SplitOptimizedExecutor(BlockedMemoryDescCPtr inDesc, const std::vector<BlockedMemoryDescCPtr> &outDescs,
+                                                                const size_t axis) {
+    // find axis order position
+    const auto& order = inDesc->getOrder();
+    unsigned axisOrderPos = std::numeric_limits<unsigned>::max();
+    for (size_t i = 0; i < order.size(); ++i) {
+        if (order[i] == axis) {
+            axisOrderPos = i;
+            break;
         }
     }
+    if (std::numeric_limits<unsigned>::max() == axisOrderPos) {
+        IE_THROW() << "Can't create split executor, because can't find the axis in the input tensor order list";
+    }
+
+    const auto outputPortsCount = outDescs.size();
+
+    uint8_t srcDataSize = inDesc->getPrecision().size();
+    const auto& srcDims = inDesc->getBlockDims();
+    const auto getRank = srcDims.size();
+
+    countStrides = 1;
+    for (int i = 0; i < axisOrderPos; i++)
+        countStrides *= srcDims[i];
+
+    srcDataStride = 0;
+    dataSize.resize(outputPortsCount);
+
+    for (size_t i = 0; i < outputPortsCount; i++) {
+        dataSize[i] = srcDataSize;
+        for (size_t j = axisOrderPos; j < getRank; j++)
+            dataSize[i] *= outDescs[i]->getBlockDims()[j];
+
+        srcDataStride += dataSize[i];
+    }
+
+    srcDataOffsets.resize(outputPortsCount);
+    srcDataOffsets[0] = 0;
+    for (size_t i = 1; i < outputPortsCount; i++) {
+        srcDataOffsets[i] = srcDataOffsets[i - 1] + dataSize[i - 1];
+    }
+}
+
+void MKLDNNSplitNode::SplitOptimizedExecutor::exec(const uint8_t* srcData, const std::vector<uint8_t*> &dstMemPtrs,
+                                                   const Dim origBatch, const Dim perInferBatch) {
+    size_t execCountStrides = countStrides;
+    if (origBatch != perInferBatch)
+        execCountStrides = execCountStrides / origBatch * perInferBatch;
+
+    parallel_for2d(dstMemPtrs.size(), execCountStrides, [&](size_t i, size_t j) {
+        uint8_t* dstData = dstMemPtrs[i];
+
+        cpu_memcpy(&dstData[j * dataSize[i]],
+                   &srcData[srcDataOffsets[i] + j * srcDataStride],
+                   dataSize[i]);
+    });
 }
 
 REG_MKLDNN_PRIM_FOR(MKLDNNSplitNode, Split);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h
index e7f99105d1d4d2..20f18555d905e0 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_split_node.h
@@ -30,9 +30,31 @@ class MKLDNNSplitNode : public MKLDNNNode {
         return !isOptimized();
     }
 
+    bool needPrepareParams() const override;
+    void prepareParams() override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+
 private:
-    void prepareOptimizedParams();
-    void initializeDstMemPtrs();
+    struct SplitExecutor {
+        virtual void exec(const uint8_t* srcData, const std::vector<uint8_t*> &dstMemPtrs,
+                          const Dim origBatch, const Dim perInferBatch) = 0;
+        virtual ~SplitExecutor() = default;
+    };
+    std::shared_ptr<SplitExecutor> execPtr = nullptr;
+
+    struct SplitOptimizedExecutor : public SplitExecutor {
+        public:
+            SplitOptimizedExecutor(BlockedMemoryDescCPtr inDesc, const std::vector<BlockedMemoryDescCPtr> &outDescs, const size_t axis);
+            void exec(const uint8_t* srcData, const std::vector<uint8_t*> &dstMemPtrs,
+                      const Dim origBatch, const Dim perInferBatch) override;
+
+        private:
+            std::vector<size_t> dataSize;
+            std::vector<size_t> srcDataOffsets;
+            size_t srcDataStride;
+            size_t countStrides;
+    };
+
     void optimizedNspc2Ncsp(size_t MB);
 
     bool canUseOptimizedNspc2Ncsp;
@@ -40,13 +62,6 @@ class MKLDNNSplitNode : public MKLDNNNode {
     size_t axis = 1;
     std::vector<uint8_t*> dstMemPtrs;
 
-    struct {
-        std::vector<size_t> dataSize;
-        std::vector<size_t> srcDataOffsets;
-        size_t srcDataStride;
-        size_t countStrides;
-    } optimizedParams;
-
     size_t INPUTS_NUM = 2;
 };
 
diff --git a/inference-engine/src/mkldnn_plugin/utils/cpu_utils.hpp b/inference-engine/src/mkldnn_plugin/utils/cpu_utils.hpp
index 14fd043dce0824..e9c213f23fb608 100644
--- a/inference-engine/src/mkldnn_plugin/utils/cpu_utils.hpp
+++ b/inference-engine/src/mkldnn_plugin/utils/cpu_utils.hpp
@@ -38,7 +38,8 @@ inline std::vector<size_t> getNormalizedDimsBySize(const InferenceEngine::SizeVe
 */
 inline bool isPerTensorOrPerChannelBroadcastable(const InferenceEngine::SizeVector &firstInputDims, const InferenceEngine::SizeVector& secondInputDims,
                                                  bool weakComparison = false) {
-    bool (*dimsEqual)(size_t, size_t) = weakComparison ? static_cast<bool (*)(size_t, size_t)>(dimsEqualWeak) : dimsEqualStrong;
+    bool (*dimsEqual)(size_t, size_t) = weakComparison ? static_cast<bool (*)(size_t, size_t)>(dimsEqualWeak) :
+                                                         static_cast<bool (*)(size_t, size_t)>(dimsEqualStrong);
     if (secondInputDims.size() > firstInputDims.size())
         return false;
     if (std::accumulate(secondInputDims.begin(), secondInputDims.end(), 1, std::multiplies<size_t>()) == 1)
@@ -72,12 +73,17 @@ inline InferenceEngine::Precision normalizeToSupportedPrecision(InferenceEngine:
         case InferenceEngine::Precision::FP32: {
             break;
         }
+        case InferenceEngine::Precision::FP64: {
+            precision = InferenceEngine::Precision::FP32;
+            break;
+        }
         case InferenceEngine::Precision::BOOL: {
             precision = InferenceEngine::Precision::U8;
             break;
         }
         case InferenceEngine::Precision::U16:
         case InferenceEngine::Precision::I16:
+        case InferenceEngine::Precision::U32:
         case InferenceEngine::Precision::I64:
         case InferenceEngine::Precision::U64: {
             precision = InferenceEngine::Precision::I32;
diff --git a/inference-engine/src/mkldnn_plugin/utils/general_utils.h b/inference-engine/src/mkldnn_plugin/utils/general_utils.h
index 012054e59e10c5..7a6c1e51022dd4 100644
--- a/inference-engine/src/mkldnn_plugin/utils/general_utils.h
+++ b/inference-engine/src/mkldnn_plugin/utils/general_utils.h
@@ -67,6 +67,26 @@ inline bool dimsEqualStrong(size_t lhs, size_t rhs) {
     return (lhs == rhs && lhs != Shape::UNDEFINED_DIM && rhs != Shape::UNDEFINED_DIM);
 }
 
+/**
+ * @brief Compares that two shapes are equal
+ * @param lhs
+ * first shape
+ * @param rhs
+ * second shape
+ * @return result of comparison
+ */
+inline bool dimsEqualStrong(const std::vector<size_t>& lhs, const std::vector<size_t>& rhs, size_t skipAxis = Shape::UNDEFINED_DIM) {
+    if (lhs.size() != rhs.size())
+        return false;
+
+    for (size_t i = 0; i < lhs.size(); i++) {
+        if (i != skipAxis && !dimsEqualStrong(lhs[i], rhs[i]))
+            return false;
+    }
+
+    return true;
+}
+
 /**
  * @brief Compares that two dims are equal or undefined
  * @param lhs
@@ -87,7 +107,7 @@ inline bool dimsEqualWeak(size_t lhs, size_t rhs) {
  * second shape
  * @param skipAxis
  * marks shape axis which shouldn't be validated
- * @return order
+ * @return result of comparison
  */
 inline bool dimsEqualWeak(const std::vector<size_t>& lhs, const std::vector<size_t>& rhs, size_t skipAxis = Shape::UNDEFINED_DIM) {
     if (lhs.size() != rhs.size())
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.hpp b/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.hpp
index 03aee9d63f94c6..20efc49cdc70c6 100644
--- a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.hpp
+++ b/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_dimension.hpp
@@ -9,6 +9,9 @@
 #include <stdexcept>
 #include <ostream>
 
+#include "openvino/core/dimension.hpp"
+#include "openvino/core/except.hpp"
+
 namespace ov {
 /// \brief Class representing a dimension, which must be static,
 ///        in a shape or shape-like object.
@@ -25,6 +28,10 @@ class StaticDimension {
     /// \brief Construct a zero dimension
     StaticDimension() = default;
 
+    StaticDimension(const Dimension &) {
+        OPENVINO_UNREACHABLE("[shape infer] Shoudn't convert from Dimension to StaticDimension.");
+    }
+
     bool operator==(const StaticDimension& dimension) const;
     bool operator!=(const StaticDimension& dimension) const;
 
@@ -35,6 +42,12 @@ class StaticDimension {
     value_type get_min_length() const;
     value_type get_max_length() const;
 
+    Interval& get_interval() const {
+        static Interval dummy{};
+        OPENVINO_UNREACHABLE("[shape infer] Shoudn't call get_interval() in StaticDimension.");
+        return dummy;
+    }
+
     bool same_scheme(const StaticDimension& dim) const;
     bool compatible(const StaticDimension& d) const;
     static bool merge(StaticDimension& dst, const StaticDimension& d1, const StaticDimension& d2);
diff --git a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.hpp b/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.hpp
index f0dd557f7f3f4a..1afa6f2b02ddf7 100644
--- a/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.hpp
+++ b/inference-engine/src/mkldnn_plugin/utils/shape_inference/static_shape.hpp
@@ -12,6 +12,7 @@
 #include "openvino/core/rank.hpp"
 #include "openvino/core/shape.hpp"
 #include "openvino/core/partial_shape.hpp"
+#include "openvino/core/except.hpp"
 
 namespace ov {
 namespace op {
@@ -26,6 +27,10 @@ class StaticShape : public std::vector<StaticDimension>  {
     StaticShape(const std::vector<StaticDimension::value_type>& dimensions);
     StaticShape(std::vector<StaticDimension> dimensions);
 
+    StaticShape(const PartialShape &) {
+        OPENVINO_UNREACHABLE("[shape infer] Shouldn't convert from PartialShape to StaticShape at runtime.");
+    }
+
     static bool is_static() { return true; }
     static bool is_dynamic() { return false; }
 
diff --git a/inference-engine/src/multi_device/multi_device_exec_network.cpp b/inference-engine/src/multi_device/multi_device_exec_network.cpp
index 2e90d94f2f5aa3..30d39e382d6f7c 100644
--- a/inference-engine/src/multi_device/multi_device_exec_network.cpp
+++ b/inference-engine/src/multi_device/multi_device_exec_network.cpp
@@ -21,6 +21,7 @@
 #include "ngraph/opsets/opset1.hpp"
 #include "transformations/utils/utils.hpp"
 
+#include "multi_itt.hpp"
 // ------------------------------MultiDeviceExecutableNetwork----------------------------
 namespace MultiDevicePlugin {
 using namespace InferenceEngine;
@@ -159,7 +160,7 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
     _config[MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES] = strDevices;
 
     std::vector<DeviceInformation> needLoadDevices;
-
+    std::string profilingTask = "MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork:AutoMode";
     // check if have cpu device
     const auto CPUIter = std::find_if(metaDevices.begin(), metaDevices.end(),
                                       [=](const DeviceInformation& d)->bool{return d.deviceName.find("CPU") != std::string::npos;});
@@ -168,6 +169,7 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
         _config.insert(_cpuDevice.config.begin(), _cpuDevice.config.end());
         needLoadDevices.push_back(_cpuDevice);
         _cpuFuture = _cpuPromise.get_future();
+        profilingTask += _cpuDevice.deviceName;
     }
 
     // get accelerator device, like GPU
@@ -179,8 +181,9 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
         _config.insert(_acceleratorDevice.config.begin(), _acceleratorDevice.config.end());
         needLoadDevices.push_back(_acceleratorDevice);
         _acceleratorFuture = _acceleratorPromise.get_future();
+        profilingTask += _acceleratorDevice.deviceName;
     }
-
+    OV_ITT_SCOPED_TASK(itt::domains::MULTIPlugin, openvino::itt::handle(profilingTask));
     if (needLoadDevices.size() == 0) {
         IE_THROW() << "No device set";
     }
@@ -254,6 +257,7 @@ void MultiDeviceExecutableNetwork::WaitFirstNetworkReady() {
 void MultiDeviceExecutableNetwork::WaitActualNetworkReady() const {
     // Maybe different API will call this function, so add call once here
     // for every MultiDeviceExecutableNetwork instance
+    OV_ITT_SCOPED_TASK(itt::domains::MULTIPlugin, "MultiDeviceExecutableNetwork::WaitActualNetworkReady");
     std::call_once(_oc, [&] () {
             if (_acceleratorFuture.valid()) {
                 _networkActualNeeded = _acceleratorFuture.get();
@@ -350,7 +354,6 @@ std::shared_ptr<InferenceEngine::RemoteContext> MultiDeviceExecutableNetwork::Ge
         WaitActualNetworkReady();
         return _networkActualNeeded->GetContext();
     }
-
     auto devices = [&] {
         std::lock_guard<std::mutex> lock(_mutex);
         return _devicePriorities;
@@ -380,6 +383,11 @@ InferenceEngine::IInferRequestInternal::Ptr MultiDeviceExecutableNetwork::Create
     InferenceEngine::SoIInferRequestInternal request_to_share_blobs_with;
 
     if (_workModeIsAUTO) {
+        if (!_networkFirstReady && _networkActualNeeded) {
+            auto& dev_requests = _workerRequests[_acceleratorDevice.deviceName];
+            auto index = num % dev_requests.size();
+            request_to_share_blobs_with = dev_requests.at(index)._inferRequest;
+        }
         return std::make_shared<MultiDeviceInferRequest>(inputs, outputs, request_to_share_blobs_with);
     }
 
@@ -403,6 +411,11 @@ InferenceEngine::IInferRequestInternal::Ptr MultiDeviceExecutableNetwork::Create
     InferenceEngine::SoIInferRequestInternal request_to_share_blobs_with;
 
     if (_workModeIsAUTO) {
+        if (!_networkFirstReady && _networkActualNeeded) {
+            auto& dev_requests = _workerRequests[_acceleratorDevice.deviceName];
+            auto index = num % dev_requests.size();
+            request_to_share_blobs_with = dev_requests.at(index)._inferRequest;
+        }
         return std::make_shared<MultiDeviceInferRequest>(networkInputs, networkOutputs, request_to_share_blobs_with);
     }
 
diff --git a/inference-engine/src/multi_device/multi_device_plugin.cpp b/inference-engine/src/multi_device/multi_device_plugin.cpp
index 79d3eee9312a54..8fc3fc7a621e94 100644
--- a/inference-engine/src/multi_device/multi_device_plugin.cpp
+++ b/inference-engine/src/multi_device/multi_device_plugin.cpp
@@ -20,6 +20,7 @@
 #include <ie_algorithm.hpp>
 #include <ie_icore.hpp>
 
+#include "multi_itt.hpp"
 // ------------------------------MultiDeviceInferencePlugin----------------------------
 namespace MultiDevicePlugin {
     using namespace InferenceEngine;
@@ -57,6 +58,8 @@ namespace {
                     auto res = PerfHintsConfig::SupportedKeys();
                     res.push_back(MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES);
                     res.push_back(CONFIG_KEY_INTERNAL(MULTI_WORK_MODE_AS_AUTO));
+                    res.push_back(PluginConfigParams::KEY_PERF_COUNT);
+                    res.push_back(PluginConfigParams::KEY_EXCLUSIVE_ASYNC_REQUESTS);
                     return res;
                 }();
 }  // namespace
@@ -159,16 +162,12 @@ InferenceEngine::Parameter MultiDeviceInferencePlugin::GetConfig(const std::stri
 }
 
 void MultiDeviceInferencePlugin::SetConfig(const std::map<std::string, std::string> & config) {
-    const auto perf_hints_configs = PerfHintsConfig::SupportedKeys();
+    bool needPerfCounters = false;
+    std::map<std::string, std::string> filterConfig;
+    CheckConfig(config, needPerfCounters, filterConfig);
     for (auto && kvp : config) {
         const auto& name = kvp.first;
-        if (supported_configKeys.end() != std::find(supported_configKeys.begin(), supported_configKeys.end(), name)) {
-            if (std::find(perf_hints_configs.begin(), perf_hints_configs.end(), kvp.first) != perf_hints_configs.end())
-                PerfHintsConfig::CheckConfigAndValue(kvp);
-            _config[name] = kvp.second;
-        } else {
-            IE_THROW() << "Unsupported config key: " << name;
-        }
+        _config[name] = kvp.second;
     }
 }
 
@@ -237,6 +236,7 @@ IExecutableNetworkInternal::Ptr MultiDeviceInferencePlugin::LoadNetworkImpl(cons
     if (workModeAuto) {
         // check the configure and check if need to set PerfCounters configure to device
         // and set filter configure
+        OV_ITT_SCOPED_TASK(itt::domains::MULTIPlugin, "MultiDeviceInferencePlugin::LoadNetworkImpl::AutoMode");
         bool needPerfCounters = false;
         std::map<std::string, std::string> filterConfig;
         CheckConfig(fullConfig, needPerfCounters, filterConfig);
@@ -268,7 +268,7 @@ IExecutableNetworkInternal::Ptr MultiDeviceInferencePlugin::LoadNetworkImpl(cons
 
         return std::make_shared<MultiDeviceExecutableNetwork>(modelPath, network, supportDevices, strDevices, this, needPerfCounters);
     }
-
+    OV_ITT_SCOPED_TASK(itt::domains::MULTIPlugin, "MultiDeviceInferencePlugin::LoadNetworkImpl:MultiMode");
     if (priorities == fullConfig.end()) {
         IE_THROW() << "KEY_MULTI_DEVICE_PRIORITIES key is not set for " << GetName() << " device";
     } else {  // for use case -d MULTI:xPU or -d AUTO:xPU
@@ -375,6 +375,7 @@ QueryNetworkResult MultiDeviceInferencePlugin::QueryNetwork(const CNNNetwork&
 }
 
 DeviceInformation MultiDeviceInferencePlugin::SelectDevice(const std::vector<DeviceInformation>& metaDevices, const std::string& networkPrecision) {
+    OV_ITT_SCOPED_TASK(itt::domains::MULTIPlugin, "MultiDeviceInferencePlugin::SelectDevice");
     if (metaDevices.empty()) {
         IE_THROW(NotFound) << "No available device to select in " << GetName() <<  " plugin";
     }
diff --git a/inference-engine/src/multi_device/multi_itt.hpp b/inference-engine/src/multi_device/multi_itt.hpp
new file mode 100644
index 00000000000000..dd3b7a03f9f210
--- /dev/null
+++ b/inference-engine/src/multi_device/multi_itt.hpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * @brief Defines openvino domains for tracing
+ * @file multi_itt.h
+ */
+
+#pragma once
+
+#include <openvino/itt.hpp>
+
+namespace MultiDevicePlugin {
+namespace itt {
+namespace domains {
+    OV_ITT_DOMAIN(MULTIPlugin);
+}
+}
+}
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp
index b454d6a326fca9..bd0c074eb4c8d1 100644
--- a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp
+++ b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp
@@ -115,13 +115,6 @@ class INFERENCE_ENGINE_API_CLASS(IExecutableNetworkInternal)
      */
     virtual std::shared_ptr<ngraph::Function> GetExecGraphInfo();
 
-    /**
-     * @deprecated Need to implement GetVariablesInfo for ExecutableNetwork
-     * @brief Queries memory states.
-     * @return Returns memory states
-     */
-    virtual std::vector<std::shared_ptr<IVariableStateInternal>> QueryState();
-
     /**
      * @brief      Sets the pointer to plugin internal.
      * @param[in]  plugin  The plugin
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
index 2a4db632fd757d..09383330e22b86 100644
--- a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
+++ b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
@@ -331,7 +331,6 @@ using CreatePluginEngineFunc = void(std::shared_ptr<IInferencePlugin>&);
  * @private
  */
 using CreateExtensionFunc = void(std::shared_ptr<IExtension>&);
-;
 
 /**
  * @def IE_CREATE_PLUGIN
diff --git a/inference-engine/src/plugin_api/performance_heuristics.hpp b/inference-engine/src/plugin_api/performance_heuristics.hpp
index 3b58ae88570b6c..aeb4ebcfaf007a 100644
--- a/inference-engine/src/plugin_api/performance_heuristics.hpp
+++ b/inference-engine/src/plugin_api/performance_heuristics.hpp
@@ -28,7 +28,7 @@ static MemBandwidthPressure MemBandwidthPressureTolerance(
     const float memThresholdAssumeLimited = MemBandwidthPressure::LIMITED) {
     int total_convs = 0, mem_limited_convs = 0, compute_convs = 0, total_gemms = 0, mem_limited_gemms = 0,
         total_deconvs = 0, compute_deconvs = 0, mem_limited_deconvs = 0;
-    auto memLimitedFactor = [&](int size_data_moved, int datatype_size = 4) -> float {
+    auto memLimitedFactor = [&](int size_data_moved, int datatype_size) -> float {
         return (L2_cache_size * 1.0f /*util factor, tbd */
                 / (size_data_moved * datatype_size));
     };
diff --git a/inference-engine/src/preprocessing/CMakeLists.txt b/inference-engine/src/preprocessing/CMakeLists.txt
index e3c52c8236d21b..29e415e9ade136 100644
--- a/inference-engine/src/preprocessing/CMakeLists.txt
+++ b/inference-engine/src/preprocessing/CMakeLists.txt
@@ -139,8 +139,9 @@ else()
     target_link_libraries(inference_engine PRIVATE ${TARGET_NAME})
 endif()
 
-target_include_directories(${TARGET_NAME} INTERFACE "${CMAKE_CURRENT_SOURCE_DIR}"
-                                                    $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
+target_include_directories(${TARGET_NAME} INTERFACE
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>
+    $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
 
 # Workaround to avoid warnings caused with bug in the avx512intrin.h of GCC5
 if((CMAKE_CXX_COMPILER_ID STREQUAL "GNU") AND
@@ -185,5 +186,9 @@ openvino_developer_export_targets(COMPONENT inference_engine TARGETS ${TARGET_NA
 
 # install
 
-install(TARGETS ${TARGET_NAME}
-        LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core)
+if(BUILD_SHARED_LIBS)
+    install(TARGETS ${TARGET_NAME}
+            LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core)
+else()
+    ov_install_static_lib(${TARGET_NAME} core)
+endif()
diff --git a/inference-engine/src/preprocessing/ie_preprocess_data.hpp b/inference-engine/src/preprocessing/ie_preprocess_data.hpp
index 347c239f943cf6..4204aa69b54bc6 100644
--- a/inference-engine/src/preprocessing/ie_preprocess_data.hpp
+++ b/inference-engine/src/preprocessing/ie_preprocess_data.hpp
@@ -102,7 +102,7 @@ class PreProcessDataPlugin {
     }
 
     void isApplicable(const Blob::Ptr &src, const Blob::Ptr &dst) {
-        OV_PREPROC_PLUGIN_CALL_STATEMENT(return _ptr->isApplicable(src, dst));
+        OV_PREPROC_PLUGIN_CALL_STATEMENT(_ptr->isApplicable(src, dst));
     }
 };
 
diff --git a/inference-engine/src/readers/CMakeLists.txt b/inference-engine/src/readers/CMakeLists.txt
index 46028205d37932..29cd6e6c3277f2 100644
--- a/inference-engine/src/readers/CMakeLists.txt
+++ b/inference-engine/src/readers/CMakeLists.txt
@@ -8,9 +8,11 @@ set(TARGET_NAME inference_engine_reader_api)
 add_library(${TARGET_NAME} INTERFACE)
 
 target_include_directories(${TARGET_NAME} INTERFACE
-    "${CMAKE_CURRENT_SOURCE_DIR}/reader_api"
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/reader_api>
     $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
 
+ov_install_static_lib(${TARGET_NAME} core)
+
 file(GLOB_RECURSE reader_api_hpp "${CMAKE_CURRENT_SOURCE_DIR}/reader_api/*.hpp")
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_SOURCES ${reader_api_hpp})
diff --git a/inference-engine/src/readers/ir_reader_v7/CMakeLists.txt b/inference-engine/src/readers/ir_reader_v7/CMakeLists.txt
index 846b831825be81..d53fc13abc7d1e 100644
--- a/inference-engine/src/readers/ir_reader_v7/CMakeLists.txt
+++ b/inference-engine/src/readers/ir_reader_v7/CMakeLists.txt
@@ -55,6 +55,8 @@ endif()
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
 
+# Install rules
+
 # for ieFuncTests
 install(TARGETS ${TARGET_NAME}
         LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
@@ -67,3 +69,5 @@ else()
     install(TARGETS ${TARGET_NAME}
             LIBRARY DESTINATION tests/lib COMPONENT tests EXCLUDE_FROM_ALL)
 endif()
+
+ov_install_static_lib(${TARGET_NAME} core)
diff --git a/inference-engine/src/snippets/CMakeLists.txt b/inference-engine/src/snippets/CMakeLists.txt
index db2c8413083c4e..334c2b8fb312a6 100644
--- a/inference-engine/src/snippets/CMakeLists.txt
+++ b/inference-engine/src/snippets/CMakeLists.txt
@@ -17,7 +17,7 @@ source_group("include" FILES ${PUBLIC_HEADERS})
 
 # Create library
 
-add_library(${TARGET_NAME}
+add_library(${TARGET_NAME} EXCLUDE_FROM_ALL
             ${LIBRARY_SRC}
             ${PUBLIC_HEADERS})
 
@@ -53,9 +53,13 @@ openvino_developer_export_targets(COMPONENT inference_engine TARGETS ${TARGET_NA
 # install
 
 # TODO: uncomment once snippets are integrated into CPU plugin
-# install(TARGETS ${TARGET_NAME}
-#         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
-#         LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
+# if(BUILD_SHARED_LIBS)
+#     install(TARGETS ${TARGET_NAME}
+#             RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT core
+#             LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT core)
+# else()
+#     ov_install_static_lib(${TARGET_NAME} core)
+# endif()
 
 # TODO: remove once install commands above are commented out
 install(TARGETS ${TARGET_NAME}
diff --git a/inference-engine/src/transformations/include/ngraph_ops/type_relaxed.hpp b/inference-engine/src/transformations/include/ngraph_ops/type_relaxed.hpp
index f59818a93f4106..2c331f3fb468d6 100644
--- a/inference-engine/src/transformations/include/ngraph_ops/type_relaxed.hpp
+++ b/inference-engine/src/transformations/include/ngraph_ops/type_relaxed.hpp
@@ -8,6 +8,7 @@
 #include <vector>
 #include <algorithm>
 #include <string>
+#include <mutex>
 
 #include <transformations_visibility.hpp>
 
@@ -92,7 +93,9 @@ class NGRAPH_API TypeRelaxedBase {
         for (size_t i = 0; i < node.get_input_size(); ++i) {
             auto origin_input_type = get_origin_input_type(i);
             if (origin_input_type != element::undefined) {
+                OPENVINO_SUPPRESS_DEPRECATED_START
                 node.get_input_tensor(i).set_tensor_type(origin_input_type, node.get_input_partial_shape(i));
+                OPENVINO_SUPPRESS_DEPRECATED_END
             }
         }
     }
@@ -100,7 +103,9 @@ class NGRAPH_API TypeRelaxedBase {
     void restore_input_data_types(Node &node, const element::TypeVector &old_input_types) {
         // Restore original input data types
         for (size_t i = 0; i < node.get_input_size(); ++i) {
+            OPENVINO_SUPPRESS_DEPRECATED_START
             node.get_input_tensor(i).set_tensor_type(old_input_types[i], node.get_input_partial_shape(i));
+            OPENVINO_SUPPRESS_DEPRECATED_END
         }
 
         if (m_original_output_data_types.empty()) {
@@ -158,7 +163,9 @@ class TemporaryReplaceOutputType {
     TemporaryReplaceOutputType(Output<Node> output, element::Type tmp_type) : m_output(output) {
         // save original element type in order to restore it in the destructor
         orig_type = m_output.get_element_type();
+        OPENVINO_SUPPRESS_DEPRECATED_START
         m_output.get_tensor().set_element_type(tmp_type);
+        OPENVINO_SUPPRESS_DEPRECATED_END
     }
 
     /// Return the output port that was used in the constructor
@@ -168,7 +175,9 @@ class TemporaryReplaceOutputType {
 
     /// Restores the original element type for the output
     ~TemporaryReplaceOutputType() {
+        OPENVINO_SUPPRESS_DEPRECATED_START
         m_output.get_tensor().set_element_type(orig_type);
+        OPENVINO_SUPPRESS_DEPRECATED_END
     }
 };
 
@@ -227,6 +236,7 @@ class TypeRelaxed : public BaseOp, public TypeRelaxedBase {
     bool visit_attributes(AttributeVisitor& visitor) override;
 
 private:
+    mutable std::mutex type_relax_mutex;
     void init() {
         validate_and_infer_types();
     }
@@ -307,6 +317,7 @@ void TypeRelaxed<BaseOp>::validate_and_infer_types() {
 
 template <typename BaseOp>
 std::shared_ptr<Node> TypeRelaxed<BaseOp>::clone_with_new_inputs(const OutputVector& new_args) const {
+    std::lock_guard<std::mutex> lock(type_relax_mutex);
     // copy then modify inputs
     std::shared_ptr<Node> new_node = std::make_shared<TypeRelaxed<BaseOp>>((BaseOp&)(*this), m_input_data_types, m_output_data_types);
     for (size_t i = 0; i < new_node->get_input_size(); ++i) {
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/compress_float_constants.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/compress_float_constants.hpp
new file mode 100644
index 00000000000000..a0a4ef18580e7d
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/compress_float_constants.hpp
@@ -0,0 +1,51 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "transformations_visibility.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+
+namespace ov {
+namespace pass {
+
+class TRANSFORMATIONS_API CompressFloatConstantsImpl;
+class TRANSFORMATIONS_API AddOldApiMapToParameters;
+class TRANSFORMATIONS_API CompressFloatConstants;
+
+}  // namespace pass
+}  // namespace ov
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief CompressFloatConstantsImpl transformation replaces FP32/FP64 Constants with FP16 ones.
+ */
+class ov::pass::CompressFloatConstantsImpl : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("CompressFloatConstantsImpl", "0");
+    CompressFloatConstantsImpl();
+};
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief AddOldApiMapToParameters transformation adds OldApiMap to each float input to the model.
+ */
+class ov::pass::AddOldApiMapToParameters : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("AddOldApiMapToParameters", "0");
+    AddOldApiMapToParameters();
+};
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief CompressFloatConstants transformation replaces FP32/FP64 Constants with FP16 ones.
+ */
+class ov::pass::CompressFloatConstants : public ov::pass::GraphRewrite {
+public:
+    OPENVINO_RTTI("CompressFloatConstants", "0");
+    CompressFloatConstants() {
+        add_matcher<ov::pass::CompressFloatConstantsImpl>();
+        add_matcher<ov::pass::AddOldApiMapToParameters>();
+    }
+};
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/convert_compression_only_to_legacy.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/convert_compression_only_to_legacy.hpp
new file mode 100644
index 00000000000000..40a297f65913b8
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/convert_compression_only_to_legacy.hpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "transformations_visibility.hpp"
+#include "openvino/pass/pass.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+
+namespace ov {
+namespace pass {
+
+class TRANSFORMATIONS_API ConvertPrecisionCompressedOnly;
+class TRANSFORMATIONS_API EnableDecompressionConvertConstantFolding;
+class TRANSFORMATIONS_API ConvertCompressedOnlyToLegacy;
+
+}  // namespace pass
+}  // namespace ov
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief ConvertPrecisionCompressedOnly transformation runs ConvertPrecision transformation for CompressedOnly format.
+ */
+
+class ov::pass::ConvertPrecisionCompressedOnly : public ov::pass::FunctionPass {
+public:
+    OPENVINO_RTTI("ConvertPrecisionCompressedOnly", "0");
+    bool run_on_function(std::shared_ptr<Function> f) override;
+};
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief Enables ConstantFolding for Convert operation in compressed function.
+ */
+class ov::pass::EnableDecompressionConvertConstantFolding : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("EnableDecompressionConvertConstantFolding", "0");
+    EnableDecompressionConvertConstantFolding();
+};
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief ConvertCompressedOnlyToLegacy transformation converts compression only FP16 format to legacy FP16 format.
+ */
+class ov::pass::ConvertCompressedOnlyToLegacy : public ov::pass::FunctionPass {
+public:
+    OPENVINO_RTTI("ConvertCompressedOnlyToLegacy", "0");
+    bool run_on_function(std::shared_ptr<Function> f) override;
+};
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/divide_fusion.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/divide_fusion.hpp
new file mode 100644
index 00000000000000..e469b0a57985f7
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/divide_fusion.hpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <ngraph/pass/graph_rewrite.hpp>
+#include <transformations_visibility.hpp>
+
+namespace ngraph {
+namespace pass {
+
+class TRANSFORMATIONS_API DivideFusion;
+
+}  // namespace pass
+}  // namespace ngraph
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief DivideFusion transformation replaces a sub-graph
+ * Pow(y, -1) * x or x * Pow(y, -1) with Divide(x,y)
+ */
+class ngraph::pass::DivideFusion : public ngraph::pass::MatcherPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    DivideFusion();
+};
\ No newline at end of file
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp
new file mode 100644
index 00000000000000..ad72e23e1df091
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp
@@ -0,0 +1,29 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "transformations_visibility.hpp"
+#include "openvino/pass/pass.hpp"
+
+
+namespace ov {
+namespace pass {
+
+class TRANSFORMATIONS_API MarkPrecisionSensitiveSubgraphs;
+
+}  // namespace pass
+}  // namespace ov
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief MarkPrecisionSensitiveSubgraphs transformation marks the constants
+ * inside the subgraph starting from precision-sensitive input and ending at
+ * the ShapeOf node as disabled for FP16 compression.
+ */
+class ov::pass::MarkPrecisionSensitiveSubgraphs : public FunctionPass {
+public:
+    OPENVINO_RTTI("MarkPrecisionSensitiveSubgraphs", "0");
+    bool run_on_function(std::shared_ptr<ov::Function> f) override;
+};
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp
index 9b4f7f80d7f2ec..1d6c10a5909da1 100644
--- a/inference-engine/src/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp
@@ -68,6 +68,11 @@ class ngraph::pass::GroupedStridedSliceOptimizer: public ngraph::pass::FunctionP
  */
 class ngraph::pass::StridedSliceOptimization: public ngraph::pass::FunctionPass {
 public:
+    StridedSliceOptimization(bool use_shapes = true);
+
     NGRAPH_RTTI_DECLARATION;
     bool run_on_function(std::shared_ptr<ngraph::Function> f) override;
+
+private:
+    bool m_use_shapes = true;
 };
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp
index 1aa4ee16bddcf3..4ae870ccc6f45e 100644
--- a/inference-engine/src/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp
@@ -14,11 +14,18 @@
 namespace ngraph {
 namespace pass {
 
+class TRANSFORMATIONS_API FuseFilteringBoxesBySize;
 class TRANSFORMATIONS_API RemoveFilteringBoxesBySize;
 
 }  // namespace pass
 }  // namespace ngraph
 
+class ngraph::pass::FuseFilteringBoxesBySize: public ngraph::pass::GraphRewrite {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    FuseFilteringBoxesBySize();
+};
+
 class ngraph::pass::RemoveFilteringBoxesBySize: public ngraph::pass::MatcherPass {
 public:
     NGRAPH_RTTI_DECLARATION;
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/subtract_fusion.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/subtract_fusion.hpp
new file mode 100644
index 00000000000000..c3b1c8afec6cbf
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/subtract_fusion.hpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <ngraph/pass/graph_rewrite.hpp>
+#include <transformations_visibility.hpp>
+
+namespace ngraph {
+namespace pass {
+
+class TRANSFORMATIONS_API SubtractFusion;
+
+}  // namespace pass
+}  // namespace ngraph
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief SubtractFusion transformation replaces a sub-graph
+ * Mul(y, -1) + x or x + Mul(y, -1) with Subtract(x,y)
+ */
+class ngraph::pass::SubtractFusion : public ngraph::pass::MatcherPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    SubtractFusion();
+};
\ No newline at end of file
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/wrap_interpolate_into_transposes.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/wrap_interpolate_into_transposes.hpp
new file mode 100644
index 00000000000000..e7b2442b255b02
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/common_optimizations/wrap_interpolate_into_transposes.hpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <vector>
+#include <memory>
+
+#include <transformations_visibility.hpp>
+#include <ngraph/pass/graph_rewrite.hpp>
+
+namespace ngraph {
+namespace pass {
+
+class TRANSFORMATIONS_API WrapInterpolateIntoTransposes;
+
+}  // namespace pass
+}  // namespace ngraph
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief WrapInterpolateIntoTransposes transformation replaces
+ *      Interpolate
+ *  with
+ *      Transpose -> Interpolate -> Transpose
+ *  when
+ *    1) the source Interpolate has the static input rank;
+ *    2) 'axes' input is a Constant;
+ *    3) number of axes is equal to input rank minus 2;
+ *    4) axes contain 0 or 1.
+ *  The reason of this transformation is that now CPU plugin supports interpolation only
+ *  with respect to spatial dimensions, but TensorFlow frontend gives Interpolate with
+ *  axes {1, 2} for 4D tensors.
+ */
+class ngraph::pass::WrapInterpolateIntoTransposes: public ngraph::pass::MatcherPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    WrapInterpolateIntoTransposes();
+};
diff --git a/inference-engine/src/transformations/include/transformations/disable_decompression_convert_constant_folding.hpp b/inference-engine/src/transformations/include/transformations/disable_decompression_convert_constant_folding.hpp
new file mode 100644
index 00000000000000..ea9935e10029d0
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/disable_decompression_convert_constant_folding.hpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "transformations_visibility.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+
+namespace ov {
+namespace pass {
+
+class TRANSFORMATIONS_API DisableDecompressionConvertConstantFolding;
+
+}  // namespace pass
+}  // namespace ov
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief Disables ConstantFolding for Convert operation in compressed function.
+ */
+class ov::pass::DisableDecompressionConvertConstantFolding : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("DisableDecompressionConvertConstantFolding", "0");
+    DisableDecompressionConvertConstantFolding();
+};
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_divide.hpp b/inference-engine/src/transformations/include/transformations/op_conversions/convert_divide.hpp
index 13158b47ad13f4..b7d66501a06c91 100644
--- a/inference-engine/src/transformations/include/transformations/op_conversions/convert_divide.hpp
+++ b/inference-engine/src/transformations/include/transformations/op_conversions/convert_divide.hpp
@@ -15,6 +15,7 @@ namespace ngraph {
 namespace pass {
 
 class TRANSFORMATIONS_API ConvertDivide;
+class TRANSFORMATIONS_API ConvertDivideWithConstant;
 
 }  // namespace pass
 }  // namespace ngraph
@@ -24,3 +25,9 @@ class ngraph::pass::ConvertDivide: public ngraph::pass::MatcherPass {
     NGRAPH_RTTI_DECLARATION;
     ConvertDivide();
 };
+
+class ngraph::pass::ConvertDivideWithConstant: public ngraph::pass::MatcherPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    ConvertDivideWithConstant();
+};
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_slice_to_strided_slice.hpp b/inference-engine/src/transformations/include/transformations/op_conversions/convert_slice_to_strided_slice.hpp
new file mode 100644
index 00000000000000..8ae55a67065510
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/op_conversions/convert_slice_to_strided_slice.hpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <transformations_visibility.hpp>
+#include <ngraph/pass/graph_rewrite.hpp>
+
+namespace ngraph {
+namespace pass {
+
+class TRANSFORMATIONS_API SliceToStridedSlice;
+
+}  // namespace pass
+}  // namespace ngraph
+
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief SliceToStridedSlice transformation convert v8::Slice to v1::StridedSlice
+ */
+class ngraph::pass::SliceToStridedSlice: public ngraph::pass::MatcherPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    SliceToStridedSlice(bool use_shapes);
+};
diff --git a/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp b/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp
index f8bd5493a6f88d..92a5cbe7da685a 100644
--- a/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp
+++ b/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp
@@ -12,11 +12,13 @@
 #include <openvino/core/variant.hpp>
 #include <set>
 #include <transformations/rt_info/disable_constant_folding.hpp>
+#include <transformations/rt_info/disable_fp16_compression.hpp>
 #include <transformations/rt_info/fused_names_attribute.hpp>
 #include <transformations/rt_info/nms_selected_indices.hpp>
 #include <transformations/rt_info/old_api_map_attribute.hpp>
 #include <transformations/rt_info/primitives_priority_attribute.hpp>
 #include <transformations/rt_info/strides_property.hpp>
+#include <transformations/rt_info/decompression.hpp>
 #include <transformations_visibility.hpp>
 #include <utility>
 
@@ -37,5 +39,5 @@ class TRANSFORMATIONS_API Attributes {
 
     ngraph::FactoryRegistry<Variant> m_factory_registry;
 };
-}  // namespace pass
-}  // namespace ov
+} // namespace pass
+} // namespace ov
diff --git a/inference-engine/src/transformations/include/transformations/rt_info/decompression.hpp b/inference-engine/src/transformations/include/transformations/rt_info/decompression.hpp
new file mode 100644
index 00000000000000..18965d1aee26b0
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/rt_info/decompression.hpp
@@ -0,0 +1,42 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <assert.h>
+#include <functional>
+#include <memory>
+#include <string>
+#include <set>
+
+#include "openvino/core/node.hpp"
+#include "openvino/core/variant.hpp"
+#include "transformations_visibility.hpp"
+
+
+namespace ov {
+
+TRANSFORMATIONS_API void mark_as_decompression(const std::shared_ptr<Node>& node);
+
+TRANSFORMATIONS_API void unmark_as_decompression(const std::shared_ptr<Node>& node);
+
+TRANSFORMATIONS_API bool is_decompression(const std::shared_ptr<Node>& node);
+
+/**
+ * @ingroup ie_runtime_attr_api
+ * @brief Decompression class represents runtime info attribute that marks operation
+ * as used as decompression for Compressed Only format.
+ */
+class TRANSFORMATIONS_API Decompression : public VariantImpl<void> {
+public:
+    OPENVINO_RTTI("decompression", "0");
+
+    Decompression() = default;
+
+    bool visit_attributes(AttributeVisitor& visitor) override { return true; }
+
+    bool is_copyable() const override { return false; }
+};
+
+}  // namespace ov
diff --git a/inference-engine/src/transformations/include/transformations/rt_info/disable_fp16_compression.hpp b/inference-engine/src/transformations/include/transformations/rt_info/disable_fp16_compression.hpp
new file mode 100644
index 00000000000000..00352928ba2f07
--- /dev/null
+++ b/inference-engine/src/transformations/include/transformations/rt_info/disable_fp16_compression.hpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/core/node.hpp"
+#include "openvino/core/variant.hpp"
+#include "transformations_visibility.hpp"
+
+
+namespace ov {
+
+TRANSFORMATIONS_API void disable_fp16_compression(const std::shared_ptr<Node>& node);
+
+TRANSFORMATIONS_API void enable_fp16_compression(const std::shared_ptr<Node>& node);
+
+TRANSFORMATIONS_API bool fp16_compression_is_disabled(const std::shared_ptr<Node>& node);
+
+/**
+ * @ingroup ie_runtime_attr_api
+ * @brief DisableFP16Compression class represents runtime info attribute that marks operation
+ * as prohibitted to convert to FP16 as part of Compressed Only format.
+ */
+class TRANSFORMATIONS_API DisableFP16Compression : public VariantImpl<void> {
+public:
+    OPENVINO_RTTI("disable_fp16_compression", "0");
+
+    DisableFP16Compression() = default;
+
+    bool is_copyable() const override { return false; }
+};
+
+}  // namespace ov
diff --git a/inference-engine/src/transformations/include/transformations/utils/utils.hpp b/inference-engine/src/transformations/include/transformations/utils/utils.hpp
index b4e4a8e6142505..d6342c28fe138f 100644
--- a/inference-engine/src/transformations/include/transformations/utils/utils.hpp
+++ b/inference-engine/src/transformations/include/transformations/utils/utils.hpp
@@ -15,10 +15,12 @@
 #include <ngraph/op/constant.hpp>
 #include <ngraph/opsets/opset3.hpp>
 #include <ngraph/opsets/opset4.hpp>
+#include <ngraph/opsets/opset8.hpp>
 
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pattern/op/wrap_type.hpp>
 #include <ngraph/pass/graph_rewrite.hpp>
+#include <transformations/rt_info/attributes.hpp>
 
 namespace ngraph {
 namespace op {
@@ -49,6 +51,17 @@ bool has_op_with_type(const std::shared_ptr<const ngraph::Function> &function) {
     }
     return false;
 }
+
+inline bool has_decompression_converts(const std::shared_ptr<const ngraph::Function>& function) {
+    for (const auto& op : function->get_ops()) {
+        if (std::dynamic_pointer_cast<ngraph::opset8::Convert>(op)) {
+            if (ov::is_decompression(op))
+                return true;
+        }
+    }
+    return false;
+}
+
 inline std::string create_ie_output_name(const ngraph::Output<const ngraph::Node>& output) {
     const auto& prev_layer = output.get_node_shared_ptr();
     std::string out_name = prev_layer->get_friendly_name();
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/clamp_fusion.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/clamp_fusion.cpp
index 2bd8bb6cd5e39e..93af8683f4b09a 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/clamp_fusion.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/clamp_fusion.cpp
@@ -11,9 +11,9 @@
 #include <ngraph/opsets/opset5.hpp>
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/pattern/op/or.hpp>
 #include "itt.hpp"
 
-
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ClampFusion, "ClampFusion", 0);
 
 ngraph::pass::ClampFusion::ClampFusion() {
@@ -21,8 +21,11 @@ ngraph::pass::ClampFusion::ClampFusion() {
     auto data_pattern = ngraph::pattern::any_input();
     auto min_const_pattern = ngraph::pattern::wrap_type<opset5::Constant>();
     auto max_const_pattern = ngraph::pattern::wrap_type<opset5::Constant>();
-    auto max_pattern = ngraph::pattern::wrap_type<opset5::Maximum>({data_pattern, min_const_pattern}, pattern::consumers_count(1));
-    auto min_pattern = ngraph::pattern::wrap_type<opset5::Minimum>({max_pattern, max_const_pattern});
+    auto max_pattern1 = ngraph::pattern::wrap_type<opset5::Maximum>({data_pattern, min_const_pattern}, pattern::consumers_count(1));
+    auto min_pattern1 = ngraph::pattern::wrap_type<opset5::Minimum>({max_pattern1, max_const_pattern});
+    auto min_pattern2 = ngraph::pattern::wrap_type<opset5::Minimum>({data_pattern, max_const_pattern});
+    auto max_pattern2 = ngraph::pattern::wrap_type<opset5::Maximum>({min_pattern2, min_const_pattern}, pattern::consumers_count(1));
+    auto root = std::make_shared<ngraph::pattern::op::Or>(ngraph::OutputVector{min_pattern1, max_pattern2});
 
     ngraph::matcher_pass_callback callback = [=](pattern::Matcher& m) {
         auto pattern_map = m.get_pattern_value_map();
@@ -42,19 +45,27 @@ ngraph::pass::ClampFusion::ClampFusion() {
         double max_value = max_const->cast_vector<double>()[0];
 
         auto clamp = register_new_node<ngraph::opset5::Clamp>(data, min_value, max_value);
-        auto minimum = pattern_map.at(min_pattern);
-        clamp->set_friendly_name(minimum.get_node()->get_friendly_name());
 
-        copy_runtime_info({
-                            pattern_map.at(max_pattern).get_node_shared_ptr(),
-                            minimum.get_node_shared_ptr()
-                          },
-                          clamp);
-        replace_node(minimum.get_node_shared_ptr(), clamp);
+        std::shared_ptr<ov::Node> root_node;
+        NodeVector nodes;
+        auto min_pattern1_it = pattern_map.find(min_pattern1);
+        if (min_pattern1_it != std::end(pattern_map)) {
+            root_node = min_pattern1_it->second.get_node_shared_ptr();
+            nodes.push_back(pattern_map.at(max_pattern1).get_node_shared_ptr());
+        } else {
+            root_node = pattern_map.at(max_pattern2).get_node_shared_ptr();
+            nodes.push_back(pattern_map.at(min_pattern2).get_node_shared_ptr());
+        }
+        nodes.push_back(root_node);
+
+        clamp->set_friendly_name(root_node->get_friendly_name());
+
+        copy_runtime_info(nodes, clamp);
+        replace_node(root_node, clamp);
 
         return true;
     };
 
-    auto m = std::make_shared<ngraph::pattern::Matcher>(min_pattern, matcher_name);
+    auto m = std::make_shared<ngraph::pattern::Matcher>(root, matcher_name);
     this->register_matcher(m, callback);
 }
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp
index d55ab3ca5ceeb9..7e22faf8b1fd49 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp
@@ -49,6 +49,7 @@
 #include "transformations/common_optimizations/strides_optimization.hpp"
 #include "transformations/common_optimizations/convert_nms_gather_path_to_unsigned.hpp"
 #include "transformations/common_optimizations/mul_conv_fusion.hpp"
+#include "transformations/common_optimizations/convert_compression_only_to_legacy.hpp"
 #include "transformations/op_conversions/bidirectional_sequences_decomposition.hpp"
 #include "transformations/op_conversions/convert_pad_to_group_conv.hpp"
 #include "transformations/op_conversions/convert_divide.hpp"
@@ -78,6 +79,7 @@
 #include "transformations/op_conversions/gather_normalize_negative_indices.hpp"
 #include "transformations/op_conversions/convert_deformable_conv_v8_to_v1.hpp"
 #include "transformations/op_conversions/convert_maxpool_downgrade.hpp"
+#include "transformations/disable_decompression_convert_constant_folding.hpp"
 
 #include <ngraph/pass/manager.hpp>
 #include <ngraph/pass/constant_folding.hpp>
@@ -95,10 +97,14 @@ bool ngraph::pass::CommonOptimizations::run_on_function(std::shared_ptr<ngraph::
     RUN_ON_FUNCTION_SCOPE(CommonOptimizations);
     ngraph::pass::Manager manager(get_pass_config());
 
+    manager.register_pass<ov::pass::DisableDecompressionConvertConstantFolding>();
+
     // Disable low_precision_enabled as all plugins handle low-precision sub-graph manually
     // before CommonOptimization pipeline execution
     manager.register_pass<ngraph::pass::MOCTransformations>(true, false);
 
+    manager.register_pass<ov::pass::ConvertCompressedOnlyToLegacy, false>();
+
     // TODO: move to KMB
     manager.register_pass<ngraph::pass::WeightsDequantizeToFakeQuantize>();
 
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/compress_float_constants.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/compress_float_constants.cpp
new file mode 100644
index 00000000000000..8133c108a6f85c
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/compress_float_constants.cpp
@@ -0,0 +1,125 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/common_optimizations/compress_float_constants.hpp"
+
+#include "openvino/opsets/opset8.hpp"
+#include "ngraph/rt_info.hpp"
+#include "openvino/pass/pattern/op/wrap_type.hpp"
+#include "transformations/rt_info/decompression.hpp"
+#include "transformations/rt_info/disable_fp16_compression.hpp"
+#include "transformations/rt_info/old_api_map_attribute.hpp"
+#include "itt.hpp"
+
+
+namespace {
+template <ov::element::Type_t PREC_FROM>
+std::shared_ptr<ov::Node> change_constant_precision_to_fp16(std::shared_ptr<ov::opset8::Constant>& constant) {
+    using src_type = typename ov::element_type_traits<PREC_FROM>::value_type;
+
+    const auto* src_data = constant->get_data_ptr<src_type>();
+    const auto size = ov::shape_size(constant->get_shape());
+
+    auto new_constant = std::make_shared<ov::opset8::Constant>(ov::element::f16, constant->get_shape());
+    auto* dst_data = const_cast<ov::float16*>(reinterpret_cast<const ov::float16*>(new_constant->get_data_ptr()));
+    if (dst_data == nullptr)
+        return nullptr;
+
+    bool is_overflow = false;
+    for (size_t i = 0; i < size; ++i) {
+        if (src_data[i] > std::numeric_limits<ov::float16>::max()) {
+            dst_data[i] = std::numeric_limits<ov::float16>::max();
+            is_overflow = true;
+        } else if (src_data[i] < std::numeric_limits<ov::float16>::lowest()) {
+            dst_data[i] = std::numeric_limits<ov::float16>::lowest();
+            is_overflow = true;
+        } else {
+            dst_data[i] = static_cast<ov::float16>(src_data[i]);
+        }
+    }
+    if (is_overflow) {
+        std::cerr << "Warning: One or more of the values of the Constant can't fit in the float16 data type."
+            " Those values were casted to the nearest limit value, the model can produce incorrect results." << std::endl;
+    }
+    return new_constant;
+}
+}  // namespace
+
+ov::pass::CompressFloatConstantsImpl::CompressFloatConstantsImpl() {
+    MATCHER_SCOPE(CompressFloatConstantsImpl);
+    auto const_pattern = pattern::wrap_type<opset8::Constant>();
+
+    ov::matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        const auto& pattern_map = m.get_pattern_value_map();
+        const auto& const_node_pattern = pattern_map.at(const_pattern);
+
+        auto const_node = std::dynamic_pointer_cast<ov::opset8::Constant>(
+            const_node_pattern.get_node_shared_ptr());
+        if (!const_node)
+            return false;
+
+        if (ov::fp16_compression_is_disabled(const_node))
+            return false;
+
+        auto c_type = const_node->get_element_type();
+        std::shared_ptr<ov::Node> new_const;
+        if (c_type == ov::element::f32) {
+            new_const = change_constant_precision_to_fp16<ov::element::Type_t::f32>(const_node);
+        } else if (c_type == ov::element::f64) {
+            new_const = change_constant_precision_to_fp16<ov::element::Type_t::f64>(const_node);
+        } else {
+            return false;
+        }
+        auto convert = std::make_shared<ov::opset8::Convert>(new_const, const_node->get_element_type());
+
+        convert->set_friendly_name(const_node->get_friendly_name());
+        ngraph::copy_runtime_info(const_node, convert);
+        ov::mark_as_decompression(convert);
+
+        ov::replace_node(const_node, convert);
+
+        return true;
+    };
+
+    auto m = std::make_shared<pattern::Matcher>(const_pattern, matcher_name);
+    this->register_matcher(m, callback);
+}
+
+ov::pass::AddOldApiMapToParameters::AddOldApiMapToParameters() {
+    MATCHER_SCOPE(AddOldApiMapToParameters);
+    auto param_pattern = pattern::wrap_type<opset8::Parameter>();
+
+    ov::matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        const auto& pattern_map = m.get_pattern_value_map();
+        auto node = pattern_map.at(param_pattern).get_node_shared_ptr();
+
+        auto param_node = std::dynamic_pointer_cast<ov::opset8::Parameter>(node);
+        if (!param_node)
+            return false;
+        auto p_type = param_node->get_element_type();
+        if (p_type == ov::element::f32 || p_type == ov::element::f64) {
+            std::vector<uint64_t> order;
+            if (ov::has_old_api_map(node)) {
+                auto old_api = ov::get_old_api_map(node).get();
+                order = old_api.get_order();
+            } else {
+                auto p_rank = param_node->get_partial_shape().rank();
+                if (p_rank.is_static()) {
+                    auto r = p_rank.get_length();
+                    order.resize(r);
+                    std::iota(order.begin(), order.end(), 0);
+                } else {
+                    return false;
+                }
+            }
+            ov::set_old_api_map(node, ov::OldApiMap(ov::OldApiMapAttr(order, ov::element::Type_t::f16)));
+        } else {
+            return false;
+        }
+        return true;
+    };
+
+    auto m = std::make_shared<pattern::Matcher>(param_pattern, matcher_name);
+    this->register_matcher(m, callback);
+}
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp
index 4dddec4adab9fb..2cec36d7bf5e1f 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp
@@ -99,7 +99,7 @@ bool conv_callback(ngraph::pattern::Matcher &m) {
     auto expected_shape = Shape(output_rank, 1);
     expected_shape[1] = channel_dim;
 
-    if (op::util::check_for_broadcast(expected_shape, const_shape)) {
+    if (ngraph::op::util::check_for_broadcast(expected_shape, const_shape)) {
         return false;
     }
 
@@ -107,7 +107,7 @@ bool conv_callback(ngraph::pattern::Matcher &m) {
     // the number of weights dimensions.
     Output<Node> final_const = m_const;
     if (is_scalar_multiplier) {
-        final_const = op::util::broadcastTo(m_const, expected_shape);
+        final_const = ngraph::op::util::broadcastTo(m_const, expected_shape);
     }
 
     if (final_const.get_shape().size() > 1) {
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/convert_compression_only_to_legacy.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/convert_compression_only_to_legacy.cpp
new file mode 100644
index 00000000000000..4725e64e5ff81b
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/convert_compression_only_to_legacy.cpp
@@ -0,0 +1,52 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/common_optimizations/convert_compression_only_to_legacy.hpp"
+
+#include "transformations/convert_precision.hpp"
+#include "transformations/utils/utils.hpp"
+#include "openvino/opsets/opset8.hpp"
+#include "openvino/pass/manager.hpp"
+#include "itt.hpp"
+
+using namespace ov;
+
+bool ov::pass::ConvertPrecisionCompressedOnly::run_on_function(std::shared_ptr<ov::Function> f) {
+    if (ngraph::op::util::has_decompression_converts(f)) {
+        const precisions_array convert_precision_list{
+            {ov::element::f32, ov::element::f16}
+        };
+        auto convert_precision = ngraph::pass::ConvertPrecision(convert_precision_list);
+        return convert_precision.run_on_function(f);
+    }
+    return false;
+}
+
+ov::pass::EnableDecompressionConvertConstantFolding::EnableDecompressionConvertConstantFolding() {
+    MATCHER_SCOPE(EnableDecompressionConvertConstantFolding);
+    auto convert = pattern::wrap_type<opset8::Convert>();
+
+    ov::matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        const auto& node = m.get_match_root();
+        if (!ov::is_decompression(node))
+            return false;
+        enable_constant_folding(node);
+        return true;
+    };
+
+    auto m = std::make_shared<ov::pass::pattern::Matcher>(convert, matcher_name);
+    this->register_matcher(m, callback);
+}
+
+bool ov::pass::ConvertCompressedOnlyToLegacy::run_on_function(std::shared_ptr<ov::Function> f) {
+    Manager manager(get_pass_config());
+
+    manager.register_pass<ov::pass::ConvertPrecisionCompressedOnly>();
+    manager.register_pass<ov::pass::EnableDecompressionConvertConstantFolding>();
+    manager.register_pass<ov::pass::ConstantFolding>();
+
+    manager.run_passes(f);
+
+    return false;
+}
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/divide_fusion.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/divide_fusion.cpp
new file mode 100644
index 00000000000000..adba32ad57ffdb
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/divide_fusion.cpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/common_optimizations/divide_fusion.hpp"
+
+#include <memory>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/rt_info.hpp>
+
+#include "itt.hpp"
+#include "transformations/utils/utils.hpp"
+
+NGRAPH_RTTI_DEFINITION(ngraph::pass::DivideFusion, "DivideFusion", 0);
+
+ngraph::pass::DivideFusion::DivideFusion() {
+    MATCHER_SCOPE(DivideFusion);
+    auto p_pow_input = pattern::any_input();
+    auto p_pow_const = pattern::wrap_type<opset8::Constant>();
+    auto p_pow = pattern::wrap_type<opset8::Power>({p_pow_input, p_pow_const});
+    auto p_mul_input = pattern::any_input();
+    auto p_mul = ngraph::pattern::wrap_type<opset8::Multiply>({p_mul_input, p_pow});
+
+    matcher_pass_callback callback = [=](pattern::Matcher &m) {
+        const auto & pattern_to_output = m.get_pattern_value_map();
+        const auto & minuend_input = pattern_to_output.at(p_mul_input);
+        const auto & subtrahend_input = pattern_to_output.at(p_pow_input);
+        const auto & mul = pattern_to_output.at(p_mul).get_node_shared_ptr();
+        const auto & pow = pattern_to_output.at(p_pow).get_node_shared_ptr();
+        const auto & minus_one = pattern_to_output.at(p_pow_const).get_node_shared_ptr();
+
+        auto minus_one_const = std::dynamic_pointer_cast<opset8::Constant>(minus_one);
+        if (!minus_one_const || !op::util::has_constant_value<float>(minus_one_const, -1.)) {
+            return false;
+        }
+
+        auto div = register_new_node<opset8::Divide>(minuend_input, subtrahend_input);
+        div->set_friendly_name(mul->get_friendly_name());
+        copy_runtime_info({mul, pow}, div);
+        replace_node(mul, div);
+        return true;
+    };
+
+    auto m = std::make_shared<ngraph::pattern::Matcher>(p_mul, matcher_name);
+    register_matcher(m, callback);
+}
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/mark_precision_sensitive_subgraphs.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/mark_precision_sensitive_subgraphs.cpp
new file mode 100644
index 00000000000000..c31181edfcab3d
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/mark_precision_sensitive_subgraphs.cpp
@@ -0,0 +1,73 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp"
+
+#include <memory>
+#include <vector>
+
+#include "transformations/utils/utils.hpp"
+#include "transformations/rt_info/disable_fp16_compression.hpp"
+#include "openvino/opsets/opset1.hpp"
+#include "openvino/opsets/opset3.hpp"
+#include "openvino/opsets/opset8.hpp"
+#include "openvino/pass/pattern/op/wrap_type.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
+
+using namespace std;
+
+namespace {
+void visit_shape_path(const shared_ptr<ov::Node>& node, unordered_set<shared_ptr<ov::Node>>& visited) {
+    if (!node)
+        return;
+    visited.insert(node);
+    deque<shared_ptr<ov::Node>> nodes{ node };
+    while (!nodes.empty()) {
+        auto curr_node = nodes.front();
+        nodes.pop_front();
+        // Do not check if already visited
+        if (ov::is_type<ov::opset1::ShapeOf>(curr_node) || ov::is_type<ov::opset3::ShapeOf>(curr_node)) {
+            continue;
+        }
+        visited.insert(curr_node);
+        if (ov::is_type<ov::opset8::Constant>(curr_node)) {
+            ov::disable_fp16_compression(curr_node);
+        } else {
+            for (auto& input_value : curr_node->input_values()) {
+                // continue searching
+                const auto& input_node = input_value.get_node_shared_ptr();
+                nodes.push_front(input_node);
+            }
+        }
+    }
+}
+}  // namespace
+
+bool ov::pass::MarkPrecisionSensitiveSubgraphs::run_on_function(std::shared_ptr<ov::Function> f) {
+    deque<shared_ptr<Node>> nodes;
+    unordered_set<shared_ptr<Node>> visited;
+    for (auto& r : f->get_results())
+        nodes.push_back(r);
+    for (auto& r : f->get_sinks())
+        nodes.emplace_back(r);
+
+    while (!nodes.empty()) {
+        auto curr_node = nodes.front();
+        nodes.pop_front();
+        if (visited.count(curr_node))
+            continue;
+        for (auto& input : curr_node->inputs()) {
+            if (ov::is_precision_sensitive(input))
+                visit_shape_path(input.get_source_output().get_node_shared_ptr(), visited);
+        }
+        visited.insert(curr_node);
+
+        for (auto& input_value : curr_node->input_values()) {
+            // continue searching
+            const auto& input_node = input_value.get_node_shared_ptr();
+            nodes.push_front(input_node);
+        }
+    }
+    return true;
+}
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/moc_transformations.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/moc_transformations.cpp
index 781e0d972fa584..88f68a22082205 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/moc_transformations.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/moc_transformations.cpp
@@ -48,7 +48,10 @@
 #include <transformations/common_optimizations/transpose_to_reshape.hpp>
 #include <transformations/common_optimizations/batch_to_space_fusion.hpp>
 #include <transformations/common_optimizations/mul_conv_fusion.hpp>
-#include "transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.hpp"
+#include <transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.hpp>
+#include <transformations/op_conversions/convert_divide.hpp>
+#include <transformations/common_optimizations/divide_fusion.hpp>
+#include <transformations/common_optimizations/subtract_fusion.hpp>
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::MOCTransformations, "MOCTransformations", 0);
 
@@ -76,7 +79,10 @@ bool ngraph::pass::MOCTransformations::run_on_function(std::shared_ptr<ngraph::F
     }
     manager.register_pass<ngraph::pass::DisableRandomUniformConstantFolding>();
     manager.register_pass<ngraph::pass::ConstantFolding>();
-    manager.register_pass<ngraph::pass::RemoveFilteringBoxesBySize>();
+    // FusedFilteringBoxesBySize transformation has the complex pattern
+    // which can be affected by further transformations. So we have to
+    // execute it at the beginning of the pipeline.
+    manager.register_pass<ngraph::pass::FuseFilteringBoxesBySize>();
     manager.register_pass<ngraph::pass::ConvertQuantizeDequantize>();
     manager.register_pass<ngraph::pass::SimplifyShapeOfSubGraph>();
     if (!m_use_shapes) {
@@ -85,9 +91,7 @@ bool ngraph::pass::MOCTransformations::run_on_function(std::shared_ptr<ngraph::F
     // workaround until dynamism in NMS is not supported
     manager.register_pass<ngraph::pass::ConvertNmsGatherPathToUnsigned>();
 
-    if (m_use_shapes) {
-        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
-    }
+    manager.register_pass<ngraph::pass::StridedSliceOptimization>(m_use_shapes);
 
     manager.register_pass<ngraph::pass::BroadcastElementwiseFusion>();
 
@@ -122,6 +126,8 @@ bool ngraph::pass::MOCTransformations::run_on_function(std::shared_ptr<ngraph::F
     common_fusions->add_matcher<ngraph::pass::LeakyReluFusion>();
     common_fusions->add_matcher<ngraph::pass::RandomUniformFusion>();
     common_fusions->add_matcher<ngraph::pass::SplitConcatPairToInterpolateFusion>(m_use_shapes);
+    common_fusions->add_matcher<ngraph::pass::DivideFusion>();
+    common_fusions->add_matcher<ngraph::pass::SubtractFusion>();
     common_fusions->set_name("ngraph::pass::CommonFusions");
 
     manager.register_pass<ngraph::pass::BinarizeWeights>();
@@ -129,6 +135,7 @@ bool ngraph::pass::MOCTransformations::run_on_function(std::shared_ptr<ngraph::F
 
     auto decomp = manager.register_pass<ngraph::pass::GraphRewrite>();
     decomp->add_matcher<ngraph::pass::BatchNormDecomposition>();
+    decomp->add_matcher<ngraph::pass::ConvertDivideWithConstant>();
 
     manager.register_pass<ngraph::pass::LinOpSequenceFusion>();
 
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/optimize_strided_slice.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/optimize_strided_slice.cpp
index f7b92fb031548b..37511260ae4741 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/optimize_strided_slice.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/optimize_strided_slice.cpp
@@ -6,11 +6,15 @@
 #include <vector>
 
 #include "itt.hpp"
+#include "transformations/op_conversions/convert_slice_to_strided_slice.hpp"
+
 #include <transformations/common_optimizations/optimize_strided_slice.hpp>
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/opsets/opset3.hpp>
+#include <ngraph/pass/manager.hpp>
 #include <ngraph/rt_info.hpp>
 
+
 NGRAPH_RTTI_DEFINITION(ngraph::pass::StridedSliceOptimization, "StridedSliceOptimization", 0);
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::UselessStridedSliceEraser, "UselessStridedSliceEraser", 0);
@@ -245,10 +249,21 @@ bool ngraph::pass::GroupedStridedSliceOptimizer::run_on_function(std::shared_ptr
     return graph_rewritten;
 }
 
+ngraph::pass::StridedSliceOptimization::StridedSliceOptimization(bool use_shapes) {
+    m_use_shapes = use_shapes;
+}
+
 bool ngraph::pass::StridedSliceOptimization::run_on_function(std::shared_ptr<ngraph::Function> f) {
     RUN_ON_FUNCTION_SCOPE(StridedSliceOptimization);
-    bool rewritten = UselessStridedSliceEraser().run_on_function(f);
-    rewritten |= SharedStridedSliceEraser().run_on_function(f);
-    rewritten |= GroupedStridedSliceOptimizer().run_on_function(f);
+    ngraph::pass::Manager manager(get_pass_config());
+    manager.register_pass<ngraph::pass::SliceToStridedSlice>(m_use_shapes);
+    manager.run_passes(f);
+
+    bool rewritten = false;
+    if (m_use_shapes) {
+        rewritten = UselessStridedSliceEraser().run_on_function(f);
+        rewritten |= SharedStridedSliceEraser().run_on_function(f);
+        rewritten |= GroupedStridedSliceOptimizer().run_on_function(f);
+    }
     return rewritten;
 }
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/pull_transpose_through_fq.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/pull_transpose_through_fq.cpp
index d07dda95face24..d4f61e7e728ae4 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/pull_transpose_through_fq.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/pull_transpose_through_fq.cpp
@@ -11,12 +11,14 @@
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pattern/op/wrap_type.hpp>
+#include <transformations/utils/utils.hpp>
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::PullTransposeThroughFQUp, "PullTransposeThroughFQUp", 0);
 
 ngraph::pass::PullTransposeThroughFQUp::PullTransposeThroughFQUp() {
     MATCHER_SCOPE(PullTransposeThroughFQUp);
-    auto m_fq = pattern::wrap_type<opset1::FakeQuantize>({pattern::any_input(pattern::has_static_rank()),
+    const auto weights = ngraph::pattern::wrap_type<ngraph::opset1::Constant>();
+    auto m_fq = pattern::wrap_type<opset1::FakeQuantize>({weights,
                                                           pattern::any_input(pattern::has_static_shape()),
                                                           pattern::any_input(pattern::has_static_shape()),
                                                           pattern::any_input(pattern::has_static_shape()),
@@ -59,7 +61,7 @@ ngraph::pass::PullTransposeThroughFQUp::PullTransposeThroughFQUp() {
                                                                        opset1::Constant::create(element::i64, Shape{unsqueeze_axes.size()}, unsqueeze_axes));
                 new_ops.push_back(fq_input.get_node_shared_ptr());
             }
-            fq_input = transpose->clone_with_new_inputs({fq_input, transpose->input_value(1)});
+            fq_input = op::util::make_try_fold<opset1::Transpose>(fq_input, transpose->input_value(1));
             ngraph::copy_runtime_info(transpose, fq_input.get_node_shared_ptr());
             fq_inputs.push_back(fq_input);
         }
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/remove_filtering_boxes_by_size.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/remove_filtering_boxes_by_size.cpp
index 8772858d2653cd..734d0fe3b992af 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/remove_filtering_boxes_by_size.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/remove_filtering_boxes_by_size.cpp
@@ -8,11 +8,19 @@
 
 #include <ngraph/opsets/opset3.hpp>
 #include <ngraph/rt_info.hpp>
+#include <ngraph/pass/manager.hpp>
 
 #include "transformations/common_optimizations/remove_filtering_boxes_by_size.hpp"
+#include "transformations/common_optimizations/subtract_fusion.hpp"
 
+NGRAPH_RTTI_DEFINITION(ngraph::pass::FuseFilteringBoxesBySize, "FuseFilteringBoxesBySize", 0);
 NGRAPH_RTTI_DEFINITION(ngraph::pass::RemoveFilteringBoxesBySize, "RemoveFilteringBoxesBySize", 0);
 
+ngraph::pass::FuseFilteringBoxesBySize::FuseFilteringBoxesBySize() {
+    add_matcher<SubtractFusion>();
+    add_matcher<RemoveFilteringBoxesBySize>();
+}
+
 ngraph::pass::RemoveFilteringBoxesBySize::RemoveFilteringBoxesBySize() {
     MATCHER_SCOPE(RemoveFilteringBoxesBySize);
     // variadic split
@@ -85,9 +93,9 @@ ngraph::pass::RemoveFilteringBoxesBySize::RemoveFilteringBoxesBySize() {
         auto start = opset3::Constant::create(element::i64, Shape{}, std::vector<int64_t >({0}));
         auto step = opset3::Constant::create(element::i64, Shape{}, std::vector<int64_t >({1}));
 
-        auto pattern_map = m.get_pattern_map();
+        const auto & pattern_map = m.get_pattern_map();
 
-        auto input = pattern_map[data];
+        auto input = pattern_map.at(data);
         auto output = m.get_match_root();
 
         auto input_shape = std::make_shared<ngraph::opset3::ShapeOf>(input);
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/subtract_fusion.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/subtract_fusion.cpp
new file mode 100644
index 00000000000000..1dfa3590b9e489
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/subtract_fusion.cpp
@@ -0,0 +1,60 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/common_optimizations/subtract_fusion.hpp"
+
+#include <memory>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pattern/op/or.hpp>
+#include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/rt_info.hpp>
+
+#include "itt.hpp"
+#include "transformations/utils/utils.hpp"
+
+NGRAPH_RTTI_DEFINITION(ngraph::pass::SubtractFusion, "SubtractFusion", 0);
+
+ngraph::pass::SubtractFusion::SubtractFusion() {
+    MATCHER_SCOPE(SubtractFusion);
+    auto p_input = pattern::any_input();
+
+    auto p_mul_const = pattern::wrap_type<opset8::Constant>();
+    auto p_mul = pattern::wrap_type<opset8::Multiply>({p_input, p_mul_const});
+
+    auto p_neg = pattern::wrap_type<opset8::Negative>({p_input});
+
+    auto p_mul_or_neg = std::make_shared<pattern::op::Or>(OutputVector({p_mul, p_neg}));
+
+    auto p_add_input = pattern::any_input();
+    auto p_add = ngraph::pattern::wrap_type<opset8::Add>({p_add_input, p_mul_or_neg});
+
+    matcher_pass_callback callback = [=](pattern::Matcher &m) {
+        const auto & pattern_to_output = m.get_pattern_value_map();
+        const auto & minuend_input = pattern_to_output.at(p_add_input);
+        const auto & subtrahend_input = pattern_to_output.at(p_input);
+
+        const auto & add = pattern_to_output.at(p_add).get_node_shared_ptr();
+
+        NodeVector nodes_to_replace{add};
+
+        if (pattern_to_output.count(p_mul_const)) {
+            auto minus_one_const = std::dynamic_pointer_cast<opset8::Constant>(pattern_to_output.at(p_mul_const).get_node_shared_ptr());
+            if (!op::util::has_constant_value<float>(minus_one_const, -1.)) {
+                return false;
+            }
+            nodes_to_replace.emplace_back(pattern_to_output.at(p_mul).get_node_shared_ptr());
+        } else {
+            nodes_to_replace.emplace_back(pattern_to_output.at(p_neg).get_node_shared_ptr());
+        }
+
+        auto sub = register_new_node<opset8::Subtract>(minuend_input, subtrahend_input);
+        sub->set_friendly_name(add->get_friendly_name());
+        copy_runtime_info(nodes_to_replace, sub);
+        replace_node(add, sub);
+        return true;
+    };
+
+    auto m = std::make_shared<ngraph::pattern::Matcher>(p_add, matcher_name);
+    register_matcher(m, callback);
+}
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/transpose_sinking.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
index f5b62cd7c9cb87..d72d15d19baa28 100644
--- a/inference-engine/src/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
@@ -63,7 +63,7 @@ std::shared_ptr<ngraph::opset6::Constant> get_reversed_order_constant(const std:
 ngraph::pass::TransposeReduction::TransposeReduction() {
     MATCHER_SCOPE(TransposeReduction);
 
-    auto transpose_label = pattern::wrap_type<opset6::Transpose>({pattern::any_input(), pattern::wrap_type<opset6::Constant>()});
+    auto transpose_label = pattern::wrap_type<opset6::Transpose>({pattern::any_input(), pattern::wrap_type<opset6::Constant>()}, pattern::consumers_count(1));
     auto reduce_or_squeeze_label = pattern::wrap_type<op::util::ArithmeticReductionKeepDims, op::util::LogicalReductionKeepDims, opset6::Squeeze>(
             {transpose_label, pattern::wrap_type<opset6::Constant>()});
 
diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/wrap_interpolate_into_transposes.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/wrap_interpolate_into_transposes.cpp
new file mode 100644
index 00000000000000..3b74ce1cefb47d
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/common_optimizations/wrap_interpolate_into_transposes.cpp
@@ -0,0 +1,95 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "itt.hpp"
+#include "transformations/common_optimizations/wrap_interpolate_into_transposes.hpp"
+
+#include <algorithm>
+#include <memory>
+#include <numeric>
+#include <tuple>
+#include <set>
+#include <utility>
+#include <vector>
+
+#include <ngraph/rt_info.hpp>
+#include <openvino/opsets/opset8.hpp>
+#include <openvino/pass/pattern/op/wrap_type.hpp>
+
+namespace {
+std::vector<int64_t> reverse_permutation(const std::vector<int64_t>& perm) {
+    if (perm.empty()) return {};
+
+    std::vector<int64_t> result(perm.size());
+    for (int64_t i = 0; i < static_cast<int64_t>(perm.size()); ++i) {
+        result[perm[i]] = i;
+    }
+
+    return result;
+}
+
+std::vector<int64_t> build_transposition_for_axes(const std::vector<int64_t>& axes, size_t input_rank) {
+    std::set<int64_t> non_interpolated_axes_set;
+    for (size_t i = 0; i < input_rank; ++i) {
+        non_interpolated_axes_set.insert(static_cast<int64_t>(i));
+    }
+    for (const auto& axis : axes) {
+        non_interpolated_axes_set.erase(axis);
+    }
+    std::vector<int64_t> result(non_interpolated_axes_set.begin(), non_interpolated_axes_set.end());
+    result.insert(result.end(), axes.begin(), axes.end());
+
+    return result;
+}
+
+std::vector<int64_t> build_new_axes(size_t num_of_axes, size_t rank) {
+    std::vector<int64_t> result(num_of_axes);
+    std::iota(result.begin(), result.end(), static_cast<int64_t>(rank - num_of_axes));
+    return result;
+}
+} // namespace
+
+NGRAPH_RTTI_DEFINITION(ngraph::pass::WrapInterpolateIntoTransposes, "WrapInterpolateIntoTransposes", 0);
+
+ngraph::pass::WrapInterpolateIntoTransposes::WrapInterpolateIntoTransposes() {
+    MATCHER_SCOPE(WrapInterpolateIntoTransposes);
+    auto interpolate_pattern = ov::pass::pattern::wrap_type<ov::opset8::Interpolate>();
+    ngraph::matcher_pass_callback callback = [=](ngraph::pattern::Matcher& m) {
+        auto interpolate = std::dynamic_pointer_cast<ov::opset8::Interpolate>(m.get_match_root());
+        if (!interpolate || interpolate->get_input_partial_shape(0).rank().is_dynamic() || interpolate->inputs().size() != 4) return false;
+
+        int64_t input_rank = interpolate->get_input_partial_shape(0).rank().get_length();
+        // If the input rank is equal to 1 or 2, then such Interpolate is supported by MKLDNN.
+        if (input_rank < 3) return false;
+
+        auto axes_node = std::dynamic_pointer_cast<ov::opset8::Constant>(interpolate->input_value(3).get_node_shared_ptr());
+        if (!axes_node) return false;
+
+        const auto axes = axes_node->cast_vector<int64_t>();
+        if (static_cast<int64_t>(axes.size()) > input_rank - 2 ||
+            std::all_of(axes.begin(), axes.end(), [](int64_t axis){ return axis != 0 && axis != 1; })) {
+            return false;
+        }
+
+        const auto first_perm = build_transposition_for_axes(axes, input_rank);
+        const auto last_perm = reverse_permutation(first_perm);
+
+        auto first_transpose_perm = ov::opset8::Constant::create(element::i64, {first_perm.size()}, first_perm);
+        auto first_transpose = std::make_shared<ov::opset8::Transpose>(interpolate->input_value(0), first_transpose_perm);
+        auto new_axes = build_new_axes(axes.size(), input_rank);
+        auto new_axes_node = ov::opset8::Constant::create(element::i64, {new_axes.size()}, new_axes);
+        auto new_interpolate = interpolate->clone_with_new_inputs({first_transpose, interpolate->input_value(1), interpolate->input_value(2), new_axes_node});
+        auto last_transpose_perm = ov::opset8::Constant::create(element::i64, {last_perm.size()}, last_perm);
+        auto last_transpose = std::make_shared<ov::opset8::Transpose>(new_interpolate, last_transpose_perm);
+
+        last_transpose->set_friendly_name(interpolate->get_friendly_name());
+        copy_runtime_info(interpolate, {first_transpose_perm, first_transpose, new_axes_node, new_interpolate, last_transpose_perm, last_transpose});
+        replace_node(interpolate, last_transpose);
+
+        return true;
+    };
+
+    auto m = std::make_shared<ngraph::pattern::Matcher>(interpolate_pattern, matcher_name);
+    register_matcher(m, callback);
+}
diff --git a/inference-engine/src/transformations/src/transformations/disable_decompression_convert_constant_folding.cpp b/inference-engine/src/transformations/src/transformations/disable_decompression_convert_constant_folding.cpp
new file mode 100644
index 00000000000000..3b6af212327bf6
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/disable_decompression_convert_constant_folding.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/disable_decompression_convert_constant_folding.hpp"
+
+#include "openvino/opsets/opset8.hpp"
+#include "openvino/pass/pattern/op/wrap_type.hpp"
+#include "transformations/rt_info/disable_constant_folding.hpp"
+#include "transformations/rt_info/decompression.hpp"
+#include "itt.hpp"
+
+ov::pass::DisableDecompressionConvertConstantFolding::DisableDecompressionConvertConstantFolding() {
+    MATCHER_SCOPE(DisableDecompressionConvertConstantFolding);
+    auto convert = pattern::wrap_type<opset8::Convert>();
+
+    ov::matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        const auto& node = m.get_match_root();
+        if (!ov::is_decompression(node))
+            return false;
+        disable_constant_folding(node);
+        return true;
+    };
+
+    auto m = std::make_shared<ov::pass::pattern::Matcher>(convert, matcher_name);
+    this->register_matcher(m, callback);
+}
diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_divide.cpp b/inference-engine/src/transformations/src/transformations/op_conversions/convert_divide.cpp
index 03aa5025861b08..07c3624839b4fe 100644
--- a/inference-engine/src/transformations/src/transformations/op_conversions/convert_divide.cpp
+++ b/inference-engine/src/transformations/src/transformations/op_conversions/convert_divide.cpp
@@ -11,31 +11,65 @@
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/validation_util.hpp>
+#include <ngraph/log.hpp>
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertDivide, "ConvertDivide", 0);
+NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertDivideWithConstant, "ConvertDivideWithConstant", 0);
+
+namespace {
+bool convert_divide(std::shared_ptr<ngraph::Node> node) {
+    auto div = std::dynamic_pointer_cast<ngraph::opset1::Divide>(node);
+    // We can not apply this transformation in case with integer input data type
+    if (!div || div->get_input_element_type(0).is_integral()
+             || div->get_input_element_type(1).is_integral()) {
+        return false;
+    }
+
+    ngraph::Output<ngraph::Node> pow = std::make_shared<ngraph::opset1::Power>(div->input_value(1),
+                                                       ngraph::op::Constant::create(div->get_input_element_type(1), ngraph::Shape{}, {-1}));
+
+    if (std::dynamic_pointer_cast<ngraph::op::Constant>(div->get_input_node_shared_ptr(1))) {
+        if (auto const_pow = ngraph::get_constant_from_source(pow)) {
+            pow = const_pow;
+        } else {
+            NGRAPH_DEBUG << "ConvertDivide has failed due to unsupported evaluate type in " << pow.get_node();
+            return false;
+        }
+    } else {
+        ngraph::copy_runtime_info(div, pow.get_node_shared_ptr());
+    }
+
+    auto mul = std::make_shared<ngraph::opset1::Multiply>(div->input(0).get_source_output(), pow);
+
+    mul->set_friendly_name(div->get_friendly_name());
+    ngraph::copy_runtime_info(div, mul);
+    ngraph::replace_node(div, mul);
+    return true;
+}
+} // namespace
 
 ngraph::pass::ConvertDivide::ConvertDivide() {
     MATCHER_SCOPE(ConvertDivide);
     auto div = ngraph::pattern::wrap_type<ngraph::opset1::Divide>();
 
     ngraph::matcher_pass_callback callback = [](pattern::Matcher& m) {
-        auto div = std::dynamic_pointer_cast<ngraph::opset1::Divide> (m.get_match_root());
-        // We can not apply this transformation in case with integer input data type
-        if (!div || div->input(0).get_element_type().is_integral()) {
-            return false;
-        }
+        return convert_divide(m.get_match_root());
+    };
 
-        auto pow = std::make_shared<ngraph::opset1::Power>(div->input(1).get_source_output(),
-                                                           op::Constant::create(div->get_input_element_type(1), Shape{}, {-1}));
+    auto m = std::make_shared<ngraph::pattern::Matcher>(div, matcher_name);
+    this->register_matcher(m, callback);
+}
 
-        auto mul = std::make_shared<ngraph::opset1::Multiply>(div->input(0).get_source_output(), pow);
+ngraph::pass::ConvertDivideWithConstant::ConvertDivideWithConstant() {
+    MATCHER_SCOPE(ConvertDivideWithConstant);
+    auto div = ngraph::pattern::wrap_type<ngraph::opset1::Divide>(
+            {pattern::any_input(), pattern::wrap_type<op::Constant>()});
 
-        mul->set_friendly_name(div->get_friendly_name());
-        ngraph::copy_runtime_info(div, {pow, mul});
-        ngraph::replace_node(div, mul);
-        return true;
+    ngraph::matcher_pass_callback callback = [](pattern::Matcher& m) {
+        return convert_divide(m.get_match_root());
     };
 
     auto m = std::make_shared<ngraph::pattern::Matcher>(div, matcher_name);
     this->register_matcher(m, callback);
-}
+}
\ No newline at end of file
diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_slice_to_strided_slice.cpp b/inference-engine/src/transformations/src/transformations/op_conversions/convert_slice_to_strided_slice.cpp
new file mode 100644
index 00000000000000..f0a1e0b6c468ca
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/op_conversions/convert_slice_to_strided_slice.cpp
@@ -0,0 +1,149 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <memory>
+#include <vector>
+
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/rt_info.hpp>
+
+#include "transformations/op_conversions/convert_slice_to_strided_slice.hpp"
+#include "transformations/utils/utils.hpp"
+#include "ngraph/node.hpp"
+#include "ngraph/op/constant.hpp"
+#include "ngraph/op/util/op_types.hpp"
+#include "ngraph/validation_util.hpp"
+
+#include "itt.hpp"
+
+using namespace ngraph;
+
+NGRAPH_RTTI_DEFINITION(ngraph::pass::SliceToStridedSlice, "SliceToStridedSlice", 0);
+
+namespace {
+    Output<ngraph::Node> align_indices(const Output<ngraph::Node>& indices,
+                                   const Output<ngraph::Node>& slice_axes,
+                                   const Output<ngraph::Node>& scatter_axis,
+                                   size_t slice_indices_length,
+                                   int64_t fill_in_value,
+                                   NodeVector& new_ops) {
+    // Handle a case when starts/ends/steps lengths are less than provided axes
+    // in order to ensure compatibility with `StridedSlice:v1` interface
+    // Example:
+    // data_shape: {3, 3, 3, 3}
+    // starts: [1, 1] - after extending --> [0, 0, 1, 1]
+    // ends: [2, 2] - after extending --> [0, 0, 2, 2]
+    // steps : [1, 1] - after extending --> [1, 1, 1, 1]
+    // axes: [2, 3] - apply slice values to 2 and 3 dimension of input data
+    // expected_output_shape: {3, 3, 1, 1}
+
+    const auto default_indices = ngraph::opset8::Constant::create(indices.get_element_type(), Shape{slice_indices_length}, {fill_in_value});
+    std::shared_ptr<ngraph::Node> adjusted_indices = ngraph::op::util::make_try_fold<ngraph::opset8::ScatterUpdate>(
+                                                                                         default_indices,
+                                                                                         slice_axes,
+                                                                                         indices, // updates
+                                                                                         scatter_axis);
+
+    if (!ngraph::op::is_constant(adjusted_indices)) {
+        new_ops.push_back(default_indices);
+    }
+    return adjusted_indices;
+}
+
+std::vector<int64_t> axes_to_mask(const std::vector<int64_t>& axes, size_t slice_indices_length) {
+    std::vector<int64_t> mask(slice_indices_length, 1);
+    for (auto axis : axes) {
+        mask[axis] = 0;
+    }
+    return mask;
+}
+
+}  // namespace
+
+ngraph::pass::SliceToStridedSlice::SliceToStridedSlice(bool use_shapes) {
+    MATCHER_SCOPE(SliceToStridedSlice);
+    auto slice = pattern::wrap_type<opset8::Slice>();
+    ngraph::matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        auto slice_node = std::dynamic_pointer_cast<opset8::Slice>(m.get_match_root());
+        if (!slice_node)
+            return false;
+
+        if (slice_node->get_input_size() < 4)
+            return false;
+
+        auto arg = slice_node->input_value(0);
+
+        std::shared_ptr<opset8::Constant> start_const;
+        std::shared_ptr<opset8::Constant> stop_const;
+        std::shared_ptr<opset8::Constant> step_const;
+
+        if (use_shapes) {
+            start_const = get_constant_from_source(slice_node->input_value(1));
+            stop_const = get_constant_from_source(slice_node->input_value(2));
+            step_const = get_constant_from_source(slice_node->input_value(3));
+        } else {
+            start_const = std::dynamic_pointer_cast<opset8::Constant>(slice_node->input_value(1).get_node_shared_ptr());
+            stop_const = std::dynamic_pointer_cast<opset8::Constant>(slice_node->input_value(2).get_node_shared_ptr());
+            step_const = std::dynamic_pointer_cast<opset8::Constant>(slice_node->input_value(3).get_node_shared_ptr());
+        }
+
+        auto start_input = start_const ? start_const : slice_node->input_value(1);
+        auto stop_input = stop_const ? stop_const : slice_node->input_value(2);
+        auto step_input = step_const ? step_const : slice_node->input_value(3);
+
+        std::shared_ptr<opset8::Constant> axes_const;
+        if (slice_node->get_input_size() > 4) {
+            axes_const = use_shapes ? get_constant_from_source(slice_node->input_value(4))
+                                    : std::dynamic_pointer_cast<opset8::Constant>(slice_node->input_value(4).get_node_shared_ptr());
+        } else {
+            axes_const = slice_node->get_default_const_axes(start_input);
+        }
+        if (!axes_const)
+            return false;
+
+        const auto& data_shape = slice_node->get_input_partial_shape(0);
+        auto axes_vec = axes_const->cast_vector<int64_t>();
+        if (data_shape.rank().is_static()) {
+            auto norm_axes_vec = normalize_axes(slice_node->get_friendly_name(), axes_vec, data_shape.rank());
+            axes_vec = std::vector<int64_t>(norm_axes_vec.begin(), norm_axes_vec.end());
+        } else {
+            const bool need_normalization = std::any_of(axes_vec.begin(),
+                                                        axes_vec.end(),
+                                                        [](int64_t axis) {
+                                                            return axis < 0;
+                                                        });
+            if (need_normalization)
+                return false;
+        }
+        const size_t slice_indices_length = *std::max_element(std::begin(axes_vec), std::end(axes_vec)) + 1;
+        const auto begin_end_mask = axes_to_mask(axes_vec, slice_indices_length);
+
+        const bool are_axes_sorted = std::is_sorted(axes_vec.begin(), axes_vec.end());
+        const bool are_indices_aligned = are_axes_sorted && (axes_vec.size() == slice_indices_length);
+
+        NodeVector new_ops;
+        if (!are_indices_aligned) {
+            const auto scatter_axis = opset8::Constant::create(element::i32, Shape{1}, {0});
+            const auto slice_axes = opset8::Constant::create(element::i64, Shape{axes_vec.size()}, axes_vec);
+            new_ops.insert(new_ops.end(), {scatter_axis, slice_axes});
+
+            start_input = align_indices(start_input, slice_axes, scatter_axis, slice_indices_length, 0, new_ops);
+            stop_input = align_indices(stop_input, slice_axes, scatter_axis, slice_indices_length, 0, new_ops);
+            step_input = align_indices(step_input, slice_axes, scatter_axis, slice_indices_length, 1, new_ops);
+        }
+        new_ops.insert(new_ops.end(), {start_input.get_node_shared_ptr(), stop_input.get_node_shared_ptr(), step_input.get_node_shared_ptr()});
+
+        const auto strided_slice = std::make_shared<opset8::StridedSlice>(arg, start_input, stop_input, step_input, begin_end_mask, begin_end_mask);
+        new_ops.push_back(strided_slice);
+
+        strided_slice->set_friendly_name(slice_node->get_friendly_name());
+        ngraph::copy_runtime_info(slice_node, new_ops);
+        ngraph::replace_node(slice_node, strided_slice);
+        return true;
+    };
+
+    auto m = std::make_shared<pattern::Matcher>(slice, matcher_name);
+    register_matcher(m, callback);
+}
diff --git a/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp b/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp
index 26c42ab0fbceb5..910788d6c43ef9 100644
--- a/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp
+++ b/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp
@@ -8,10 +8,12 @@ ov::pass::Attributes::Attributes() {
     register_factory<VariantWrapper<ngraph::FusedNames>>();
     register_factory<PrimitivesPriority>();
     register_factory<DisableConstantFolding>();
+    register_factory<DisableFP16Compression>();
     register_factory<NmsSelectedIndices>();
     register_factory<StridesPropagation>();
     register_factory<OldApiMap>();
     register_factory<LayoutAttribute>();
+    register_factory<Decompression>();
 }
 
 ov::Variant* ov::pass::Attributes::create_by_type_info(const ov::DiscreteTypeInfo& type_info) {
diff --git a/inference-engine/src/transformations/src/transformations/rt_info/decompression.cpp b/inference-engine/src/transformations/src/transformations/rt_info/decompression.cpp
new file mode 100644
index 00000000000000..01b45f2bfaba67
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/rt_info/decompression.cpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/rt_info/decompression.hpp"
+
+void ov::mark_as_decompression(const std::shared_ptr<Node>& node) {
+    auto& rt_info = node->get_rt_info();
+    rt_info[Decompression::get_type_info_static()] = std::make_shared<Decompression>();
+}
+
+void ov::unmark_as_decompression(const std::shared_ptr<Node>& node) {
+    auto& rt_info = node->get_rt_info();
+    rt_info.erase(Decompression::get_type_info_static());
+}
+
+bool ov::is_decompression(const std::shared_ptr<Node>& node) {
+    const auto& rt_info = node->get_rt_info();
+    return rt_info.count(Decompression::get_type_info_static());
+}
diff --git a/inference-engine/src/transformations/src/transformations/rt_info/disable_fp16_compression.cpp b/inference-engine/src/transformations/src/transformations/rt_info/disable_fp16_compression.cpp
new file mode 100644
index 00000000000000..79513ddf2b7158
--- /dev/null
+++ b/inference-engine/src/transformations/src/transformations/rt_info/disable_fp16_compression.cpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/rt_info/disable_fp16_compression.hpp"
+
+void ov::disable_fp16_compression(const std::shared_ptr<Node>& node) {
+    auto& rt_info = node->get_rt_info();
+    rt_info[DisableFP16Compression::get_type_info_static()] = std::make_shared<DisableFP16Compression>();
+}
+
+void ov::enable_fp16_compression(const std::shared_ptr<Node>& node) {
+    auto& rt_info = node->get_rt_info();
+    rt_info.erase(DisableFP16Compression::get_type_info_static());
+}
+
+bool ov::fp16_compression_is_disabled(const std::shared_ptr<Node>& node) {
+    const auto& rt_info = node->get_rt_info();
+    return rt_info.count(DisableFP16Compression::get_type_info_static());
+}
diff --git a/inference-engine/src/vpu/common/CMakeLists.txt b/inference-engine/src/vpu/common/CMakeLists.txt
index d8b55be48257e8..6b9800394cb2ad 100644
--- a/inference-engine/src/vpu/common/CMakeLists.txt
+++ b/inference-engine/src/vpu/common/CMakeLists.txt
@@ -33,7 +33,7 @@ function(add_common_target TARGET_NAME STATIC_IE)
 
     target_include_directories(${TARGET_NAME}
         PUBLIC
-            "${CMAKE_CURRENT_SOURCE_DIR}/include")
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
 
     target_include_directories(${TARGET_NAME} SYSTEM PUBLIC
             $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>
@@ -58,6 +58,8 @@ function(add_common_target TARGET_NAME STATIC_IE)
     if(NOT STATIC_IE)
         target_link_libraries(${TARGET_NAME} PUBLIC inference_engine_legacy)
     endif()
+
+    ov_install_static_lib(${TARGET_NAME} myriad)
 endfunction()
 
 add_common_target("vpu_common_lib" FALSE)
diff --git a/inference-engine/src/vpu/common/include/vpu/configuration/options/ir_with_scales_directory.hpp b/inference-engine/src/vpu/common/include/vpu/configuration/options/vpu_scales_option.hpp
similarity index 85%
rename from inference-engine/src/vpu/common/include/vpu/configuration/options/ir_with_scales_directory.hpp
rename to inference-engine/src/vpu/common/include/vpu/configuration/options/vpu_scales_option.hpp
index e4ed56fd0d51fd..e1736aa88f6f4f 100644
--- a/inference-engine/src/vpu/common/include/vpu/configuration/options/ir_with_scales_directory.hpp
+++ b/inference-engine/src/vpu/common/include/vpu/configuration/options/vpu_scales_option.hpp
@@ -19,8 +19,8 @@ enum class Category;
 
 class PluginConfiguration;
 
-struct IRWithScalesDirectoryOption : public AsParameterEnabler {
-    using value_type = std::string;
+struct VPUScalesOption : public AsParameterEnabler {
+    using value_type = std::map<std::string, float>;
 
     static std::string key();
     static void validate(const std::string&);
diff --git a/inference-engine/src/vpu/common/include/vpu/private_plugin_config.hpp b/inference-engine/src/vpu/common/include/vpu/private_plugin_config.hpp
index 78f1964e0804bb..97791374cab149 100644
--- a/inference-engine/src/vpu/common/include/vpu/private_plugin_config.hpp
+++ b/inference-engine/src/vpu/common/include/vpu/private_plugin_config.hpp
@@ -21,7 +21,7 @@ DECLARE_VPU_CONFIG(MYRIAD_TILING_CMX_LIMIT_KB);
 
 DECLARE_VPU_CONFIG(MYRIAD_TENSOR_STRIDES);
 
-DECLARE_VPU_CONFIG(MYRIAD_IR_WITH_SCALES_DIRECTORY);
+DECLARE_VPU_CONFIG(MYRIAD_SCALES_PATTERN);
 DECLARE_VPU_CONFIG(MYRIAD_DETECT_NETWORK_BATCH);
 DECLARE_VPU_CONFIG(MYRIAD_COPY_OPTIMIZATION);
 DECLARE_VPU_CONFIG(MYRIAD_HW_INJECT_STAGES);
diff --git a/inference-engine/src/vpu/common/src/configuration/options/ir_with_scales_directory.cpp b/inference-engine/src/vpu/common/src/configuration/options/ir_with_scales_directory.cpp
deleted file mode 100644
index 42037bdc9f0769..00000000000000
--- a/inference-engine/src/vpu/common/src/configuration/options/ir_with_scales_directory.cpp
+++ /dev/null
@@ -1,38 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "vpu/private_plugin_config.hpp"
-#include "vpu/configuration/options/ir_with_scales_directory.hpp"
-#include "vpu/utils/containers.hpp"
-#include "vpu/configuration/plugin_configuration.hpp"
-
-namespace vpu {
-
-void IRWithScalesDirectoryOption::validate(const std::string& value) {}
-
-void IRWithScalesDirectoryOption::validate(const PluginConfiguration& configuration) {
-    validate(configuration[key()]);
-}
-
-std::string IRWithScalesDirectoryOption::key() {
-    return InferenceEngine::MYRIAD_IR_WITH_SCALES_DIRECTORY;
-}
-
-details::Access IRWithScalesDirectoryOption::access() {
-    return details::Access::Private;
-}
-
-details::Category IRWithScalesDirectoryOption::category() {
-    return details::Category::CompileTime;
-}
-
-std::string IRWithScalesDirectoryOption::defaultValue() {
-    return std::string();
-}
-
-IRWithScalesDirectoryOption::value_type IRWithScalesDirectoryOption::parse(const std::string& value) {
-    return value;
-}
-
-}  // namespace vpu
diff --git a/inference-engine/src/vpu/common/src/configuration/options/vpu_scales_option.cpp b/inference-engine/src/vpu/common/src/configuration/options/vpu_scales_option.cpp
new file mode 100644
index 00000000000000..0c67f1c99cac2f
--- /dev/null
+++ b/inference-engine/src/vpu/common/src/configuration/options/vpu_scales_option.cpp
@@ -0,0 +1,75 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "vpu/private_plugin_config.hpp"
+#include "vpu/configuration/options/vpu_scales_option.hpp"
+#include "vpu/utils/containers.hpp"
+#include "vpu/configuration/plugin_configuration.hpp"
+
+#if defined(__GNUC__) && (__GNUC__ <= 4) && (__GNUC_MINOR__ < 9) && !defined(__clang__) && !defined(IE_GCC_4_8)
+# define IE_GCC_4_8
+#else
+# include <regex>
+#endif
+
+namespace vpu {
+
+void VPUScalesOption::validate(const std::string& value) {}
+
+void VPUScalesOption::validate(const PluginConfiguration& configuration) {
+    validate(configuration[key()]);
+}
+
+std::string VPUScalesOption::key() {
+    return InferenceEngine::MYRIAD_SCALES_PATTERN;
+}
+
+details::Access VPUScalesOption::access() {
+    return details::Access::Private;
+}
+
+details::Category VPUScalesOption::category() {
+    return details::Category::CompileTime;
+}
+
+std::string VPUScalesOption::defaultValue() {
+    return std::string();
+}
+
+VPUScalesOption::value_type VPUScalesOption::parse(const std::string& value) {
+    value_type vpuScaleMap;
+    #ifdef IE_GCC_4_8
+        VPU_THROW_UNLESS(value.empty(), "It is not possible to parse the 'scale' value from the config because you are using a gcc version less than 4.9.");
+    #else
+    std::vector<std::string> parsedStrings;
+
+    auto delimiterToken = std::regex(";");
+    auto regexScales =  std::sregex_token_iterator(value.begin(), value.end(), delimiterToken, -1);
+    std::sregex_token_iterator end;
+    for ( ; regexScales != end; ++regexScales) {
+        parsedStrings.push_back(*regexScales);
+    }
+
+    for (auto& paramStr : parsedStrings) {
+        paramStr.erase(
+            std::remove_if(paramStr.begin(), paramStr.end(), ::isspace),
+            paramStr.end());
+    }
+
+    parsedStrings.erase(
+        std::remove_if(parsedStrings.begin(), parsedStrings.end(),
+                       [](std::string str) { return str.empty(); }),
+        parsedStrings.end());
+    for (const auto& vpuScale : parsedStrings) {
+        const auto delimeterPos = vpuScale.find(':');
+        VPU_THROW_UNLESS(delimeterPos != std::string::npos, "Unable to parse string \"{}\"", vpuScale);
+        vpuScaleMap.insert({std::string(vpuScale.substr(0, delimeterPos)),
+                            std::stof(vpuScale.substr(delimeterPos + 1))});
+    }
+
+    #endif
+    return vpuScaleMap;
+}
+
+}  // namespace vpu
diff --git a/inference-engine/src/vpu/graph_transformer/CMakeLists.txt b/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
index 8fdae7418b4174..4bcf6c3295b269 100644
--- a/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
+++ b/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
@@ -38,7 +38,7 @@ function(add_graph_transformer_target TARGET_NAME STATIC_IE)
 
     target_include_directories(${TARGET_NAME}
         PUBLIC
-            "${CMAKE_CURRENT_SOURCE_DIR}/include")
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
 
     target_include_directories(${TARGET_NAME}
         PUBLIC
@@ -67,6 +67,7 @@ function(add_graph_transformer_target TARGET_NAME STATIC_IE)
     if(NOT STATIC_IE)
         add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME} CUSTOM_FILTERS "+runtime/explicit")
         openvino_developer_export_targets(COMPONENT inference_engine_vpu TARGETS ${TARGET_NAME})
+        ov_install_static_lib(${TARGET_NAME} myriad)
     endif()
 
     set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
diff --git a/inference-engine/src/vpu/graph_transformer/src/frontend/unroll_loops.cpp b/inference-engine/src/vpu/graph_transformer/src/frontend/unroll_loops.cpp
index 219c92a5772d82..1b86921fe7086e 100644
--- a/inference-engine/src/vpu/graph_transformer/src/frontend/unroll_loops.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/frontend/unroll_loops.cpp
@@ -11,7 +11,7 @@
 
 #include <vpu/compile_env.hpp>
 
-#include <vpu/configuration/options/ir_with_scales_directory.hpp>
+#include <vpu/configuration/options/vpu_scales_option.hpp>
 #include <vpu/configuration/options/force_pure_tensor_iterator.hpp>
 #include <vpu/configuration/options/enable_tensor_iterator_unrolling.hpp>
 
@@ -25,7 +25,7 @@ void FrontEnd::unrollLoops(ie::CNNNetwork& network) {
     env.log->trace("Unroll TensorIterator loops");
     VPU_LOGGER_SECTION(env.log);
 
-    if (!env.config.get<IRWithScalesDirectoryOption>().empty()) {
+    if (!env.config.get<VPUScalesOption>().empty()) {
         // TODO: Scale dumps does not work with IR, which contain Tensor Iterator layers, because we cannot serialize them. #-23429
         for (auto iterator = ie::details::CNNNetworkIterator(network); iterator != ie::details::CNNNetworkIterator(); ++iterator) {
             const auto& layer = *iterator;
diff --git a/inference-engine/src/vpu/graph_transformer/src/graph_transformer.cpp b/inference-engine/src/vpu/graph_transformer/src/graph_transformer.cpp
index c82a75515d0bd6..b23d401a09ff85 100644
--- a/inference-engine/src/vpu/graph_transformer/src/graph_transformer.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/graph_transformer.cpp
@@ -50,7 +50,7 @@
 #include <vpu/configuration/options/number_of_shaves.hpp>
 #include <vpu/configuration/options/throughput_streams.hpp>
 #include <vpu/configuration/options/number_of_cmx_slices.hpp>
-#include <vpu/configuration/options/ir_with_scales_directory.hpp>
+#include <vpu/configuration/options/vpu_scales_option.hpp>
 
 namespace vpu {
 
@@ -173,11 +173,6 @@ CompiledGraph::Ptr compileImpl(const ie::CNNNetwork& network, const std::shared_
 
     middleEnd->run(model);
 
-    if (!env.config.get<IRWithScalesDirectoryOption>().empty()) {
-        network.serialize(env.config.get<IRWithScalesDirectoryOption>() + "/" + network.getName() + "_scales.xml",
-                          env.config.get<IRWithScalesDirectoryOption>() + "/" + network.getName() + "_scales.bin");
-    }
-
     return backEnd->build(model, frontEnd->origLayers());
 }
 
diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/weights_analysis.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/weights_analysis.cpp
index f29432aeaff56e..5caf439fa16737 100644
--- a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/weights_analysis.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/weights_analysis.cpp
@@ -9,7 +9,7 @@
 #include <vpu/model/data_contents/replicated_data_content.hpp>
 #include <vpu/model/data_contents/scaled_content.hpp>
 
-#include <vpu/configuration/options/ir_with_scales_directory.hpp>
+#include <vpu/configuration/options/vpu_scales_option.hpp>
 #include <vpu/configuration/options/check_preprocessing_inside_model.hpp>
 
 #include <precision_utils.h>
@@ -28,6 +28,17 @@
 #include <list>
 #include <set>
 
+#if defined(__GNUC__) && (__GNUC__ <= 4) && (__GNUC_MINOR__ < 9) && !defined(__clang__) && !defined(IE_GCC_4_8)
+# define IE_GCC_4_8
+#endif
+
+#ifndef IE_GCC_4_8
+# include <regex>
+# define STD_REGEX_SEARCH(SRC, PATTERN) std::regex_search(SRC, std::regex(PATTERN))
+#else
+# define STD_REGEX_SEARCH(SRC, PATTERN) false
+#endif
+
 namespace vpu {
 
 namespace {
@@ -206,6 +217,16 @@ void scaleWeightableStage(const Model& model, const Stage& stage, float factor)
     stage->attrs().set<float>("scaleFactor", factor);
 }
 
+float getScaleValue(const std::string layerName, const std::map<std::string, float>& vpuScalemap) {
+    float scaleForAnyLayer = 0.0;
+    for (const auto& pair : vpuScalemap) {
+        if (STD_REGEX_SEARCH(layerName, pair.first)) {
+            return pair.second;
+        }
+    }
+    return scaleForAnyLayer;
+}
+
 class PassImpl final : public Pass {
 public:
     void run(const Model& model) override;
@@ -227,9 +248,9 @@ void PassImpl::run(const Model& model) {
             continue;
         }
         IE_ASSERT(stage->origLayer() != nullptr);
-
-        // Get scale from IR, compute if it was absent
-        auto scale = stage->origLayer()->GetParamAsFloat("vpu_scale", 0);
+        // Get scale from config, compute if it was absent
+        const auto map = env.config.get<VPUScalesOption>();
+        auto scale = getScaleValue(stage->origLayerName(), map);
         if (!scale) {
             auto weights = stage->input(1);
 
@@ -260,10 +281,6 @@ void PassImpl::run(const Model& model) {
             if (shift >= scaleThreshold) {
                 scale = static_cast<float>(1ULL << static_cast<std::uint32_t>(shift));
             }
-
-            if (!env.config.get<IRWithScalesDirectoryOption>().empty()) {
-                stage->origLayer()->params["vpu_scale"] = toString(scale);
-            }
         }
         scaleWeightableStage(model, stage, scale);
     }
diff --git a/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp b/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp
index 1136f71f345803..6b6f88ab25ca09 100644
--- a/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp
+++ b/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp
@@ -39,7 +39,7 @@
 #include <vpu/configuration/options/number_of_shaves.hpp>
 #include <vpu/configuration/options/number_of_cmx_slices.hpp>
 #include <vpu/configuration/options/throughput_streams.hpp>
-#include <vpu/configuration/options/ir_with_scales_directory.hpp>
+#include <vpu/configuration/options/vpu_scales_option.hpp>
 #include <vpu/configuration/options/tensor_strides.hpp>
 #include <vpu/configuration/options/ignore_unknown_layers.hpp>
 #include <vpu/configuration/options/force_pure_tensor_iterator.hpp>
@@ -208,7 +208,7 @@ Engine::Engine(std::shared_ptr<IMvnc> mvnc) :
     _parsedConfig.registerOption<NumberOfSHAVEsOption>();
     _parsedConfig.registerOption<NumberOfCMXSlicesOption>();
     _parsedConfig.registerOption<ThroughputStreamsOption>();
-    _parsedConfig.registerOption<IRWithScalesDirectoryOption>();
+    _parsedConfig.registerOption<VPUScalesOption>();
     _parsedConfig.registerOption<TensorStridesOption>();
     _parsedConfig.registerOption<IgnoreUnknownLayersOption>();
     _parsedConfig.registerOption<ForcePureTensorIteratorOption>();
diff --git a/inference-engine/tests/functional/inference_engine/CMakeLists.txt b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
index a107a133ca8b42..1aed4b84b0eea7 100644
--- a/inference-engine/tests/functional/inference_engine/CMakeLists.txt
+++ b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
@@ -4,7 +4,7 @@
 
 set(TARGET_NAME ieFuncTests)
 
-set(INCLUDES ${IE_MAIN_SOURCE_DIR}/src/inference_engine/src)
+set(INCLUDES $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src)
 
 set(LINK_LIBRARIES
     gmock
@@ -123,32 +123,44 @@ function(ie_headers_compilation_with_custom_flags)
     cmake_parse_arguments(IE_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
     if(IE_TEST_PLUGIN_API)
-        set(IE_TEST_INCLUDE_DIRECTORY
-            $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>)
+        set(include_dirs
+            "${OpenVINO_SOURCE_DIR}/inference-engine/src/plugin_api/")
     else()
-        set(IE_TEST_INCLUDE_DIRECTORY
-            $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
+        set(include_dirs
+            "${OpenVINO_SOURCE_DIR}/ngraph/core/include"
+            "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/ie"
+            "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/")
     endif()
 
-    file(GLOB_RECURSE header_files RELATIVE "${IE_TEST_INCLUDE_DIRECTORY}"
-        "${IE_TEST_INCLUDE_DIRECTORY}/*")
+    foreach(include_dir IN LISTS include_dirs)
+        file(GLOB_RECURSE header_files_local RELATIVE "${include_dir}" "${include_dir}/*.hpp")
+        list(APPEND header_files ${header_files_local})
+    endforeach()
 
     if(NOT IE_TEST_CXX_STANDARD)
         set(IE_TEST_CXX_STANDARD ${CMAKE_CXX_STANDARD})
     endif()
 
     if(NOT IE_TEST_PLUGIN_API)
+        list(APPEND IE_TEST_HEADERS_TO_SKIP "_tbl.hpp")
         if(NOT OPENCL_HEADERS_DIR)
             list(APPEND IE_TEST_HEADERS_TO_SKIP "gpu/gpu_ocl_wrapper.hpp"
                                                 "gpu/gpu_context_api_ocl.hpp"
                                                 "gpu/gpu_context_api_va.hpp"
-                                                "gpu/gpu_context_api_dx.hpp")
+                                                "gpu/gpu_context_api_dx.hpp"
+                                                "gpu/gpu_ocl_wrapper.hpp"
+                                                "openvino/runtime/gpu/ocl/ocl_wrapper.hpp"
+                                                "openvino/runtime/gpu/ocl/ocl.hpp"
+                                                "openvino/runtime/gpu/ocl/va.hpp"
+                                                "openvino/runtime/gpu/ocl/dx.hpp")
         endif()
         if(NOT WIN32)
-            list(APPEND IE_TEST_HEADERS_TO_SKIP "gpu/gpu_context_api_dx.hpp")
+            list(APPEND IE_TEST_HEADERS_TO_SKIP "gpu/gpu_context_api_dx.hpp"
+                                                "openvino/runtime/gpu/ocl/dx.hpp")
         endif()
         if(NOT LIBVA_FOUND)
-            list(APPEND IE_TEST_HEADERS_TO_SKIP "gpu/gpu_context_api_va.hpp")
+            list(APPEND IE_TEST_HEADERS_TO_SKIP "gpu/gpu_context_api_va.hpp"
+                                                "openvino/runtime/gpu/ocl/va.hpp")
         endif()
     endif()
 
@@ -157,7 +169,7 @@ function(ie_headers_compilation_with_custom_flags)
         # skip user-passed headers
         set(skip_current_file OFF)
         foreach(skip_file IN LISTS IE_TEST_HEADERS_TO_SKIP)
-            if(header_file STREQUAL skip_file)
+            if("${header_file}" MATCHES "${skip_file}")
                 set(skip_current_file ON)
             endif()
         endforeach()
@@ -187,8 +199,7 @@ function(ie_headers_compilation_with_custom_flags)
             $<TARGET_PROPERTY:pugixml::static,INTERFACE_INCLUDE_DIRECTORIES>
             $<TARGET_PROPERTY:openvino::itt,INTERFACE_INCLUDE_DIRECTORIES>)
         target_include_directories(${target_name} PRIVATE
-            $<TARGET_PROPERTY:ngraph,INTERFACE_INCLUDE_DIRECTORIES>
-            $<TARGET_PROPERTY:inference_engine_preproc,INTERFACE_INCLUDE_DIRECTORIES>)
+            $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>)
     else()
         # OpenCL headers if any
         if(OPENCL_HEADERS_DIR)
@@ -233,12 +244,19 @@ endfunction()
 ie_headers_compilation_with_custom_flags(TEST_SUFFIX Cxx17
                                          CXX_STANDARD 17)
 
+if(CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.0)
+    ie_headers_compilation_with_custom_flags(TEST_SUFFIX Cxx20
+                                             CXX_STANDARD 20)
+endif()
+
 if(UNIX)
     if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
         ie_headers_compilation_with_custom_flags(TEST_SUFFIX WarningsAreErrors
                                                  FLAGS -Wremarks -Wcheck -Werror-all -Werror -Wall)
-    else()
-        ie_headers_compilation_with_custom_flags(TEST_SUFFIX Pedantic FLAGS -Wpedantic)
+    elseif(BUILD_SHARED_LIBS)
+        # fix warnings wit RTTI
+        # ie_headers_compilation_with_custom_flags(TEST_SUFFIX Pedantic
+        #                                          FLAGS -Wpedantic)
     endif()
 else()
     ie_headers_compilation_with_custom_flags(TEST_SUFFIX WindowsAreErrors
@@ -260,14 +278,21 @@ endif()
 ie_headers_compilation_with_custom_flags(TEST_SUFFIX PluginApiCxx17
                                          CXX_STANDARD 17 PLUGIN_API)
 
+if(CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.0)
+    ie_headers_compilation_with_custom_flags(TEST_SUFFIX PluginApiCxx20
+                                            CXX_STANDARD 20 PLUGIN_API)
+endif()
+
 if(UNIX)
     if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
         ie_headers_compilation_with_custom_flags(TEST_SUFFIX PluginApiWarningsAreErrors
                                                  FLAGS -Werror-all -Werror -Wall
                                                  PLUGIN_API)
-    else()
-        ie_headers_compilation_with_custom_flags(TEST_SUFFIX PluginApiPedantic FLAGS -Wpedantic
-                                                 PLUGIN_API)
+    elseif(BUILD_SHARED_LIBS)
+        # fix warnings wit RTTI
+        # ie_headers_compilation_with_custom_flags(TEST_SUFFIX PluginApiPedantic
+        #                                          FLAGS -Wpedantic
+        #                                          PLUGIN_API)
     endif()
 else()
     ie_headers_compilation_with_custom_flags(TEST_SUFFIX PluginApiWindowsAreErrors
diff --git a/inference-engine/tests/functional/inference_engine/core_threading.cpp b/inference-engine/tests/functional/inference_engine/core_threading.cpp
index c59e3538bbce90..5540fc933dbfbe 100644
--- a/inference-engine/tests/functional/inference_engine/core_threading.cpp
+++ b/inference-engine/tests/functional/inference_engine/core_threading.cpp
@@ -87,6 +87,9 @@ TEST_F(CoreThreadingTests, RegisterPlugin) {
     }, 4000);
 }
 
+// TODO: CVS-68982
+#ifndef OPENVINO_STATIC_LIBRARY
+
 // tested function: RegisterPlugins
 TEST_F(CoreThreadingTests, RegisterPlugins) {
     InferenceEngine::Core ie;
@@ -123,6 +126,8 @@ TEST_F(CoreThreadingTests, RegisterPlugins) {
     }, 1000);
 }
 
+#endif // !OPENVINO_STATIC_LIBRARY
+
 // tested function: GetAvailableDevices, UnregisterPlugin
 // TODO: some initialization (e.g. thread/dlopen) sporadically fails during such stress-test scenario
 TEST_F(CoreThreadingTests, DISABLED_GetAvailableDevices) {
diff --git a/inference-engine/tests/functional/inference_engine/executable_network.cpp b/inference-engine/tests/functional/inference_engine/executable_network.cpp
index 89653bdc5ee7f1..3ca191b529dcf6 100644
--- a/inference-engine/tests/functional/inference_engine/executable_network.cpp
+++ b/inference-engine/tests/functional/inference_engine/executable_network.cpp
@@ -35,13 +35,6 @@ TEST(ExecutableNetworkTests, throwsOnUninitializedGetExecGraphInfo) {
     ASSERT_THROW(exec.GetExecGraphInfo(), InferenceEngine::NotAllocated);
 }
 
-TEST(ExecutableNetworkTests, throwsOnUninitializedQueryState) {
-    IE_SUPPRESS_DEPRECATED_START
-    ExecutableNetwork exec;
-    ASSERT_THROW(exec.QueryState(), InferenceEngine::NotAllocated);
-    IE_SUPPRESS_DEPRECATED_END
-}
-
 TEST(ExecutableNetworkTests, throwsOnUninitializedSetConfig) {
     ExecutableNetwork exec;
     ASSERT_THROW(exec.SetConfig({{}}), InferenceEngine::NotAllocated);
diff --git a/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp b/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
index c5f779aebb7417..e5e130dbb0361c 100644
--- a/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
+++ b/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
@@ -11,6 +11,7 @@
 #include "common_test_utils/file_utils.hpp"
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "ie_core.hpp"
+#include "openvino/runtime/core.hpp"
 #include "ngraph/ngraph.hpp"
 #include "transformations/serialize.hpp"
 
@@ -27,6 +28,11 @@ static std::string get_extension_path() {
         {}, std::string("template_extension") + IE_BUILD_POSTFIX);
 }
 
+static std::string get_ov_extension_path() {
+    return FileUtils::makePluginLibraryName<char>(
+        {}, std::string("template_ov_extension") + IE_BUILD_POSTFIX);
+}
+
 class CustomOpsSerializationTest : public ::testing::Test {
 protected:
     std::string test_name =
@@ -158,3 +164,25 @@ TEST_F(CustomOpsSerializationTest, CustomOpNoExtensions) {
 
     ASSERT_TRUE(success) << message;
 }
+
+TEST_F(CustomOpsSerializationTest, CustomOpOVExtensions) {
+    const std::string model = CommonTestUtils::getModelFromTestModelZoo(
+        IR_SERIALIZATION_MODELS_PATH "custom_identity.xml");
+
+    ov::runtime::Core core;
+    core.add_extension(get_ov_extension_path());
+    auto expected = core.read_model(model);
+    ngraph::pass::Manager manager;
+    manager.register_pass<ngraph::pass::Serialize>(
+            m_out_xml_path, m_out_bin_path,
+            ngraph::pass::Serialize::Version::IR_V10);
+    manager.run_passes(expected);
+    auto result = core.read_model(m_out_xml_path, m_out_bin_path);
+
+    bool success;
+    std::string message;
+    std::tie(success, message) =
+            compare_functions(result, expected, true, false, false, true, true);
+
+    ASSERT_TRUE(success) << message;
+}
diff --git a/inference-engine/tests/functional/inference_engine/ir_serialization/models/custom_identity.xml b/inference-engine/tests/functional/inference_engine/ir_serialization/models/custom_identity.xml
new file mode 100644
index 00000000000000..e7ae6bc8374a9f
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/ir_serialization/models/custom_identity.xml
@@ -0,0 +1,51 @@
+<?xml version="1.0" ?>
+<!--This is syntetic model created by hand desined only for white-box unit testing-->
+<net name="Network" version="10">
+    <layers>
+        <layer name="in1" type="Parameter" id="0" version="opset1">
+            <data element_type="f32" shape="2,2,2,1"/>
+            <output>
+                <port id="0" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </output>
+        </layer>
+        <layer name="operation" id="1" type="Identity" version="extension">
+            <data  add="11"/>
+            <input>
+                <port id="1" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </input>
+            <output>
+                <port id="2" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </output>
+        </layer>
+        <layer name="output" type="Result" id="2" version="opset1">
+            <input>
+                <port id="0" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </input>
+        </layer>
+    </layers>
+    <edges>
+        <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
+        <edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
+    </edges>
+</net>
+
diff --git a/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp b/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
index ad74c2bcb7842f..76cd2c7f9917ac 100644
--- a/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
+++ b/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp
@@ -35,8 +35,8 @@ class RTInfoDeserialization : public testing::Test {
         std::istringstream modelStringStream(model);
         std::istream& modelStream = modelStringStream;
 
-        ngraph::frontend::FrontEnd::Ptr FE;
-        ngraph::frontend::InputModel::Ptr inputModel;
+        ov::frontend::FrontEnd::Ptr FE;
+        ov::frontend::InputModel::Ptr inputModel;
 
         ov::VariantVector params{ov::make_variant(&modelStream)};
 
@@ -51,7 +51,7 @@ class RTInfoDeserialization : public testing::Test {
     }
 
 private:
-    ngraph::frontend::FrontEndManager manager;
+    ov::frontend::FrontEndManager manager;
 };
 
 TEST_F(RTInfoDeserialization, NodeV10) {
@@ -553,6 +553,75 @@ TEST_F(RTInfoDeserialization, NodeV11) {
     }
 }
 
+TEST_F(RTInfoDeserialization, NodeV11MultipleRTKeys) {
+    std::string model = R"V0G0N(
+<net name="Network" version="11">
+    <layers>
+        <layer name="in1" type="Parameter" id="0" version="opset8">
+            <data element_type="f32" shape="1,22,22,3"/>
+            <rt_info>
+                <attribute name="old_api_map" version="0" order="0,2,3,1" element_type="f16"/>
+                <attribute name="old_api_map" version="0" order="0,1,2,3" element_type="f32"/>
+                <attribute name="fused_names" version="0" value="in1"/>
+            </rt_info>
+            <output>
+                <port id="0" precision="FP32" names="input_tensor">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>22</dim>
+                    <dim>22</dim>
+                </port>
+            </output>
+        </layer>
+        <layer name="Round" id="1" type="Round" version="opset8">
+            <data mode="half_to_even"/>
+            <rt_info>
+                <attribute name="fused_names" version="0" value="Round1,Round2"/>
+            </rt_info>
+            <input>
+                <rt_info>
+                    <attribute name="fused_names" version="0" value="check"/>
+                    <attribute name="fused_names" version="0" value="multiple_keys"/>
+                </rt_info>
+                <port id="1" precision="FP32">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>22</dim>
+                    <dim>22</dim>
+                </port>
+            </input>
+            <output>
+                <port id="2" precision="FP32" names="output_tensor">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>22</dim>
+                    <dim>22</dim>
+                </port>
+            </output>
+        </layer>
+        <layer name="output" type="Result" id="2" version="opset8">
+            <rt_info>
+                <attribute name="old_api_map" version="0" order="0,3,1,2" element_type="f16"/>
+            </rt_info>
+            <input>
+                <port id="0" precision="FP32">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>22</dim>
+                    <dim>22</dim>
+                </port>
+            </input>
+        </layer>
+    </layers>
+    <edges>
+        <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
+        <edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
+    </edges>
+</net>
+)V0G0N";
+    ASSERT_ANY_THROW(getWithIRFrontend(model));
+}
+
 TEST_F(RTInfoDeserialization, InputAndOutputV11) {
     std::string model = R"V0G0N(
 <net name="Network" version="11">
diff --git a/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp b/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp
index a851b27026f2e0..8b6841f1772566 100644
--- a/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp
+++ b/inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp
@@ -30,8 +30,8 @@ class RTInfoSerializationTest : public CommonTestUtils::TestsCommon {
 
     std::shared_ptr<ngraph::Function> getWithIRFrontend(const std::string& model_path,
                                                         const std::string& weights_path) {
-        ngraph::frontend::FrontEnd::Ptr FE;
-        ngraph::frontend::InputModel::Ptr inputModel;
+        ov::frontend::FrontEnd::Ptr FE;
+        ov::frontend::InputModel::Ptr inputModel;
 
         ov::VariantVector params{ov::make_variant(model_path), ov::make_variant(weights_path)};
 
@@ -46,7 +46,7 @@ class RTInfoSerializationTest : public CommonTestUtils::TestsCommon {
     }
 
 private:
-    ngraph::frontend::FrontEndManager manager;
+    ov::frontend::FrontEndManager manager;
 };
 
 TEST_F(RTInfoSerializationTest, all_attributes_latest) {
@@ -57,6 +57,7 @@ TEST_F(RTInfoSerializationTest, all_attributes_latest) {
                 std::make_shared<ov::PrimitivesPriority>("priority");
         info[ov::OldApiMap::get_type_info_static()] = std::make_shared<ov::OldApiMap>(
                 ov::OldApiMapAttr(std::vector<uint64_t>{0, 2, 3, 1}, ngraph::element::Type_t::f32));
+        info[ov::Decompression::get_type_info_static()] = std::make_shared<ov::Decompression>();
     };
 
     std::shared_ptr<ngraph::Function> function;
@@ -100,6 +101,11 @@ TEST_F(RTInfoSerializationTest, all_attributes_latest) {
         auto old_api_map_attr_val = old_api_map_attr->get();
         ASSERT_EQ(old_api_map_attr_val.get_order(), std::vector<uint64_t>({0, 2, 3, 1}));
         ASSERT_EQ(old_api_map_attr_val.get_type(), ngraph::element::Type_t::f32);
+
+        const std::string& dkey = ov::Decompression::get_type_info_static();
+        ASSERT_TRUE(info.count(dkey));
+        auto decompression_attr = std::dynamic_pointer_cast<ov::Decompression>(info.at(dkey));
+        ASSERT_TRUE(decompression_attr);
     };
 
     auto add = f->get_results()[0]->get_input_node_ptr(0);
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/add_transformation.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/add_transformation.cpp
index 88b133ad116271..ce2e69043e499b 100644
--- a/inference-engine/tests/functional/inference_engine/lp_transformations/add_transformation.cpp
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/add_transformation.cpp
@@ -931,6 +931,28 @@ const std::vector<AddTransformationTestValues> testValues = {
         },
         ""
     },
+    // float path without subtract
+    {
+        false,
+        -1,
+        LayerTransformation::createParamsU8I8(),
+        {
+            ngraph::element::f32,
+            { },
+            ngraph::element::u8,
+            { {ngraph::element::f32},  {}, { 4.f }},
+            { }
+        },
+        {
+            ngraph::element::f32,
+            { {ngraph::element::f32},  {}, { 0.25f } },
+            ngraph::element::u8,
+            { },
+            { {},  {}, { 4.f }},
+            { }
+        },
+        ""
+    },
 };
 
 INSTANTIATE_TEST_SUITE_P(
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/concat_transformation.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/concat_transformation.cpp
index 64e71691075a20..3483d37fed1c3f 100644
--- a/inference-engine/tests/functional/inference_engine/lp_transformations/concat_transformation.cpp
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/concat_transformation.cpp
@@ -967,7 +967,7 @@ const std::vector<ConcatTransformationTestValues> testValues = {
             { {element::f32}, {}, { 0.01f } },
         }
     },
-    // unexpected quantization levels, concat
+    // INT4+INT8 quantization levels, concat
     {
         LayerTransformation::createParamsU8I8(),
         false,
@@ -990,16 +990,16 @@ const std::vector<ConcatTransformationTestValues> testValues = {
             ngraph::element::f32,
             {},
         },
-        false,
+        true,
         false,
     },
-    // unexpected quantization levels, concat multi channels
+    // INT4+INT8 quantization levels, concat multi channels
     {
         LayerTransformation::createParamsU8I8(),
         true,
         1,
         {
-            { 16ul, {}, {0.f}, {1.5f}, {0.f}, {15.f} },
+            { 16ul, {}, {0.f}, {1.5f}, {0.f}, {1.5f} },
             {},
             {},
             { 256ul, {}, {0.f}, {2.55f}, {0.f}, {2.55f} },
@@ -1007,16 +1007,16 @@ const std::vector<ConcatTransformationTestValues> testValues = {
             {}
         },
         {
-            { 16ul, {}, {0.f}, {1.5f}, {0.f}, {15.f} },
-            {},
+            {16ul, {}, {0.f}, {1.5f}, {0.f}, {15.f}, ngraph::element::u8},
             {},
-            { 256ul, {}, {0.f}, {2.55f}, {0.f}, {2.55f} },
             {},
+            {256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8},
             {},
-            ngraph::element::f32,
             {},
+            ngraph::element::u8,
+            { ngraph::element::f32, {}, {{ 0.1f, 0.1f, 0.1f, 0.01f, 0.01f, 0.01f }} }
         },
-        false,
+        true,
         false
     }
 };
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/fake_quantize_transformation.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/fake_quantize_transformation.cpp
index 0e292336291a1c..f4e808bfd53bf4 100644
--- a/inference-engine/tests/functional/inference_engine/lp_transformations/fake_quantize_transformation.cpp
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/fake_quantize_transformation.cpp
@@ -244,11 +244,22 @@ const std::vector<FakeQuantizeTransformationTestValues> fakeQuantizeTransformati
     {
         LayerTransformation::createParamsU8I8AndI8(),
         { 256ul, {}, { 0.f }, { 25.5f }, { -1.0686283872061019e-38 }, { 1.0686283872061019e-38 } },
-        { 256ul, {}, { 0.f }, { 25.5f }, { 0.f }, { 255.f } },
+        { 256ul, {}, { 0.f }, { 25.5f }, { -128.f }, { 127.f } },
+        ngraph::element::i8,
+        {
+            { ngraph::element::f32, {{ngraph::element::f32}, {}, { 1e-32f }} },
+            { ngraph::element::f16, {{ngraph::element::f16}, {}, { 1e-32f }} }
+        }
+    },
+    // denormal values
+    {
+        LayerTransformation::createParamsU8I8AndI8(),
+        { 256ul, {}, { 0.f }, { 25.5f }, { 0.0 }, { 1.0686283872061019e-38 } },
+        { 256ul, {}, { 0.f }, { 25.5f }, { 0.0 }, { 255 } },
         ngraph::element::u8,
         {
-            { ngraph::element::f32, {{ngraph::element::f32}, { 127.5 }, { 1e-32f }} },
-            { ngraph::element::f16, {{ngraph::element::f16}, { 127.5 }, { 1e-32f }} }
+            { ngraph::element::f32, {{ngraph::element::f32}, {}, { 1e-32f }} },
+            { ngraph::element::f16, {{ngraph::element::f16}, {}, { 1e-32f }} }
         }
     },
     // U16
@@ -311,6 +322,28 @@ const std::vector<FakeQuantizeTransformationTestValues> fakeQuantizeTransformati
     //        { ngraph::element::f16, { {ngraph::element::f16}, {}, { {0.01f, 0.1f, 1.f} }} }
     //    }
     //},
+    // u4 through u8
+    {
+        LayerTransformation::createParamsU8I8(),
+        { 16ul, {}, { 0.f }, { 1.5f }, { 0.f }, { 1.5f } },
+        { 16ul, {}, { 0.f }, { 1.5f }, { 0.f }, { 15.f } },
+        ngraph::element::u8,
+        {
+            { ngraph::element::f32, { {ngraph::element::f32}, {}, { 0.1f }} },
+            { ngraph::element::f16, { {ngraph::element::f16}, {}, { 0.1f }} }
+        }
+    },
+    // i4 through i8
+    {
+        LayerTransformation::createParamsI8I8(),
+        { 16ul, {}, { -0.8f }, { 0.7f }, { -0.8f }, { 0.7f } },
+        { 16ul, {}, { -0.8f }, { 0.7f }, { -8.f }, { 7.f } },
+        ngraph::element::i8,
+        {
+            { ngraph::element::f32, {{ngraph::element::f32}, { }, { 0.1f }} },
+            { ngraph::element::f16, {{ngraph::element::f16}, { }, { 0.1f }} }
+        }
+    },
 };
 
 INSTANTIATE_TEST_SUITE_P(
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_dequantization.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_dequantization.cpp
new file mode 100644
index 00000000000000..e5caba16f00faa
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_dequantization.cpp
@@ -0,0 +1,128 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "layer_transformation.hpp"
+
+#include <sstream>
+#include <memory>
+#include <utility>
+
+#include <gtest/gtest.h>
+
+#include <transformations/utils/utils.hpp>
+#include <transformations/init_node_info.hpp>
+#include <low_precision/weightable_layer_transformation.hpp>
+#include "lpt_ngraph_functions/convolution_function.hpp"
+
+using namespace testing;
+using namespace ngraph;
+using namespace ngraph::pass;
+
+class IsAsymmetricOnWeightsDequantizationTestValues {
+public:
+    ngraph::element::Type precisionBeforeDequantization;
+    ngraph::builder::subgraph::DequantizationOperations dequantizationOnActivations;
+    std::shared_ptr<ngraph::opset1::Constant> weights;
+    builder::subgraph::DequantizationOperations dequantizationOnWeights;
+    bool isAsymmetricOnWeights;
+};
+
+typedef std::tuple<
+    element::Type,
+    ngraph::PartialShape,
+    IsAsymmetricOnWeightsDequantizationTestValues> IsAsymmetricOnWeightsDequantizationParams;
+
+class IsAsymmetricOnWeightsDequantizationTransformation :
+    public LayerTransformation,
+    public testing::WithParamInterface<IsAsymmetricOnWeightsDequantizationParams> {
+public:
+    void SetUp() override {
+        const auto netPrecision = std::get<0>(GetParam());
+        const auto inputShape = std::get<1>(GetParam());
+        auto testValues = std::get<2>(GetParam());
+
+        actualFunction = ngraph::builder::subgraph::ConvolutionFunction::getOriginal(
+            netPrecision,
+            testValues.precisionBeforeDequantization,
+            inputShape,
+            testValues.dequantizationOnActivations,
+            testValues.weights,
+            {},
+            testValues.dequantizationOnWeights);
+    }
+
+    static std::string getTestCaseName(testing::TestParamInfo<IsAsymmetricOnWeightsDequantizationParams> obj) {
+        const auto netPrecision = std::get<0>(obj.param);
+        auto inputShape = std::get<1>(obj.param);
+        IsAsymmetricOnWeightsDequantizationTestValues testValues = std::get<2>(obj.param);
+
+        std::ostringstream result;
+        result <<
+            netPrecision << "_" <<
+            inputShape << "_" <<
+            testValues.precisionBeforeDequantization << "_" <<
+            testValues.dequantizationOnActivations << "_" << "_weights_" <<
+            testValues.weights->get_element_type() << "_" << "{ " <<
+            testValues.weights->cast_vector<float>()[0] << " }_" <<
+            testValues.dequantizationOnWeights;
+        return result.str();
+    }
+};
+
+TEST_P(IsAsymmetricOnWeightsDequantizationTransformation, CompareFunctions) {
+    actualFunction->validate_nodes_and_infer_types();
+
+    const auto convolutions = LayerTransformation::get<opset1::Convolution>(actualFunction);
+    ASSERT_TRUE(convolutions.size() == 1ul) << "convolution was not found";
+
+    IsAsymmetricOnWeightsDequantizationTestValues testValues = std::get<2>(GetParam());
+
+    const auto isAsymmetricOnWeights = ngraph::pass::low_precision::WeightableLayerTransformation::isAsymmetricOnWeights(convolutions[0]);
+    ASSERT_EQ(testValues.isAsymmetricOnWeights, isAsymmetricOnWeights);
+}
+
+const std::vector<element::Type> netPrecisions = {
+    element::f32
+};
+
+const std::vector<ngraph::PartialShape> suitablePartialShapes = {
+    ngraph::PartialShape({ 1, 3, 72, 48 }),
+    ngraph::PartialShape({ 4, 3, 72, 48 }),
+    ngraph::PartialShape({ Dimension::dynamic(), 3, 72, 48 }),
+    ngraph::PartialShape({ 1, 3, Dimension::dynamic(), Dimension::dynamic() }),
+};
+
+const std::vector<IsAsymmetricOnWeightsDequantizationTestValues> testValues = {
+    {
+        ngraph::element::u8,
+        {{ngraph::element::f32}, { 128.f }, { 0.02f }},
+        op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
+        {
+            {ngraph::element::f32},
+            {{1, 2, 3, 4, 5, 6}, ngraph::element::f32, {6, 1, 1, 1}},
+            {{1, 2, 3, 4, 5, 6}, ngraph::element::f32, {6, 1, 1, 1}}
+        },
+        true
+    },
+    {
+        ngraph::element::u8,
+        {{ngraph::element::f32}, { 128.f }, { 0.02f }},
+        op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
+        {
+            {ngraph::element::f32},
+            {{0, 0, 1.e-7, 0, 0, 0}, ngraph::element::f32, {6, 1, 1, 1}},
+            {{1, 2, 3, 4, 5, 6}, ngraph::element::f32, {6, 1, 1, 1}}
+        },
+        false
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(
+    smoke_LPT,
+    IsAsymmetricOnWeightsDequantizationTransformation,
+    ::testing::Combine(
+        ::testing::ValuesIn(netPrecisions),
+        ::testing::ValuesIn(suitablePartialShapes),
+        ::testing::ValuesIn(testValues)),
+    IsAsymmetricOnWeightsDequantizationTransformation::getTestCaseName);
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_fq.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_fq.cpp
new file mode 100644
index 00000000000000..78bd7aa548320c
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_fq.cpp
@@ -0,0 +1,136 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "layer_transformation.hpp"
+
+#include <sstream>
+#include <memory>
+#include <utility>
+
+#include <gtest/gtest.h>
+
+#include <transformations/utils/utils.hpp>
+#include <transformations/init_node_info.hpp>
+#include <low_precision/weightable_layer_transformation.hpp>
+#include "lpt_ngraph_functions/convolution_function.hpp"
+
+using namespace testing;
+using namespace ngraph;
+using namespace ngraph::pass;
+
+class IsAsymmetricOnWeightsFakeQuantizeTestValues {
+public:
+    ngraph::element::Type precisionBeforeDequantization;
+    ngraph::builder::subgraph::DequantizationOperations dequantizationOnActivations;
+    std::shared_ptr<ngraph::opset1::Constant> weights;
+    builder::subgraph::FakeQuantizeOnWeights fakeQuantizeOnWeights;
+};
+
+typedef std::tuple<
+    element::Type,
+    ngraph::PartialShape,
+    IsAsymmetricOnWeightsFakeQuantizeTestValues,
+    std::pair<std::vector<bool>, bool> > IsAsymmetricOnWeightsFakeQuantizeParams;
+
+class IsAsymmetricOnWeightsFakeQuantizeTransformation :
+    public LayerTransformation,
+    public testing::WithParamInterface<IsAsymmetricOnWeightsFakeQuantizeParams> {
+public:
+    void SetUp() override {
+        const auto netPrecision = std::get<0>(GetParam());
+        const auto inputShape = std::get<1>(GetParam());
+        auto testValues = std::get<2>(GetParam());
+        std::pair<std::vector<bool>, bool> transposeAndIsAsymmetricOnWeights = std::get<3>(GetParam());
+
+        actualFunction = ngraph::builder::subgraph::ConvolutionFunction::getOriginal(
+            netPrecision,
+            testValues.precisionBeforeDequantization,
+            inputShape,
+            testValues.dequantizationOnActivations,
+            testValues.weights,
+            testValues.fakeQuantizeOnWeights,
+            {},
+            transposeAndIsAsymmetricOnWeights.first[0],
+            transposeAndIsAsymmetricOnWeights.first[1],
+            transposeAndIsAsymmetricOnWeights.first[2],
+            transposeAndIsAsymmetricOnWeights.first[3],
+            transposeAndIsAsymmetricOnWeights.first[4]);
+    }
+
+    static std::string getTestCaseName(testing::TestParamInfo<IsAsymmetricOnWeightsFakeQuantizeParams> obj) {
+        const auto netPrecision = std::get<0>(obj.param);
+        auto inputShape = std::get<1>(obj.param);
+        IsAsymmetricOnWeightsFakeQuantizeTestValues testValues = std::get<2>(obj.param);
+        std::pair<std::vector<bool>, bool> transposeAndIsAsymmetricOnWeights = std::get<3>(obj.param);
+
+        std::ostringstream result;
+        result <<
+            netPrecision << "_" <<
+            inputShape << "_" <<
+            testValues.precisionBeforeDequantization << "_" <<
+            testValues.dequantizationOnActivations << "_" << "_weights_" <<
+            testValues.weights->get_element_type() << "_" << "{ " <<
+            testValues.weights->cast_vector<float>()[0] << " }_" <<
+            testValues.fakeQuantizeOnWeights << "_" <<
+            transposeAndIsAsymmetricOnWeights.first[0] << "_" <<
+            transposeAndIsAsymmetricOnWeights.first[1] << "_" <<
+            transposeAndIsAsymmetricOnWeights.first[2] << "_" <<
+            transposeAndIsAsymmetricOnWeights.first[3] << "_" <<
+            transposeAndIsAsymmetricOnWeights.first[4];
+        return result.str();
+    }
+};
+
+TEST_P(IsAsymmetricOnWeightsFakeQuantizeTransformation, CompareFunctions) {
+    actualFunction->validate_nodes_and_infer_types();
+
+    const auto convolutions = LayerTransformation::get<opset1::Convolution>(actualFunction);
+    ASSERT_TRUE(convolutions.size() == 1ul) << "convolution was not found";
+
+    const auto isAsymmetricOnWeights = ngraph::pass::low_precision::WeightableLayerTransformation::isAsymmetricOnWeights(convolutions[0]);
+    std::pair<std::vector<bool>, bool> transposeAndIsAsymmetricOnWeights = std::get<3>(GetParam());
+    ASSERT_EQ(transposeAndIsAsymmetricOnWeights.second, isAsymmetricOnWeights);
+}
+
+const std::vector<element::Type> netPrecisions = {
+    element::f32
+};
+
+const std::vector<ngraph::PartialShape> suitablePartialShapes = {
+    ngraph::PartialShape({ 1, 3, 72, 48 }),
+    ngraph::PartialShape({ 4, 3, 72, 48 }),
+    ngraph::PartialShape({ Dimension::dynamic(), 3, 72, 48 }),
+    ngraph::PartialShape({ 1, 3, Dimension::dynamic(), Dimension::dynamic() }),
+};
+
+const std::vector<IsAsymmetricOnWeightsFakeQuantizeTestValues> testValues = {
+    {
+        ngraph::element::u8,
+        {{ngraph::element::f32}, { 128.f }, { 0.02f }},
+        op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ 2.f }),
+        { 255ul, Shape({ 1, 1, 1, 1 }), { 0.f }, { 254.f }, { -1.f }, { 1.27f } },
+    }
+};
+
+const std::vector<std::pair<std::vector<bool>, bool> > transposeFlags = {
+    // asymmetric quantization
+    {{false, false, false, false, false}, true},
+    {{true, false, false, false, false}, true},
+
+    // not supported FakeQuantize
+    {{false, true, false, false, false}, false},
+    {{false, false, true, false, false}, false},
+    {{false, false, false, true, false}, false},
+    {{false, false, false, false, true}, false}
+};
+
+INSTANTIATE_TEST_SUITE_P(
+    smoke_LPT,
+    IsAsymmetricOnWeightsFakeQuantizeTransformation,
+    ::testing::Combine(
+        ::testing::ValuesIn(netPrecisions),
+        ::testing::ValuesIn(suitablePartialShapes),
+        ::testing::ValuesIn(testValues),
+        ::testing::ValuesIn(transposeFlags)),
+    IsAsymmetricOnWeightsFakeQuantizeTransformation::getTestCaseName);
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/lpt_avoid_shapeof_propagation_test.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/lpt_avoid_shapeof_propagation_test.cpp
new file mode 100644
index 00000000000000..a49749405fd741
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/lpt_avoid_shapeof_propagation_test.cpp
@@ -0,0 +1,734 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <string>
+#include <sstream>
+#include <memory>
+
+#include "low_precision/add.hpp"
+#include "low_precision/avg_pool.hpp"
+#include "low_precision/clamp.hpp"
+#include "low_precision/concat.hpp"
+#include "low_precision/convolution.hpp"
+#include "low_precision/convolution_backprop_data.hpp"
+#include "low_precision/depth_to_space.hpp"
+#include "low_precision/fake_quantize_decomposition.hpp"
+#include "low_precision/group_convolution.hpp"
+#include "low_precision/interpolate.hpp"
+#include "low_precision/mat_mul.hpp"
+#include "low_precision/max_pool.hpp"
+#include "low_precision/multiply.hpp"
+#include "low_precision/mvn.hpp"
+#include "low_precision/normalize_l2.hpp"
+#include "low_precision/pad.hpp"
+#include "low_precision/prelu.hpp"
+#include "low_precision/reduce_max.hpp"
+#include "low_precision/reduce_mean.hpp"
+#include "low_precision/reduce_min.hpp"
+#include "low_precision/reduce_sum.hpp"
+#include "low_precision/reshape.hpp"
+#include "low_precision/relu.hpp"
+#include "low_precision/squeeze.hpp"
+#include "low_precision/split.hpp"
+#include "low_precision/shuffle_channels.hpp"
+#include "low_precision/strided_slice.hpp"
+#include "low_precision/transpose.hpp"
+#include "low_precision/unsqueeze.hpp"
+#include "low_precision/variadic_split.hpp"
+
+#include "low_precision/network_helper.hpp"
+#include "lpt_ngraph_functions/common/builders.hpp"
+
+#include <gtest/gtest.h>
+#include <transformations/utils/utils.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+using namespace ngraph;
+using namespace ngraph::pass;
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationAddTransformation) {
+    auto input1 = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto input2 = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+
+    auto convert1 = std::make_shared<opset1::Convert>(input1, element::f32);
+    auto convert2 = std::make_shared<opset1::Convert>(input2, element::f32);
+
+    auto mul1 = std::make_shared<opset1::Multiply>(convert1, opset1::Constant::create(element::f32, {}, { 2.f }));
+    auto mul2 = std::make_shared<opset1::Multiply>(convert2, opset1::Constant::create(element::f32, {}, { 4.f }));
+
+    auto add = std::make_shared<opset1::Add>(mul1, mul2);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(add);
+
+    auto result1 = std::make_shared<opset1::Result>(add);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input1, input2 });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::AddTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationAvgPoolTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto avgPool = std::make_shared<opset1::AvgPool>(mul, Strides{ 1, 1 }, Shape{ 1, 1 }, Shape{ 0, 0 }, Shape{ 2, 2 }, true);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(avgPool);
+
+    auto result1 = std::make_shared<opset1::Result>(avgPool);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::AvgPoolTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationClampTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto clamp = std::make_shared<opset1::Clamp>(mul, 0.0, 6.0);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(clamp);
+
+    auto result1 = std::make_shared<opset1::Result>(clamp);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ClampTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationConcatTransformation) {
+    auto input1 = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto input2 = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+
+    auto convert1 = std::make_shared<opset1::Convert>(input1, element::f32);
+    auto convert2 = std::make_shared<opset1::Convert>(input2, element::f32);
+
+    auto mul1 = std::make_shared<opset1::Multiply>(convert1, opset1::Constant::create(element::f32, {}, { 2.f }));
+    auto mul2 = std::make_shared<opset1::Multiply>(convert2, opset1::Constant::create(element::f32, {}, { 4.f }));
+
+    auto concat = std::make_shared<opset1::Concat>(OutputVector{ mul1, mul2 }, 1);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(concat);
+
+    auto result1 = std::make_shared<opset1::Result>(concat);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input1, input2 });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ConcatTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationConvolutionTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto weights = opset1::Constant::create(element::i8, { 6, 3, 1, 1 }, { 3 });
+    auto convertOnWeights = std::make_shared<opset1::Convert>(weights, element::f32);
+    auto mulOnWeights = std::make_shared<opset1::Multiply>(convertOnWeights, opset1::Constant::create(element::f32, {}, { 4.f }));
+
+    auto convolution = std::make_shared<opset1::Convolution>(
+        mul,
+        mulOnWeights,
+        ngraph::Strides{ 1, 1 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::Strides{ 1, 1 });
+
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(convolution);
+
+    auto result1 = std::make_shared<opset1::Result>(convolution);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ConvolutionTransformation>();
+    m.run_passes(f);
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationConvolutionBackpropDataTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 8, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto weights = opset1::Constant::create(element::i8, { 8, 2, 1, 1 }, { 3 });
+    auto convertOnWeights = std::make_shared<opset1::Convert>(weights, element::f32);
+    auto mulOnWeights = std::make_shared<opset1::Multiply>(convertOnWeights, opset1::Constant::create(element::f32, {}, { 4.f }));
+
+    auto convolutionBackpropData = std::make_shared<opset1::ConvolutionBackpropData>(
+        mul,
+        mulOnWeights,
+        ngraph::Strides{ 1, 1 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::Strides{ 1, 1 });
+
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(convolutionBackpropData);
+
+    auto result1 = std::make_shared<opset1::Result>(convolutionBackpropData);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ConvolutionBackpropDataTransformation>();
+    m.run_passes(f);
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationDepthToSpaceTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto d2s = std::make_shared<opset1::DepthToSpace>(mul, op::v0::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(d2s);
+
+    auto result1 = std::make_shared<opset1::Result>(d2s);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::DepthToSpaceTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationFakeQuantizeDecompositionTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::f32, PartialShape{ 1, 3, 16, 16 });
+
+    ngraph::builder::subgraph::FakeQuantizeOnData fqValues{ 256ul, {}, {0.f}, {2.55f}, {0.f}, {2.55f} };
+    auto fakeQuantize = ngraph::builder::subgraph::makeFakeQuantize(input, element::f32, fqValues);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(fakeQuantize);
+
+    auto& outInfo = fakeQuantize->output(0).get_rt_info();
+    auto attribute = ngraph::pass::low_precision::make_shared_attribute<PrecisionsAttribute>(element::TypeVector{ element::u8, element::i8 });
+    auto attributeWrapper = std::make_shared<ngraph::VariantWrapper<std::shared_ptr<PrecisionsAttribute>>>(attribute);
+    outInfo.emplace(ngraph::VariantWrapper<std::shared_ptr<PrecisionsAttribute>>::type_info.name, attributeWrapper);
+
+    auto result1 = std::make_shared<opset1::Result>(fakeQuantize);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::FakeQuantizeDecompositionTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationGroupConvolutionTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 2 * 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto weights = opset1::Constant::create(element::i8, { 6, 3, 7, 7 }, { 2 });
+    auto convertOnWeights = std::make_shared<opset1::Convert>(weights, element::f32);
+    auto mulOnWeights = std::make_shared<opset1::Multiply>(convertOnWeights, opset1::Constant::create(element::f32, {}, { 4.f }));
+    auto reshapeConst = opset1::Constant::create(element::i32, { 5 }, { 2, 3, 3, 7, 7 });
+    auto reshapeOnWeights = std::make_shared<opset1::Reshape>(mulOnWeights, reshapeConst, true);
+
+    auto groupConvolution = std::make_shared<opset1::GroupConvolution>(
+        mul,
+        reshapeOnWeights,
+        ngraph::Strides{ 1, 1 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::CoordinateDiff{ 0, 0 },
+        ngraph::Strides{ 1, 1 });
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(groupConvolution);
+
+    auto result1 = std::make_shared<opset1::Result>(groupConvolution);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::GroupConvolutionTransformation>();
+    m.run_passes(f);
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationInterpolateTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto outShape = opset1::Constant::create(element::i32, { 4 }, { 1, 3, 18, 18});
+    op::v0::InterpolateAttrs attributes;
+    attributes.align_corners = false;
+    attributes.antialias = false;
+    attributes.axes = AxisSet{ 2, 3 };
+    attributes.mode = "nearest";
+    attributes.pads_begin = { 0 };
+    attributes.pads_end = { 0 };
+    auto interpolate = std::make_shared<opset1::Interpolate>(mul, outShape, attributes);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(interpolate);
+
+    auto result1 = std::make_shared<opset1::Result>(interpolate);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::InterpolateTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationMatMulTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 1024 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto weights = opset1::Constant::create(element::i8, { 2048, 1024 }, { 3 });
+    auto convertOnWeights = std::make_shared<opset1::Convert>(weights, element::f32);
+    auto mulOnWeights = std::make_shared<opset1::Multiply>(convertOnWeights, opset1::Constant::create(element::f32, {}, { 4.f }));
+
+    auto matmul = std::make_shared<opset1::MatMul>(mul, mulOnWeights, false, true);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(matmul);
+
+    auto result1 = std::make_shared<opset1::Result>(matmul);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::MatMulTransformation>();
+    m.run_passes(f);
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationMaxPoolTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto maxPool = std::make_shared<opset1::MaxPool>(mul, Strides{ 1, 1 }, Shape{ 1, 1 }, Shape{ 0, 0 }, Shape{ 2, 2 });
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(maxPool);
+
+    auto result1 = std::make_shared<opset1::Result>(maxPool);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::MaxPoolTransformation>();
+    m.run_passes(f);
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationMultiplyTransformation) {
+    auto input1 = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto input2 = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+
+    auto convert1 = std::make_shared<opset1::Convert>(input1, element::f32);
+    auto convert2 = std::make_shared<opset1::Convert>(input2, element::f32);
+
+    auto mul1 = std::make_shared<opset1::Multiply>(convert1, opset1::Constant::create(element::f32, {}, { 2.f }));
+    auto mul2 = std::make_shared<opset1::Multiply>(convert2, opset1::Constant::create(element::f32, {}, { 4.f }));
+
+    auto mul = std::make_shared<opset1::Multiply>(mul1, mul2);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(mul);
+
+    auto result1 = std::make_shared<opset1::Result>(mul);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input1, input2 });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::MultiplyTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationMVNTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto MVN = std::make_shared<op::TypeRelaxed<op::v0::MVN>>(mul);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(MVN);
+
+    auto result1 = std::make_shared<opset1::Result>(MVN);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::MVNTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationNormalizeL2Transformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axes = opset1::Constant::create(element::i32, { 2 }, { 2, 3 });
+    auto normalize = std::make_shared<opset1::NormalizeL2>(mul, axes, 0.01, ov::op::EpsMode::ADD);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(normalize);
+
+    auto result1 = std::make_shared<opset1::Result>(normalize);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::NormalizeL2Transformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationPadTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto pads_begin = opset1::Constant::create(element::i32, { 4 }, { 0, 0, 1, 1 });
+    auto pads_end = opset1::Constant::create(element::i32, { 4 }, { 0, 0, 1, 1 });
+    auto pad = std::make_shared<opset1::Pad>(mul, pads_begin, pads_end, op::PadMode::CONSTANT);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(pad);
+
+    auto result1 = std::make_shared<opset1::Result>(pad);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::PadTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationPReluTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto slope = opset1::Constant::create(element::f32, { 1, 3, 1, 1 }, { 0.01f });
+    auto prelu = std::make_shared<opset1::PRelu>(mul, slope);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(prelu);
+
+    auto result1 = std::make_shared<opset1::Result>(prelu);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::PReluTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationReduceMaxTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axes = opset1::Constant::create(element::i32, { 2 }, { 2, 3 });
+    auto reduce = std::make_shared<opset1::ReduceMax>(mul, axes);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(reduce);
+
+    auto result1 = std::make_shared<opset1::Result>(reduce);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ReduceMaxTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationReduceMeanTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axes = opset1::Constant::create(element::i32, { 2 }, { 2, 3 });
+    auto reduce = std::make_shared<opset1::ReduceMean>(mul, axes);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(reduce);
+
+    auto result1 = std::make_shared<opset1::Result>(reduce);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ReduceMeanTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationReduceMinTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axes = opset1::Constant::create(element::i32, { 2 }, { 2, 3 });
+    auto reduce = std::make_shared<opset1::ReduceMin>(mul, axes);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(reduce);
+
+    auto result1 = std::make_shared<opset1::Result>(reduce);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ReduceMinTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationReduceSumTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axes = opset1::Constant::create(element::i32, { 2 }, { 2, 3 });
+    auto reduce = std::make_shared<opset1::ReduceSum>(mul, axes);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(reduce);
+
+    auto result1 = std::make_shared<opset1::Result>(reduce);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ReduceSumTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationReshapeTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto outShape = opset1::Constant::create(element::i32, { 3 }, { 1, 3, -1 });
+    auto reshape = std::make_shared<opset1::Reshape>(mul, outShape, true);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(reshape);
+
+    auto result1 = std::make_shared<opset1::Result>(reshape);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ReshapeTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationReluTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto relu = std::make_shared<opset1::Relu>(mul);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(relu);
+
+    auto result1 = std::make_shared<opset1::Result>(relu);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ReluTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationSqueezeTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axes = opset1::Constant::create(element::i32, { 1 }, { 0 });
+    auto squeeze = std::make_shared<opset1::Squeeze>(mul, axes);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(squeeze);
+
+    auto result1 = std::make_shared<opset1::Result>(squeeze);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::SqueezeTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationSplitTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axis = opset1::Constant::create(element::i32, {}, { 1 });
+    auto split = std::make_shared<opset1::Split>(mul, axis, 3);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(split);
+
+    auto result1 = std::make_shared<opset1::Result>(split->output(0));
+    auto result2 = std::make_shared<opset1::Result>(split->output(1));
+    auto result3 = std::make_shared<opset1::Result>(split->output(2));
+    auto result4 = std::make_shared<opset1::Result>(shapeOf->output(0));
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2, result3, result4 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::SplitTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationShuffleChannelsTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto shuffleChannels = std::make_shared<opset1::ShuffleChannels>(mul);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(shuffleChannels);
+
+    auto result1 = std::make_shared<opset1::Result>(shuffleChannels);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::ShuffleChannelsTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationStridedSliceTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto beginParam = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 0, 0, 0 });
+    auto endParam = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 1, 2, 1, 1 });
+    auto stridesParam = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 1, 1, 1, 1 });
+    auto stridedSlice = std::make_shared<ngraph::opset1::StridedSlice>(
+        mul, beginParam, endParam, stridesParam,
+        std::vector<std::int64_t>{ 1, 0, 1, 1 },
+        std::vector<std::int64_t>{ 1, 0, 1, 1 });
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(stridedSlice);
+
+    auto result1 = std::make_shared<opset1::Result>(stridedSlice);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::StridedSliceTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationTransposeTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto constant = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 1, 3, 2 });
+    auto transpose = std::make_shared<ngraph::opset1::Transpose>(mul, constant);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(transpose);
+
+    auto result1 = std::make_shared<opset1::Result>(transpose);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::TransposeTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationUnsqueezeTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axes = opset1::Constant::create(element::i32, { 1 }, { 3 });
+    auto unsqueeze = std::make_shared<opset1::Unsqueeze>(mul, axes);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(unsqueeze);
+
+    auto result1 = std::make_shared<opset1::Result>(unsqueeze);
+    auto result2 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::UnsqueezeTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
+
+TEST(LPT, AvoidDequantizationToShapeOfPropagationVariadicSplitTransformation) {
+    auto input = std::make_shared<opset1::Parameter>(element::u8, PartialShape{ 1, 3, 16, 16 });
+    auto convert = std::make_shared<opset1::Convert>(input, element::f32);
+    auto mul = std::make_shared<opset1::Multiply>(convert, opset1::Constant::create(element::f32, {}, { 2.f }));
+
+    auto axis = opset1::Constant::create(element::i32, {}, { 1 });
+    auto lengths = opset1::Constant::create(element::i32, { 2 }, { 1, 2 });
+    auto variadicSplit = std::make_shared<opset1::VariadicSplit>(mul, axis, lengths);
+    auto shapeOf = std::make_shared<opset1::ShapeOf>(variadicSplit->output(0));
+
+    auto result1 = std::make_shared<opset1::Result>(variadicSplit->output(0));
+    auto result2 = std::make_shared<opset1::Result>(variadicSplit->output(1));
+    auto result3 = std::make_shared<opset1::Result>(shapeOf);
+
+    auto f = std::make_shared<Function>(ResultVector{ result1, result2, result3 }, ParameterVector{ input });
+    pass::Manager m;
+    m.register_pass<pass::low_precision::VariadicSplitTransformation>();
+    m.run_passes(f);
+
+    auto dqBeforeShapeOf = low_precision::NetworkHelper::getDequantization(result2->get_input_node_shared_ptr(0));
+    ASSERT_TRUE(dqBeforeShapeOf.empty());
+}
diff --git a/inference-engine/tests/functional/inference_engine/ngraph_reshape_tests.cpp b/inference-engine/tests/functional/inference_engine/ngraph_reshape_tests.cpp
index 1965bf6e86b5c0..e911a0829dcef4 100644
--- a/inference-engine/tests/functional/inference_engine/ngraph_reshape_tests.cpp
+++ b/inference-engine/tests/functional/inference_engine/ngraph_reshape_tests.cpp
@@ -119,6 +119,11 @@ TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLU) {
     ASSERT_EQ(changedFunction->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
     ASSERT_EQ(ngraph->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
     ASSERT_EQ(ngraph->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 22, 22}));
+
+    ASSERT_EQ(Layout::NCHW, cnnNetwork.getInputsInfo()["data"]->getLayout());
+    IE_SUPPRESS_DEPRECATED_START
+    ASSERT_FALSE(cnnNetwork.getInputsInfo()["data"]->getInputData()->isDynamic());
+    IE_SUPPRESS_DEPRECATED_END
 }
 
 TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUWithoutCloneFunction) {
@@ -152,6 +157,11 @@ TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUWithoutCloneFunction) {
     ASSERT_EQ(changedFunction->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
     ASSERT_EQ(ngraph->get_parameters()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
     ASSERT_EQ(ngraph->get_results()[0]->get_shape(), ngraph::Shape({1, 3, 25, 25}));
+
+    ASSERT_EQ(Layout::NCHW, cnnNetwork.getInputsInfo()["data"]->getLayout());
+    IE_SUPPRESS_DEPRECATED_START
+    ASSERT_FALSE(cnnNetwork.getInputsInfo()["data"]->getInputData()->isDynamic());
+    IE_SUPPRESS_DEPRECATED_END
 }
 
 TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUStaticToDynamic) {
@@ -192,6 +202,11 @@ TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUStaticToDynamic) {
     ASSERT_EQ(changedFunction->get_results()[0]->get_output_partial_shape(0), refShape);
     ASSERT_EQ(ngraph->get_parameters()[0]->get_output_partial_shape(0), refShape);
     ASSERT_EQ(ngraph->get_results()[0]->get_output_partial_shape(0), refShape);
+
+    ASSERT_EQ(Layout::NCHW, cnnNetwork.getInputsInfo()["data"]->getLayout());
+    IE_SUPPRESS_DEPRECATED_START
+    ASSERT_TRUE(cnnNetwork.getInputsInfo()["data"]->getInputData()->isDynamic());
+    IE_SUPPRESS_DEPRECATED_END
 }
 
 TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUStaticToFullyDynamic) {
@@ -232,10 +247,16 @@ TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUStaticToFullyDynamic) {
     ASSERT_EQ(changedFunction->get_results()[0]->get_output_partial_shape(0), refShape);
     ASSERT_EQ(ngraph->get_parameters()[0]->get_output_partial_shape(0), refShape);
     ASSERT_EQ(ngraph->get_results()[0]->get_output_partial_shape(0), refShape);
+
+    ASSERT_EQ(Layout::BLOCKED, cnnNetwork.getInputsInfo()["data"]->getLayout());
+    IE_SUPPRESS_DEPRECATED_START
+    ASSERT_TRUE(cnnNetwork.getInputsInfo()["data"]->getInputData()->isDynamic());
+    IE_SUPPRESS_DEPRECATED_END
 }
 
 TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUDynamicToDynamic) {
     const ngraph::PartialShape refShape{1, 3, ngraph::Dimension::dynamic(), 25};
+    const Layout refLayout = Layout::NHWC;
     std::shared_ptr<ngraph::Function> ngraph;
     {
         ngraph::PartialShape shape({1, 3, 22, ngraph::Dimension::dynamic()});
@@ -255,6 +276,7 @@ TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUDynamicToDynamic) {
     ASSERT_EQ(ngraph->get_results()[0]->get_output_partial_shape(0), ngraph::PartialShape({1, 3, 22, ngraph::Dimension::dynamic()}));
 
     CNNNetwork cnnNetwork(ngraph);
+    cnnNetwork.getInputsInfo()["data"]->setLayout(refLayout);
     std::map<std::string, ngraph::PartialShape> shapes;
     shapes["data"] = refShape;
 
@@ -272,6 +294,11 @@ TEST_F(NGraphReshapeTests, CNNReshapeSpatialReLUDynamicToDynamic) {
     ASSERT_EQ(changedFunction->get_results()[0]->get_output_partial_shape(0), refShape);
     ASSERT_EQ(ngraph->get_parameters()[0]->get_output_partial_shape(0), refShape);
     ASSERT_EQ(ngraph->get_results()[0]->get_output_partial_shape(0), refShape);
+
+    ASSERT_EQ(refLayout, cnnNetwork.getInputsInfo()["data"]->getLayout());
+    IE_SUPPRESS_DEPRECATED_START
+    ASSERT_TRUE(cnnNetwork.getInputsInfo()["data"]->getInputData()->isDynamic());
+    IE_SUPPRESS_DEPRECATED_END
 }
 
 class CustomTestOp: public ngraph::op::Op {
diff --git a/inference-engine/tests/functional/inference_engine/ov_shared_object_test.cpp b/inference-engine/tests/functional/inference_engine/ov_shared_object_test.cpp
index 8ae3850c0ee54a..30d7a7f0c92153 100644
--- a/inference-engine/tests/functional/inference_engine/ov_shared_object_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/ov_shared_object_test.cpp
@@ -14,7 +14,7 @@ using namespace std;
 class SharedObjectOVTests : public ::testing::Test {
 protected:
     std::string get_mock_engine_name() {
-        return FileUtils::makePluginLibraryName<char>(InferenceEngine::getIELibraryPath(),
+        return FileUtils::makePluginLibraryName<char>({},
                                                       std::string("mock_engine") + IE_BUILD_POSTFIX);
     }
 
diff --git a/inference-engine/tests/functional/inference_engine/ov_variable_state_test.cpp b/inference-engine/tests/functional/inference_engine/ov_variable_state_test.cpp
index 3dfa33ed31cecf..d2ecec7e08282f 100644
--- a/inference-engine/tests/functional/inference_engine/ov_variable_state_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/ov_variable_state_test.cpp
@@ -4,30 +4,30 @@
 
 #include <gtest/gtest.h>
 
-#include "ie_blob.h"
-
 #include <openvino/core/except.hpp>
 #include <openvino/runtime/variable_state.hpp>
 
 using namespace ::testing;
 using namespace std;
 
-TEST(VariableStateOVTests, throwsOnUninitializedReset) {
+using VariableStateOVTests = ::testing::Test;
+
+TEST_F(VariableStateOVTests, throwsOnUninitializedReset) {
     ov::runtime::VariableState state;
     ASSERT_THROW(state.reset(), ov::Exception);
 }
 
-TEST(VariableStateOVTests, throwsOnUninitializedGetname) {
+TEST_F(VariableStateOVTests, throwsOnUninitializedGetname) {
     ov::runtime::VariableState state;
     ASSERT_THROW(state.get_name(), ov::Exception);
 }
 
-TEST(VariableStateOVTests, throwsOnUninitializedGetState) {
+TEST_F(VariableStateOVTests, throwsOnUninitializedGetState) {
     ov::runtime::VariableState state;
     ASSERT_THROW(state.get_state(), ov::Exception);
 }
 
-TEST(VariableStateOVTests, throwsOnUninitializedSetState) {
+TEST_F(VariableStateOVTests, throwsOnUninitializedSetState) {
     ov::runtime::VariableState state;
     ov::runtime::Tensor tensor;
     ASSERT_THROW(state.set_state(tensor), ov::Exception);
diff --git a/inference-engine/tests/functional/inference_engine/pre_allocator_test.cpp b/inference-engine/tests/functional/inference_engine/pre_allocator_test.cpp
index 3ac7e5a6ff6dfe..1b99b4e36ea148 100644
--- a/inference-engine/tests/functional/inference_engine/pre_allocator_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/pre_allocator_test.cpp
@@ -3,7 +3,6 @@
 //
 
 #include <gtest/gtest.h>
-#include <gmock/gmock-spec-builders.h>
 
 #include "ie_allocator.hpp"
 #include "details/ie_pre_allocator.hpp"
diff --git a/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_bag_offsets_sum.cpp b/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_bag_offsets_sum.cpp
new file mode 100644
index 00000000000000..a6fde2c1453073
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_bag_offsets_sum.cpp
@@ -0,0 +1,48 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "shared_test_classes/single_layer/embedding_bag_offsets_sum.hpp"
+
+using namespace LayerTestsDefinitions;
+
+namespace {
+    TEST_P(EmbeddingBagOffsetsSumLayerTest, Serialize) {
+        Serialize();
+    }
+
+    const std::vector<InferenceEngine::Precision> netPrecisions = {
+        InferenceEngine::Precision::FP32,
+        InferenceEngine::Precision::I32,
+        InferenceEngine::Precision::U8};
+
+    const std::vector<InferenceEngine::Precision> indPrecisions = {
+        InferenceEngine::Precision::I64,
+        InferenceEngine::Precision::I32};
+
+    const std::vector<std::vector<size_t>> emb_table_shape = {{5, 6}, {5, 4, 16}};
+    const std::vector<std::vector<size_t>> indices =
+        {{0, 1, 2, 2, 3}, {4, 4, 3, 1, 0}, {1, 2, 1, 2, 1, 2, 1, 2, 1, 2}};
+    const std::vector<std::vector<size_t>> offsets = {{0, 2}, {0, 0, 2, 2}, {2, 4}};
+    const std::vector<size_t> default_index = {0, 4};
+    const std::vector<bool> with_weights = {false, true};
+    const std::vector<bool> with_default_index = {false, true};
+
+    const auto EmbeddingBagOffsetsSumParams = ::testing::Combine(
+        ::testing::ValuesIn(emb_table_shape),
+        ::testing::ValuesIn(indices),
+        ::testing::ValuesIn(offsets),
+        ::testing::ValuesIn(default_index),
+        ::testing::ValuesIn(with_weights),
+        ::testing::ValuesIn(with_default_index));
+
+    INSTANTIATE_TEST_SUITE_P(
+        smoke_EmbeddingBagOffsetsSumLayerTest_Serialization, EmbeddingBagOffsetsSumLayerTest,
+        ::testing::Combine(EmbeddingBagOffsetsSumParams,
+                           ::testing::ValuesIn(netPrecisions),
+                           ::testing::ValuesIn(indPrecisions),
+                           ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        EmbeddingBagOffsetsSumLayerTest::getTestCaseName);
+} // namespace
diff --git a/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_bag_packed_sum.cpp b/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_bag_packed_sum.cpp
new file mode 100644
index 00000000000000..7bb3327c9fa6bd
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_bag_packed_sum.cpp
@@ -0,0 +1,42 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "shared_test_classes/single_layer/embedding_bag_packed_sum.hpp"
+
+using namespace LayerTestsDefinitions;
+
+namespace {
+    TEST_P(EmbeddingBagPackedSumLayerTest, Serialize) {
+        Serialize();
+    }
+
+    const std::vector<InferenceEngine::Precision> netPrecisions = {
+        InferenceEngine::Precision::FP32,
+        InferenceEngine::Precision::I32,
+        InferenceEngine::Precision::U8};
+
+    const std::vector<InferenceEngine::Precision> indPrecisions = {
+        InferenceEngine::Precision::I64,
+        InferenceEngine::Precision::I32};
+
+    const std::vector<std::vector<size_t>> emb_table_shape = {{5, 6}, {10, 35}, {5, 4, 16}};
+    const std::vector<std::vector<std::vector<size_t>>> indices =
+        {{{0, 1}, {2, 2}}, {{4, 4, 3}, {1, 0, 2}}, {{1, 2, 1, 2}}};
+    const std::vector<bool> with_weights = {false, true};
+
+    const auto EmbeddingBagPackedSumParams = ::testing::Combine(
+        ::testing::ValuesIn(emb_table_shape),
+        ::testing::ValuesIn(indices),
+        ::testing::ValuesIn(with_weights));
+
+    INSTANTIATE_TEST_SUITE_P(
+        smoke_EmbeddingBagPackedSumLayerTest_Serialization, EmbeddingBagPackedSumLayerTest,
+        ::testing::Combine(EmbeddingBagPackedSumParams,
+                           ::testing::ValuesIn(netPrecisions),
+                           ::testing::ValuesIn(indPrecisions),
+                           ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        EmbeddingBagPackedSumLayerTest::getTestCaseName);
+} // namespace
diff --git a/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_segments_sum.cpp b/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_segments_sum.cpp
new file mode 100644
index 00000000000000..7b5d44f4354d47
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/serialization/single_layer/embedding_segments_sum.cpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "shared_test_classes/single_layer/embedding_segments_sum.hpp"
+
+using namespace LayerTestsDefinitions;
+
+namespace {
+    TEST_P(EmbeddingSegmentsSumLayerTest, Serialize) {
+        Serialize();
+    }
+
+    const std::vector<InferenceEngine::Precision> netPrecisions = {
+        InferenceEngine::Precision::FP32,
+        InferenceEngine::Precision::I32,
+        InferenceEngine::Precision::U8};
+
+    const std::vector<InferenceEngine::Precision> indPrecisions = {
+        InferenceEngine::Precision::I64,
+        InferenceEngine::Precision::I32};
+
+    const std::vector<std::vector<size_t>> emb_table_shape = {{5, 6}, {5, 4, 16}};
+    const std::vector<std::vector<size_t>> indices =
+        {{0, 1, 2, 2, 3}, {4, 4, 3, 1, 2}};
+    const std::vector<std::vector<size_t>> segment_ids = {{0, 1, 2, 3, 4}, {0, 0, 2, 2, 4}};
+    const std::vector<size_t> num_segments = {5, 7};
+    const std::vector<size_t> default_index = {0, 4};
+    const std::vector<bool> with_weights = {false, true};
+    const std::vector<bool> with_default_index = {false, true};
+
+    const auto EmbeddingSegmentsSumParams = ::testing::Combine(
+        ::testing::ValuesIn(emb_table_shape),
+        ::testing::ValuesIn(indices),
+        ::testing::ValuesIn(segment_ids),
+        ::testing::ValuesIn(num_segments),
+        ::testing::ValuesIn(default_index),
+        ::testing::ValuesIn(with_weights),
+        ::testing::ValuesIn(with_default_index));
+
+    INSTANTIATE_TEST_SUITE_P(
+        smoke_EmbeddingSegmentsSumLayerTest_Serialization, EmbeddingSegmentsSumLayerTest,
+        ::testing::Combine(EmbeddingSegmentsSumParams,
+                           ::testing::ValuesIn(netPrecisions),
+                           ::testing::ValuesIn(indPrecisions),
+                           ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        EmbeddingSegmentsSumLayerTest::getTestCaseName);
+} // namespace
diff --git a/inference-engine/tests/functional/inference_engine/shared_object_loader_test.cpp b/inference-engine/tests/functional/inference_engine/shared_object_loader_test.cpp
index 411d87b3036548..fb1ab852c27305 100644
--- a/inference-engine/tests/functional/inference_engine/shared_object_loader_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/shared_object_loader_test.cpp
@@ -15,7 +15,7 @@ using namespace InferenceEngine::details;
 class SharedObjectLoaderTests: public ::testing::Test {
 protected:
     std::string get_mock_engine_name() {
-        return FileUtils::makePluginLibraryName<char>(getIELibraryPath(),
+        return FileUtils::makePluginLibraryName<char>({},
             std::string("mock_engine") + IE_BUILD_POSTFIX);
     }
 
diff --git a/inference-engine/tests/functional/inference_engine/transformations/clamp_fusion.cpp b/inference-engine/tests/functional/inference_engine/transformations/clamp_fusion.cpp
index 769c6d781d7734..4fd0353330e38f 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/clamp_fusion.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/clamp_fusion.cpp
@@ -18,11 +18,9 @@
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
-
 using namespace testing;
 using namespace ngraph;
 
-
 TEST_F(TransformationTestsF, ClampFusion) {
     {
         auto data = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
@@ -82,3 +80,64 @@ TEST_F(TransformationTestsF, ClampFusionNonConstMin) {
         function_ref = std::make_shared<Function>(NodeVector{min}, ParameterVector{data, min_val});
     }
 }
+
+TEST_F(TransformationTestsF, ClampFusionMinMax) {
+    {
+        auto data = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
+        auto min_const = opset5::Constant::create(element::f32, Shape{1}, {0.1});
+        auto max_const = opset5::Constant::create(element::f32, Shape{1}, {5});
+        auto min = std::make_shared<opset5::Minimum>(data, max_const);
+        auto max = std::make_shared<opset5::Maximum>(min, min_const);
+
+        function = std::make_shared<Function>(NodeVector{max}, ParameterVector{data});
+
+        manager.register_pass<pass::ClampFusion>();
+    }
+
+    {
+        auto data = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 2});
+        auto clamp = std::make_shared<opset5::Clamp>(data, 0.1, 5);
+        function_ref = std::make_shared<Function>(NodeVector{clamp}, ParameterVector{data});
+    }
+}
+
+TEST_F(TransformationTestsF, ClampFusionMinMaxScalars) {
+    {
+        auto data = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
+        auto min_const = opset5::Constant::create(element::f32, Shape{}, {0.1});
+        auto max_const = opset5::Constant::create(element::f32, Shape{}, {5});
+        auto min = std::make_shared<opset5::Minimum>(data, max_const);
+        auto max = std::make_shared<opset5::Maximum>(min, min_const);
+        function = std::make_shared<Function>(NodeVector{max}, ParameterVector{data});
+
+        manager.register_pass<pass::ClampFusion>();
+    }
+
+    {
+        auto data = std::make_shared<opset1::Parameter>(element::f32, Shape{2, 2});
+        auto clamp = std::make_shared<opset5::Clamp>(data, 0.1, 5);
+        function_ref = std::make_shared<Function>(NodeVector{clamp}, ParameterVector{data});
+    }
+}
+
+TEST_F(TransformationTestsF, ClampFusionMinMaxNonConstMax) {
+    {
+        auto data = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
+        auto max_val = std::make_shared<opset5::Parameter>(element::f32, Shape{});
+        auto max_const = opset5::Constant::create(element::f32, Shape{}, {5});
+        auto min = std::make_shared<opset5::Minimum>(data, max_const);
+        auto max = std::make_shared<opset5::Maximum>(min, max_val);
+        function = std::make_shared<Function>(NodeVector{max}, ParameterVector{data, max_val});
+
+        manager.register_pass<pass::ClampFusion>();
+    }
+
+    {
+        auto data = std::make_shared<opset5::Parameter>(element::f32, Shape{2, 2});
+        auto min_val = std::make_shared<opset5::Parameter>(element::f32, Shape{});
+        auto max_const = opset5::Constant::create(element::f32, Shape{}, {5});
+        auto min = std::make_shared<opset5::Minimum>(data, max_const);
+        auto max = std::make_shared<opset5::Maximum>(min, min_val);
+        function_ref = std::make_shared<Function>(NodeVector{max}, ParameterVector{data, min_val});
+    }
+}
\ No newline at end of file
diff --git a/inference-engine/tests/functional/inference_engine/transformations/compress_float_constants_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/compress_float_constants_test.cpp
new file mode 100644
index 00000000000000..1c3b6bfe4396cd
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/transformations/compress_float_constants_test.cpp
@@ -0,0 +1,138 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+
+#include "openvino/core/function.hpp"
+#include "openvino/opsets/opset8.hpp"
+#include "openvino/pass/manager.hpp"
+#include "transformations/common_optimizations/compress_float_constants.hpp"
+#include "transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp"
+#include "transformations/init_node_info.hpp"
+#include "transformations/utils/utils.hpp"
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST(TransformationTests, CompressConstants_f32) {
+    std::shared_ptr<ov::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f32,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            const_weights,
+            ov::Strides{1, 1},
+            ov::CoordinateDiff{0, 0},
+            ov::CoordinateDiff{0, 0},
+            ov::Strides{1, 1});
+        auto const_scales = ov::opset8::Constant::create(ov::element::f32, ov::Shape{ 1 }, { 1.4 });
+
+        auto shape = std::make_shared<ov::opset8::ShapeOf>(conv);
+        auto convert1 = std::make_shared<ov::opset8::Convert>(shape, ov::element::f32);
+        auto mul = std::make_shared<ov::opset8::Multiply>(convert1, const_scales);
+        auto convert2 = std::make_shared<ov::opset8::Convert>(mul, ov::element::i32);
+
+        auto default_scales_node = ov::opset8::Constant::create(ov::element::f32, ov::Shape{ 4 }, { 1., 1., 1.4, 1.4 });
+        auto axes_node = ov::opset8::Constant::create(ov::element::i64, ov::Shape{ 4 }, { 0, 1, 2, 3 });
+
+        auto interpolate4_attr = ov::opset8::Interpolate::InterpolateAttrs(ov::opset8::Interpolate::InterpolateMode::NEAREST,
+            ov::opset8::Interpolate::ShapeCalcMode::SIZES, std::vector<size_t>{0, 0, 0, 0}, std::vector<size_t>{0, 0, 0, 0},
+            ov::opset8::Interpolate::CoordinateTransformMode::ASYMMETRIC, ov::opset8::Interpolate::NearestMode::SIMPLE,
+            false, -0.75);
+
+        auto resize = std::make_shared<ov::opset8::Interpolate>(conv, convert2, default_scales_node, axes_node, interpolate4_attr);
+
+        f = std::make_shared<ov::Function>(ov::NodeVector{ resize }, ov::ParameterVector{ input });
+
+        ov::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ov::pass::MarkPrecisionSensitiveSubgraphs>();
+        manager.register_pass<ov::pass::CompressFloatConstants>();
+        manager.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f16,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto convert_ins1 = std::make_shared<ov::opset8::Convert>(const_weights, ov::element::f32);
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            convert_ins1,
+            ov::Strides{ 1, 1 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::Strides{ 1, 1 });
+        auto const_scales = ov::opset8::Constant::create(ov::element::f32, ov::Shape{ 1 }, { 1.4 });
+
+        auto shape = std::make_shared<ov::opset8::ShapeOf>(conv);
+        auto convert1 = std::make_shared<ov::opset8::Convert>(shape, ov::element::f32);
+        auto mul = std::make_shared<ov::opset8::Multiply>(convert1, const_scales);
+        auto convert2 = std::make_shared<ov::opset8::Convert>(mul, ov::element::i32);
+
+        auto default_scales_node = ov::opset8::Constant::create(ov::element::f32, ov::Shape{ 4 }, { 1., 1., 1.4, 1.4 });
+        auto axes_node = ov::opset8::Constant::create(ov::element::i64, ov::Shape{ 4 }, { 0, 1, 2, 3 });
+
+        auto interpolate4_attr = ov::opset8::Interpolate::InterpolateAttrs(ov::opset8::Interpolate::InterpolateMode::NEAREST,
+            ov::opset8::Interpolate::ShapeCalcMode::SIZES, std::vector<size_t>{0, 0, 0, 0}, std::vector<size_t>{0, 0, 0, 0},
+            ov::opset8::Interpolate::CoordinateTransformMode::ASYMMETRIC, ov::opset8::Interpolate::NearestMode::SIMPLE,
+            false, -0.75);
+
+        auto resize = std::make_shared<ov::opset8::Interpolate>(conv, convert2, default_scales_node, axes_node, interpolate4_attr);
+
+        f_ref = std::make_shared<ov::Function>(ov::NodeVector{ resize }, ov::ParameterVector{ input });
+    }
+
+    auto res = compare_functions(f, f_ref, true);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, CompressConstants_f64) {
+    std::shared_ptr<ov::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f64, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f64,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            const_weights,
+            ov::Strides{ 1, 1 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::Strides{ 1, 1 });
+        f = std::make_shared<ov::Function>(ov::NodeVector{ conv }, ov::ParameterVector{ input });
+
+        ov::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ov::pass::MarkPrecisionSensitiveSubgraphs>();
+        manager.register_pass<ov::pass::CompressFloatConstants>();
+        manager.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f64, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f16,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto convert_ins1 = std::make_shared<ov::opset8::Convert>(const_weights, ov::element::f64);
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            convert_ins1,
+            ov::Strides{ 1, 1 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::Strides{ 1, 1 });
+        f_ref = std::make_shared<ov::Function>(ov::NodeVector{ conv }, ov::ParameterVector{ input });
+    }
+
+    auto res = compare_functions(f, f_ref, true);
+    ASSERT_TRUE(res.first) << res.second;
+}
diff --git a/inference-engine/tests/functional/inference_engine/transformations/const_folding_for_if.cpp b/inference-engine/tests/functional/inference_engine/transformations/const_folding_for_if.cpp
index 84dea503137fc7..1cd9aebd685595 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/const_folding_for_if.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/const_folding_for_if.cpp
@@ -14,6 +14,7 @@
 #include "ngraph/opsets/opset8.hpp"
 #include <ngraph/pass/constant_folding.hpp>
 
+#include <ngraph/pass/manager.hpp>
 using namespace testing;
 using namespace std;
 using namespace ngraph;
@@ -49,7 +50,9 @@ TEST(TransformationTests, DISABLED_if_constant_folding) {
         auto add = make_shared<op::v1::Add>(if_res, param_add);
         auto add_res = make_shared<op::Result>(add);
         fun = make_shared<Function>(OutputVector{ add_res }, ParameterVector{ param_add });
-        ngraph::pass::ConstantFolding().run_on_function(fun);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.run_passes(fun);
     }
     std::shared_ptr<ngraph::Function> f_ref(nullptr);
     {
diff --git a/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp b/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp
index cb77d6b6bbca84..a56ea2b612e9b0 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp
@@ -15,6 +15,7 @@
 #include <ngraph/pass/constant_folding.hpp>
 #include <ngraph/ops.hpp>
 #include "common_test_utils/ngraph_test_utils.hpp"
+#include <ngraph/pass/manager.hpp>
 
 using namespace testing;
 
@@ -35,8 +36,10 @@ TEST(TransformationTests, ConstFoldingPriorBox) {
         auto pb = std::make_shared<ngraph::opset3::PriorBox>(layer_shape, image_shape, attrs);
         auto res = std::make_shared<ngraph::opset3::Result>(pb);
         f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res}, ngraph::ParameterVector{in});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
-        ngraph::pass::ConstantFolding().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
 
@@ -76,8 +79,10 @@ TEST(TransformationTests, ConstFoldingPriorBoxClustered) {
         auto pb = std::make_shared<ngraph::opset3::PriorBoxClustered>(layer_shape, image_shape, attrs);
         auto res = std::make_shared<ngraph::opset3::Result>(pb);
         f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res}, ngraph::ParameterVector{in});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
-        ngraph::pass::ConstantFolding().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
 
@@ -135,8 +140,10 @@ TEST(TransformationTests, ConstFoldingPriorBoxSubgraph) {
         auto pb = std::make_shared<ngraph::opset3::PriorBox>(ss_data, ss_image, attrs);
         auto res = std::make_shared<ngraph::opset3::Result>(pb);
         f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res}, ngraph::ParameterVector{in, in_2});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
-        ngraph::pass::ConstantFolding().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
 
@@ -185,8 +192,10 @@ TEST(TransformationTests, ConstFoldingPriorBoxClusteredSubgraph) {
         auto pb = std::make_shared<ngraph::opset3::PriorBoxClustered>(ss_data, ss_image, attrs);
         auto res = std::make_shared<ngraph::opset3::Result>(pb);
         f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res}, ngraph::ParameterVector{in, in_2});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
-        ngraph::pass::ConstantFolding().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.run_passes(f);
         ASSERT_NO_THROW(check_rt_info(f));
     }
 
diff --git a/inference-engine/tests/functional/inference_engine/transformations/conv_fusion_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/conv_fusion_test.cpp
index 389388f29584fa..0fa792b611537d 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/conv_fusion_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/conv_fusion_test.cpp
@@ -23,6 +23,8 @@
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace ngraph;
 using namespace testing;
 
@@ -50,7 +52,9 @@ class ConvFusionTests: public CommonTestUtils::TestsCommon,
             f_ref = get_initial_function(input_shape, weights_shape, eltwise_type, eltwise_shape);
         } else {
             f_ref = get_reference_function(input_shape, weights_shape, eltwise_type, eltwise_shape);
-            ngraph::pass::ConstantFolding().run_on_function(f_ref);
+            ngraph::pass::Manager manager;
+            manager.register_pass<ngraph::pass::ConstantFolding>();
+            manager.run_passes(f_ref);
         }
     }
 
@@ -165,15 +169,15 @@ INSTANTIATE_TEST_SUITE_P(ConvAddFusion, ConvFusionTests,
                         std::make_tuple(InputShape{3, 3, DYN},          WeightsShape{1, 3, 3},
                                         add::get_type_info_static(), EltwiseShape{1, 1}, false)));
 
-INSTANTIATE_TEST_SUITE_P(DISABLED_ConvAddFusionNegative, ConvFusionTests,
+INSTANTIATE_TEST_SUITE_P(ConvAddFusionNegative, ConvFusionTests,
         testing::Values(std::make_tuple(InputShape{DYN, DYN, DYN, DYN, DYN}, WeightsShape{8, 3, 1, 2, 3},
                                         add::get_type_info_static(), EltwiseShape{2, 1}, true),
                         std::make_tuple(InputShape{DYN, 3, 64, 64, 64}, WeightsShape{8, 3, 1, 2, 3},
-                                        add::get_type_info_static(), EltwiseShape{8, 1, 1}, true),
+                                        add::get_type_info_static(), EltwiseShape{8, 1, 1, 1, 1}, true),
                         std::make_tuple(InputShape{2, DYN, 64, 64, 64}, WeightsShape{9, 3, 2, 3, 1},
-                                        add::get_type_info_static(), EltwiseShape{9, 1, 1, 1, 1}, true)));
+                                        add::get_type_info_static(), EltwiseShape{2, 1, 1, 1, 1}, true)));
 
-INSTANTIATE_TEST_SUITE_P(DISABLED_ConvMulFusion, ConvFusionTests,
+INSTANTIATE_TEST_SUITE_P(ConvMulFusion, ConvFusionTests,
         testing::Values(std::make_tuple(InputShape{DYN, DYN, DYN, DYN, DYN}, WeightsShape{8, 3, 1, 2, 3},
                                         mul::get_type_info_static(), EltwiseShape{8, 1, 1, 1}, false),
                         std::make_tuple(InputShape{DYN, 3, 64, 64, 64}, WeightsShape{8, 3, 1, 2, 3},
@@ -207,12 +211,12 @@ INSTANTIATE_TEST_SUITE_P(DISABLED_ConvMulFusion, ConvFusionTests,
                         std::make_tuple(InputShape{3, 3, DYN},          WeightsShape{1, 3, 3},
                                         mul::get_type_info_static(), EltwiseShape{1, 1}, false)));
 
-INSTANTIATE_TEST_SUITE_P(DISABLED_ConvMulFusionNegative, ConvFusionTests,
+INSTANTIATE_TEST_SUITE_P(ConvMulFusionNegative, ConvFusionTests,
         testing::Values(std::make_tuple(InputShape{DYN, DYN, DYN, DYN, DYN}, WeightsShape{8, 3, 1, 2, 3},
                                         mul::get_type_info_static(), EltwiseShape{2, 1}, true),
-                        std::make_tuple(InputShape{DYN, 3, 64, 64}, WeightsShape{8, 3, 1, 2, 3},
-                                        mul::get_type_info_static(), EltwiseShape{8, 1, 1}, true),
-                        std::make_tuple(InputShape{2, DYN, 64, 64}, WeightsShape{9, 3, 2, 3, 1},
+                        std::make_tuple(InputShape{DYN, 3, 64, 64}, WeightsShape{ 3, 3, 2, 3},
+                                        mul::get_type_info_static(), EltwiseShape{8, 1, 1, 1}, true),
+                        std::make_tuple(InputShape{2, DYN, 64, 64}, WeightsShape{ 3, 2, 3, 1},
                                         mul::get_type_info_static(), EltwiseShape{9, 1, 1, 1, 1}, true)));
 
 
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_compression_only_to_legacy_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_compression_only_to_legacy_test.cpp
new file mode 100644
index 00000000000000..1ce8582ff95a3c
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_compression_only_to_legacy_test.cpp
@@ -0,0 +1,106 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+
+#include "openvino/core/function.hpp"
+#include "openvino/opsets/opset8.hpp"
+#include "openvino/pass/manager.hpp"
+#include "transformations/common_optimizations/convert_compression_only_to_legacy.hpp"
+#include "transformations/rt_info/decompression.hpp"
+#include "transformations/init_node_info.hpp"
+#include "transformations/utils/utils.hpp"
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST(TransformationTests, ConvertCompressionOnlyToLegacy) {
+    std::shared_ptr<ov::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f16,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto convert_ins1 = std::make_shared<ov::opset8::Convert>(const_weights, ov::element::f32);
+        ov::mark_as_decompression(convert_ins1);
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            convert_ins1,
+            ov::Strides{ 1, 1 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::Strides{ 1, 1 });
+
+        f = std::make_shared<ov::Function>(ov::NodeVector{ conv }, ov::ParameterVector{ input });
+
+        ov::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ov::pass::ConvertCompressedOnlyToLegacy>();
+        manager.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f16, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f16,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            const_weights,
+            ov::Strides{ 1, 1 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::Strides{ 1, 1 });
+
+        f_ref = std::make_shared<ov::Function>(ov::NodeVector{ conv }, ov::ParameterVector{ input });
+    }
+
+    auto res = compare_functions(f, f_ref, true);
+    ASSERT_TRUE(res.first) << res.second;
+}
+
+TEST(TransformationTests, ConvertCompressionOnlyToLegacyNoConvertion) {
+    std::shared_ptr<ov::Function> f(nullptr), f_ref(nullptr);
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f32,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            const_weights,
+            ov::Strides{ 1, 1 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::Strides{ 1, 1 });
+
+        f = std::make_shared<ov::Function>(ov::NodeVector{ conv }, ov::ParameterVector{ input });
+
+        ov::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ov::pass::ConvertCompressedOnlyToLegacy>();
+        manager.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto input = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::Shape{ 1, 3, 12, 12 });
+        auto const_weights = ov::opset8::Constant::create(ov::element::f32,
+            ov::Shape{ 1, 3, 3, 3 },
+            { 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9 });
+        auto conv = std::make_shared<ov::opset8::Convolution>(input,
+            const_weights,
+            ov::Strides{ 1, 1 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::CoordinateDiff{ 0, 0 },
+            ov::Strides{ 1, 1 });
+
+        f_ref = std::make_shared<ov::Function>(ov::NodeVector{ conv }, ov::ParameterVector{ input });
+    }
+
+    auto res = compare_functions(f, f_ref, true);
+    ASSERT_TRUE(res.first) << res.second;
+}
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_convolution_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_convolution_test.cpp
index 1a8e196be5950e..27f173304d156c 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_convolution_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_convolution_test.cpp
@@ -24,6 +24,8 @@
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 using namespace ngraph;
 using namespace ngraph::opset1;
@@ -71,9 +73,12 @@ class ConvertConvolutionTest: public CommonTestUtils::TestsCommon,
 };
 
 TEST_P(ConvertConvolutionTest, CompareFunctions) {
-    const auto & orig_shape = f->get_output_partial_shape(0);
-    pass::InitNodeInfo().run_on_function(f);
-    pass::ConvertConvolutions().run_on_function(f);
+    const auto orig_shape = f->get_output_partial_shape(0);
+    pass::Manager manager;
+    manager.register_pass<pass::InitNodeInfo>();
+    manager.register_pass<pass::ConvertConvolutions>();
+    manager.run_passes(f);
+
     ASSERT_NO_THROW(check_rt_info(f));
     auto res = compare_functions(f, f_ref);
     ASSERT_TRUE(res.first) << res.second;
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp
index 3c456c7f083c6d..242325390d248a 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_deconvolution_test.cpp
@@ -24,6 +24,8 @@
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 
 using InputShape = ngraph::PartialShape;
@@ -67,9 +69,11 @@ class ConvertDeconvolutionTest: public CommonTestUtils::TestsCommon,
 };
 
 TEST_P(ConvertDeconvolutionTest, CompareFunctions) {
-    const auto & orig_shape = f->get_output_partial_shape(0);
-    ngraph::pass::InitNodeInfo().run_on_function(f);
-    ngraph::pass::ConvertConvolutions().run_on_function(f);
+    const auto orig_shape = f->get_output_partial_shape(0);
+    ngraph::pass::Manager manager;
+    manager.register_pass<ngraph::pass::InitNodeInfo>();
+    manager.register_pass<ngraph::pass::ConvertConvolutions>();
+    manager.run_passes(f);
     ASSERT_NO_THROW(check_rt_info(f));
     auto res = compare_functions(f, f_ref);
     ASSERT_TRUE(res.first) << res.second;
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_divide.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_divide.cpp
index 7a308b89a41ec0..b4df08a778b611 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_divide.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_divide.cpp
@@ -32,10 +32,8 @@ TEST_F(TransformationTestsF, ConvertDivide) {
 
     {
         auto data = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
-        auto divide_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {1.5});
-        auto pow = std::make_shared<ngraph::opset1::Power>(divide_constant,
-                                                           ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{}, {-1}));
-        auto mul = std::make_shared<ngraph::opset1::Multiply>(data, pow);
+        auto divide_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {1. / 1.5});
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data, divide_constant);
 
         function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{data});
     }
@@ -63,11 +61,11 @@ TEST_F(TransformationTestsF, ConvertDivideNegative) {
 
 TEST_F(TransformationTestsF, ConvertDivideScalar) {
     {
-        auto data = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
-        auto divide_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1.5});
-        auto divide = std::make_shared<ngraph::opset1::Divide>(data, divide_constant);
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto divide = std::make_shared<ngraph::opset1::Divide>(data1, data2);
 
-        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data});
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data1, data2});
 
         NGRAPH_CHECK(divide->get_output_partial_shape(0).rank().get_length() == 0);
 
@@ -76,13 +74,51 @@ TEST_F(TransformationTestsF, ConvertDivideScalar) {
 
     {
         auto data = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
-        auto divide_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1.5});
-        auto pow = std::make_shared<ngraph::opset1::Power>(divide_constant,
+        auto pow_input = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto pow = std::make_shared<ngraph::opset1::Power>(pow_input,
                                                            ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{}, {-1}));
         auto mul = std::make_shared<ngraph::opset1::Multiply>(data, pow);
 
-        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{data});
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{data, pow_input});
 
         NGRAPH_CHECK(mul->get_output_partial_shape(0).rank().get_length() == 0);
     }
 }
+
+TEST_F(TransformationTestsF, ConvertDivideWithConstantPositive) {
+    {
+        auto data = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto divide_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1.5});
+        auto divide = std::make_shared<ngraph::opset1::Divide>(data, divide_constant);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data});
+        manager.register_pass<ngraph::pass::ConvertDivideWithConstant>();
+    }
+
+    {
+        auto data = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto divide_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1. / 1.5});
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data, divide_constant);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{data});
+    }
+}
+
+TEST_F(TransformationTestsF, ConvertDivideWithConstantNegative) {
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto divide = std::make_shared<ngraph::opset1::Divide>(data1, data2);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data1, data2});
+        manager.register_pass<ngraph::pass::ConvertDivideWithConstant>();
+    }
+
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{});
+        auto divide = std::make_shared<ngraph::opset1::Divide>(data1, data2);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data1, data2});
+    }
+}
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
index 44caeeb1a4b141..c62de865091744 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
@@ -23,6 +23,8 @@
 #include <transformations/init_node_info.hpp>
 #include <legacy/convert_function_to_cnn_network.hpp>
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 using namespace InferenceEngine;
 
@@ -37,7 +39,9 @@ TEST(ConvertFunctionToCNNNetworkTests, ConvertPReLUNetwork) {
 
         f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result},
                                                ngraph::ParameterVector{param1, param2});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.run_passes(f);
     }
 
     InferenceEngine::CNNNetwork nGraphImpl(f);
@@ -66,7 +70,9 @@ TEST(ConvertFunctionToCNNNetworkTests, ConvertConvolutionNetwork) {
 
         f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result},
                                                ngraph::ParameterVector{param1, param2});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.run_passes(f);
     }
 
     InferenceEngine::CNNNetwork nGraphImpl(f);
@@ -162,7 +168,9 @@ TEST(ConvertFunctionToCNNNetworkTests, ConvertTopKWithOneInput) {
 
         f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result},
                                                ngraph::ParameterVector{param});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.run_passes(f);
     }
 
     ngraph::pass::Manager manager;
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_reduce_to_pooling_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_reduce_to_pooling_test.cpp
index fec70c317badeb..866f8a8bcc31f7 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_reduce_to_pooling_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_reduce_to_pooling_test.cpp
@@ -21,6 +21,8 @@
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 
 using InputShape = ngraph::PartialShape;
@@ -149,7 +151,9 @@ INSTANTIATE_TEST_SUITE_P(ReduceToReshapePoolReshape, ConvertReduceToPoolingTests
 
 TEST(ConvertReduceToPooling, Negative) {
     auto f = ConvertReduceToPoolingTests::get_initial_function(ngraph::PartialShape::dynamic(), {3}, MAX, true);
-    ASSERT_NO_THROW(ngraph::pass::ConvertReduceToPooling().run_on_function(f));
+    ngraph::pass::Manager manager;
+    manager.register_pass<ngraph::pass::ConvertReduceToPooling>();
+    ASSERT_NO_THROW(manager.run_passes(f));
 }
 
 #undef MAX
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_scatter_elements_to_scatter_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_scatter_elements_to_scatter_test.cpp
index b40c4dc4e6cc80..31fbb691f8493f 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_scatter_elements_to_scatter_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_scatter_elements_to_scatter_test.cpp
@@ -22,6 +22,8 @@
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 
 std::shared_ptr<ngraph::Function> get_initial_function(const ngraph::PartialShape & data_shape,
@@ -82,9 +84,11 @@ void test(std::shared_ptr<ngraph::Function> f, std::shared_ptr<ngraph::Function>
     manager.register_pass<ngraph::pass::InitNodeInfo>();
     manager.register_pass<ngraph::pass::ConvertScatterElementsToScatter>();
     manager.register_pass<ngraph::pass::CheckUniqueNames>(unh);
-    manager.run_passes(f);
-    ASSERT_NO_THROW(check_rt_info(f));
-    ngraph::pass::ConstantFolding().run_on_function(f);
+    manager.register_pass<ngraph::pass::InjectionPass>([](std::shared_ptr<ngraph::Function> f) {
+        check_rt_info(f);
+    });
+    manager.register_pass<ngraph::pass::ConstantFolding>();
+    ASSERT_NO_THROW(manager.run_passes(f));
 
     auto fc = FunctionsComparator::no_default().enable(FunctionsComparator::PRECISIONS);
     auto res = fc.compare(f, f_ref);
diff --git a/inference-engine/tests/functional/inference_engine/transformations/divide_fusion.cpp b/inference-engine/tests/functional/inference_engine/transformations/divide_fusion.cpp
new file mode 100644
index 00000000000000..841697532eb0b4
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/transformations/divide_fusion.cpp
@@ -0,0 +1,88 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+#include <queue>
+
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <transformations/init_node_info.hpp>
+#include <transformations/utils/utils.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <transformations/common_optimizations/divide_fusion.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST(TransformationTests, DivideFusion) {
+    std::shared_ptr<ngraph::Function> f, f_ref;
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto pow_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {-1});
+        auto pow = std::make_shared<ngraph::opset1::Power>(data2, pow_constant);
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data1, pow);
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{data1, data2});
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::DivideFusion>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto divide = std::make_shared<ngraph::opset1::Divide>(data1, data2);
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data1, data2});
+    }
+
+    const auto res = FunctionsComparator::with_default()
+            .enable(FunctionsComparator::CONST_VALUES)
+            .enable(FunctionsComparator::ATTRIBUTES)
+            .compare(f, f_ref);
+    ASSERT_TRUE(res.valid) << res.message;
+}
+
+TEST(TransformationTests, DivideFusionNegative) {
+    std::shared_ptr<ngraph::Function> f, f_ref;
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto pow_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {-1.01});
+        auto pow = std::make_shared<ngraph::opset1::Power>(data2, pow_constant);
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data1, pow);
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{data1, data2});
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::DivideFusion>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto pow_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {-1.01});
+        auto pow = std::make_shared<ngraph::opset1::Power>(data2, pow_constant);
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data1, pow);
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{mul}, ngraph::ParameterVector{data1, data2});
+    }
+
+    const auto res = FunctionsComparator::with_default()
+            .enable(FunctionsComparator::CONST_VALUES)
+            .enable(FunctionsComparator::ATTRIBUTES)
+            .compare(f, f_ref);
+    ASSERT_TRUE(res.valid) << res.message;
+}
diff --git a/inference-engine/tests/functional/inference_engine/transformations/eliminate_split_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/eliminate_split_test.cpp
new file mode 100644
index 00000000000000..e6f17ba777428f
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/transformations/eliminate_split_test.cpp
@@ -0,0 +1,81 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <queue>
+#include <ngraph/op/parameter.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <transformations/common_optimizations/nop_elimination.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST_F(TransformationTestsF, EliminateSplit) {
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::PartialShape::dynamic());
+        auto mul_constant = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {89.2});
+        auto mul = std::make_shared<ngraph::opset8::Multiply>(input, mul_constant);
+        auto axis_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {2});
+        auto split = std::make_shared<ngraph::opset8::Split>(mul, axis_const, 1);
+        auto res = std::make_shared<ngraph::opset8::Result>(split);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{res}, ngraph::ParameterVector{input});
+
+        manager.register_pass<ngraph::pass::EliminateSplit>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::PartialShape::dynamic());
+        auto mul_constant = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {89.2});
+        auto mul = std::make_shared<ngraph::opset8::Multiply>(input, mul_constant);
+        auto res = std::make_shared<ngraph::opset8::Result>(mul);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{res}, ngraph::ParameterVector{input});
+    }
+}
+
+TEST_F(TransformationTestsF, EliminateSplitNegative) {
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::PartialShape::dynamic());
+        auto mul_constant = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {89.2});
+        auto mul = std::make_shared<ngraph::opset8::Multiply>(input, mul_constant);
+        auto axis_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {2});
+        auto split = std::make_shared<ngraph::opset8::Split>(mul, axis_const, 3);
+        auto res1 = std::make_shared<ngraph::opset8::Result>(split->output(0));
+        auto res2 = std::make_shared<ngraph::opset8::Result>(split->output(1));
+        auto res3 = std::make_shared<ngraph::opset8::Result>(split->output(2));
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{res1, res2, res3}, ngraph::ParameterVector{input});
+
+        manager.register_pass<ngraph::pass::EliminateSplit>();
+    }
+}
+
+TEST_F(TransformationTestsF, EliminateSequenceOfSplits) {
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::PartialShape::dynamic());
+        auto axis_const1 = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
+        auto axis_const2 = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+        auto axis_const3 = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {2});
+        auto split1 = std::make_shared<ngraph::opset8::Split>(input, axis_const1, 1);
+        auto split2 = std::make_shared<ngraph::opset8::Split>(split1, axis_const2, 1);
+        auto split3 = std::make_shared<ngraph::opset8::Split>(split2, axis_const3, 1);
+        auto axis_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {2});
+        auto true_split = std::make_shared<ngraph::opset8::Split>(split3, axis_const, 3);
+        auto res1 = std::make_shared<ngraph::opset8::Result>(true_split->output(0));
+        auto res2 = std::make_shared<ngraph::opset8::Result>(true_split->output(1));
+        auto res3 = std::make_shared<ngraph::opset8::Result>(true_split->output(2));
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{res1, res2, res3}, ngraph::ParameterVector{input});
+
+        manager.register_pass<ngraph::pass::EliminateSplit>();
+    }
+
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::PartialShape::dynamic());
+        auto axis_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {2});
+        auto split = std::make_shared<ngraph::opset8::Split>(input, axis_const, 3);
+        auto res1 = std::make_shared<ngraph::opset8::Result>(split->output(0));
+        auto res2 = std::make_shared<ngraph::opset8::Result>(split->output(1));
+        auto res3 = std::make_shared<ngraph::opset8::Result>(split->output(2));
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{res1, res2, res3}, ngraph::ParameterVector{input});
+    }
+}
\ No newline at end of file
diff --git a/inference-engine/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp b/inference-engine/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp
index 93f91995506b88..bb97268d787eb8 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/keep_constant_inputs_tests.cpp
@@ -23,6 +23,8 @@
 #include <transformations/opset_conversions/convert_opset3_to_opset2.hpp>
 #include "shared_test_classes/base/low_precision_transformations/layer_transformation.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 using namespace InferenceEngine;
 
@@ -44,10 +46,12 @@ int numberOfInputsForLayerInCNNNetwork(const InferenceEngine::CNNNetwork & netwo
 void transformNetwork(InferenceEngine::CNNNetwork & clonedNetwork, bool keep_constant_inputs) {
     if (clonedNetwork.getFunction()) {
         auto nGraphFunc = clonedNetwork.getFunction();
-        ngraph::pass::CommonOptimizations().run_on_function(nGraphFunc);
-        ngraph::pass::ConvertOpSet3ToOpSet2().run_on_function(nGraphFunc);
-        ngraph::pass::ConvertOpSet2ToOpSet1().run_on_function(nGraphFunc);
-        ngraph::pass::ConvertOpSet1ToLegacy().run_on_function(nGraphFunc);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::CommonOptimizations>();
+        manager.register_pass<ngraph::pass::ConvertOpSet3ToOpSet2>();
+        manager.register_pass<ngraph::pass::ConvertOpSet2ToOpSet1>();
+        manager.register_pass<ngraph::pass::ConvertOpSet1ToLegacy>();
+        manager.run_passes(nGraphFunc);
         IE_SUPPRESS_DEPRECATED_START
         clonedNetwork = InferenceEngine::CNNNetwork(
             InferenceEngine::details::convertFunctionToICNNNetwork(nGraphFunc, clonedNetwork, keep_constant_inputs));
diff --git a/inference-engine/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp
index d2e8dd0cb4f464..9358886a63afc7 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/mul_add_conversion_test.cpp
@@ -28,6 +28,8 @@
 #include "common_test_utils/ngraph_test_utils.hpp"
 #include "lpt_ngraph_functions/common/builders.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 
 using InputShape = ngraph::PartialShape;
@@ -148,9 +150,11 @@ TEST_P(MulAddConversionTests, CompareFunctions) {
     manager.register_pass<ngraph::pass::InitNodeInfo>();
     manager.register_pass<ngraph::pass::ConvertMulAddToScaleShiftOrPower>();
     manager.register_pass<ngraph::pass::CheckUniqueNames>(unh);
-    manager.run_passes(f);
-    ASSERT_NO_THROW(check_rt_info(f));
-    ngraph::pass::ConstantFolding().run_on_function(f);
+    manager.register_pass<ngraph::pass::InjectionPass>([](std::shared_ptr<ngraph::Function> f) {
+        check_rt_info(f);
+    });
+    manager.register_pass<ngraph::pass::ConstantFolding>();
+    ASSERT_NO_THROW(manager.run_passes(f));
     f->validate_nodes_and_infer_types();
 
     auto fc = FunctionsComparator::no_default().enable(FunctionsComparator::PRECISIONS);
@@ -166,9 +170,12 @@ TEST_P(MulOrAddConversionTests, CompareFunctions) {
     manager.register_pass<ngraph::pass::InitNodeInfo>();
     manager.register_pass<ngraph::pass::ConvertMulOrAddFinally>();
     manager.register_pass<ngraph::pass::CheckUniqueNames>(unh);
-    manager.run_passes(f);
-    ASSERT_NO_THROW(check_rt_info(f));
-    ngraph::pass::ConstantFolding().run_on_function(f);
+    manager.register_pass<ngraph::pass::InjectionPass>([](std::shared_ptr<ngraph::Function> f) {
+        check_rt_info(f);
+    });
+    manager.register_pass<ngraph::pass::ConstantFolding>();
+    ASSERT_NO_THROW(manager.run_passes(f));
+
     f->validate_nodes_and_infer_types();
 
     auto fc = FunctionsComparator::no_default().enable(FunctionsComparator::PRECISIONS);
diff --git a/inference-engine/tests/functional/inference_engine/transformations/ngraph_1d_ops_reshape_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/ngraph_1d_ops_reshape_test.cpp
index 841f310318780f..72bbbcf64a5023 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/ngraph_1d_ops_reshape_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/ngraph_1d_ops_reshape_test.cpp
@@ -21,6 +21,8 @@
 #include <ngraph/pass/manager.hpp>
 #include "common_test_utils/ngraph_test_utils.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace testing;
 using namespace ngraph;
 
@@ -36,10 +38,14 @@ TEST(TransformationTests, ConvReshapeTest1) {
         auto conv = std::make_shared<ngraph::op::ConvolutionIE>(input, w, strides, dilations, pads_begin, pads_end, ngraph::element::f32, 1);
 
         f = std::make_shared<ngraph::Function>(ngraph::NodeVector{conv}, ngraph::ParameterVector{});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
-        ngraph::pass::Reshape1DOps().run_on_function(f);
-        ASSERT_NO_THROW(check_rt_info(f));
-        ngraph::pass::ConstantFolding().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::Reshape1DOps>();
+        manager.register_pass<ngraph::pass::InjectionPass>([](std::shared_ptr<ngraph::Function> f) {
+            check_rt_info(f);
+        });
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        ASSERT_NO_THROW(manager.run_passes(f));
     }
 
     std::vector<size_t> ref_shape{1, 6, 1, 62};
@@ -69,10 +75,14 @@ TEST(TransformationTests, ConvBiasReshapeTest1) {
         auto conv = std::make_shared<ngraph::op::ConvolutionIE>(input, w, b, strides, dilations, pads_begin, pads_end, ngraph::element::f32, 1);
 
         f = std::make_shared<ngraph::Function>(ngraph::NodeVector{conv}, ngraph::ParameterVector{});
-        ngraph::pass::InitNodeInfo().run_on_function(f);
-        ngraph::pass::Reshape1DOps().run_on_function(f);
-        ASSERT_NO_THROW(check_rt_info(f));
-        ngraph::pass::ConstantFolding().run_on_function(f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::Reshape1DOps>();
+        manager.register_pass<ngraph::pass::InjectionPass>([](std::shared_ptr<ngraph::Function> f) {
+            check_rt_info(f);
+        });
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        ASSERT_NO_THROW(manager.run_passes(f));
     }
 
     std::vector<size_t> ref_shape{1, 6, 1, 62};
diff --git a/inference-engine/tests/functional/inference_engine/transformations/ngraph_fq_transpose_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/ngraph_fq_transpose_test.cpp
index 6b5f2d82b6a1b8..822c575ac11faf 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/ngraph_fq_transpose_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/ngraph_fq_transpose_test.cpp
@@ -56,28 +56,28 @@ TEST_F(TransformationTestsF, FQTransposeTest1) {
     }
 }
 
-TEST(TransformationTests, FQTransposeDynamic) {
-    auto data = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::PartialShape::dynamic());
-    auto input_low = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {2});
-    auto input_high = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {3});
-    auto output_low = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {2});
-    auto output_high = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {3});
-    auto transpose_order = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {0, 2, 1});
+TEST_F(TransformationTestsF, FQTransposeNegativeCase) {
+    auto create_graph = []() -> std::shared_ptr<ngraph::Function> {
+        auto data = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::PartialShape{1, 3, 1});
+        auto sigmoid = std::make_shared<ngraph::op::Sigmoid>(data);
+        auto input_low = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {2});
+        auto input_high = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {3});
+        auto output_low = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {2});
+        auto output_high = ngraph::op::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {3});
+        auto transpose_order = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {0, 2, 1});
 
-    std::shared_ptr<ngraph::Function> f(nullptr);
-    {
-        auto fq = std::make_shared<ngraph::op::FakeQuantize>(data, input_low, input_high, output_low, output_high, 1);
+        auto fq = std::make_shared<ngraph::op::FakeQuantize>(sigmoid, input_low, input_high, output_low, output_high, 1);
         auto transpose = std::make_shared<ngraph::op::Transpose>(fq, transpose_order);
 
-        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{transpose}, ngraph::ParameterVector{data});
+        return std::make_shared<ngraph::Function>(ngraph::NodeVector{transpose}, ngraph::ParameterVector{data});
+    };
+    function = create_graph();
 
-        ngraph::pass::Manager manager;
-        manager.register_pass<ngraph::pass::InitNodeInfo>();
-        manager.register_pass<ngraph::pass::PullTransposeThroughFQUp>();
-        manager.register_pass<ngraph::pass::InjectionPass>([](std::shared_ptr<ngraph::Function> f) {
-            check_rt_info(f);
-        });
-        manager.register_pass<ngraph::pass::ConstantFolding>();
-        ASSERT_NO_THROW(manager.run_passes(f));
-    }
+    manager.register_pass<ngraph::pass::InitNodeInfo>();
+    manager.register_pass<ngraph::pass::PullTransposeThroughFQUp>();
+    manager.register_pass<ngraph::pass::InjectionPass>([](std::shared_ptr<ngraph::Function> f) {
+        check_rt_info(f);
+    });
+
+    function_ref = create_graph();
 }
diff --git a/inference-engine/tests/functional/inference_engine/transformations/nop_elimination.cpp b/inference-engine/tests/functional/inference_engine/transformations/nop_elimination.cpp
index 1bab10d9b4a3cf..ef654188194878 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/nop_elimination.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/nop_elimination.cpp
@@ -21,6 +21,8 @@
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
+#include <ngraph/pass/manager.hpp>
+
 using namespace ngraph;
 using namespace std;
 
@@ -559,7 +561,9 @@ TEST(nop_elimination, unsqueeze_squeeze_elimination) {
     auto check_usecase = [&](const Shape& shape, const std::vector<int64_t>& axes_val) {
         auto baseline_f = generate_func(shape, axes_val);
         auto optimized_f = generate_func(shape, axes_val);
-        pass::NopElimination().run_on_function(optimized_f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
 
         ASSERT_EQ(count_ops_of_type<op::v0::Squeeze>(baseline_f), 1);
         ASSERT_EQ(count_ops_of_type<op::v0::Unsqueeze>(baseline_f), 1);
@@ -589,7 +593,9 @@ TEST(nop_elimination, reshape_unsqueeze_elimination) {
         auto B1 = make_shared<op::v0::Unsqueeze>(B, axes);
         auto baseline_f = make_shared<Function>(make_shared<op::v0::Abs>(B1), ParameterVector{A});
         auto optimized_f = clone_function(*baseline_f);
-        pass::NopElimination().run_on_function(optimized_f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
 
         ASSERT_EQ(count_ops_of_type<op::v1::Reshape>(baseline_f), 1);
         ASSERT_EQ(count_ops_of_type<op::v0::Unsqueeze>(baseline_f), 1);
@@ -618,7 +624,9 @@ TEST(nop_elimination, reshape_squeeze_elimination) {
         auto B1 = make_shared<op::v0::Squeeze>(B, axes);
         auto baseline_f = make_shared<Function>(make_shared<op::v0::Abs>(B1), ParameterVector{A});
         auto optimized_f = clone_function(*baseline_f);
-        pass::NopElimination().run_on_function(optimized_f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
 
         ASSERT_EQ(count_ops_of_type<op::v1::Reshape>(baseline_f), 1);
         ASSERT_EQ(count_ops_of_type<op::v0::Squeeze>(baseline_f), 1);
@@ -644,7 +652,9 @@ TEST(nop_elimination, reshape_reshape_elimination) {
         auto B1 = make_shared<op::v1::Reshape>(B, pat2, true);
         auto baseline_f = make_shared<Function>(make_shared<op::v0::Abs>(B1), ParameterVector{A});
         auto optimized_f = clone_function(*baseline_f);
-        pass::NopElimination().run_on_function(optimized_f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
 
         ASSERT_EQ(count_ops_of_type<op::v1::Reshape>(baseline_f), 2);
         ASSERT_EQ(count_ops_of_type<op::v1::Reshape>(optimized_f), 1);
@@ -669,7 +679,9 @@ TEST(nop_elimination, squeeze_reshape_elimination) {
         auto B1 = make_shared<op::v1::Reshape>(B, pat2, false);
         auto baseline_f = make_shared<Function>(make_shared<op::v0::Abs>(B1), ParameterVector{A});
         auto optimized_f = clone_function(*baseline_f);
-        pass::NopElimination().run_on_function(optimized_f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
 
         ASSERT_EQ(count_ops_of_type<op::v1::Reshape>(baseline_f), 1);
         ASSERT_EQ(count_ops_of_type<op::v0::Squeeze>(baseline_f), 1);
@@ -695,7 +707,9 @@ TEST(nop_elimination, unsqueeze_reshape_elimination) {
         auto B1 = make_shared<op::v1::Reshape>(B, pat2, false);
         auto baseline_f = make_shared<Function>(make_shared<op::v0::Abs>(B1), ParameterVector{A});
         auto optimized_f = clone_function(*baseline_f);
-        pass::NopElimination().run_on_function(optimized_f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
 
         ASSERT_EQ(count_ops_of_type<op::v1::Reshape>(baseline_f), 1);
         ASSERT_EQ(count_ops_of_type<op::v0::Unsqueeze>(baseline_f), 1);
@@ -716,8 +730,9 @@ TEST(nop_elimination, squeeze_unsqueeze_elimination_negative) {
         auto squeeze = make_shared<ngraph::opset1::Squeeze>(input, indices);
         auto baseline_f = make_shared<Function>(squeeze, ParameterVector{input});
         auto optimized_f = clone_function(*baseline_f);
-        pass::NopElimination().run_on_function(optimized_f);
-
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
         ASSERT_EQ(count_ops_of_type<ngraph::opset1::Squeeze>(baseline_f), 1);
         ASSERT_EQ(count_ops_of_type<ngraph::opset1::Squeeze>(optimized_f), 1);
     };
@@ -733,7 +748,9 @@ TEST(nop_elimination, topk_convert_elimination) {
         auto C = make_shared<op::Convert>(B->output(0), B->output(0).get_element_type());
         auto baseline_f = make_shared<Function>(make_shared<op::v0::Abs>(C), ParameterVector{A});
         auto optimized_f = clone_function(*baseline_f);
-        pass::NopElimination().run_on_function(optimized_f);
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::NopElimination>();
+        manager.run_passes(optimized_f);
 
         ASSERT_EQ(count_ops_of_type<op::Convert>(baseline_f), 1);
         ASSERT_EQ(count_ops_of_type<op::Convert>(optimized_f), 0);
diff --git a/inference-engine/tests/functional/inference_engine/transformations/optimize_strided_slice_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/optimize_strided_slice_test.cpp
index 9c64bd308cec7e..de7b78bda9d014 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/optimize_strided_slice_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/optimize_strided_slice_test.cpp
@@ -15,9 +15,11 @@
 #include <ngraph/function.hpp>
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/opsets/opset3.hpp>
+#include <ngraph/opsets/opset8.hpp>
 #include <ngraph/pass/constant_folding.hpp>
 #include <transformations/common_optimizations/optimize_strided_slice.hpp>
 #include <transformations/utils/utils.hpp>
+#include "openvino/core/partial_shape.hpp"
 
 #include "common_test_utils/ngraph_test_utils.hpp"
 
@@ -277,3 +279,707 @@ TEST_F(TransformationTestsF, OptimizeSS_Groupped_Test) {
         function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{concat}, ngraph::ParameterVector{source});
     }
 }
+
+TEST_F(TransformationTestsF, OptimizeSS_UselessDeletion_use_shapes_false) {
+    {
+        auto data = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5});
+        auto relu = std::make_shared<ngraph::opset1::Relu>(data);
+        auto begin = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto stride = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+
+        std::vector<int64_t> begin_mask = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask = {1, 1, 1, 1};   // ignoring end -- slicing to the end
+
+        auto ss = std::make_shared<ngraph::opset1::StridedSlice>(relu, begin, end, stride, begin_mask, end_mask);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ss}, ngraph::ParameterVector{data});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(false);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    // No UselessStridedSliceEraser transformation if use_shapes == false
+}
+
+TEST_F(TransformationTestsF, OptimizeSS_Shared_Test_use_shapes_false) {
+    {
+        auto source = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5});
+
+        auto begin1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto stride1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+        std::vector<int64_t> begin_mask1 = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask1 = {0, 0, 0, 0};
+        auto ss1 = std::make_shared<ngraph::opset1::StridedSlice>(source, begin1, end1, stride1, begin_mask1, end_mask1);
+
+        auto begin2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto stride2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+        std::vector<int64_t> begin_mask2 = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask2 = {0, 0, 0, 0};
+        auto ss2 = std::make_shared<ngraph::opset1::StridedSlice>(source, begin2, end2, stride2, begin_mask2, end_mask2);
+
+        auto concat = std::make_shared<ngraph::opset1::Concat>(ngraph::NodeVector{ss1, ss2}, 0);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{concat}, ngraph::ParameterVector{source});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(false);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    // No SharedStridedSliceEraser transformation if use_shapes == false
+}
+
+TEST_F(TransformationTestsF, OptimizeSS_Groupped_Test_use_shapes_false) {
+    {
+        auto source = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5});
+
+        auto begin1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {5, 3, 5, 5});
+        auto stride1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+        std::vector<int64_t> begin_mask1 = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask1 = {0, 0, 0, 0};
+        auto ss1 = std::make_shared<ngraph::opset1::StridedSlice>(source, begin1, end1, stride1, begin_mask1, end_mask1);
+
+        auto begin2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 3, 0, 0});
+        auto end2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {5, 5, 5, 5});
+        auto stride2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+        std::vector<int64_t> begin_mask2 = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask2 = {0, 0, 0, 0};
+        auto ss2 = std::make_shared<ngraph::opset1::StridedSlice>(source, begin2, end2, stride2, begin_mask2, end_mask2);
+
+        auto concat = std::make_shared<ngraph::opset1::Concat>(ngraph::NodeVector{ss1, ss2}, 1);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{concat}, ngraph::ParameterVector{source});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(false);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    // No GroupedStridedSliceOptimizer transformation if use_shapes == false
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_default_axes) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1, 1, 1, 1});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1, 1, 1, 1});
+
+        std::vector<int64_t> begin_mask = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask = {0, 0, 0, 0};
+
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_axes_const_sorted_full) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1, 1, 1, 1});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 1, 2, 3});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1, 1, 1, 1});
+
+        std::vector<int64_t> begin_mask = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask = {0, 0, 0, 0};
+
+        auto strided_slice = std::make_shared<ngraph::opset1::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_all_const) {
+    {
+        auto data = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{4}, {2, 3, 4, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {-1});
+        auto step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {-1});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{4}, {2, 3, 4, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {-1});
+        auto stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+
+        std::vector<int64_t> begin_end_mask = {0};
+        auto strided_slice = std::make_shared<ngraph::opset1::StridedSlice>(data, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_all_const_fold) {
+    {
+        auto data = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{4}, {2, 3, 4, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {-1});
+        auto step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {-1});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    {
+        auto sliced_const = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, {3, 4});
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{sliced_const}, ngraph::ParameterVector{});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_sss_params_axes_const_sorted_less) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 2});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin, end, step});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto start = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto stop = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {1, 2});
+        auto zero = ngraph::opset8::Constant::create(ngraph::element::i32, ngraph::Shape{1}, {0});
+
+        const auto default_begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {0});
+        const auto begin = std::make_shared<ngraph::opset8::ScatterUpdate>(default_begin,
+                                                                           axes,
+                                                                           start,
+                                                                           zero);
+
+        const auto default_end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {0});
+        const auto end = std::make_shared<ngraph::opset8::ScatterUpdate>(default_end,
+                                                                         axes,
+                                                                         stop,
+                                                                         zero);
+
+        const auto default_stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1});
+        const auto stride = std::make_shared<ngraph::opset8::ScatterUpdate>(default_stride,
+                                                                            axes,
+                                                                            step,
+                                                                            zero);
+        std::vector<int64_t> begin_end_mask = {1, 0, 0};
+        auto strided_slice = std::make_shared<ngraph::opset1::StridedSlice>(data, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data, start, stop, step});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_sss_params_axes_const_unsorted) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {3, 1});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin, end, step});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto start = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto stop = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+
+        auto zero = ngraph::opset8::Constant::create(ngraph::element::i32, ngraph::Shape{1}, {0});
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {3, 1});
+
+        const auto default_begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        const auto begin = std::make_shared<ngraph::opset8::ScatterUpdate>(default_begin,
+                                                                           axes,
+                                                                           start,
+                                                                           zero);
+
+        const auto default_end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        const auto end = std::make_shared<ngraph::opset8::ScatterUpdate>(default_end,
+                                                                         axes,
+                                                                         stop,
+                                                                         zero);
+
+        const auto default_stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+        const auto stride = std::make_shared<ngraph::opset8::ScatterUpdate>(default_stride,
+                                                                            axes,
+                                                                            step,
+                                                                            zero);
+
+        std::vector<int64_t> begin_end_mask = {1, 0, 1, 0};
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(data, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data, start, stop, step});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_sss_params_axes_const_negative_sorted) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, -3, 2, -1});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin, end, step});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto stride = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+
+        std::vector<int64_t> begin_mask = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask = {0, 0, 0, 0};
+
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data, begin, end, stride});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_sss_params_dyn_shape_axes_const_negative_unsorted) {
+    {
+        auto data_shape = ov::PartialShape{ov::Dimension(-1), ov::Dimension(2, 6), 4, ov::Dimension(-1)};
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, data_shape);
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::PartialShape{ov::Dimension(-1)});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::PartialShape{ov::Dimension(-1)});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::PartialShape{ov::Dimension(-1)});
+
+        auto axes = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {-1, -3});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin, end, step});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data_shape = ov::PartialShape{ov::Dimension(-1), ov::Dimension(2, 6), 4, ov::Dimension(-1)};
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, data_shape);
+        auto start = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::PartialShape{ov::Dimension(-1)});
+        auto stop = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::PartialShape{ov::Dimension(-1)});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::PartialShape{ov::Dimension(-1)});
+
+        auto zero = ngraph::opset8::Constant::create(ngraph::element::i32, ngraph::Shape{1}, {0});
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {3, 1});
+
+        const auto default_begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        const auto begin = std::make_shared<ngraph::opset8::ScatterUpdate>(default_begin,
+                                                                           axes,
+                                                                           start,
+                                                                           zero);
+
+        const auto default_end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        const auto end = std::make_shared<ngraph::opset8::ScatterUpdate>(default_end,
+                                                                         axes,
+                                                                         stop,
+                                                                         zero);
+
+        const auto default_stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+        const auto stride = std::make_shared<ngraph::opset8::ScatterUpdate>(default_stride,
+                                                                            axes,
+                                                                            step,
+                                                                            zero);
+
+        std::vector<int64_t> begin_end_mask = {1, 0, 1, 0};
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(data, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data, start, stop, step});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_sss_params_static_shape_axes_const_negative_unsorted) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+
+        auto axes = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {-1, -3});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin, end, step});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto start = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto stop = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{2});
+
+        auto zero = ngraph::opset8::Constant::create(ngraph::element::i32, ngraph::Shape{1}, {0});
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {3, 1});
+
+        const auto default_begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        const auto begin = std::make_shared<ngraph::opset8::ScatterUpdate>(default_begin,
+                                                                           axes,
+                                                                           start,
+                                                                           zero);
+
+        const auto default_end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        const auto end = std::make_shared<ngraph::opset8::ScatterUpdate>(default_end,
+                                                                         axes,
+                                                                         stop,
+                                                                         zero);
+
+        const auto default_stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+        const auto stride = std::make_shared<ngraph::opset8::ScatterUpdate>(default_stride,
+                                                                            axes,
+                                                                            step,
+                                                                            zero);
+
+        std::vector<int64_t> begin_end_mask = {1, 0, 1, 0};
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(data, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data, start, stop, step});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_dyn_rank_axes_const_positive) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 1, 2, 3});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin, end, step});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto stride = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+
+        std::vector<int64_t> begin_mask = {0, 0, 0, 0};
+        std::vector<int64_t> end_mask = {0, 0, 0, 0};
+
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(data, begin, end, stride, begin_mask, end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data, begin, end, stride});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_dyn_rank_axes_const_negative) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ov::PartialShape::dynamic());
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto end = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+        auto step = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+
+        auto axes = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, -3, 2, -1});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin, end, step});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    // No transformation for negative axes and dynamic data rank
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_axes_param) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 4, 3, 5});
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1});
+        auto step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1, 1, 1, 1});
+
+        auto axes = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{4});
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(data, begin, end, step, axes);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, axes});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    // No transformation for non-const axes input
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_begin_param_shape_of_use_shapes_true) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto shape_of_data = std::make_shared<ngraph::opset8::ShapeOf>(data, ngraph::element::i64);
+        auto data_rank = std::make_shared<ngraph::opset8::ShapeOf>(shape_of_data, ngraph::element::i64);
+
+        auto zero_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+        auto three_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {3});
+
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1});
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(three_const, begin);
+        auto step = std::make_shared<ngraph::opset8::Broadcast>(one_const, begin);
+
+        auto axes = std::make_shared<ngraph::opset8::Range>(zero_const, one_const, one_const, ngraph::element::i64);
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(shape_of_data, begin, end, step, axes);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin});
+
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(true);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    {
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+        auto three_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {3});
+
+        auto data = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {2, 3, 4, 5});
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1});
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(three_const, begin);
+        auto stride = std::make_shared<ngraph::opset8::Broadcast>(one_const, begin);
+
+        std::vector<int64_t> begin_end_mask = {0};
+        auto strided_slice = std::make_shared<ngraph::opset1::StridedSlice>(data, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{begin});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_begin_param_shape_of_use_shapes_false) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto shape_of_data = std::make_shared<ngraph::opset8::ShapeOf>(data, ngraph::element::i64);
+        auto data_rank = std::make_shared<ngraph::opset8::ShapeOf>(shape_of_data, ngraph::element::i64);
+
+        auto zero_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+        auto three_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {3});
+
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1});
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(three_const, begin);
+        auto step = std::make_shared<ngraph::opset8::Broadcast>(one_const, begin);
+
+        auto axes = std::make_shared<ngraph::opset8::Range>(zero_const, one_const, one_const, ngraph::element::i64);
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(shape_of_data, begin, end, step, axes);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data, begin});
+
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(false);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    {
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+        auto three_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {3});
+
+        auto data = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {2, 3, 4, 5});
+        auto begin = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape{1});
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(three_const, begin);
+        auto stride = std::make_shared<ngraph::opset8::Broadcast>(one_const, begin);
+
+        std::vector<int64_t> begin_end_mask = {0};
+        auto strided_slice = std::make_shared<ngraph::opset1::StridedSlice>(data, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{begin});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_const_fold_params_slice_shape_of_use_shapes_true) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto shape_of_data = std::make_shared<ngraph::opset8::ShapeOf>(data, ngraph::element::i64);
+        auto data_rank = std::make_shared<ngraph::opset8::ShapeOf>(shape_of_data, ngraph::element::i64);
+
+        auto zero_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+        auto three_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {3});
+
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(three_const, begin);
+        auto step = std::make_shared<ngraph::opset8::Broadcast>(one_const, begin);
+
+        auto axes = std::make_shared<ngraph::opset8::Range>(zero_const, one_const, one_const, ngraph::element::i64);
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(shape_of_data, begin, end, step, axes);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data});
+
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(true);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    {
+        auto sliced_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {3, 4});
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{sliced_const}, ngraph::ParameterVector{});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_const_fold_params_slice_shape_of_use_shapes_false) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto shape_of_data = std::make_shared<ngraph::opset8::ShapeOf>(data, ngraph::element::i64);
+        auto data_rank = std::make_shared<ngraph::opset8::ShapeOf>(shape_of_data, ngraph::element::i64);
+
+        auto zero_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+        auto three_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {3});
+
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(three_const, begin);
+        auto step = std::make_shared<ngraph::opset8::Broadcast>(one_const, begin);
+
+        auto axes = std::make_shared<ngraph::opset8::Range>(zero_const, one_const, one_const, ngraph::element::i64);
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(shape_of_data, begin, end, step, axes);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data});
+
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(false);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    {
+        auto sliced_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {3, 4});
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{sliced_const}, ngraph::ParameterVector{});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_slice_all_use_shapes_true) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto relu = std::make_shared<ngraph::opset8::Relu>(data);
+
+        auto shape_of_data = std::make_shared<ngraph::opset8::ShapeOf>(relu, ngraph::element::i64);
+        auto data_rank = std::make_shared<ngraph::opset8::ShapeOf>(shape_of_data, ngraph::element::i64);
+
+        auto zero_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+
+        auto begin = std::make_shared<ngraph::opset8::Broadcast>(zero_const, data_rank);
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(data_rank, data_rank);
+        auto step = std::make_shared<ngraph::opset8::Broadcast>(one_const, data_rank);
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(relu, begin, end, step);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(true);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto relu = std::make_shared<ngraph::opset8::Relu>(data);
+
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {4});
+        auto stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+
+        std::vector<int64_t> begin_end_mask = {0, 0, 0, 0};
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(relu, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
+
+TEST_F(TransformationTestsF, SliceToStridedSlice_slice_all_use_shapes_false) {
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto relu = std::make_shared<ngraph::opset8::Relu>(data);
+
+        auto shape_of_data = std::make_shared<ngraph::opset8::ShapeOf>(relu, ngraph::element::i64);
+        auto data_rank = std::make_shared<ngraph::opset8::ShapeOf>(shape_of_data, ngraph::element::i64);
+
+        auto zero_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {0});
+        auto one_const = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1});
+
+        auto begin = std::make_shared<ngraph::opset8::Broadcast>(zero_const, data_rank);
+        auto end = std::make_shared<ngraph::opset8::Broadcast>(data_rank, data_rank);
+        auto step = std::make_shared<ngraph::opset8::Broadcast>(one_const, data_rank);
+
+        auto slice = std::make_shared<ngraph::opset8::Slice>(relu, begin, end, step);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{slice}, ngraph::ParameterVector{data});
+        manager.register_pass<ngraph::pass::StridedSliceOptimization>(false);
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+    }
+    {
+        auto data = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{2, 3, 4, 5});
+        auto relu = std::make_shared<ngraph::opset8::Relu>(data);
+
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {4});
+        auto stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1});
+
+        std::vector<int64_t> begin_end_mask = {0, 0, 0, 0};
+        auto strided_slice = std::make_shared<ngraph::opset8::StridedSlice>(relu, begin, end, stride, begin_end_mask, begin_end_mask);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{strided_slice}, ngraph::ParameterVector{data});
+    }
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+}
diff --git a/inference-engine/tests/functional/inference_engine/transformations/subtract_fusion.cpp b/inference-engine/tests/functional/inference_engine/transformations/subtract_fusion.cpp
new file mode 100644
index 00000000000000..a271061bf5da0f
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/transformations/subtract_fusion.cpp
@@ -0,0 +1,121 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+#include <queue>
+
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <transformations/init_node_info.hpp>
+#include <transformations/utils/utils.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <transformations/common_optimizations/divide_fusion.hpp>
+#include <transformations/common_optimizations/subtract_fusion.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST(TransformationTests, SubtractFusionMultiply) {
+    std::shared_ptr<ngraph::Function> f, f_ref;
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto mul_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {-1});
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data2, mul_constant);
+        auto add = std::make_shared<ngraph::opset1::Add>(data1, mul);
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{data1, data2});
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::SubtractFusion>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto divide = std::make_shared<ngraph::opset1::Subtract>(data1, data2);
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data1, data2});
+    }
+
+    const auto res = FunctionsComparator::with_default()
+            .enable(FunctionsComparator::CONST_VALUES)
+            .enable(FunctionsComparator::ATTRIBUTES)
+            .compare(f, f_ref);
+    ASSERT_TRUE(res.valid) << res.message;
+}
+
+TEST(TransformationTests, SubtractFusionMultiplyNegative) {
+    std::shared_ptr<ngraph::Function> f, f_ref;
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto mul_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {-1.01});
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data2, mul_constant);
+        auto add = std::make_shared<ngraph::opset1::Add>(data1, mul);
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{data1, data2});
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::SubtractFusion>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto mul_constant = ngraph::opset1::Constant::create(ngraph::element::f32, ngraph::Shape{1}, {-1.01});
+        auto mul = std::make_shared<ngraph::opset1::Multiply>(data2, mul_constant);
+        auto add = std::make_shared<ngraph::opset1::Add>(data1, mul);
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{data1, data2});
+    }
+
+    const auto res = FunctionsComparator::with_default()
+            .enable(FunctionsComparator::CONST_VALUES)
+            .enable(FunctionsComparator::ATTRIBUTES)
+            .compare(f, f_ref);
+    ASSERT_TRUE(res.valid) << res.message;
+}
+
+TEST(TransformationTests, SubtractFusionNeg) {
+    std::shared_ptr<ngraph::Function> f, f_ref;
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto neg = std::make_shared<ngraph::opset1::Negative>(data2);
+        auto add = std::make_shared<ngraph::opset1::Add>(neg, data1);
+
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector{add}, ngraph::ParameterVector{data1, data2});
+
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<ngraph::pass::SubtractFusion>();
+        m.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto data1 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto data2 = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ngraph::Shape{3, 1, 2});
+        auto divide = std::make_shared<ngraph::opset1::Subtract>(data1, data2);
+
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{divide}, ngraph::ParameterVector{data1, data2});
+    }
+
+    const auto res = FunctionsComparator::with_default()
+            .enable(FunctionsComparator::CONST_VALUES)
+            .enable(FunctionsComparator::ATTRIBUTES)
+            .compare(f, f_ref);
+    ASSERT_TRUE(res.valid) << res.message;
+}
diff --git a/inference-engine/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp
index acef05b876dd6f..56007f814b4536 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/transpose_sinking_test.cpp
@@ -265,3 +265,17 @@ TEST_F(TransformationTestsF, TransposeFuses) {
         function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ add }, ngraph::ParameterVector{ input });
     }
 }
+
+TEST_F(TransformationTestsF, TransposeReduceNegative) {
+    {
+        auto input = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f32, ngraph::Shape{ 1, 3, 64});
+        auto order = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{ 3 }, { 0, 2, 1});
+        auto transpose = std::make_shared<ngraph::opset6::Transpose>(input, order);
+        auto axes = ngraph::opset6::Constant::create(ngraph::element::i64, ngraph::Shape{}, {-1});
+        auto reduce_mean = std::make_shared<ngraph::opset6::ReduceMean>(transpose, axes, true);
+        auto sub = std::make_shared<opset6::Subtract>(transpose, reduce_mean);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ sub }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::TransposeReduction>();
+    }
+}
\ No newline at end of file
diff --git a/inference-engine/tests/functional/inference_engine/transformations/type_relaxed_tests.cpp b/inference-engine/tests/functional/inference_engine/transformations/type_relaxed_tests.cpp
index 810fa36999e8a9..2aac98140907e9 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/type_relaxed_tests.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/type_relaxed_tests.cpp
@@ -8,6 +8,7 @@
 #include <ngraph/pass/constant_folding.hpp>
 #include <ngraph_ops/type_relaxed.hpp>
 
+#include <ngraph/pass/manager.hpp>
 
 namespace element = ngraph::element;
 using std::make_shared;
@@ -328,8 +329,9 @@ TEST_F(TypeRelaxedTests, ConstantFoldingCheck) {
         auto relaxed_equal = make_shared<ngraph::op::TypeRelaxed<ngraph::opset1::Equal>>(equal, TypeVector{}, TypeVector{ element::u8 });
 
         f = make_shared<ngraph::Function>(ngraph::OutputVector{ relaxed_equal }, ngraph::ParameterVector{});
-
-        ASSERT_NO_THROW(ngraph::pass::ConstantFolding().run_on_function(f));
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        ASSERT_NO_THROW(manager.run_passes(f));
         auto layer_before_result = f->get_result()->get_input_node_shared_ptr(0);
         ASSERT_TRUE(ngraph::is_type<ngraph::opset1::Constant>(layer_before_result));
     }
@@ -344,8 +346,9 @@ TEST_F(TypeRelaxedTests, ConstantFoldingCheck1) {
         auto relaxed_equal = make_shared<ngraph::op::TypeRelaxed<ngraph::opset1::Equal>>(equal, TypeVector{}, TypeVector{ element::boolean });
 
         f = make_shared<ngraph::Function>(ngraph::OutputVector{ relaxed_equal }, ngraph::ParameterVector{});
-
-        ASSERT_NO_THROW(ngraph::pass::ConstantFolding().run_on_function(f));
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        ASSERT_NO_THROW(manager.run_passes(f));
         auto layer_before_result = f->get_result()->get_input_node_shared_ptr(0);
         ASSERT_TRUE(ngraph::is_type<ngraph::opset1::Constant>(layer_before_result));
     }
@@ -365,8 +368,9 @@ TEST_F(TypeRelaxedTests, ConstantFoldingCheck2) {
             ngraph::op::TemporaryReplaceOutputType(const2, element::i32).get());
 
         f = make_shared<ngraph::Function>(ngraph::OutputVector{ relaxed_equal }, ngraph::ParameterVector{});
-
-        ASSERT_NO_THROW(ngraph::pass::ConstantFolding().run_on_function(f));
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        ASSERT_NO_THROW(manager.run_passes(f));
         auto layer_before_result = f->get_result()->get_input_node_shared_ptr(0);
         ASSERT_TRUE(ngraph::is_type<ngraph::opset1::Constant>(layer_before_result));
     }
@@ -383,8 +387,9 @@ TEST_F(TypeRelaxedTests, ConstantFoldingCheck3) {
         auto relaxed_equal = make_shared<ngraph::op::TypeRelaxed<ngraph::opset1::Equal>>(equal, original_input_types, TypeVector{ element::u8 });
 
         f = make_shared<ngraph::Function>(ngraph::OutputVector{ relaxed_equal }, ngraph::ParameterVector{});
-
-        ASSERT_NO_THROW(ngraph::pass::ConstantFolding().run_on_function(f));
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        ASSERT_NO_THROW(manager.run_passes(f));
         auto layer_before_result = f->get_result()->get_input_node_shared_ptr(0);
         ASSERT_TRUE(ngraph::is_type<ngraph::opset1::Constant>(layer_before_result));
     }
diff --git a/inference-engine/tests/functional/inference_engine/transformations/wrap_interpolate_into_transposes_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/wrap_interpolate_into_transposes_test.cpp
new file mode 100644
index 00000000000000..5a2c3f055ef3c4
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/transformations/wrap_interpolate_into_transposes_test.cpp
@@ -0,0 +1,564 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <ngraph/pass/visualize_tree.hpp>
+#include <transformations/common_optimizations/wrap_interpolate_into_transposes.hpp>
+#include <transformations/init_node_info.hpp>
+#include <transformations/utils/utils.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes4DScales) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 2});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{2, 3});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 3, 1, 2 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 2, 3, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes4DSizes) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SIZES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 2});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{2, 3});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 3, 1, 2 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 2, 3, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes5DScales) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150, 18 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 2, 3});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{2, 3, 4});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 4, 1, 2, 3 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 2, 3, 4, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes5DSizes) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SIZES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150, 18 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 2, 3});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{2, 3, 4});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 4, 1, 2, 3 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 2, 3, 4, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes4DScalesDynamic) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::PartialShape input_shape { 1, ngraph::Dimension::dynamic(), 120, ngraph::Dimension::dynamic() };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 2});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{2, 3});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 3, 1, 2 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 2, 3, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes4DSizesDynamic) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SIZES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::PartialShape input_shape { 1, ngraph::Dimension::dynamic(), 120, ngraph::Dimension::dynamic() };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 2});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 60 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{2, 3});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 3, 1, 2 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{4}, { 0, 2, 3, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes5DScalesDynamic) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::PartialShape input_shape { ngraph::Dimension::dynamic(), 100, ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), 18 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 2, 3});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{2, 3, 4});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 4, 1, 2, 3 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 2, 3, 4, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes5DSizesDynamic) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SIZES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::PartialShape input_shape { ngraph::Dimension::dynamic(), 100, ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), 18 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 2, 3});
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axis_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 60, 240, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.6, 2.0, 0.5 });
+        auto axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{2, 3, 4});
+
+        auto first_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 4, 1, 2, 3 });
+        auto last_transpose_perm = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{5}, { 0, 2, 3, 4, 1 });
+
+        auto first_transpose = std::make_shared<ngraph::opset8::Transpose>(input, first_transpose_perm);
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(first_transpose, sizes_node, scales_node, axis_node, attrs);
+        auto last_transpose = std::make_shared<ngraph::opset8::Transpose>(interpolate, last_transpose_perm);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ last_transpose }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes4DScalesNotApplicable) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 4 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 3});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 4 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 3});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes4DSizesNotApplicable) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SIZES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 4 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 3});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{2}, { 50, 75 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{2}, { 0.5, 0.5 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 4 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 3});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes5DScalesNotApplicable) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150, 800 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 50, 75, 600 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.5, 0.5, 0.75 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 5 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 3, 4});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 50, 75, 600 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.5, 0.5, 0.75 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 5 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 3, 4});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, WrapInterpolateIntoTransposes5DSizesNotApplicable) {
+    ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+    attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+    attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SIZES;
+    attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    attrs.pads_begin = std::vector<size_t>{0};
+    attrs.pads_end = std::vector<size_t>{0};
+    attrs.antialias = false;
+    attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+    attrs.cube_coeff = -0.75f;
+
+    ngraph::Shape input_shape { 1, 100, 120, 150, 800 };
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 50, 75, 600 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.5, 0.5, 0.75 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 5 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 3, 4});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+    }
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{3}, { 50, 75, 600 });
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{3}, { 0.5, 0.5, 0.75 });
+
+        auto range_start = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 0 });
+        auto range_stop = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 5 });
+        auto range_step = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape{}, { 1 });
+        auto range = std::make_shared<ngraph::opset8::Range>(range_start, range_stop, range_step, ngraph::element::i64);
+
+        auto indices = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 3, 4});
+        auto gather_axis_node = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto gather_node = std::make_shared<ngraph::opset8::Gather>(range, indices, gather_axis_node);
+
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, gather_node, attrs);
+
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+    }
+}
diff --git a/inference-engine/tests/functional/plugin/cpu/CMakeLists.txt b/inference-engine/tests/functional/plugin/cpu/CMakeLists.txt
index 5e973d1f57ade3..6535fca61e3712 100644
--- a/inference-engine/tests/functional/plugin/cpu/CMakeLists.txt
+++ b/inference-engine/tests/functional/plugin/cpu/CMakeLists.txt
@@ -8,13 +8,14 @@ add_library(cpuSpecificRtInfo STATIC ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin/uti
                                      ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp)
 target_link_libraries(cpuSpecificRtInfo PRIVATE ngraph)
 
-set(INCLUDES ${CMAKE_CURRENT_SOURCE_DIR} ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin)
+set(INCLUDES ${CMAKE_CURRENT_SOURCE_DIR} $<TARGET_PROPERTY:MKLDNNPlugin,SOURCE_DIR>)
 set(DEPENDENCIES MKLDNNPlugin)
 set(LINK_LIBRARIES funcSharedTests cpuSpecificRtInfo)
 if (NGRAPH_ONNX_FRONTEND_ENABLE)
     list(APPEND INCLUDES "${OpenVINO_SOURCE_DIR}/docs/onnx_custom_op")
     list(APPEND LINK_LIBRARIES onnx_custom_op)
     list(APPEND DEPENDENCIES template_extension onnx_custom_op)
+    list(APPEND DEFINES TEST_MODELS="${TEST_MODEL_ZOO}")
 else()
     set(EXCLUDED_SOURCE_PATHS ${CMAKE_CURRENT_SOURCE_DIR}/extension ${CMAKE_CURRENT_SOURCE_DIR}/onnx)
 endif()
@@ -24,6 +25,7 @@ addIeTargetTest(
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
         INCLUDES ${INCLUDES}
         EXCLUDED_SOURCE_PATHS ${EXCLUDED_SOURCE_PATHS}
+        DEFINES ${DEFINES}
         DEPENDENCIES ${DEPENDENCIES}
         LINK_LIBRARIES ${LINK_LIBRARIES}
         ADD_CPPLINT
diff --git a/inference-engine/tests/functional/plugin/cpu/extension/extension.cpp b/inference-engine/tests/functional/plugin/cpu/extension/extension.cpp
index 1f9a430a24d1d7..baf3dc3e7f8ca7 100644
--- a/inference-engine/tests/functional/plugin/cpu/extension/extension.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/extension/extension.cpp
@@ -119,10 +119,8 @@ class CustomAbsExtension : public InferenceEngine::IExtension {
     }
 };
 
-void infer_model(InferenceEngine::Core& ie, const std::string& model,
+static void infer_model(InferenceEngine::Core& ie, InferenceEngine::CNNNetwork& network,
                  const std::vector<float>& input_values, const std::vector<float>& expected) {
-    InferenceEngine::Blob::CPtr weights;
-    auto network = ie.ReadNetwork(model, weights);
     auto function = network.getFunction();
 
     auto network_inputs = network.getInputsInfo();
@@ -150,53 +148,11 @@ void infer_model(InferenceEngine::Core& ie, const std::string& model,
     ASSERT_EQ(expected, computed_values);
 }
 
-
-TEST(Extension, OnnxModelWithCustomAbs) {
-    std::string model = R"V0G0N(
-ir_version: 3
-producer_name: "nGraph ONNX Importer"
-graph {
-  node {
-    input: "A"
-    output: "Y"
-    name: "customrelu"
-    op_type: "CustomAbs"
-    domain: "custom_domain"
-  }
-  name: "test_graph"
-  input {
-    name: "A"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 10
-          }
-        }
-      }
-    }
-  }
-  output {
-    name: "Y"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 10
-          }
-        }
-      }
-    }
-  }
-}
-opset_import {
-  version: 1
-  domain: "custom_domain"
+static std::string model_full_path(const char* path) {
+    return FileUtils::makePath<char>(TEST_MODELS, path);
 }
-)V0G0N";
 
+TEST(Extension, OnnxModelWithCustomAbs) {
     std::vector<float> input_values{1, -2, 3, -4, 5, -6, 7, -8, 9, -10};
     std::vector<float> expected{1, 4, 3, 8, 5, 12, 7, 16, 9, 20};
     InferenceEngine::Core ie;
@@ -207,7 +163,8 @@ opset_import {
             return {std::make_shared<CustomAbs>(ng_inputs.at(0))};
     });
 
-    infer_model(ie, model, input_values, expected);
+    auto network = ie.ReadNetwork(model_full_path("func_tests/models/custom_abs_op.onnx"));
+    infer_model(ie, network, input_values, expected);
     ngraph::onnx_import::unregister_operator(CustomAbs::get_type_info_static().name, 1, "custom_domain");
 }
 
@@ -255,7 +212,9 @@ TEST(Extension, XmlModelWithCustomAbs) {
     std::vector<float> expected{1, 4, 3, 8, 5, 12, 7, 16, 9, 20};
     InferenceEngine::Core ie;
     ie.AddExtension(std::make_shared<CustomAbsExtension>());
-    infer_model(ie, model, input_values, expected);
+    InferenceEngine::Blob::CPtr weights;
+    auto network = ie.ReadNetwork(model, weights);
+    infer_model(ie, network, input_values, expected);
 }
 
 
@@ -320,84 +279,19 @@ TEST(Extension, XmlModelWithExtensionFromDSO) {
     std::vector<float> expected{12, 13, 14, 15, 16, 17, 18, 19};
     InferenceEngine::Core ie;
     ie.AddExtension(std::make_shared<InferenceEngine::Extension>(get_extension_path()));
-    infer_model(ie, model, input_values, expected);
+    InferenceEngine::Blob::CPtr weights;
+    auto network = ie.ReadNetwork(model, weights);
+    infer_model(ie, network, input_values, expected);
 }
 
 
 TEST(Extension, OnnxModelWithExtensionFromDSO) {
-    std::string model = R"V0G0N(
-ir_version: 3
-producer_name: "nGraph ONNX Importer"
-graph {
-  node {
-    input: "A"
-    output: "Y"
-    name: "operation"
-    op_type: "Template"
-    domain: "custom_domain"
-    attribute {
-        name: "add"
-        type: INT
-        i: 11
-    }
-  }
-  name: "test_graph"
-  input {
-    name: "A"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 1
-          }
-        }
-      }
-    }
-  }
-  output {
-    name: "Y"
-    type {
-      tensor_type {
-        elem_type: 1
-        shape {
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 2
-          }
-          dim {
-            dim_value: 1
-          }
-        }
-      }
-    }
-  }
-}
-opset_import {
-  version: 1
-  domain: "com.example"
-}
-)V0G0N";
-
     std::vector<float> input_values{1, 2, 3, 4, 5, 6, 7, 8};
     std::vector<float> expected{12, 13, 14, 15, 16, 17, 18, 19};
     InferenceEngine::Core ie;
     ie.AddExtension(std::make_shared<InferenceEngine::Extension>(get_extension_path()));
-    infer_model(ie, model, input_values, expected);
+    auto network = ie.ReadNetwork(model_full_path("func_tests/models/custom_template_op.onnx"));
+    infer_model(ie, network, input_values, expected);
 }
 
 
@@ -407,6 +301,7 @@ TEST(Extension, OnnxModelWithCustomReluDocsExample) {
 
     register_custom_relu_operator();
     InferenceEngine::Core ie;
-    infer_model(ie, custom_relu_model(), input_values, expected);
+    auto network = ie.ReadNetwork(model_full_path("docs/models/custom_relu_model.onnx"));
+    infer_model(ie, network, input_values, expected);
     unregister_custom_relu_operator();
 }
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
index c90cd8b7249eee..a772c660d5b1d7 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
@@ -63,6 +63,14 @@ namespace {
                 {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::THROUGHPUT}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
                 {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                {InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, InferenceEngine::PluginConfigParams::YES}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                {InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, InferenceEngine::PluginConfigParams::NO}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                {InferenceEngine::PluginConfigParams::KEY_EXCLUSIVE_ASYNC_REQUESTS, InferenceEngine::PluginConfigParams::YES}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                {InferenceEngine::PluginConfigParams::KEY_EXCLUSIVE_ASYNC_REQUESTS, InferenceEngine::PluginConfigParams::NO}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
                 {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY},
                     {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "1"}}
@@ -106,6 +114,10 @@ namespace {
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
              {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::THROUGHPUT},
                     {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "should be int"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, "OFF"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_EXCLUSIVE_ASYNC_REQUESTS, "OFF"}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
                     {InferenceEngine::PluginConfigParams::KEY_CPU_THROUGHPUT_STREAMS, "OFF"}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
@@ -170,6 +182,10 @@ namespace {
             {{InferenceEngine::PluginConfigParams::KEY_CPU_THROUGHPUT_STREAMS, "8"}},
             {{InferenceEngine::PluginConfigParams::KEY_CPU_BIND_THREAD, InferenceEngine::PluginConfigParams::NO}},
             {{InferenceEngine::PluginConfigParams::KEY_CPU_BIND_THREAD, InferenceEngine::PluginConfigParams::YES}},
+            {{InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, InferenceEngine::PluginConfigParams::NO}},
+            {{InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, InferenceEngine::PluginConfigParams::YES}},
+            {{InferenceEngine::PluginConfigParams::KEY_EXCLUSIVE_ASYNC_REQUESTS, InferenceEngine::PluginConfigParams::NO}},
+            {{InferenceEngine::PluginConfigParams::KEY_EXCLUSIVE_ASYNC_REQUESTS, InferenceEngine::PluginConfigParams::YES}},
             {{InferenceEngine::PluginConfigParams::KEY_DYN_BATCH_LIMIT, "10"}}
     };
 
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp
index 8c531cb2c151fb..7fdb749714146a 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/hetero/synthetic.cpp
@@ -11,6 +11,9 @@
 namespace {
 using namespace HeteroTests;
 
+// this tests load plugin by library name: this is not available during static linkage
+#ifndef OPENVINO_STATIC_LIBRARY
+
 INSTANTIATE_TEST_SUITE_P(smoke_SingleMajorNode, HeteroSyntheticTest,
                         ::testing::Combine(
                                 ::testing::Values(std::vector<PluginParameter>{{"CPU0", "MKLDNNPlugin"}, {"CPU1", "MKLDNNPlugin"}}),
@@ -22,4 +25,7 @@ INSTANTIATE_TEST_SUITE_P(nightly_RandomMajorNodes, HeteroSyntheticTest,
                                 ::testing::Values(std::vector<PluginParameter>{{"CPU0", "MKLDNNPlugin"}, {"CPU1", "MKLDNNPlugin"}}),
                                 ::testing::ValuesIn(HeteroTests::HeteroSyntheticTest::_randomMajorNodeFunctions)),
                         HeteroSyntheticTest::getTestCaseName);
+
+#endif // !OPENVINO_STATIC_LIBRARY
+
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/concat_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/concat_transformation.cpp
index ffd5c2b1d43d80..818ebe8ec32d40 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/concat_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/concat_transformation.cpp
@@ -38,10 +38,20 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         { 256ul, ngraph::Shape({}), {0.f}, {2.55f}, {0.f}, {2.55f} }
     },
     // FQ with unexpected quantizationLevels
+    {
+        { 14ul, ngraph::Shape({}), {0.f}, {15.f}, {0.f}, {1.5f} },
+        { 14ul, ngraph::Shape({}), {0.f}, {15.f}, {0.f}, {1.5f} }
+    },
+    // FQ with INT4 quantizationLevels
     {
         { 16ul, ngraph::Shape({}), {0.f}, {15.f}, {0.f}, {1.5f} },
         { 16ul, ngraph::Shape({}), {0.f}, {15.f}, {0.f}, {1.5f} }
     },
+    // FQ with INT4+INT8 quantizationLevels
+    {
+        { 16ul, ngraph::Shape({}), {0.f}, {15.f}, {0.f}, {1.5f} },
+        { 256ul, ngraph::Shape({}), {0.f}, {2.55f}, {0.f}, {2.55f} }
+    },
 };
 
 const std::vector<ngraph::PartialShape> shapes = {
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
index 4662a7648ccfd3..b2d223c8f317a4 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
@@ -13,12 +13,11 @@ using namespace LayerTestsDefinitions;
 namespace {
 const std::vector<ngraph::element::Type> netPrecisions = {
     ngraph::element::f32,
-    // ngraph::element::f16
+    // ngraph::element::f16,
 };
 
 const std::vector<ngraph::pass::low_precision::LayerTransformation::Params> trasformationParamValues = {
-    LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams().setUpdatePrecisions(true),
-    LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams().setUpdatePrecisions(false),
+    LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams()
 };
 
 const std::vector<LayerTestsDefinitions::ConvolutionTransformationParam> params = {
@@ -55,15 +54,15 @@ const std::vector<LayerTestsDefinitions::ConvolutionTransformationParam> params
         "U8"
     },
     {
-        { 16ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 25.5f } },
+        { 14ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 25.5f } },
         false,
-        { 16ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 254.f }, { -12.7f }, { 12.7f } },
+        { 14ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 254.f }, { -12.7f }, { 12.7f } },
         false,
         "Convolution",
         "FP32"
     },
     {
-        { 16ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
+        { 14ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
         false,
         { 255ul, ngraph::Shape { 1, 1, 1, 1 }, { -12.7f }, { 12.7f }, { -12.7f }, { 12.7f } },
         false,
@@ -73,7 +72,7 @@ const std::vector<LayerTestsDefinitions::ConvolutionTransformationParam> params
     {
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 25.5f } },
         false,
-        { 16ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 254.f }, { -12.7f }, { 12.7f } },
+        { 14ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 254.f }, { -12.7f }, { 12.7f } },
         false,
         "Convolution",
         "FP32"
@@ -97,8 +96,22 @@ const std::vector<LayerTestsDefinitions::ConvolutionTransformationParam> params
     {
         { 256ul, ngraph::Shape { 1 }, { 0.f }, { 255.f }, { -18.7f }, { 18.8f } },
         true,
-        { 255ul, ngraph::Shape { 6, 1, 1, 1 }, { -0.6f }, { 0.6f },
-            { -1.52806e-39f, -0.2, -0.3, -0.3, -0.2, -0.1 }, { 1.52806e-39f, 0.2, 0.3, 0.3, 0.2, 0.1 } },
+        {
+            255ul, ngraph::Shape { 6, 1, 1, 1 }, { -0.6f }, { 0.6f },
+            { -1.52806e-39f, -0.2, -0.3, -0.3, -0.2, -0.1 }, { 1.52806e-39f, 0.2, 0.3, 0.3, 0.2, 0.1 }
+        },
+        false,
+        "Convolution",
+        "U8"
+    },
+    {
+        { 256ul, ngraph::Shape { 1 }, { 0.f }, { 255.f }, { -18.7f }, { 18.8f } },
+        true,
+        {
+            255ul, ngraph::Shape { 6, 1, 1, 1 }, { -0.6f }, { 0.6f },
+            { -1.52806e-39f, -1.52806e-39f, -1.52806e-39f, -1.52806e-39f, -1.52806e-39f, -1.52806e-39f },
+            { 1.52806e-39f, 1.52806e-39f, 1.52806e-39f, 1.52806e-39f, 1.52806e-39f, 1.52806e-39f }
+        },
         false,
         "Convolution",
         "U8"
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp
index 4ed701efeca1f0..5b1166b1577e15 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp
@@ -46,14 +46,13 @@ const std::vector<FakeQuantizeTransformationParam> fakeQuantizeOnDataValues = {
         { 256ul, {}, { -127.5f }, { 0.f }, { -127.5f }, { 0.f } },
         "Pooling", "U8"
     },
-    // INT4 FQ's are not transformed and inferred via FP32
     {
         { 16ul, {}, { 0.f }, { 1.5f }, { 0.f }, { 1.5f } },
-        "Pooling", "FP32"
+        "Pooling", "U8"
     },
     {
-        { 16ul, {}, { -8.f }, { 7.f }, { -0.8f }, { 0.7f } },
-        "Pooling", "FP32"
+        { 16ul, {}, { -0.8f }, { 0.7f }, { -0.8f }, { 0.7f } },
+        "Pooling", "I8"
     },
     // INT16, INT32 FQ's are transformed, but updatePrecision = false for inference on CPU Plugin and inferred via FP32
     {
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/experimental_detectron_roifeatureextractor.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/experimental_detectron_roifeatureextractor.cpp
new file mode 100644
index 00000000000000..c2746ef43f6cb9
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/experimental_detectron_roifeatureextractor.cpp
@@ -0,0 +1,70 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+#include "single_layer_tests/experimental_detectron_roifeatureextractor.hpp"
+
+using namespace ov::test;
+using namespace ov::test::subgraph;
+
+namespace {
+
+const std::vector<int64_t> outputSize = {7, 14};
+const std::vector<int64_t> samplingRatio = {1, 2, 3};
+
+const std::vector<std::vector<int64_t>> pyramidScales = {
+        {8, 16, 32, 64},
+        {4, 8, 16, 32},
+        {2, 4, 8, 16}
+};
+
+const std::vector<std::vector<InputShape>> staticInputShape = {
+        static_shapes_to_test_representation({{1000, 4}, {1, 8, 200, 336}, {1, 8, 100, 168}, {1, 8, 50, 84}, {1, 8, 25, 42}}),
+        static_shapes_to_test_representation({{1000, 4}, {1, 16, 200, 336}, {1, 16, 100, 168}, {1, 16, 50, 84}, {1, 16, 25, 42}}),
+        static_shapes_to_test_representation({{1200, 4}, {1, 8, 200, 42}, {1, 8, 100, 336}, {1, 8, 50, 168}, {1, 8, 25, 84}})
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalROI_static, ExperimentalDetectronROIFeatureExtractorLayerTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(staticInputShape),
+                                 ::testing::ValuesIn(outputSize),
+                                 ::testing::ValuesIn(samplingRatio),
+                                 ::testing::ValuesIn(pyramidScales),
+                                 ::testing::Values(false),
+                                 ::testing::Values(ov::element::Type_t::f32),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         ExperimentalDetectronROIFeatureExtractorLayerTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> dynamicInputShape = {
+        {
+                {
+                        {{-1, 4}, {{1000, 4}, {1500, 4}, {2000, 4}}},
+                        {{1, 8, -1, -1}, {{1, 8, 200, 336}, {1, 8, 200, 42}, {1, 8, 200, 84}}},
+                        {{1, 8, -1, -1}, {{1, 8, 100, 168}, {1, 8, 100, 336}, {1, 8, 25, 42}}},
+                        {{1, 8, -1, -1}, {{1, 8, 50, 84}, {1, 8, 50, 168}, {1, 8, 100, 336}}},
+                        {{1, 8, -1, -1}, {{1, 8, 25, 42}, {1, 8, 25, 84}, {1, 8, 50, 168}}}
+                }
+        },
+        {
+                {
+                        {{-1, 4}, {{1000, 4}, {1100, 4}, {1200, 4}}},
+                        {{1, {8, 16}, -1, -1}, {{1, 8, 200, 336}, {1, 12, 200, 336}, {1, 16, 200, 336}}},
+                        {{1, {8, 16}, -1, -1}, {{1, 8, 100, 168}, {1, 12, 100, 168}, {1, 16, 100, 168}}},
+                        {{1, {8, 16}, -1, -1}, {{1, 8, 50, 84}, {1, 12, 50, 84}, {1, 16, 50, 84}}},
+                        {{1, {8, 16}, -1, -1}, {{1, 8, 25, 42}, {1, 12, 25, 42}, {1, 16, 25, 42}}}
+                }
+        }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalROI_dynamic, ExperimentalDetectronROIFeatureExtractorLayerTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(dynamicInputShape),
+                                 ::testing::ValuesIn(outputSize),
+                                 ::testing::ValuesIn(samplingRatio),
+                                 ::testing::ValuesIn(pyramidScales),
+                                 ::testing::Values(false),
+                                 ::testing::Values(ov::element::Type_t::f32),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         ExperimentalDetectronROIFeatureExtractorLayerTest::getTestCaseName);
+} // namespace
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/gather_nd.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/gather_nd.cpp
index 1639dabcb557d6..b85c7f4e646cec 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/gather_nd.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/gather_nd.cpp
@@ -31,7 +31,8 @@ const auto gatherNDArgsSubset1 = ::testing::Combine(
             {{2, 1}, {2, 1, 1}})),                                // Indices shape
         ::testing::ValuesIn(std::vector<int>({0, 1}))             // Batch dims
 );
-INSTANTIATE_TEST_SUITE_P(smoke_Set1, GatherNDLayerTest,
+
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND5_Set1, GatherNDLayerTest,
                         ::testing::Combine(
                             gatherNDArgsSubset1,
                             ::testing::ValuesIn(dPrecisions),
@@ -40,6 +41,15 @@ INSTANTIATE_TEST_SUITE_P(smoke_Set1, GatherNDLayerTest,
                             ::testing::Values<Config>({})),
                         GatherNDLayerTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND8_Set1, GatherNDLayerTest,
+                         ::testing::Combine(
+                                 gatherNDArgsSubset1,
+                                 ::testing::ValuesIn(dPrecisions),
+                                 ::testing::ValuesIn(iPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
+                                 ::testing::Values<Config>({})),
+                         GatherNDLayerTest::getTestCaseName);
+
 const auto gatherNDArgsSubset2 = ::testing::Combine(
         ::testing::ValuesIn(std::vector<std::vector<size_t>>(
             {{15, 12, 20, 15, 2}, {15, 12, 18, 7, 17}})),          // Data shape
@@ -47,7 +57,8 @@ const auto gatherNDArgsSubset2 = ::testing::Combine(
             {{15, 12, 2}, {15, 12, 5, 9, 1, 3}})),                 // Indices shape
         ::testing::ValuesIn(std::vector<int>({0, 1, 2}))           // Batch dims
 );
-INSTANTIATE_TEST_SUITE_P(smoke_Set2, GatherNDLayerTest,
+
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND5_Set2, GatherNDLayerTest,
                         ::testing::Combine(
                             gatherNDArgsSubset2,
                             ::testing::ValuesIn(dPrecisions),
@@ -55,4 +66,14 @@ INSTANTIATE_TEST_SUITE_P(smoke_Set2, GatherNDLayerTest,
                             ::testing::Values(CommonTestUtils::DEVICE_CPU),
                             ::testing::Values<Config>({})),
                         GatherNDLayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND8_Set2, GatherNDLayerTest,
+                         ::testing::Combine(
+                                 gatherNDArgsSubset2,
+                                 ::testing::ValuesIn(dPrecisions),
+                                 ::testing::ValuesIn(iPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
+                                 ::testing::Values<Config>({})),
+                         GatherNDLayerTest::getTestCaseName);
+
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/slice.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/slice.cpp
new file mode 100644
index 00000000000000..2543d44f99dd6f
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/slice.cpp
@@ -0,0 +1,82 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "single_layer_tests/slice.hpp"
+#include "common_test_utils/test_constants.hpp"
+
+using namespace LayerTestsDefinitions;
+
+namespace {
+
+const std::vector<InferenceEngine::Precision> inputPrecision = {
+    InferenceEngine::Precision::I8,
+    InferenceEngine::Precision::U8,
+    InferenceEngine::Precision::I16,
+    InferenceEngine::Precision::I32,
+    InferenceEngine::Precision::FP32
+};
+
+std::vector<SliceSpecificParams> test_cases = {
+        SliceSpecificParams{ { 16 }, { 4 }, { 12 }, { 1 }, { 0 } },
+        SliceSpecificParams{ { 16 }, { 0 }, { 8 }, { 2 }, { 0 } },
+        SliceSpecificParams{ { 20, 10, 5 }, { 0, 0}, { 10, 20}, { 1, 1 }, { 1, 0 } },
+        SliceSpecificParams{ { 1, 2, 12, 100 }, { 0, 1, 0, 1 }, { 1, 2, 5, 100 }, { 1, 1, 1, 10 }, {} },
+        SliceSpecificParams{ { 1, 12, 100 }, { 0, 9, 0 }, { 1, 11, 1 }, { 1, 1, 1 }, {} },
+        SliceSpecificParams{ { 1, 12, 100 }, { 0, 1, 0 }, { 10, -1, 10 }, { 1, 1, 1 }, {} },
+        SliceSpecificParams{ { 2, 12, 100 }, { 1, 12, 100 }, { 0, 7, 0 }, { -1, -1, -1 }, {} },
+        SliceSpecificParams{ { 2, 12, 100 }, { 1, 4, 99 }, { 0, 9, 0 }, { -1, 2, -1 }, {} },
+        SliceSpecificParams{ { 2, 12, 100 }, { -1, -1, -1 }, { 0, 4, 0 }, { -1, -2, -1 }, {} },
+        SliceSpecificParams{ { 2, 12, 100 }, { -1, -1, -1 }, { 0, 0, 4 }, { -1, -1, -1 }, {2, 0, 1} },
+        SliceSpecificParams{ { 2, 12, 100 }, { 0, 0, 4 }, { -5, -1, -1 }, { 1, 2, 1 }, {2, 0, 1} },
+        SliceSpecificParams{ { 2, 2, 2, 2 }, { 0, 0, 0, 0 }, { 2, 2, 2, 2 }, { 1, 1, 1, 1 }, {} },
+        SliceSpecificParams{ { 2, 2, 2, 2 }, { 1, 1, 1, 1 }, { 2, 2, 2, 2 }, { 1, 1, 1, 1 }, {} },
+        SliceSpecificParams{ { 2, 2, 4, 3 }, { 0, 0, 0, 0 }, { 2, 2, 4, 3 }, { 1, 1, 2, 1 }, {} },
+        SliceSpecificParams{ { 2, 2, 4, 2 }, { 1, 0, 0, 1 }, { 2, 2, 4, 2 }, { 1, 1, 2, 1 }, {} },
+        SliceSpecificParams{ { 1, 2, 4, 2 }, { 0, 1, 0, 1 }, { 10, 2, 4, 2 }, { 1, 1, 2, 1 }, {} },
+        SliceSpecificParams{ { 10, 2, 4, 2 }, { 9, 1, 3, 0 }, { 0, 0, 0, 1 }, { -1, -1, -1, 1 }, {} },
+        SliceSpecificParams{ { 10, 2, 4, 2 }, { 19, 1, -1, 0 }, { -10, 0, 0, -1 }, { -1, -1, -1, 1 }, {} },
+        SliceSpecificParams{ { 3, 2, 4, 200 }, { 0, 1, -1, -1 }, { 3, 2, 0, 0 }, { 1, 1, -2, -1 }, {} },
+        SliceSpecificParams{ { 2, 4, 5, 5, 68 }, { 0, 1, 0, 0, 0 }, {
+                std::numeric_limits<std::int64_t>::max(),
+                std::numeric_limits<std::int64_t>::max(),
+                std::numeric_limits<std::int64_t>::max(),
+                std::numeric_limits<std::int64_t>::max(),
+                std::numeric_limits<std::int64_t>::max() }, { 1, 1, 1, 1, 16 }, {} },
+        SliceSpecificParams{ { 10, 12 }, { -1, 1 }, { -9999, 10 }, { -1, 1 }, {} },
+        SliceSpecificParams{ { 5, 5, 5, 5 }, { -1, 0, -1, 0 }, { -50, -1, -60, -1 }, { -1, 1, -1, 1 }, {} },
+        SliceSpecificParams{ { 1, 5, 32, 32 }, { 0, 2, 5, 4 }, { 1, 4, 28, 27 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 1, 5, 32, 20 }, { 0, 1, 0, 0 }, { 1, 3, 32, 20 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 2, 5, 32, 20 }, { 0, 0, 10, 0 }, { 1, 3, 20, 20 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 1, 5, 32, 32 }, { 0, 0, 20, 20 }, { 1, 5, 25, 26 }, { 1, 1, 1, 2 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 2, 5, 32, 32 }, { 0, 0, 0, 20 }, { 1, 2, 30, 30 }, { 1, 1, 2, 1 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 1, 5, 32, 20 }, { 0, 0, 2, 10 }, { 1, 3, 32, 20 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 2, 5, 32, 32 }, { 0, 1, 0, 10 }, { 1, 5, 32, 30 }, { 1, 1, 1, 1 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 1, 5, 32, 20 }, { 0, 1, 2, 10 }, { 1, 5, 32, 18 }, { 1, 1, 1, 2 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 2, 8, 32, 20 }, { 0, 0, 2, 10 }, { 1, 8, 32, 18 }, { 1, 2, 1, 2 }, { 0, 1, 2, 3 } },
+        SliceSpecificParams{ { 2, 8, 32, 20 }, { 0, -20, -15 }, { 2, -5, 3 }, { 1, 1, 1 }, { 0, 2, 1 } },
+
+        // Plugin Error: Slice has zero dimension which is not allowed
+        // SliceSpecificParams{ { 2, 8, 32, 20 }, { 0, 0, 10 }, { 0, 32, 18 }, { 1, 1, 1 }, { 0, 1, 2 } },
+        // SliceSpecificParams{ { 2, 8, 32, 20 }, { 0, 0, 10 }, { 1, 0, 20 }, { 1, 1, 1 }, { 0, 1, 2 } },
+        // SliceSpecificParams{ { 2, 8, 32, 20 }, { 0, 4, 10 }, { 2, 8, 0 }, { 1, 1, 1 }, { 0, 1, 2 } },
+        // SliceSpecificParams{ { 2, 8, 32, 20 }, { 0, 4, 10 }, { 2, 8, 0 }, { 1, 1, 1 }, { 0, 2, 1 } },
+        // SliceSpecificParams{ { 2, 8, 32, 20 }, { 0, 4, 10 }, { 2, 8, 0 }, { 1, 1, 1 }, { 0, -2, -1 } },
+};
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_MKLDNN, SliceLayerTest,
+        ::testing::Combine(
+            ::testing::ValuesIn(test_cases),
+            ::testing::ValuesIn(inputPrecision),
+            ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+            ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+            ::testing::Values(InferenceEngine::Layout::ANY),
+            ::testing::Values(InferenceEngine::Layout::ANY),
+            ::testing::Values(CommonTestUtils::DEVICE_CPU),
+            ::testing::Values(std::map<std::string, std::string>())),
+        SliceLayerTest::getTestCaseName);
+
+}  // namespace
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index 32bd52a790b0fd..291d564533fd0c 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -39,7 +39,7 @@ std::vector<std::string> disabledTestPatterns() {
         // TODO: Issue 43417 sporadic issue, looks like an issue in test, reproducible only on Windows platform
         R"(.*decomposition1_batch=5_hidden_size=10_input_size=30_.*tanh.relu.*_clip=0_linear_before_reset=1.*_targetDevice=CPU_.*)",
         // Skip platforms that do not support BF16 (i.e. sse, avx, avx2)
-        R"(.*BF16.*(jit_avx(?!5)|jit_sse|ref).*)",
+        R"(.*(BF|bf)16.*(jit_avx(?!5)|jit_sse|ref).*)",
         // TODO: Incorrect blob sizes for node BinaryConvolution_X
         R"(.*BinaryConvolutionLayerTest.*)",
         R"(.*ClampLayerTest.*netPrc=(I64|I32).*)",
@@ -84,8 +84,6 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*(Auto|Multi).*Behavior.*IncorrectConfigTests.*CanNotLoadNetworkWithIncorrectConfig.*)",
         R"(.*OVExecutableNetworkBaseTest.*(CanGetInputsInfoAndCheck|CanSetConfigToExecNet|canLoadCorrectNetworkToGetExecutableWithIncorrectConfig).*)",
         R"(.*Behavior.*CorrectConfigCheck.*(canSetConfigAndCheckGetConfig|canSetConfigTwiceAndCheckGetConfig).*CPU_BIND_THREAD=YES.*)",
-        // azure is failing after #6199
-        R"(.*/NmsLayerTest.*)",
         // TODO: 56520 Accuracy mismatch
         R"(.*ReduceOpsLayerTest.*type=Mean_.*netPRC=(I64|I32).*)",
         R"(.*ReduceOpsLayerTest.*type=Mean_.*netPRC=U64.*)",
@@ -103,8 +101,6 @@ std::vector<std::string> disabledTestPatterns() {
 
         // CPU plugin does not support some precisions
         R"(smoke_CachingSupportCase_CPU/LoadNetworkCacheTestBase.CompareWithRefImpl/ReadConcatSplitAssign_f32_batch1_CPU)",
-        // Issue 66685
-        R"(smoke_PrePostProcess.*resize_linear_nhwc.*)",
         // CPU plugin does not support some precisions
         R"(.*Behavior.*OVExecGraphImportExportTest.*elementType=(i8|u32).*)",
         R"(.*Behavior.*OVExecGraphImportExportTest.*elementType=(f16).*)",
@@ -153,6 +149,8 @@ std::vector<std::string> disabledTestPatterns() {
         // bad accuracy
         R"(.*smoke_FakeQuantizeLayerCPUTest_Decompos.
             *IS=_TS=\(\(4\.5\.6\.7\)\)_RS=\(\(1\.1\.6\.1\)\)_\(\(1\.5\.6\.1\)\)_\(\(1\.1\.1\.1\)\)_\(\(1\.1\.6\.1\)\).*)",
+        // Issue: 71121
+        R"(.*smoke_Proposal*.*TS=\(2.*)",
     };
 
 #define FIX_62820 0
@@ -167,7 +165,7 @@ std::vector<std::string> disabledTestPatterns() {
     if (!InferenceEngine::with_cpu_x86_avx512_core()) {
         // on platforms which do not support bfloat16, we are disabling bf16 tests since there are no bf16 primitives,
         // tests are useless on such platforms
-        retVector.emplace_back(R"(.*BF16.*)");
+        retVector.emplace_back(R"(.*(BF|bf)16.*)");
         retVector.emplace_back(R"(.*bfloat16.*)");
     }
 
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/activation.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/activation.cpp
index c7342e95517800..f767af4a310f9a 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/activation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/activation.cpp
@@ -3,72 +3,118 @@
 //
 
 #include <shared_test_classes/single_layer/activation.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
 #include "test_utils/cpu_test_utils.hpp"
 
 using namespace InferenceEngine;
 using namespace CPUTestUtils;
 using namespace ngraph::helpers;
+using namespace ov::test;
 
 namespace CPULayerTestsDefinitions  {
 
-typedef std::tuple<
-        LayerTestsDefinitions::activationParams,
-        CPUSpecificParams>
-        ActivationLayerCPUTestParamSet;
+using ActivationLayerCPUTestParamSet = std::tuple<
+        std::vector<InputShape>,                                         // Input shapes
+        std::vector<size_t>,                                             // Activation shapes
+        std::pair<ngraph::helpers::ActivationTypes, std::vector<float>>, // Activation type and constant value
+        InferenceEngine::Precision,                                      // Net precision
+        InferenceEngine::Precision,                                      // Input precision
+        InferenceEngine::Precision,                                      // Output precision
+        CPUSpecificParams>;
 
 class ActivationLayerCPUTest : public testing::WithParamInterface<ActivationLayerCPUTestParamSet>,
-                            virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+                            virtual public SubgraphBaseTest, public CPUTestsBase {
 public:
     ActivationTypes activationType;
     static std::string getTestCaseName(const testing::TestParamInfo<ActivationLayerCPUTestParamSet> &obj) {
-        LayerTestsDefinitions::activationParams basicParamsSet;
+        std::vector<InputShape> inputShapes;
+        std::vector<size_t> activationShapes;
+        std::pair<ngraph::helpers::ActivationTypes, std::vector<float>> activationTypeAndConstValue;
+        Precision netPrecision, inPrecision, outPrecision;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = obj.param;
+        std::tie(inputShapes, activationShapes, activationTypeAndConstValue, netPrecision, inPrecision, outPrecision, cpuParams) = obj.param;
 
         std::ostringstream result;
-        result << LayerTestsDefinitions::ActivationLayerTest::getTestCaseName(testing::TestParamInfo<LayerTestsDefinitions::activationParams>(
-                basicParamsSet, 0));
-
+        result << LayerTestsDefinitions::activationNames[activationTypeAndConstValue.first] << "_";
+        if (inputShapes.front().first.size() != 0) {
+            result << "IS=(";
+            for (const auto &shape : inputShapes) {
+                result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+            }
+            result.seekp(-1, result.cur);
+            result << ")_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            for (const auto& item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "AS=" << CommonTestUtils::vec2str(activationShapes) << "_";
+        result << "ConstantsValue=" << CommonTestUtils::vec2str(activationTypeAndConstValue.second) << "_";
+        result << "netPRC=" << netPrecision.name() << "_";
+        result << "inPRC=" << inPrecision.name() << "_";
+        result << "outPRC=" << outPrecision.name() << "_";
         result << CPUTestsBase::getTestCaseName(cpuParams);
 
         return result.str();
     }
-    InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo &info) const override {
-        int32_t data_start_from;
-        uint32_t data_range;
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        int32_t startFrom;
+        uint32_t range;
         int32_t resolution;
-
         if (activationType == ActivationTypes::Exp && netPrecision == Precision::BF16) {
-            data_start_from = 0;
-            data_range = 2;
+            startFrom = 0;
+            range = 2;
             resolution = 32768;
+        } else if (activationType == ActivationTypes::Acosh) {
+            startFrom = 2;
+            range = 2;
+            resolution = 128;
         } else {
-            data_start_from = 0;
-            data_range = 15;
+            startFrom = 0;
+            range = 15;
             resolution = 32768;
         }
-
-        return FuncTestUtils::createAndFillBlob(info.getTensorDesc(), data_range, data_start_from, resolution);
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+            if (funcInput.get_element_type().is_real()) {
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i],
+                                                                 range, startFrom, resolution);
+            } else {
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+            }
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
     }
 
 protected:
     void SetUp() override {
-        LayerTestsDefinitions::activationParams basicParamsSet;
-        CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+        targetDevice = CommonTestUtils::DEVICE_CPU;
 
+        std::vector<InputShape> inputShapes;
+        std::vector<size_t> activationShapes;
+        std::pair<ngraph::helpers::ActivationTypes, std::vector<float>> activationTypeAndConstValue;
+        Precision inPrecision, outPrecision;
+        CPUSpecificParams cpuParams;
+        std::tie(inputShapes, activationShapes, activationTypeAndConstValue, netPrecision, inPrecision, outPrecision, cpuParams) = this->GetParam();
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+        activationType = activationTypeAndConstValue.first;
+        auto constantsValue = activationTypeAndConstValue.second;
 
-        std::pair<std::vector<size_t>, std::vector<size_t>> shapes;
-        std::pair<ActivationTypes, std::vector<float>> activationDecl;
-        std::tie(activationDecl, netPrecision, inPrc, outPrc, inLayout, outLayout, shapes, targetDevice) = basicParamsSet;
+        inType = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inPrecision);
+        outType = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(outPrecision);
         selectedType = getPrimitiveType() + "_" + netPrecision.name();
 
-        activationType = activationDecl.first;
-        auto constantsValue = activationDecl.second;
+        init_input_shapes(inputShapes);
+
         auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, {shapes.first});
-        auto activation = ngraph::builder::makeActivation(params[0], ngPrc, activationType, shapes.second, constantsValue);
+        auto params = ngraph::builder::makeDynamicParams(ngPrc, {inputDynamicShapes.front()});
+        auto activation = ngraph::builder::makeActivation(params[0], ngPrc, activationType, activationShapes, constantsValue);
         activation->get_rt_info() = getCPUInfo();
         function = std::make_shared<ngraph::Function>(ngraph::NodeVector{activation}, params, "Activation");
     }
@@ -79,13 +125,16 @@ class ActivationLayerCPUTest : public testing::WithParamInterface<ActivationLaye
 TEST_P(ActivationLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
-    CheckPluginRelatedResults(executableNetwork, "Eltwise");
+    run();
+    // TODO: Should be uncommented after updating the CheckPluginRelatedResults() method
+    // CheckPluginRelatedResults(executableNetwork, "Eltwise");
 }
 
 
 namespace {
 // list only types supported by eltwise
+const std::vector<size_t> activationShapes = {};
+
 const std::map<ActivationTypes, std::vector<std::vector<float>>> activationTypes = {
         {Sqrt,        {{}}},
         {Sigmoid,     {{}}},
@@ -97,7 +146,7 @@ const std::map<ActivationTypes, std::vector<std::vector<float>>> activationTypes
         {Swish,       {{0.1f}}},
         {HSwish,      {{}}},
         {Mish,        {{}}},
-        {PReLu, {{-0.01f}}},
+        {PReLu,       {{-0.01f}}},
         {GeluErf,     {{}}},
         {GeluTanh,    {{}}}
 };
@@ -108,24 +157,25 @@ std::vector<CPUSpecificParams> cpuParams_4D = {
         CPUSpecificParams({nchw}, {nchw}, {}, {})
 };
 
-std::map<std::vector<size_t>, std::vector<std::vector<size_t>>> basic4D = {
-        {{2, 4, 4, 1}, {{}}},
-        {{2, 17, 5, 4}, {{}}},
+std::vector<std::vector<ov::Shape>> basic4D = {
+            {{2, 4, 4, 1}},
+            {{2, 17, 5, 4}}
 };
 
-std::vector<Precision> netPrc = {Precision::BF16, Precision::FP32};
+std::vector<Precision> netPrc = {
+        // TODO: Should be uncommented after PR #8339 merge
+        // Precision::BF16
+        Precision::FP32
+};
 
 const auto basicCases4D = ::testing::Combine(
-        ::testing::Combine(
+            ::testing::ValuesIn(static_shapes_to_test_representation(basic4D)),
+            ::testing::Values(activationShapes),
             ::testing::ValuesIn(CommonTestUtils::combineParams(activationTypes)),
             ::testing::ValuesIn(netPrc),
             ::testing::Values(Precision::FP32),
             ::testing::Values(Precision::FP32),
-            ::testing::Values(InferenceEngine::Layout::ANY),
-            ::testing::Values(InferenceEngine::Layout::ANY),
-            ::testing::ValuesIn(CommonTestUtils::combineParams(basic4D)),
-            ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-        ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D))
+            ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D))
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation4D_Eltwise_CPU_BF16, ActivationLayerCPUTest, basicCases4D, ActivationLayerCPUTest::getTestCaseName);
@@ -136,24 +186,67 @@ std::vector<CPUSpecificParams> cpuParams_5D = {
         CPUSpecificParams({ncdhw}, {ncdhw}, {}, {})
 };
 
-std::map<std::vector<size_t>, std::vector<std::vector<size_t>>> basic5D = {
-        {{2, 4, 3, 4, 1}, {{}}},
-        {{2, 17, 7, 5, 4}, {{}}},
+std::vector<std::vector<ov::Shape>> basic5D = {
+        {{2, 4, 3, 4, 1}},
+        {{2, 17, 7, 5, 4}}
 };
 
 const auto basicCases5D = ::testing::Combine(
-        ::testing::Combine(
-                ::testing::ValuesIn(CommonTestUtils::combineParams(activationTypes)),
-                ::testing::ValuesIn(netPrc),
-                ::testing::Values(Precision::FP32),
-                ::testing::Values(Precision::FP32),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::ValuesIn(CommonTestUtils::combineParams(basic5D)),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        ::testing::ValuesIn(static_shapes_to_test_representation(basic5D)),
+        ::testing::Values(activationShapes),
+        ::testing::ValuesIn(CommonTestUtils::combineParams(activationTypes)),
+        ::testing::ValuesIn(netPrc),
+        ::testing::Values(Precision::FP32),
+        ::testing::Values(Precision::FP32),
         ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D))
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation5D_Eltwise_CPU_BF16, ActivationLayerCPUTest, basicCases5D, ActivationLayerCPUTest::getTestCaseName);
+
+const std::map<ActivationTypes, std::vector<std::vector<float>>> activationTypesDynamicMath = {
+        {Log,         {{}}},
+        {Sign,        {{}}},
+        {Acos,        {{}}},
+        {Acosh,       {{}}},
+        {Asin,        {{}}},
+        {Asinh,       {{}}},
+        {Atan,        {{}}},
+        {Atanh,       {{}}},
+        {Cos,         {{}}},
+        {Cosh,        {{}}},
+        {Tan,         {{}}},
+        {HardSigmoid, {{0.2f, 0.5f}}},
+        {Selu,        {{1.6732f, 1.0507f}}},
+        {Ceiling,     {{}}}
+};
+
+const std::vector<InferenceEngine::Precision> netPrecisions = {
+        InferenceEngine::Precision::FP32
+};
+
+std::vector<CPUSpecificParams> cpuParamsDynamicMath = {
+        CPUSpecificParams({}, {}, {}, {})
+};
+
+std::vector<std::vector<InputShape>> dynamicMathBasic = {
+        {
+                {{{-1, -1}, {{1, 50}, {5, 128}, {3, 64}}}},
+                {{{-1, -1, -1, -1, -1, -1, -1, -1}, {{2, 2, 2, 2, 2, 2, 2, 2}, {2, 3, 2, 3, 2, 3, 2, 3}, {3, 3, 3, 3, 3, 3, 3, 3}}}},
+                {{{{1, 5}, 128}, {{1, 128}, {3, 128}, {5, 128}}}}
+        }
+};
+
+const auto dynamicMathBasicCases = ::testing::Combine(
+        ::testing::ValuesIn(dynamicMathBasic),
+        ::testing::Values(activationShapes),
+        ::testing::ValuesIn(CommonTestUtils::combineParams(activationTypesDynamicMath)),
+        ::testing::ValuesIn(netPrecisions),
+        ::testing::Values(Precision::FP32),
+        ::testing::Values(Precision::FP32),
+        ::testing::ValuesIn(cpuParamsDynamicMath)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Activation5D_dynamicMath_CPU, ActivationLayerCPUTest, dynamicMathBasicCases, ActivationLayerCPUTest::getTestCaseName);
+
 } // namespace
 } // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/batch_to_space.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/batch_to_space.cpp
index a1c583adfcc1b2..44f4a882e50c41 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/batch_to_space.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/batch_to_space.cpp
@@ -2,56 +2,81 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <shared_test_classes/single_layer/batch_to_space.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "ngraph_functions/builders.hpp"
 #include "test_utils/cpu_test_utils.hpp"
 
 using namespace InferenceEngine;
 using namespace CPUTestUtils;
 using namespace ngraph::opset3;
+using namespace ov::test;
 
 namespace CPULayerTestsDefinitions  {
 
-typedef std::tuple<
-        LayerTestsDefinitions::batchToSpaceParamsTuple,
-        CPUSpecificParams> BatchToSpaceLayerCPUTestParamSet;
+using BatchToSpaceLayerTestCPUParams = std::tuple<
+        std::vector<InputShape>,            // Input shapes
+        std::vector<int64_t>,               // block shape
+        std::vector<int64_t>,               // crops begin
+        std::vector<int64_t>,               // crops end
+        Precision ,                         // Network precision
+        CPUSpecificParams>;
 
-class BatchToSpaceCPULayerTest : public testing::WithParamInterface<BatchToSpaceLayerCPUTestParamSet>,
-                                 virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+class BatchToSpaceCPULayerTest : public testing::WithParamInterface<BatchToSpaceLayerTestCPUParams>,
+                                 virtual public SubgraphBaseTest, public CPUTestsBase {
 public:
-    static std::string getTestCaseName(const testing::TestParamInfo<BatchToSpaceLayerCPUTestParamSet> &obj) {
-        LayerTestsDefinitions::batchToSpaceParamsTuple basicParamsSet;
+    static std::string getTestCaseName(const testing::TestParamInfo<BatchToSpaceLayerTestCPUParams> &obj) {
+        std::vector<InputShape> inputShapes;
+        std::vector<int64_t> blockShape, cropsBegin, cropsEnd;
+        Precision netPrecision;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = obj.param;
+        std::tie(inputShapes, blockShape, cropsBegin, cropsEnd, netPrecision, cpuParams) = obj.param;
         std::ostringstream result;
-        result << LayerTestsDefinitions::BatchToSpaceLayerTest::getTestCaseName(
-                testing::TestParamInfo<LayerTestsDefinitions::batchToSpaceParamsTuple>(basicParamsSet, 0));
+        if (inputShapes.front().first.size() != 0) {
+            result << "IS=(";
+            for (const auto &shape : inputShapes) {
+                result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+            }
+            result.seekp(-1, result.cur);
+            result << ")_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            for (const auto& item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "blockShape=" << CommonTestUtils::vec2str(blockShape) << "_";
+        result << "cropsBegin=" << CommonTestUtils::vec2str(cropsBegin) << "_";
+        result << "cropsEnd=" << CommonTestUtils::vec2str(cropsEnd) << "_";
+        result << "netPRC=" << netPrecision.name() << "_";
         result << CPUTestsBase::getTestCaseName(cpuParams);
         return result.str();
     }
 
 protected:
     void SetUp() override {
-        LayerTestsDefinitions::batchToSpaceParamsTuple basicParamsSet;
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        std::vector<InputShape>  inputShapes;
+        std::vector<int64_t> blockShape, cropsBegin, cropsEnd;
+        Precision netPrecision;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+        std::tie(inputShapes, blockShape, cropsBegin, cropsEnd, netPrecision, cpuParams) = this->GetParam();
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-        std::vector<size_t> inputShape;
-        std::vector<int64_t> blockShape, cropsBegin, cropsEnd;
-        InferenceEngine::Precision netPrecision;
-        std::tie(blockShape, cropsBegin, cropsEnd, inputShape, netPrecision, inPrc, outPrc, inLayout, outLayout, targetDevice) = basicParamsSet;
-        inPrc = outPrc = netPrecision;
+
+        auto ngPrec = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+        inType = outType = ngPrec;
+
+        init_input_shapes(inputShapes);
 
         if (strcmp(netPrecision.name(), "U8") == 0)
             selectedType = std::string("ref_any_") + "I8";
         else
             selectedType = std::string("ref_any_") + netPrecision.name();
 
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
-        auto paramOuts = ngraph::helpers::convert2OutputVector(
-                ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
-        auto b2s = ngraph::builder::makeBatchToSpace(paramOuts[0], ngPrc, blockShape, cropsBegin, cropsEnd);
+        auto params = ngraph::builder::makeDynamicParams(ngPrec, {inputDynamicShapes.front()});
+        auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+        auto b2s = ngraph::builder::makeBatchToSpace(paramOuts[0], ngPrec, blockShape, cropsBegin, cropsEnd);
         b2s->get_rt_info() = getCPUInfo();
         ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(b2s)};
         function = std::make_shared<ngraph::Function>(results, params, "BatchToSpace");
@@ -61,119 +86,270 @@ class BatchToSpaceCPULayerTest : public testing::WithParamInterface<BatchToSpace
 TEST_P(BatchToSpaceCPULayerTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
-    CheckPluginRelatedResults(executableNetwork, "BatchToSpace");
+    run();
+    // TODO: Should be uncommented after updating the CheckPluginRelatedResults() method
+    // CheckPluginRelatedResults(executableNetwork, "BatchToSpace");
 };
 
 namespace {
 
-const std::vector<Precision> precisions = {
+const std::vector<Precision> netPrecision = {
         Precision::U8,
         Precision::I8,
         Precision::I32,
         Precision::FP32,
-        Precision::BF16
+        // TODO: Should be uncommented after PR #8339 merge
+        // Precision::BF16
 };
 
 const std::vector<std::vector<int64_t>> blockShape4D1  = {{1, 1, 1, 2}, {1, 2, 2, 1}};
 const std::vector<std::vector<int64_t>> cropsBegin4D1  = {{0, 0, 0, 0}, {0, 0, 0, 1}, {0, 0, 2, 0}};
 const std::vector<std::vector<int64_t>> cropsEnd4D1    = {{0, 0, 0, 0}, {0, 0, 1, 0}, {0, 0, 1, 1}};
-const std::vector<std::vector<size_t>> inputShapes4D1  = {{8, 16, 10, 10}, {16, 64, 13, 16}};
+
+std::vector<std::vector<ov::Shape>> staticInputShapes4D1 = {
+        {{8, 16, 10, 10}}
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes4D1 = {
+        {
+                {{{-1, -1, -1, -1}, {{8, 8, 6, 7}, {4, 10, 5, 5}, {12, 9, 7, 5}}}},
+                {{{{4, 12}, {8, 16}, 6, -1}, {{8, 8, 6, 7}, {4, 10, 6, 5}, {12, 9, 6, 5}}}}
+        }
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes4D1Blocked = {
+        {
+                {{{-1, 16, -1, -1}, {{4, 16, 5, 8}, {8, 16, 7, 6}, {12, 16, 4, 5}}}}
+        }
+};
 
 const std::vector<std::vector<int64_t>> blockShape4D2  = {{1, 2, 3, 4}, {1, 3, 4, 2}};
 const std::vector<std::vector<int64_t>> cropsBegin4D2  = {{0, 0, 0, 1}, {0, 0, 1, 2}};
 const std::vector<std::vector<int64_t>> cropsEnd4D2    = {{0, 0, 1, 0}, {0, 0, 3, 1}};
-const std::vector<std::vector<size_t>> inputShapes4D2  = {{48, 16, 7, 8}, {24, 32, 6, 6}};
 
-const std::vector<CPUSpecificParams> cpuParams_4D = {
+std::vector<std::vector<ov::Shape>> staticInputShapes4D2 = {
+        {{24, 16, 7, 8}}
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes4D2 = {
+        {
+                {{{-1, -1, -1, -1}, {{48, 4, 7, 8}, {24, 8, 6, 7}, {24, 16, 5, 5}}}},
+                 {{{24, {4, 10}, -1, -1}, {{24, 8, 6, 7}, {24, 6, 7, 5}, {24, 4, 5, 5}}}}
+        }
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes4D2Blocked = {
+        {
+                 {{-1, 16, -1, -1}, {{24, 16, 5, 5}, {24, 16, 6, 7}, {48, 16, 4, 4}}}
+        }
+};
+
+const std::vector<CPUSpecificParams> cpuParamsWithBlock_4D = {
         CPUSpecificParams({nChw16c}, {nChw16c}, {}, {}),
         CPUSpecificParams({nChw8c}, {nChw8c}, {}, {}),
         CPUSpecificParams({nhwc}, {nhwc}, {}, {}),
         CPUSpecificParams({nchw}, {nchw}, {}, {})
 };
 
-const auto batchToSpaceParamsSet4D1 = ::testing::Combine(
-        ::testing::Combine(
-                ::testing::ValuesIn(std::vector<std::vector<int64_t>>({blockShape4D1})),
-                ::testing::ValuesIn(std::vector<std::vector<int64_t>>({cropsBegin4D1})),
-                ::testing::ValuesIn(std::vector<std::vector<int64_t>>({cropsEnd4D1})),
-                ::testing::ValuesIn(std::vector<std::vector<size_t>> ({inputShapes4D1})),
-                ::testing::ValuesIn(precisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                ::testing::ValuesIn(cpuParams_4D));
-
-const auto batchToSpaceParamsSet4D2 = ::testing::Combine(
-        ::testing::Combine(
-                ::testing::ValuesIn(std::vector<std::vector<int64_t>>({blockShape4D2})),
-                ::testing::ValuesIn(std::vector<std::vector<int64_t>>({cropsBegin4D2})),
-                ::testing::ValuesIn(std::vector<std::vector<int64_t>>({cropsEnd4D2})),
-                ::testing::ValuesIn(std::vector<std::vector<size_t>> ({inputShapes4D2})),
-                ::testing::ValuesIn(precisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+const std::vector<CPUSpecificParams> cpuParams_4D = {
+        CPUSpecificParams({nhwc}, {nhwc}, {}, {}),
+        CPUSpecificParams({nchw}, {nchw}, {}, {})
+};
+
+const auto staticBatchToSpaceParamsSet4D1 = ::testing::Combine(
+        ::testing::ValuesIn(static_shapes_to_test_representation(staticInputShapes4D1)),
+        ::testing::ValuesIn(blockShape4D1),
+        ::testing::ValuesIn(cropsBegin4D1),
+        ::testing::ValuesIn(cropsEnd4D1),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_4D));
+
+const auto dynamicBatchToSpaceParamsSet4D1 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes4D1),
+        ::testing::ValuesIn(blockShape4D1),
+        ::testing::ValuesIn(cropsBegin4D1),
+        ::testing::ValuesIn(cropsEnd4D1),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParams_4D));
+
+const auto dynamicBatchToSpaceParamsWithBlockedSet4D1 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes4D1Blocked),
+        ::testing::ValuesIn(blockShape4D1),
+        ::testing::ValuesIn(cropsBegin4D1),
+        ::testing::ValuesIn(cropsEnd4D1),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_4D));
+
+const auto staticBatchToSpaceParamsSet4D2 = ::testing::Combine(
+        ::testing::ValuesIn(static_shapes_to_test_representation(staticInputShapes4D2)),
+        ::testing::ValuesIn(blockShape4D2),
+        ::testing::ValuesIn(cropsBegin4D2),
+        ::testing::ValuesIn(cropsEnd4D2),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_4D));
+
+const auto dynamicBatchToSpaceParamsSet4D2 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes4D2),
+        ::testing::ValuesIn(blockShape4D2),
+        ::testing::ValuesIn(cropsBegin4D2),
+        ::testing::ValuesIn(cropsEnd4D2),
+        ::testing::ValuesIn(netPrecision),
         ::testing::ValuesIn(cpuParams_4D));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BatchToSpaceCPULayerTestCase1_4D, BatchToSpaceCPULayerTest,
-                            batchToSpaceParamsSet4D1, BatchToSpaceCPULayerTest::getTestCaseName);
+const auto dynamicBatchToSpaceParamsWithBlockedSet4D2 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes4D2Blocked),
+        ::testing::ValuesIn(blockShape4D2),
+        ::testing::ValuesIn(cropsBegin4D2),
+        ::testing::ValuesIn(cropsEnd4D2),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_4D));
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticBatchToSpaceCPULayerTestCase1_4D, BatchToSpaceCPULayerTest,
+                         staticBatchToSpaceParamsSet4D1, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCase1_4D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsSet4D1, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCaseWithBlocked1_4D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsWithBlockedSet4D1, BatchToSpaceCPULayerTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BatchToSpaceCPULayerTestCase2_4D, BatchToSpaceCPULayerTest,
-                            batchToSpaceParamsSet4D2, BatchToSpaceCPULayerTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_StaticBatchToSpaceCPULayerTestCase2_4D, BatchToSpaceCPULayerTest,
+                         staticBatchToSpaceParamsSet4D2, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCase2_4D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsSet4D2, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCaseWithBlocked2_4D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsWithBlockedSet4D2, BatchToSpaceCPULayerTest::getTestCaseName);
 
 const std::vector<std::vector<int64_t>> blockShape5D1  = {{1, 1, 2, 2, 1}, {1, 2, 1, 2, 2}};
 const std::vector<std::vector<int64_t>> cropsBegin5D1  = {{0, 0, 0, 0, 0}, {0, 0, 0, 3, 3}};
 const std::vector<std::vector<int64_t>> cropsEnd5D1    = {{0, 0, 0, 0, 0}, {0, 0, 1, 0, 1}};
-const  std::vector<std::vector<size_t>> inputShapes5D1 = {{8, 16, 4, 10, 10}, {16, 32, 5, 8, 12}};
+
+std::vector<std::vector<ov::Shape>> staticInputShapes5D1 = {
+        {{8, 16, 4, 10, 10}}
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes5D1 = {
+        {
+                {{{-1, -1, -1, -1, -1}, {{8, 16, 4, 10, 10}, {16, 10, 5, 11, 9}, {24, 6, 6, 8, 8}}}},
+                {{{{8, 16}, {8, 16}, {2, 7}, -1, -1}, {{8, 16, 2, 6, 8}, {8, 10, 4, 7, 5}, {16, 8, 7, 5, 10}}}}
+        }
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes5D1Blocked = {
+        {
+                {{{-1, 16, -1, -1, -1}, {{24, 16, 3, 6, 7}, {48, 16, 4, 5, 5}, {24, 16, 5, 8, 5}}}}
+        }
+};
 
 const std::vector<std::vector<int64_t>> blockShape5D2  = {{1, 2, 4, 3, 1}, {1, 1, 2, 4, 3}};
 const std::vector<std::vector<int64_t>> cropsBegin5D2  = {{0, 0, 1, 2, 0}, {0, 0, 1, 0, 1}};
 const std::vector<std::vector<int64_t>> cropsEnd5D2    = {{0, 0, 1, 0, 1}, {0, 0, 1, 1, 1}};
-const  std::vector<std::vector<size_t>> inputShapes5D2 = {{48, 16, 3, 3, 3}, {24, 32, 5, 3, 5}};
 
-const std::vector<CPUSpecificParams> cpuParams_5D = {
+std::vector<std::vector<ov::Shape>> staticInputShapes5D2 = {
+        {{48, 16, 3, 3, 3}}
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes5D2 = {
+        {
+                {{{-1, -1, -1, -1, -1}, {{48, 4, 3, 3, 3}, {24, 16, 5, 3, 5}, {24, 8, 7, 5, 5}}}},
+                {{{24, {8, 16}, {3, 5}, -1, -1}, {{24, 16, 3, 4, 3}, {24, 12, 5, 3, 5}, {24, 8, 4, 5, 5}}}},
+                // special case
+                {
+                    {{{1, 24}, {1, 16}, {1, 10}, {1, 10}, {1, 10}},
+                    {
+                        {24, 16, 5, 3, 5},
+                        {24, 16, 5, 3, 5},
+                        {24, 16, 7, 5, 5}
+                    }}
+                }
+        }
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapes5D2Blocked = {
+        {
+                {{{-1, 16, -1, -1, -1}, {{24, 16, 4, 5, 5}, {48, 16, 3, 4, 3}, {24, 16, 5, 3, 5}}}}
+        }
+};
+
+const std::vector<CPUSpecificParams> cpuParamsWithBlock_5D = {
         CPUSpecificParams({nCdhw16c}, {nCdhw16c}, {}, {}),
         CPUSpecificParams({nCdhw8c}, {nCdhw8c}, {}, {}),
         CPUSpecificParams({ndhwc}, {ndhwc}, {}, {}),
         CPUSpecificParams({ncdhw}, {ncdhw}, {}, {})
 };
 
-const auto batchToSpaceParamsSet5D1 = ::testing::Combine(
-        ::testing::Combine(
-                ::testing::ValuesIn(blockShape5D1),
-                ::testing::ValuesIn(cropsBegin5D1),
-                ::testing::ValuesIn(cropsEnd5D1),
-                ::testing::ValuesIn(inputShapes5D1),
-                ::testing::ValuesIn(precisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU)), ::testing::ValuesIn(cpuParams_5D));
-
-const auto batchToSpaceParamsSet5D2 = ::testing::Combine(
-        ::testing::Combine(
-                ::testing::ValuesIn(blockShape5D2),
-                ::testing::ValuesIn(cropsBegin5D2),
-                ::testing::ValuesIn(cropsEnd5D2),
-                ::testing::ValuesIn(inputShapes5D2),
-                ::testing::ValuesIn(precisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU)), ::testing::ValuesIn(cpuParams_5D));
-
-INSTANTIATE_TEST_SUITE_P(smoke_BatchToSpaceCPULayerTestCase1_5D, BatchToSpaceCPULayerTest,
-                            batchToSpaceParamsSet5D1, BatchToSpaceCPULayerTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_BatchToSpaceCPULayerTestCase2_5D, BatchToSpaceCPULayerTest,
-                            batchToSpaceParamsSet5D2, BatchToSpaceCPULayerTest::getTestCaseName);
+const std::vector<CPUSpecificParams> cpuParams_5D = {
+        CPUSpecificParams({ndhwc}, {ndhwc}, {}, {}),
+        CPUSpecificParams({ncdhw}, {ncdhw}, {}, {})
+};
+
+const auto staticBatchToSpaceParamsSet5D1 = ::testing::Combine(
+        ::testing::ValuesIn(static_shapes_to_test_representation(staticInputShapes5D1)),
+        ::testing::ValuesIn(blockShape5D1),
+        ::testing::ValuesIn(cropsBegin5D1),
+        ::testing::ValuesIn(cropsEnd5D1),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_5D));
+
+const auto dynamicBatchToSpaceParamsSet5D1 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes5D1),
+        ::testing::ValuesIn(blockShape5D1),
+        ::testing::ValuesIn(cropsBegin5D1),
+        ::testing::ValuesIn(cropsEnd5D1),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParams_5D));
+
+const auto dynamicBatchToSpaceParamsWithBlockedSet5D1 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes5D1Blocked),
+        ::testing::ValuesIn(blockShape5D1),
+        ::testing::ValuesIn(cropsBegin5D1),
+        ::testing::ValuesIn(cropsEnd5D1),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_5D));
+
+const auto staticBatchToSpaceParamsSet5D2 = ::testing::Combine(
+        ::testing::ValuesIn(static_shapes_to_test_representation(staticInputShapes5D2)),
+        ::testing::ValuesIn(blockShape5D2),
+        ::testing::ValuesIn(cropsBegin5D2),
+        ::testing::ValuesIn(cropsEnd5D2),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_5D));
+
+const auto dynamicBatchToSpaceParamsSet5D2 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes5D2),
+        ::testing::ValuesIn(blockShape5D2),
+        ::testing::ValuesIn(cropsBegin5D2),
+        ::testing::ValuesIn(cropsEnd5D2),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParams_5D));
+
+const auto dynamicBatchToSpaceParamsWithBlockedSet5D2 = ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShapes5D2Blocked),
+        ::testing::ValuesIn(blockShape5D2),
+        ::testing::ValuesIn(cropsBegin5D2),
+        ::testing::ValuesIn(cropsEnd5D2),
+        ::testing::ValuesIn(netPrecision),
+        ::testing::ValuesIn(cpuParamsWithBlock_5D));
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticBatchToSpaceCPULayerTestCase1_5D, BatchToSpaceCPULayerTest,
+                         staticBatchToSpaceParamsSet5D1, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCase1_5D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsSet5D1, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCaseWithBlocked1_5D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsWithBlockedSet5D1, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticBatchToSpaceCPULayerTestCase2_5D, BatchToSpaceCPULayerTest,
+                         staticBatchToSpaceParamsSet5D2, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCase2_5D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsSet5D2, BatchToSpaceCPULayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_DynamicBatchToSpaceCPULayerTestCaseWithBlocked2_5D, BatchToSpaceCPULayerTest,
+                         dynamicBatchToSpaceParamsWithBlockedSet5D2, BatchToSpaceCPULayerTest::getTestCaseName);
 
 }  // namespace
 }  // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/concat.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/concat.cpp
index 786abb33e4587e..d7281975f06670 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/concat.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/concat.cpp
@@ -2,68 +2,82 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "ngraph_functions/builders.hpp"
 #include "test_utils/cpu_test_utils.hpp"
 
-using namespace InferenceEngine;
+using namespace ov::test;
 using namespace CPUTestUtils;
 
 namespace CPULayerTestsDefinitions {
 
 typedef std::tuple<
-        size_t,                            // Concat axis
-        std::vector<std::vector<size_t>>,  // Input shapes
-        InferenceEngine::Precision,        // Network precision
-        std::string,                       // Device name
+        size_t,                   // Concat axis
+        std::vector<InputShape>,  // Input shapes
+        ElementType,              // Network precision
         CPUSpecificParams
 > concatCPUTestParams;
 
 class ConcatLayerCPUTest : public testing::WithParamInterface<concatCPUTestParams>,
-                           virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+                           virtual public SubgraphBaseTest, public CPUTestsBase {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<concatCPUTestParams> obj) {
         int axis;
-        std::vector<std::vector<size_t>> inputShapes;
-        InferenceEngine::Precision netPrecision;
-        std::string targetName;
+        std::vector<InputShape> inputShapes;
+        ElementType netPrecision;
         CPUSpecificParams cpuParams;
-        std::tie(axis, inputShapes, netPrecision, targetName, cpuParams) = obj.param;
+        std::tie(axis, inputShapes, netPrecision, cpuParams) = obj.param;
 
         std::ostringstream result;
-        result << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_";
+        result << "IS=";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            result << "(";
+            if (!shape.second.empty()) {
+                for (const auto& itr : shape.second) {
+                    result << CommonTestUtils::vec2str(itr);
+                }
+            }
+            result << ")_";
+        }
         result << "axis=" << axis << "_";
-        result << "netPRC=" << netPrecision.name() << "_";
-        result << "trgDev=" << targetName << "_";
+        result << "netPRC=" << netPrecision << "_";
         result << CPUTestsBase::getTestCaseName(cpuParams);
         return result.str();
     }
+
 protected:
     void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
         int axis;
-        std::vector<std::vector<size_t>> inputShape;
-        InferenceEngine::Precision netPrecision;
+        std::vector<InputShape> inputShape;
+        ElementType netPrecision;
         CPUSpecificParams cpuParams;
-        std::tie(axis, inputShape, netPrecision, targetDevice, cpuParams) = this->GetParam();
-        inPrc = outPrc = netPrecision;
+        std::tie(axis, inputShape, netPrecision, cpuParams) = this->GetParam();
 
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-        selectedType += std::string("_") + inPrc.name();
+        selectedType += std::string("_") + InferenceEngine::details::convertPrecision(netPrecision).name();
+
+        init_input_shapes(inputShape);
 
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, inputShape);
+        auto params = ngraph::builder::makeDynamicParams(netPrecision, inputDynamicShapes);
         auto paramOuts = ngraph::helpers::convert2OutputVector(
                 ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
         auto concat = std::make_shared<ngraph::opset1::Concat>(paramOuts, axis);
 
-        function = makeNgraphFunction(ngPrc, params, concat, "concat");
+        function = makeNgraphFunction(netPrecision, params, concat, "ConcatCPU");
     }
 };
 
 TEST_P(ConcatLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
-    CheckPluginRelatedResults(executableNetwork, "Concatenation");
+    run();
+//     CheckPluginRelatedresult(executableNetwork, "Concatenation");
 }
 
 namespace {
@@ -89,151 +103,520 @@ const auto blocked16_4D_ref = CPUSpecificParams{{nChw16c}, {nChw16c}, {}, "ref"}
 const auto blocked16_5D_ref = CPUSpecificParams{{nCdhw16c}, {nCdhw16c}, {}, "ref"};
 
 // List of precisions natively supported by mkldnn.
-const std::vector<Precision> netPrecisions = {
-        Precision::I8,
-        Precision::I32,
-        Precision::FP32,
-        Precision::BF16
+const std::vector<ElementType> netPrecisions = {
+        ElementType::i8,
+        ElementType::i32,
+        ElementType::f32,
+        ElementType::bf16
 };
 
-INSTANTIATE_TEST_SUITE_P(concat_Concat4D_CPU_Block8inPlace, ConcatLayerCPUTest,
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block8_static, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(0, 1),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{1, 8, 3, 5},
-                                                                                   {1, 8, 3, 5}}),
+                                ::testing::Values(1, 2, 3),
+                                ::testing::Values(static_shapes_to_test_representation({{2, 16, 3, 5}, {2, 16, 3, 5}})),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(planar_4D, planarChannels_4D, blocked8_4D)),
+                                ::testing::Values(planar_4D_ref, planarChannels_4D, blocked8_4D_ref)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block8, ConcatLayerCPUTest,
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block16_static, ConcatLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(1, 2, 3),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{2, 16, 3, 5},
-                                                                                   {2, 16, 3, 5}}),
+                                ::testing::Values(static_shapes_to_test_representation({{3, 32, 3, 5}, {3, 32, 3, 5}})),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(planar_4D_ref, planarChannels_4D, blocked8_4D_ref)),
+                                ::testing::Values(blocked16_4D_ref)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block16inPlace, ConcatLayerCPUTest,
+const std::vector<std::vector<InputShape>> inputShapes4D_Block_axis1 = {
+        {
+            // {{dynamic shape}, {{static shape case1}, {static shape case2}, ...}
+            {{-1, 32, -1, -1}, {{2, 32, 5, 7}, {1, 32, 10, 2}, {3, 32, 1, 8}}}, // input 0
+            {{-1, 16, -1, -1}, {{2, 16, 5, 7}, {1, 16, 10, 2}, {3, 16, 1, 8}}}, // input 1
+            {{-1, 64, -1, -1}, {{2, 64, 5, 7}, {1, 64, 10, 2}, {3, 64, 1, 8}}}  // input 2
+        },
+        {
+            {{{1, 5}, 32, {1, 10}, {2, 8}}, {{2, 32, 5, 7}, {1, 32, 10, 2}, {3, 32, 1, 8}}},
+            {{{1, 3}, 16, {1, 10}, {2, 8}}, {{2, 16, 5, 7}, {1, 16, 10, 2}, {3, 16, 1, 8}}},
+            {{{1, 3}, 64, {1, 10}, {2, 8}}, {{2, 64, 5, 7}, {1, 64, 10, 2}, {3, 64, 1, 8}}}
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block_dynamic_axis_1, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(0, 1),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{1, 32, 3, 5},
-                                                                                   {1, 32, 3, 5}}),
+                                ::testing::Values(1),
+                                ::testing::ValuesIn(inputShapes4D_Block_axis1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(blocked16_4D)),
+                                ::testing::Values(blocked8_4D_ref, blocked16_4D_ref)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block16, ConcatLayerCPUTest,
+const std::vector<std::vector<InputShape>> inputShapes4D_axis1 = {
+        {
+            {{-1, -1, -1, -1}, {{2, 32, 5, 7}, {1, 18, 10, 2}, {3, 8, 1, 8}}},
+            {{-1, -1, -1, -1}, {{2, 16, 5, 7}, {1, 5, 10, 2}, {3, 3, 1, 8}}},
+            {{-1, -1, -1, -1}, {{2, 64, 5, 7}, {1, 45, 10, 2}, {3, 1, 1, 8}}}
+        },
+        {
+            {{{1, 3}, {8, 32}, {1, 10}, {2, 8}}, {{2, 32, 5, 7}, {1, 18, 10, 2}, {3, 8, 1, 8}}},
+            {{{1, 3}, {3, 16}, {1, 10}, {2, 8}}, {{2, 16, 5, 7}, {1, 5, 10, 2}, {3, 3, 1, 8}}},
+            {{{1, 3}, {1, 64}, {1, 10}, {2, 8}}, {{2, 64, 5, 7}, {1, 45, 10, 2}, {3, 1, 1, 8}}}
+        },
+        {
+            {{{1, 18, 10, 2}}, {{1, 18, 10, 2}, {1, 18, 10, 2}}},
+            {{-1, -1, -1, -1}, {{1, 3, 10, 2}, {1, 5, 10, 2}}},
+            {{{1, 5, 10, 2}}, {{1, 5, 10, 2}, {1, 5, 10, 2}}}
+        },
+        {
+            {{{-1, 8, -1, -1}}, {{2, 8, 5, 7}, {1, 8, 10, 2}}},
+            {{{-1, 3, -1, -1}}, {{2, 3, 5, 7}, {1, 3, 10, 2}}},
+            {{{-1, -1, -1, -1}}, {{2, 16, 5, 7}, {1, 7, 10, 2}}}
+        }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_dynamic_axis_1, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(1, 2, 3),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{3, 32, 3, 5},
-                                                                                   {3, 32, 3, 5}}),
+                                ::testing::Values(1),
+                                ::testing::ValuesIn(inputShapes4D_axis1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(blocked16_4D_ref)),
+                                ::testing::Values(planar_4D_ref, planarChannels_4D)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(concat_Concat5D_CPU_Block8inPlace, ConcatLayerCPUTest,
+const std::vector<std::vector<InputShape>> inputShapes4D_Block_axis2 = {
+        {
+            {{-1, 16, -1, -1}, {{2, 16, 5, 7}, {1, 16, 16, 2}, {3, 16, 2, 8}}},
+            {{-1, 16, -1, -1}, {{2, 16, 1, 7}, {1, 16, 3, 2}, {3, 16, 11, 8}}},
+            {{-1, 16, -1, -1}, {{2, 16, 10, 7}, {1, 16, 5, 2}, {3, 16, 1, 8}}},
+        },
+        {
+            {{{1, 3}, 16, {2, 16}, {2, 8}}, {{2, 16, 5, 7}, {1, 16, 16, 2}, {3, 16, 2, 8}}},
+            {{{1, 3}, 16, {1, 11}, {2, 8}}, {{2, 16, 1, 7}, {1, 16, 3, 2}, {3, 16, 11, 8}}},
+            {{{1, 3}, 16, {1, 10}, {2, 8}}, {{2, 16, 10, 7}, {1, 16, 5, 2}, {3, 16, 1, 8}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block_dynamic_axis_2, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(0, 1),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{1, 16, 3, 5, 7},
-                                                                                   {1, 16, 3, 5, 7}}),
+                                ::testing::Values(2),
+                                ::testing::ValuesIn(inputShapes4D_Block_axis2),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(planar_5D, planarChannels_5D, blocked8_5D)),
+                                ::testing::Values(blocked8_4D_ref, blocked16_4D_ref)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block8, ConcatLayerCPUTest,
+const std::vector<std::vector<InputShape>> inputShapes4D_axis2 = {
+        {
+            {{-1, -1, -1, -1}, {{2, 16, 5, 7}, {1, 16, 16, 2}, {3, 16, 2, 8}}},
+            {{-1, -1, -1, -1}, {{2, 16, 1, 7}, {1, 16, 3, 2}, {3, 16, 11, 8}}},
+            {{-1, -1, -1, -1}, {{2, 16, 10, 7}, {1, 16, 5, 2}, {3, 16, 1, 8}}},
+        },
+        {
+            {{{1, 3}, {1, 16}, {2, 16}, {2, 8}}, {{2, 16, 5, 7}, {1, 16, 16, 2}, {3, 16, 2, 8}}},
+            {{{1, 3}, {1, 16}, {1, 11}, {2, 8}}, {{2, 16, 1, 7}, {1, 16, 3, 2}, {3, 16, 11, 8}}},
+            {{{1, 3}, {1, 16}, {1, 10}, {2, 8}}, {{2, 16, 10, 7}, {1, 16, 5, 2}, {3, 16, 1, 8}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_dynamic_axis_2, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(2, 3, 4),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{2, 16, 3, 5, 7},
-                                                                                   {2, 16, 3, 5, 7}}),
+                                ::testing::Values(2),
+                                ::testing::ValuesIn(inputShapes4D_axis2),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(planar_5D_ref, planarChannels_5D, blocked8_5D_ref)),
+                                ::testing::Values(planar_4D_ref, planarChannels_4D)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block16inPlace, ConcatLayerCPUTest,
+const std::vector<std::vector<InputShape>> inputShapes4D_Block_axis3 = {
+        {
+            {{-1, 32, -1, -1}, {{2, 32, 4, 5},  {1, 32, 1, 16}, {3, 32, 7, 2}, }},
+            {{-1, 32, -1, -1}, {{2, 32, 4, 1}, {1, 32, 1, 3}, {3, 32, 7, 11}}},
+            {{-1, 32, -1, -1}, {{2, 32, 4, 10}, {1, 32, 1, 5}, {3, 32, 7, 1}}},
+        },
+        {
+            {{{1, 3}, 32, {1, 7}, {2, 16}}, {{2, 32, 4, 5}, {1, 32, 1, 16}, {3, 32, 7, 2}}},
+            {{{1, 3}, 32, {1, 7}, {1, 11}}, {{2, 32, 4, 1},  {1, 32, 1, 3},  {3, 32, 7, 11}}},
+            {{{1, 3}, 32, {1, 7}, {1, 10}}, {{2, 32, 4, 10}, {1, 32, 1, 5}, {3, 32, 7, 1}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block_dynamic_axis_3, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(0, 1),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{1, 32, 3, 5, 7},
-                                                                                   {1, 32, 3, 5, 7}}),
+                                ::testing::Values(3),
+                                ::testing::ValuesIn(inputShapes4D_Block_axis3),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(blocked16_5D)),
+                                ::testing::Values(blocked8_4D_ref, blocked16_4D_ref)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes4D_axis3 = {
+        {
+            {{-1, -1, -1, -1}, {{2, 32, 4, 5}, {1, 32, 1, 16}, {3, 32, 7, 2}}},
+            {{-1, -1, -1, -1}, {{2, 32, 4, 1},  {1, 32, 1, 3},  {3, 32, 7, 11}}},
+            {{-1, -1, -1, -1}, {{2, 32, 4, 10}, {1, 32, 1, 5}, {3, 32, 7, 1}}},
+        },
+        {
+            {{{1, 3}, {1, 32}, {1, 7}, {2, 16}}, {{2, 32, 4, 5}, {1, 32, 1, 16}, {3, 32, 7, 2}}},
+            {{{1, 3}, {1, 32}, {1, 7}, {1, 11}}, {{2, 32, 4, 1}, {1, 32, 1, 3}, {3, 32, 7, 11}}},
+            {{{1, 3}, {1, 32}, {1, 7}, {1, 10}}, {{2, 32, 4, 10}, {1, 32, 1, 5}, {3, 32, 7, 1}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_dynamic_axis_3, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(3),
+                                ::testing::ValuesIn(inputShapes4D_axis3),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_4D_ref, planarChannels_4D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block8_static, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(2, 3, 4),
+                                ::testing::Values(static_shapes_to_test_representation({{2, 16, 3, 5, 7}, {2, 16, 3, 5, 7}})),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D_ref, planarChannels_5D, blocked8_5D_ref)),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block16, ConcatLayerCPUTest,
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block16_static, ConcatLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(2, 3, 4),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{2, 32, 3, 5, 7},
-                                                                                   {2, 32, 3, 5, 7}}),
+                                ::testing::Values(static_shapes_to_test_representation({{2, 32, 3, 5, 7}, {2, 32, 3, 5, 7}})),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(blocked16_5D_ref)),
                         ConcatLayerCPUTest::getTestCaseName);
 
+const std::vector<std::vector<InputShape>> inputShapes5D_Block_axis1 = {
+        {
+            {{-1, 32, -1, -1, -1}, {{2, 32, 5, 7, 6},  {1, 32, 10, 2, 8}, {3, 32, 1, 8, 10}}},
+            {{-1, 16, -1, -1, -1}, {{2, 16, 5, 7, 6},  {1, 16, 10, 2, 8}, {3, 16, 1, 8, 10}}},
+            {{-1, 64, -1, -1, -1}, {{2, 64, 5, 7, 6}, {1, 64, 10, 2, 8}, {3, 64, 1, 8, 10}}},
+        },
+        {
+            {{{1, 3}, 32, {1, 10}, {2, 8}, {6, 10}}, {{2, 32, 5, 7, 6}, {1, 32, 10, 2, 8}, {3, 32, 1, 8, 10}}},
+            {{{1, 3}, 16, {1, 10}, {2, 8}, {6, 10}}, {{2, 16, 5, 7, 6}, {1, 16, 10, 2, 8}, {3, 16, 1, 8, 10}}},
+            {{{1, 3}, 64, {1, 10}, {2, 8}, {6, 10}}, {{2, 64, 5, 7, 6}, {1, 64, 10, 2, 8}, {3, 64, 1, 8, 10}}},
+        },
+};
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat_inPlace, ConcatLayerCPUTest,
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block_dynamic_axis_1, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(0, 1, 2),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{1, 1, 1, 10},
-                                                                                   {1, 1, 1, 10}},
-                                                  std::vector<std::vector<size_t>>{{1, 1, 5},
-                                                                                   {1, 1, 5}}),
+                                ::testing::Values(1),
+                                ::testing::ValuesIn(inputShapes5D_Block_axis1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"})),
+                                ::testing::Values(blocked8_5D_ref, blocked16_5D_ref)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes5D_axis1 = {
+        {
+            {{-1, -1, -1, -1, -1}, {{2, 5, 5, 7, 6}, {1, 3, 10, 2, 8}, {3, 4, 1, 8, 10}}},
+            {{-1, -1, -1, -1, -1}, {{2, 16, 5, 7, 6},  {1, 20, 10, 2, 8}, {3, 5, 1, 8, 10}, }},
+            {{-1, -1, -1, -1, -1}, {{2, 1, 5, 7, 6}, {1, 17, 10, 2, 8}, {3, 5, 1, 8, 10}}},
+        },
+        {
+            {{{1, 3}, {3, 5}, {1, 10}, {2, 8}, {6, 10}}, {{2, 5, 5, 7, 6},  {1, 3, 10, 2, 8}, {3, 4, 1, 8, 10}}},
+            {{{1, 3}, {5, 20}, {1, 10}, {2, 8}, {4, 10}}, {{2, 16, 5, 7, 6}, {1, 20, 10, 2, 8}, {3, 5, 1, 8, 10}, }},
+            {{{1, 3}, {1, 17}, {1, 10}, {2, 8}, {6, 10}}, {{2, 1, 5, 7, 6}, {1, 17, 10, 2, 8}, {3, 5, 1, 8, 10}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_dynamic_axis_1, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(1),
+                                ::testing::ValuesIn(inputShapes5D_axis1),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D_ref, planarChannels_5D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes5D_Block_axis2 = {
+        {
+            {{-1, 16, -1, -1, -1}, {{2, 16, 5, 8, 7},  {1, 16, 16, 1, 2}, {3, 16, 2, 5, 8}, }},
+            {{-1, 16, -1, -1, -1}, {{2, 16, 1, 8, 7}, {1, 16, 3, 1, 2}, {3, 16, 11, 5, 8}}},
+            {{-1, 16, -1, -1, -1}, {{2, 16, 10, 8, 7}, {1, 16, 5, 1, 2}, {3, 16, 1, 5, 8}}},
+        },
+        {
+            {{{1, 3}, 16, {2, 16}, {1, 8}, {2, 8}}, {{2, 16, 5, 8, 7}, {1, 16, 16, 1, 2}, {3, 16, 2, 5, 8}, }},
+            {{{1, 5}, 16, {1, 11}, {1, 8}, {1, 8}}, {{2, 16, 1, 8, 7}, {1, 16, 3, 1, 2}, {3, 16, 11, 5, 8}}},
+            {{{1, 6}, 16, {1, 10}, {1, 8}, {2, 10}}, {{2, 16, 10, 8, 7}, {1, 16, 5, 1, 2}, {3, 16, 1, 5, 8}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block_dynamic_axis_2, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(2),
+                                ::testing::ValuesIn(inputShapes5D_Block_axis2),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked8_5D_ref, blocked16_5D_ref)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes5D_axis2 = {
+        {
+            {{-1, -1, -1, -1, -1}, {{2, 4, 5, 8, 7},  {1, 20, 16, 1, 2}, {3, 8, 2, 5, 8}}},
+            {{-1, -1, -1, -1, -1}, {{2, 4, 1, 8, 7}, {1, 20, 3, 1, 2}, {3, 8, 11, 5, 8}}},
+            {{-1, -1, -1, -1, -1}, {{2, 4, 10, 8, 7}, {1, 20, 5, 1, 2}, {3, 8, 1, 5, 8}}},
+        },
+        {
+            {{{1, 3}, {4, 20}, {1, 16}, {1, 8}, {2, 8}}, {{2, 4, 5, 8, 7},  {1, 20, 16, 1, 2}, {3, 8, 2, 5, 8}}},
+            {{{1, 3}, {4, 20}, {1, 11}, {1, 10}, {1, 15}}, {{2, 4, 1, 8, 7}, {1, 20, 3, 1, 2}, {3, 8, 11, 5, 8}}},
+            {{{1, 3}, {1, 20}, {1, 15}, {1, 10}, {2, 8}}, {{2, 4, 10, 8, 7}, {1, 20, 5, 1, 2}, {3, 8, 1, 5, 8}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_dynamic_axis_2, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(2),
+                                ::testing::ValuesIn(inputShapes5D_axis2),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D_ref, planarChannels_5D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes5D_Block_axis3 = {
+        {
+            {{-1, 32, -1, -1, -1}, {{2, 32, 4, 5, 7}, {1, 32, 1, 16, 3}, {3, 32, 7, 2, 4}}},
+            {{-1, 32, -1, -1, -1}, {{2, 32, 4, 1, 7}, {1, 32, 1, 3, 3}, {3, 32, 7, 11, 4}}},
+            {{-1, 32, -1, -1, -1}, {{2, 32, 4, 10, 7}, {1, 32, 1, 5, 3}, {3, 32, 7, 1, 4}}},
+        },
+        {
+            {{{1, 3}, 32, {1, 7}, {2, 16}, {3, 7}}, {{2, 32, 4, 5, 7}, {1, 32, 1, 16, 3}, {3, 32, 7, 2, 4}, }},
+            {{{1, 5}, 32, {1, 7}, {1, 11}, {3, 7}}, {{2, 32, 4, 1, 7}, {1, 32, 1, 3, 3}, {3, 32, 7, 11, 4}}},
+            {{{1, 6}, 32, {1, 15}, {1, 10}, {1, 20}}, {{2, 32, 4, 10, 7}, {1, 32, 1, 5, 3}, {3, 32, 7, 1, 4}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block_dynamic_axis_3, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(3),
+                                ::testing::ValuesIn(inputShapes5D_Block_axis3),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked8_5D_ref, blocked16_5D_ref)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes5D_axis3 = {
+        {
+            {{-1, -1, -1, -1, -1}, {{2, 32, 4, 5, 7}, {1, 11, 1, 16, 3}, {3, 7, 7, 2, 4}}},
+            {{-1, -1, -1, -1, -1}, {{2, 32, 4, 1, 7}, {1, 11, 1, 3, 3}, {3, 7, 7, 11, 4}}},
+            {{-1, -1, -1, -1, -1}, {{2, 32, 4, 10, 7}, {1, 11, 1, 5, 3}, {3, 7, 7, 1, 4}}},
+        },
+        {
+            {{{1, 7}, {7, 32}, {1, 7}, {1, 16}, {3, 14}}, {{2, 32, 4, 5, 7}, {1, 11, 1, 16, 3}, {3, 7, 7, 2, 4},  }},
+            {{{1, 7}, {7, 32}, {1, 10}, {1, 11}, {3, 7}}, {{2, 32, 4, 1, 7}, {1, 11, 1, 3, 3}, {3, 7, 7, 11, 4}}},
+            {{{1, 7}, {1, 32}, {1, 10}, {1, 10}, {1, 10}}, {{2, 32, 4, 10, 7}, {1, 11, 1, 5, 3}, {3, 7, 7, 1, 4}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_dynamic_axis_3, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(3),
+                                ::testing::ValuesIn(inputShapes5D_axis3),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D_ref, planarChannels_5D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes5D_Block_axis4 = {
+        {
+            {{-1, 32, -1, -1, -1}, {{2, 32, 4, 5, 5}, {1, 32, 1, 1, 16}, {3, 32, 7, 9, 2}, }},
+            {{-1, 32, -1, -1, -1}, {{2, 32, 4, 5, 1}, {1, 32, 1, 1, 3}, {3, 32, 7, 9, 11}}},
+            {{-1, 32, -1, -1, -1}, {{2, 32, 4, 5, 10}, {1, 32, 1, 1, 5}, {3, 32, 7, 9, 1}}},
+        },
+        {
+            {{{1, 15}, 32, {1, 10}, {1, 10}, {1, 16}}, {{2, 32, 4, 5, 5}, {1, 32, 1, 1, 16}, {3, 32, 7, 9, 2}, }},
+            {{{1, 15}, 32, {1, 10}, {1, 10}, {1, 11}}, {{2, 32, 4, 5, 1}, {1, 32, 1, 1, 3}, {3, 32, 7, 9, 11}}},
+            {{{1, 15}, 32, {1, 10}, {1, 10}, {1, 11}}, {{2, 32, 4, 5, 10}, {1, 32, 1, 1, 5}, {3, 32, 7, 9, 1}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block_dynamic_axis_4, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(4),
+                                ::testing::ValuesIn(inputShapes5D_Block_axis4),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked8_5D_ref, blocked16_5D_ref)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes5D_axis4 = {
+        {
+            {{-1, -1, -1, -1, -1}, {{2, 1, 4, 5, 5},  {1, 4, 1, 1, 16}, {3, 14, 7, 9, 2}}},
+            {{-1, -1, -1, -1, -1}, {{2, 1, 4, 5, 1}, {1, 4, 1, 1, 3}, {3, 14, 7, 9, 11}}},
+            {{-1, -1, -1, -1, -1}, {{2, 1, 4, 5, 10}, {1, 4, 1, 1, 5}, {3, 14, 7, 9, 1}}},
+        },
+        {
+            {{{1, 3}, {1, 14}, {1, 7}, {1, 10}, {2, 16}}, {{2, 1, 4, 5, 5}, {1, 4, 1, 1, 16}, {3, 14, 7, 9, 2}}},
+            {{{1, 3}, {1, 14}, {1, 7}, {1, 9}, {1, 11}}, {{2, 1, 4, 5, 1}, {1, 4, 1, 1, 3}, {3, 14, 7, 9, 11}}},
+            {{{1, 3}, {1, 14}, {1, 7}, {1, 9}, {1, 10}}, {{2, 1, 4, 5, 10}, {1, 4, 1, 1, 5}, {3, 14, 7, 9, 1}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_dynamic_axis_4, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(4),
+                                ::testing::ValuesIn(inputShapes5D_axis4),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D_ref, planarChannels_5D)),
                         ConcatLayerCPUTest::getTestCaseName);
 
+const std::vector<std::vector<InputShape>> inputShapes_byBatch = {
+        static_shapes_to_test_representation({{5, 2, 2, 2}, {2, 2, 2, 2}}),
+        static_shapes_to_test_representation({{1, 3, 5}, {3, 3, 5}}),
+        static_shapes_to_test_representation({{4, 3, 2}, {1, 3, 2}}),
+        // 5D
+        {
+            {{-1, -1, -1, -1, -1}, {{10, 32, 4, 5, 5}, {4, 7, 1, 1, 3},  {3, 20, 7, 9, 1}, }},
+            {{-1, -1, -1, -1, -1}, {{5, 32, 4, 5, 5}, {7, 7, 1, 1, 3}, {3, 20, 7, 9, 1}}},
+            {{-1, -1, -1, -1, -1}, {{1, 32, 4, 5, 5}, {1, 7, 1, 1, 3}, {6, 20, 7, 9, 1}}},
+        },
+        {
+            {{{3, 10}, {7, 32}, {1, 9}, {1, 10}, {1, 5}}, {{10, 32, 4, 5, 5}, {4, 7, 1, 1, 3},  {3, 20, 7, 9, 1}, }},
+            {{{3, 7}, {7, 32}, {1, 7}, {1, 9}, {1, 5}}, {{5, 32, 4, 5, 5}, {7, 7, 1, 1, 3}, {3, 20, 7, 9, 1}}},
+            {{{1, 6}, {7, 32}, {1, 7}, {1, 9}, {1, 5}}, {{1, 32, 4, 5, 5}, {1, 7, 1, 1, 3}, {6, 20, 7, 9, 1}}},
+        },
+        // 4D
+        {
+            {{-1, -1, -1, -1}, {{10, 32, 4, 5}, {4, 7, 1, 1},  {3, 20, 7, 9}, }},
+            {{-1, -1, -1, -1}, {{5, 32, 4, 5}, {7, 7, 1, 1}, {3, 20, 7, 9}}},
+            {{-1, -1, -1, -1}, {{1, 32, 4, 5}, {1, 7, 1, 1}, {6, 20, 7, 9}}},
+        },
+        {
+            {{{1, 10}, {1, 32}, {1, 7}, {1, 9}}, {{10, 32, 4, 5}, {4, 7, 1, 1},  {3, 20, 7, 9}, }},
+            {{{3, 7}, {7, 32}, {1, 7}, {1, 9}}, {{5, 32, 4, 5}, {7, 7, 1, 1}, {3, 20, 7, 9}}},
+            {{{1, 6}, {7, 32}, {1, 7}, {1, 9}}, {{1, 32, 4, 5}, {1, 7, 1, 1}, {6, 20, 7, 9}}},
+        }
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_Concat_byBatch, ConcatLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Values(0),
-                                 ::testing::Values(std::vector<std::vector<size_t>>{{5, 2, 2, 2},
-                                                                                    {2, 2, 2, 2}},
-                                                   std::vector<std::vector<size_t>>{{1, 3, 5},
-                                                                                    {3, 3, 5}},
-                                                   std::vector<std::vector<size_t>>{{4, 3, 2},
-                                                                                    {1, 3, 2}}),
+                                 ::testing::ValuesIn(inputShapes_byBatch),
                                  ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                  ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"})),
                                  ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat_3D, ConcatLayerCPUTest,
+const std::vector<std::vector<InputShape>> inputShapes3D_axis1 = {
+        static_shapes_to_test_representation({{2, 4, 5}, {2, 4, 5}}),
+        {
+            {{-1, -1, -1}, {{2, 5, 12}, {1, 16, 1}, {5, 2, 6}, }},
+            {{-1, -1, -1}, {{2, 1, 12}, {1, 3, 1}, {5, 11, 6}}},
+            {{-1, -1, -1}, {{2, 10, 12}, {1, 5, 1}, {5, 1, 6}}},
+        },
+        {
+            {{{1, 5}, {2, 16}, {1, 12}}, {{2, 5, 12}, {1, 16, 1}, {5, 2, 6}, }},
+            {{{1, 5}, {1, 11}, {1, 21}}, {{2, 1, 12}, {1, 3, 1}, {5, 11, 6}}},
+            {{{1, 5}, {1, 10}, {1, 12}}, {{2, 10, 12}, {1, 5, 1}, {5, 1, 6}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_3D_axis1, ConcatLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(1, 2),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{2, 4, 5},
-                                                                                   {2, 4, 5}}),
+                                ::testing::Values(1),
+                                ::testing::ValuesIn(inputShapes3D_axis1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
                         ConcatLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Concat_2D, ConcatLayerCPUTest,
+const std::vector<std::vector<InputShape>> inputShapes3D_axis2 = {
+        static_shapes_to_test_representation({{2, 4, 5}, {2, 4, 5}}),
+        {
+            {{-1, -1, -1}, {{4, 4, 5}, {3, 2, 16}, {1, 1, 2}}},
+            {{-1, -1, -1}, {{4, 4, 1}, {3, 2, 3}, {1, 1, 11}}},
+            {{-1, -1, -1}, {{4, 4, 10}, {3, 2, 5}, {1, 1, 1}}},
+        },
+        {
+            {{{1, 4}, {1, 4}, {2, 16}}, {{4, 4, 5}, {3, 2, 16}, {1, 1, 2}, }},
+            {{{1, 4}, {1, 4}, {1, 11}}, {{4, 4, 1}, {3, 2, 3}, {1, 1, 11}}},
+            {{{1, 4}, {1, 4}, {1, 10}}, {{4, 4, 10}, {3, 2, 5}, {1, 1, 1}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_3D_axis2, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(2),
+                                ::testing::ValuesIn(inputShapes3D_axis2),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes2D_axis1 = {
+        static_shapes_to_test_representation({{3, 2}, {3, 10}}),
+        {
+            {{-1, -1}, {{19, 5}, {1, 16}, {8, 2}, }},
+            {{-1, -1}, {{19, 1}, {1, 3}, {8, 11}}},
+            {{-1, -1}, {{19, 10}, {1, 5}, {8, 1}}},
+        },
+        {
+            {{{1, 19}, {2, 16}}, {{19, 5}, {1, 16}, {8, 2}, }},
+            {{{1, 19}, {1, 11}}, {{19, 1}, {1, 3}, {8, 11}}},
+            {{{1, 19}, {1, 10}}, {{19, 10}, {1, 5}, {8, 1}}},
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_2D_axis1, ConcatLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(1),
-                                ::testing::Values(std::vector<std::vector<size_t>>{{3, 2},
-                                                                                   {3, 10}}),
+                                ::testing::ValuesIn(inputShapes2D_axis1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
                         ConcatLayerCPUTest::getTestCaseName);
 
+const std::vector<std::vector<InputShape>> inputShapes1D = {
+        static_shapes_to_test_representation({ov::Shape{5}, ov::Shape{5}}),
+        static_shapes_to_test_representation({ov::Shape{2}, ov::Shape{2}}),
+        static_shapes_to_test_representation({ov::Shape{1}, ov::Shape{1}}),
+        static_shapes_to_test_representation({ov::Shape{3}, ov::Shape{3}}),
+        {
+            {{-1}, {{19}, {8}, {5}}},
+            {{-1}, {{19}, {8}, {5}}},
+            {{-1}, {{19}, {8}, {5}}},
+        },
+        {
+            {{{1, 20}}, {{19}, {8}, {5}}},
+            {{{1, 20}}, {{19}, {8}, {5}}},
+            {{{1, 20}}, {{19}, {8}, {5}}},
+        },
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_Concat_1D, ConcatLayerCPUTest,
-                         ::testing::Combine(
-                                 ::testing::Values(0),
-                                 ::testing::Values(std::vector<std::vector<size_t>>{{5},
-                                                                                    {2},
-                                                                                    {1},
-                                                                                    {3}}),
-                                 ::testing::ValuesIn(netPrecisions),
-                                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"})),
-                                 ConcatLayerCPUTest::getTestCaseName);
+                        ::testing::Combine(
+                                ::testing::Values(0),
+                                ::testing::ValuesIn(inputShapes1D),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"})),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+// ============================================== inPlace cases ============================================
+INSTANTIATE_TEST_SUITE_P(concat_Concat4D_CPU_Block8inPlace, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(0, 1),
+                                ::testing::Values(static_shapes_to_test_representation({{1, 8, 3, 5}, {1, 8, 3, 5}})),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_4D, planarChannels_4D, blocked8_4D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat4D_CPU_Block16inPlace, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(0, 1),
+                                ::testing::Values(static_shapes_to_test_representation({{1, 32, 3, 5}, {1, 32, 3, 5}})),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked16_4D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(concat_Concat5D_CPU_Block8inPlace, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(0, 1),
+                                ::testing::Values(static_shapes_to_test_representation({{1, 16, 3, 5, 7}, {1, 16, 3, 5, 7}})),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D, planarChannels_5D, blocked8_5D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat5D_CPU_Block16inPlace, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(0, 1),
+                                ::testing::Values(static_shapes_to_test_representation({{1, 32, 3, 5, 7}, {1, 32, 3, 5, 7}})),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked16_5D)),
+                        ConcatLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Concat_inPlace, ConcatLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(0, 1, 2),
+                                ::testing::ValuesIn(std::vector<std::vector<InputShape>>{
+                                    static_shapes_to_test_representation({{1, 1, 1, 10}, {1, 1, 1, 10}}),
+                                    static_shapes_to_test_representation({{1, 1, 5}, {1, 1, 5}})}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"})),
+                        ConcatLayerCPUTest::getTestCaseName);
 
 } // namespace
+
 } // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/convolution_backprop_data.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/convolution_backprop_data.cpp
index df0fdc1e2abb98..bf319b82ca5ecc 100755
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/convolution_backprop_data.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/convolution_backprop_data.cpp
@@ -51,6 +51,7 @@ class DeconvolutionLayerCPUTest : public testing::WithParamInterface<deconvLayer
         return result.str();
     }
 protected:
+    InferenceEngine::SizeVector kernel, stride;
     void SetUp() override {
         convBackpropDataLayerTestParamsSet basicParamsSet;
         CPUSpecificParams cpuParams;
@@ -76,7 +77,7 @@ class DeconvolutionLayerCPUTest : public testing::WithParamInterface<deconvLayer
         }
 
         ngraph::op::PadType padType;
-        InferenceEngine::SizeVector kernel, stride, dilation;
+        InferenceEngine::SizeVector dilation;
         std::vector<ptrdiff_t> padBegin, padEnd, outPadding;
         size_t convOutChannels;
         std::tie(kernel, stride, padBegin, padEnd, dilation, convOutChannels, padType, outPadding) = convParams;
@@ -102,7 +103,14 @@ TEST_P(DeconvolutionLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
     Run();
-    CheckPluginRelatedResults(executableNetwork, "Deconvolution");
+    CPUTestsBase::CheckPluginRelatedResults(executableNetwork, "Deconvolution");
+    bool isSupportedParams = stride[stride.size() - 1] <= kernel[kernel.size() - 1];
+    if (stride.size() > 1)
+        isSupportedParams &= stride[stride.size() - 2] <= kernel[kernel.size() - 2];
+    if (stride.size() > 2)
+        isSupportedParams &= stride[stride.size() - 3] <= kernel[kernel.size() - 3];
+    if (isSupportedParams)
+        CheckFusingResults(executableNetwork, "Deconvolution");
 }
 
 namespace {
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/cum_sum.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/cum_sum.cpp
new file mode 100644
index 00000000000000..90a1d12b5dfd3a
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/cum_sum.cpp
@@ -0,0 +1,259 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils/cpu_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+
+namespace CPULayerTestsDefinitions {
+
+using cumSumShape = std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>>;
+using cumSumParams = std::tuple<
+    ngraph::element::Type, // data precision
+    cumSumShape, // input shape
+    std::int64_t, // axis
+    bool, // exclusive
+    bool>; // reverse
+
+class CumSumLayerCPUTest : public testing::WithParamInterface<cumSumParams>, public ov::test::SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<cumSumParams> obj) {
+        ngraph::element::Type inputPrecision;
+        std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>> shapes;
+        std::int64_t axis;
+        bool exclusive;
+        bool reverse;
+        std::tie(inputPrecision, shapes, axis, exclusive, reverse) = obj.param;
+
+        std::ostringstream result;
+        result << inputPrecision << "_" << "IS=" << CommonTestUtils::partialShape2str(shapes.first) << "_" << "TS=";
+        for (const auto& shape : shapes.second) {
+            result << "(";
+            for (const auto& item : shape) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+            result << ")_";
+        }
+
+        result << "Axis=" << axis << "_" << (exclusive ? "exclusive" : "") << "_" << (reverse ? "reverse" : "");
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        ngraph::element::Type inputPrecision;
+        std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>> shapes;
+        std::int64_t axis;
+        bool exclusive;
+        bool reverse;
+        std::tie(inputPrecision, shapes, axis, exclusive, reverse) = this->GetParam();
+
+        for (size_t i = 0; i < shapes.second.size(); i++) {
+            targetStaticShapes.push_back(shapes.second[i]);
+        }
+        inputDynamicShapes = shapes.first;
+
+        auto params = ngraph::builder::makeDynamicParams(inputPrecision, { inputDynamicShapes.front() });
+        auto axisNode = ngraph::opset1::Constant::create(ngraph::element::i32, ngraph::Shape{}, std::vector<int64_t>{axis})->output(0);
+        auto cumSum = ngraph::builder::makeCumSum(params[0], axisNode, exclusive, reverse);
+
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ cumSum }, params, "CumSumLayerCPUTest");
+        functionRefs = ngraph::clone_function(*function);
+    }
+};
+
+TEST_P(CumSumLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    // TODO: Should be uncommented after updating the CheckPluginRelatedResults() method
+    //CheckPluginRelatedResults(executableNetwork, "CumSum");
+}
+
+const ngraph::element::TypeVector inputPrecision = {
+    ngraph::element::i8,
+    ngraph::element::u8,
+    ngraph::element::i16,
+    ngraph::element::i32,
+    ngraph::element::f32
+};
+
+const std::vector<int64_t> axes = { 0, 1, 2, 3, 4, 5, 6 };
+const std::vector<int64_t> negativeAxes = { -1, -2, -3, -4, -5, -6 };
+
+const std::vector<bool> exclusive = { true, false };
+const std::vector<bool> reverse = { true, false };
+
+const std::vector<cumSumShape> inShapes = {
+    {
+        // dynamic
+        {
+            {-1}
+        },
+        // target
+        {
+            {{16}, {18}, {12}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {-1, -1}
+        },
+        // target
+        {
+            {{9, 15}, {18, 12}, {12, 12}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {-1, -1, -1}
+        },
+        // target
+        {
+            {{16, 10, 12}, {18, 12, 10}, {12, 18, 10}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {-1, -1, -1, -1}
+        },
+        // target
+        {
+            {{18, 20, 14, 12}, {19, 20, 14, 12}, {20, 22, 23, 25}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {-1, -1, -1, -1, -1}
+        },
+        // target
+        {
+            {{2, 4, 6, 2, 4}, {3, 5, 6, 3, 5}, {1, 4, 2, 6, 8}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {-1, -1, -1, -1, -1, -1}
+        },
+        // target
+        {
+            {{2, 4, 6, 2, 4, 2}, {3, 5, 6, 3, 5, 3}, {1, 4, 2, 6, 8, 1}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {-1, -1, -1, -1, -1, -1, -1}
+        },
+        // target
+        {
+            {{2, 4, 6, 2, 4, 2, 4}, {3, 5, 6, 3, 5, 3, 5}, {1, 4, 2, 6, 8, 1, 4}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {{2, 5}, {3, 7}, {4, 8}, {5, 7}, {2, 5}, {3, 7}, {1, 2}}
+        },
+        // target
+        {
+            {{2, 4, 6, 5, 4, 3, 1}, {3, 5, 6, 6, 5, 3, 1}, {5, 7, 4, 6, 3, 7, 2}}
+        }
+    },
+    {
+        // dynamic
+        {
+            {{2, 5}, -1, {4, 8}, -1, -1, {3, 7}, -1}
+        },
+        // target
+        {
+            {{2, 4, 6, 5, 4, 3, 1}, {3, 5, 6, 6, 5, 3, 1}, {5, 7, 4, 6, 3, 7, 2}}
+        }
+    },
+};
+
+const auto testCasesAxis_0 = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(inShapes),
+    ::testing::Values(axes[0]),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+const auto testCasesAxis_1 = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(std::vector<cumSumShape>(inShapes.begin() + 1, inShapes.end())),
+    ::testing::Values(axes[1]),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+const auto testCasesAxis_2 = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(std::vector<cumSumShape>(inShapes.begin() + 2, inShapes.end())),
+    ::testing::Values(axes[2]),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+const auto testCasesAxis_3 = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(std::vector<cumSumShape>(inShapes.begin() + 3, inShapes.end())),
+    ::testing::Values(axes[3]),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+const auto testCasesAxis_4 = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(std::vector<cumSumShape>(inShapes.begin() + 4, inShapes.end())),
+    ::testing::Values(axes[4]),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+const auto testCasesAxis_5 = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(std::vector<cumSumShape>(inShapes.begin() + 5, inShapes.end())),
+    ::testing::Values(axes[5]),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+const auto testCasesAxis_6 = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(std::vector<cumSumShape>(inShapes.begin() + 6, inShapes.end())),
+    ::testing::Values(axes[6]),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+const auto testCasesAxis_negative = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecision),
+    ::testing::ValuesIn(std::vector<cumSumShape>(inShapes.begin() + 6, inShapes.end())),
+    ::testing::ValuesIn(negativeAxes),
+    ::testing::ValuesIn(exclusive),
+    ::testing::ValuesIn(reverse)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_axis_0, CumSumLayerCPUTest, testCasesAxis_0, CumSumLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_axis_1, CumSumLayerCPUTest, testCasesAxis_1, CumSumLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_axis_2, CumSumLayerCPUTest, testCasesAxis_2, CumSumLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_axis_3, CumSumLayerCPUTest, testCasesAxis_3, CumSumLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_axis_4, CumSumLayerCPUTest, testCasesAxis_4, CumSumLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_axis_5, CumSumLayerCPUTest, testCasesAxis_5, CumSumLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_axis_6, CumSumLayerCPUTest, testCasesAxis_6, CumSumLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefsNumpy_negative_axes, CumSumLayerCPUTest, testCasesAxis_negative, CumSumLayerCPUTest::getTestCaseName);
+
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/depth_to_space.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/depth_to_space.cpp
index f4d6df8e3b740e..4462137911ba0c 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/depth_to_space.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/depth_to_space.cpp
@@ -2,56 +2,78 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <shared_test_classes/single_layer/depth_to_space.hpp>
+#include "shared_test_classes/single_layer/depth_to_space.hpp"
 #include "test_utils/cpu_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
 
 using namespace InferenceEngine;
 using namespace CPUTestUtils;
 using namespace ngraph::opset3;
+using namespace ov::test;
 
 namespace CPULayerTestsDefinitions {
 
-typedef std::tuple<
-        LayerTestsDefinitions::depthToSpaceParamsTuple,
+using DepthToSpaceLayerCPUTestParamSet = std::tuple<
+        InputShape,                                     // Input shape
+        ElementType,                                    // Input element type
+        DepthToSpace::DepthToSpaceMode,                 // Mode
+        std::size_t,                                    // Block size
         CPUSpecificParams
-> DepthToSpaceLayerCPUTestParamSet;
+>;
 
 class DepthToSpaceLayerCPUTest : public testing::WithParamInterface<DepthToSpaceLayerCPUTestParamSet>,
-                        virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+                                 virtual public ov::test::SubgraphBaseTest, public CPUTestsBase {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<DepthToSpaceLayerCPUTestParamSet> obj) {
-        LayerTestsDefinitions::depthToSpaceParamsTuple basicParamsSet;
+        InputShape shapes;
+        ElementType inType;
+        DepthToSpace::DepthToSpaceMode mode;
+        std::size_t blockSize;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = obj.param;
-
-        std::ostringstream result;
-        result << LayerTestsDefinitions::DepthToSpaceLayerTest::getTestCaseName(
-                testing::TestParamInfo<LayerTestsDefinitions::depthToSpaceParamsTuple>(basicParamsSet, 0));
-
-        result << CPUTestsBase::getTestCaseName(cpuParams);
-
-        return result.str();
+        std::tie(shapes, inType, mode, blockSize, cpuParams) = obj.param;
+
+        std::ostringstream results;
+        results << "IS=" << CommonTestUtils::partialShape2str({shapes.first}) << "_";
+        results << "TS=";
+        for (const auto& item : shapes.second) {
+            results << CommonTestUtils::vec2str(item) << "_";
+        }
+        results << "Prc=" << inType << "_";
+        switch (mode) {
+            case DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST:
+                results << "BLOCKS_FIRST_";
+                break;
+            case DepthToSpace::DepthToSpaceMode::DEPTH_FIRST:
+                results << "DEPTH_FIRST_";
+                break;
+            default:
+                throw std::runtime_error("Unsupported DepthToSpaceMode");
+        }
+        results << "BS=" << blockSize << "_";
+        results << CPUTestsBase::getTestCaseName(cpuParams);
+
+        return results.str();
     }
 protected:
     void SetUp() override {
-        LayerTestsDefinitions::depthToSpaceParamsTuple basicParamsSet;
+        InputShape shapes;
+        ElementType inType;
+        DepthToSpace::DepthToSpaceMode mode;
+        std::size_t blockSize;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+        std::tie(shapes, inType, mode, blockSize, cpuParams) = this->GetParam();
 
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-
-        std::vector<size_t> inputShape;
-        DepthToSpace::DepthToSpaceMode mode;
-        std::size_t blockSize;
-        InferenceEngine::Precision netPrecision;
-        std::tie(inputShape, netPrecision, mode, blockSize, targetDevice) = basicParamsSet;
-
-        inPrc = outPrc = netPrecision;
-        selectedType = getPrimitiveType() + "_" + inPrc.name();
-        auto inPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(inPrc, {inputShape});
-        auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
-        auto d2s = ngraph::builder::makeDepthToSpace(paramOuts[0], mode, blockSize);
+        if (selectedType.empty()) {
+            selectedType = getPrimitiveType();
+        }
+        selectedType = selectedType + "_" + InferenceEngine::details::convertPrecision(inType).name();
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes({shapes});
+
+        auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+        auto d2s = ngraph::builder::makeDepthToSpace(params[0], mode, blockSize);
         d2s->get_rt_info() = getCPUInfo();
         ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(d2s)};
         function = std::make_shared<ngraph::Function>(results, params, "DepthToSpaceCPU");
@@ -61,35 +83,51 @@ class DepthToSpaceLayerCPUTest : public testing::WithParamInterface<DepthToSpace
 TEST_P(DepthToSpaceLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
-    CheckPluginRelatedResults(executableNetwork, "DepthToSpace");
+    run();
+    // TODO: need to uncomment when this method will be updated
+    // CheckPluginRelatedResults(executableNetwork, "DepthToSpace");
 }
 
 namespace {
 
-const auto cpuParams_nChw16c = CPUSpecificParams {{nChw16c}, {nChw16c}, {"jit_avx512"}, {"jit_avx512"}};
-const auto cpuParams_nCdhw16c = CPUSpecificParams {{nCdhw16c}, {nCdhw16c}, {"jit_avx512"}, {"jit_avx512"}};
+const auto cpuParams_nChw16c = CPUSpecificParams {{nChw16c}, {nChw16c}, {}, {}};
+const auto cpuParams_nCdhw16c = CPUSpecificParams {{nCdhw16c}, {nCdhw16c}, {}, {}};
 
-const auto cpuParams_nChw8c_avx2 = CPUSpecificParams {{nChw8c}, {nChw8c}, {"jit_avx2"}, {"jit_avx2"}};
-const auto cpuParams_nCdhw8c_avx2 = CPUSpecificParams {{nCdhw8c}, {nCdhw8c}, {"jit_avx2"}, {"jit_avx2"}};
+const auto cpuParams_nChw8c = CPUSpecificParams {{nChw8c}, {nChw8c}, {}, {}};
+const auto cpuParams_nCdhw8c = CPUSpecificParams {{nCdhw8c}, {nCdhw8c}, {}, {}};
 
-const auto cpuParams_nChw8c_sse42 = CPUSpecificParams {{nChw8c}, {nChw8c}, {"jit_sse42"}, {"jit_sse42"}};
-const auto cpuParams_nCdhw8c_sse42 = CPUSpecificParams {{nCdhw8c}, {nCdhw8c}, {"jit_sse42"}, {"jit_sse42"}};
+const auto cpuParams_nhwc = CPUSpecificParams {{nhwc}, {nhwc}, {}, {}};
+const auto cpuParams_ndhwc = CPUSpecificParams {{ndhwc}, {ndhwc}, {}, {}};
 
-const auto cpuParams_nhwc_avx2 = CPUSpecificParams {{nhwc}, {nhwc}, {"jit_avx2"}, {"jit_avx2"}};
-const auto cpuParams_ndhwc_avx2 = CPUSpecificParams {{ndhwc}, {ndhwc}, {"jit_avx2"}, {"jit_avx2"}};
+const auto cpuParams_nchw = CPUSpecificParams {{nchw}, {nchw}, {}, {}};
+const auto cpuParams_ncdhw = CPUSpecificParams {{ncdhw}, {ncdhw}, {}, {}};
 
-const auto cpuParams_nhwc_sse42 = CPUSpecificParams {{nhwc}, {nhwc}, {"jit_sse42"}, {"jit_sse42"}};
-const auto cpuParams_ndhwc_sse42 = CPUSpecificParams {{ndhwc}, {ndhwc}, {"jit_sse42"}, {"jit_sse42"}};
+const std::vector<CPUSpecificParams> CPUParams4D = {
+        cpuParams_nhwc,
+        cpuParams_nchw
+};
 
-const auto cpuParams_nhwc_ref = CPUSpecificParams {{nhwc}, {nhwc}, {"ref_any"}, {"ref_any"}};
-const auto cpuParams_ndhwc_ref = CPUSpecificParams {{ndhwc}, {ndhwc}, {"ref_any"}, {"ref_any"}};
+const std::vector<CPUSpecificParams> CPUParamsBlocked4D = {
+        cpuParams_nChw16c,
+        cpuParams_nChw8c,
+        cpuParams_nhwc
+};
 
+const std::vector<CPUSpecificParams> CPUParams5D = {
+        cpuParams_ndhwc,
+        cpuParams_ncdhw
+};
 
-const std::vector<InferenceEngine::Precision> inputPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::BF16,
-        InferenceEngine::Precision::I8
+const std::vector<CPUSpecificParams> CPUParamsBlocked5D = {
+        cpuParams_nCdhw16c,
+        cpuParams_nCdhw8c,
+        cpuParams_ndhwc
+};
+
+const std::vector<ElementType> inputElementType = {
+        ElementType::f32,
+        ElementType::bf16,
+        ElementType::i8
 };
 
 const std::vector<DepthToSpace::DepthToSpaceMode> depthToSpaceModes = {
@@ -97,99 +135,162 @@ const std::vector<DepthToSpace::DepthToSpaceMode> depthToSpaceModes = {
         DepthToSpace::DepthToSpaceMode::DEPTH_FIRST
 };
 
-const std::vector<std::vector<size_t >> inputShapesBS2_4D = {
-        {1, 64, 1, 1}, {1, 64, 1, 3}, {1, 128, 3, 3}, {2, 128, 1, 1}, {1, 192, 2, 2}, {2, 256, 2, 3}, {1, 512, 2, 1}
+/* *========================* Static Shapes Tests *========================* */
+
+namespace static_shapes {
+
+const std::vector<ov::Shape> inputShapesBS2_4D = {
+        {1, 64,  1, 1},
+        {1, 64,  1, 3},
+        {1, 128, 3, 3},
+        {2, 128, 1, 1},
+        {1, 192, 2, 2},
+        {2, 256, 2, 3},
+        {1, 512, 2, 1}
 };
 
-const std::vector<std::vector<size_t >> inputShapesBS3_4D = {
-        {1, 27, 1, 1}, {1, 27, 2, 3}, {1, 18, 2, 3}, {3, 18, 1, 1}, {2, 18, 3, 1}
+const std::vector<ov::Shape> inputShapesBS3_4D = {
+        {1, 27, 1, 1},
+        {1, 27, 2, 3},
+        {1, 18, 2, 3},
+        {3, 18, 1, 1},
+        {2, 18, 3, 1}
 };
 
-const std::vector<CPUSpecificParams> CPUParamsBS2_4D = {
-        cpuParams_nChw16c,
-        cpuParams_nChw8c_avx2,
-        cpuParams_nChw8c_sse42,
-        cpuParams_nhwc_avx2,
-        cpuParams_nhwc_sse42,
-        cpuParams_nhwc_ref,
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceBS2_4D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::ValuesIn(static_shapes_to_test_representation(inputShapesBS2_4D)),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::ValuesIn(depthToSpaceModes),
+                                 testing::Values(1, 2),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBlocked4D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceStaticBS3_4D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::ValuesIn(static_shapes_to_test_representation(inputShapesBS3_4D)),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::ValuesIn(depthToSpaceModes),
+                                 testing::Values(1, 3),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParams4D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
+
+const std::vector<ov::Shape> inputShapesBS2_5D = {
+        {1, 128, 1, 1, 1},
+        {1, 128, 2, 1, 2},
+        {1, 256, 2, 1, 3},
+        {2, 256, 3, 1, 1},
+        {1, 384, 1, 2, 2},
+        {2, 512, 1, 2, 1}
 };
 
-const auto depthToSpaceBS2_4DParams = testing::Combine(
-        testing::Combine(
-                testing::ValuesIn(inputShapesBS2_4D),
-                testing::ValuesIn(inputPrecisions),
-                testing::ValuesIn(depthToSpaceModes),
-                testing::Values(1, 2),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBS2_4D))
-);
-
-INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceBS2_4D, DepthToSpaceLayerCPUTest, depthToSpaceBS2_4DParams, DepthToSpaceLayerCPUTest::getTestCaseName);
-
-const std::vector<CPUSpecificParams> CPUParamsBS3_4D = {
-        cpuParams_nhwc_avx2,
-        cpuParams_nhwc_sse42,
-        cpuParams_nhwc_ref,
+const std::vector<ov::Shape> inputShapesBS3_5D = {
+        {1, 54, 1, 1, 1},
+        {1, 54, 2, 1, 2},
+        {3, 54, 1, 1, 1},
+        {2, 54, 3, 1, 2},
+        {1, 54, 3, 2, 2}
 };
 
-const auto depthToSpaceBS3_4DParams = testing::Combine(
-        testing::Combine(
-                testing::ValuesIn(inputShapesBS3_4D),
-                testing::ValuesIn(inputPrecisions),
-                testing::ValuesIn(depthToSpaceModes),
-                testing::Values(1, 3),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        ::testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBS3_4D))
-);
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceStaticBS2_5D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::ValuesIn(static_shapes_to_test_representation(inputShapesBS2_5D)),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::ValuesIn(depthToSpaceModes),
+                                 testing::Values(1, 2),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBlocked5D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceBS3_4D, DepthToSpaceLayerCPUTest, depthToSpaceBS3_4DParams, DepthToSpaceLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceStaticBS3_5D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::ValuesIn(static_shapes_to_test_representation(inputShapesBS3_5D)),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::ValuesIn(depthToSpaceModes),
+                                 testing::Values(1, 3),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParams5D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
 
-const std::vector<std::vector<size_t >> inputShapesBS2_5D = {
-        {1, 128, 1, 1, 1}, {1, 128, 2, 1, 2}, {1, 256, 2, 1, 3}, {2, 256, 3, 1, 1}, {1, 384, 1, 2, 2}, {2, 512, 1, 2, 1}
-};
+} // namespace static_shapes
+/* *========================* *==================* *========================* */
 
-const std::vector<std::vector<size_t >> inputShapesBS3_5D = {
-        {1, 54, 1, 1, 1}, {1, 54, 2, 1, 2}, {3, 54, 1, 1, 1}, {2, 54, 3, 1, 2}, {1, 54, 3, 2, 2}
+
+/* *========================* Dynamic Shapes Tests *========================* */
+namespace dynamic_shapes {
+
+const std::vector<InputShape> inputShapes4D = {
+        {{-1, -1, -1 , -1},                                  // dynamic
+         {{2, 36, 1, 1}, {1, 36, 3, 1}, {1, 72, 1, 4}}},     // target
+
+        {{-1, 576, -1 , -1},                                 // dynamic
+         {{1, 576, 1, 1}, {1, 576, 2, 2}, {3, 576, 4, 1}}},  // target
+
+        {{{1, 5}, {36, 72}, {1, 16}, {1, 16}},               // dynamic
+         {{3, 36, 4, 4}, {1, 36, 16, 12}, {3, 72, 8, 8}}},   // target
 };
 
-const std::vector<CPUSpecificParams> CPUParamsBS2_5D = {
-        cpuParams_nCdhw16c,
-        cpuParams_nCdhw8c_avx2,
-        cpuParams_nCdhw8c_sse42,
-        cpuParams_ndhwc_avx2,
-        cpuParams_ndhwc_sse42,
-        cpuParams_ndhwc_ref,
+const std::vector<InputShape> inputShapes5D = {
+        {{-1, -1, -1, -1, -1},                                        // dynamic
+         {{2, 216, 1, 1, 1}, {1, 216, 3, 1, 2}, {1, 432, 2, 3, 1}}},  // target
+
+        {{{1, 3}, {216, 432}, {1, 4}, {1, 4}, {1, 4}},                // dynamic
+         {{3, 216, 2, 2, 2}, {1, 432, 1, 1, 1}}},                     // target
 };
 
-const auto depthToSpaceBS2_5DParams = testing::Combine(
-        testing::Combine(
-                testing::ValuesIn(inputShapesBS2_5D),
-                testing::ValuesIn(inputPrecisions),
-                testing::ValuesIn(depthToSpaceModes),
-                testing::Values(1, 2),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        ::testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBS2_5D))
-);
-
-INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceBS2_5D, DepthToSpaceLayerCPUTest, depthToSpaceBS2_5DParams, DepthToSpaceLayerCPUTest::getTestCaseName);
-
-const std::vector<CPUSpecificParams> CPUParamsBS3_5D = {
-        cpuParams_ndhwc_avx2,
-        cpuParams_ndhwc_sse42,
-        cpuParams_ndhwc_ref,
+const std::vector<InputShape> inputShapesBlocked5D = {
+        {{-1, 256, -1, -1, -1},                                          // dynamic
+         {{1, 256, 1, 1, 1}, {1, 256, 2, 1, 4}, {3, 256, 4, 1, 2}}},     // target
+
+        {{{1, 3}, 256, {1, 3}, {1, 3}, {1, 3}},                          // dynamic
+         {{1, 256, 1, 1, 1}, {1, 256, 2, 1, 3}, {3, 256, 3, 1, 2}}},     // target
 };
 
-const auto depthToSpaceBS3_5DParams = testing::Combine(
-        testing::Combine(
-                testing::ValuesIn(inputShapesBS3_5D),
-                testing::ValuesIn(inputPrecisions),
-                testing::ValuesIn(depthToSpaceModes),
-                testing::Values(1, 3),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        ::testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBS3_5D))
-);
-
-INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceBS3_5D, DepthToSpaceLayerCPUTest, depthToSpaceBS3_5DParams, DepthToSpaceLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceDynamic4D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::ValuesIn(inputShapes4D),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::ValuesIn(depthToSpaceModes),
+                                 testing::Values(1, 2, 3),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParams4D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceDynamicBlocksFirstBlocked4D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::Values(inputShapes4D[1]),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::Values(DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST),
+                                 testing::Values(1, 2, 3),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBlocked4D))),
+                        DepthToSpaceLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceDynamicDepthFirstBlocked4D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::Values(inputShapes4D[1]),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::Values(DepthToSpace::DepthToSpaceMode::DEPTH_FIRST),
+                                 testing::Values(1, 2),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBlocked4D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceDynamic5D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::ValuesIn(inputShapes5D),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::ValuesIn(depthToSpaceModes),
+                                 testing::Values(1, 2, 3),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParams5D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CPUDepthToSpaceDynamicCPUSpecific5D, DepthToSpaceLayerCPUTest,
+                         testing::Combine(
+                                 testing::ValuesIn(inputShapesBlocked5D),
+                                 testing::ValuesIn(inputElementType),
+                                 testing::ValuesIn(depthToSpaceModes),
+                                 testing::Values(1, 2),
+                                 testing::ValuesIn(filterCPUInfoForDevice(CPUParamsBlocked5D))),
+                         DepthToSpaceLayerCPUTest::getTestCaseName);
+
+} // namespace dynamic_shapes
+/* *========================* *==================* *========================* */
 
 } // namespace
 } // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/gather_nd.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/gather_nd.cpp
new file mode 100644
index 00000000000000..13876aeb174ee1
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/gather_nd.cpp
@@ -0,0 +1,193 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <shared_test_classes/single_layer/gather_nd.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+
+using namespace InferenceEngine;
+using namespace ov;
+using namespace test;
+
+namespace CPULayerTestsDefinitions {
+
+using GatherNDLayerCPUTestParamSet = std::tuple<
+        InputShape,                                     // Input shapes
+        std::pair<Shape, std::vector<int>>,             // Indexes shape and values
+        ElementType,                                    // Input element type
+        ElementType,                                    // Indices element type
+        int                                             // Batch dims
+>;
+
+class GatherNDLayerCPUTest : public testing::WithParamInterface<GatherNDLayerCPUTestParamSet>,
+                             virtual public SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<GatherNDLayerCPUTestParamSet> obj) {
+        InputShape shapes;
+        std::pair<Shape, std::vector<int>> indexes;
+        ElementType dataElementType, idxElementType;
+        int batchDims;
+        std::tie(shapes, indexes, dataElementType, idxElementType, batchDims) = obj.param;
+
+        std::ostringstream results;
+        results << "IS=" << CommonTestUtils::partialShape2str({shapes.first}) << "_";
+        results << "TS=";
+        for (const auto& item : shapes.second) {
+            results << CommonTestUtils::vec2str(item) << "_";
+        }
+        results << "IDXShape=" << CommonTestUtils::vec2str(indexes.first) << "_";
+        results << "SRCPrc=" << dataElementType << "_";
+        results << "IDXPrc=" << idxElementType << "_";
+        results << "BD=" << batchDims << "_";
+
+        return results.str();
+}
+
+protected:
+    void SetUp() override {
+        InputShape shapes;
+        std::pair<Shape, std::vector<int>> indexes;
+        ElementType dataElementType, idxElementType;
+        int batchDims;
+        std::tie(shapes, indexes, dataElementType, idxElementType, batchDims) = this->GetParam();
+
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes({shapes});
+
+        auto params = ngraph::builder::makeDynamicParams(dataElementType, inputDynamicShapes);
+        auto indexes_node = ngraph::opset3::Constant::create(idxElementType, indexes.first, indexes.second);
+        auto gather_nd = std::make_shared<ngraph::opset5::GatherND>(params[0], indexes_node, batchDims);
+        ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(gather_nd)};
+        function = std::make_shared<ngraph::Function>(results, params, "gatherND");
+    }
+};
+
+class GatherND8LayerCPUTest : public testing::WithParamInterface<GatherNDLayerCPUTestParamSet>,
+                              virtual public SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<GatherNDLayerCPUTestParamSet> obj) {
+        return GatherNDLayerCPUTest::getTestCaseName(obj);
+    }
+
+protected:
+    void SetUp() override {
+        InputShape shapes;
+        std::pair<Shape, std::vector<int>> indexes;
+        ElementType dataElementType, idxElementType;
+        int batchDims;
+        std::tie(shapes, indexes, dataElementType, idxElementType, batchDims) = this->GetParam();
+
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes({shapes});
+
+        auto params = ngraph::builder::makeDynamicParams(dataElementType, inputDynamicShapes);
+        auto indexes_node = ngraph::opset3::Constant::create(idxElementType, indexes.first, indexes.second);
+        auto gather_nd = std::make_shared<ngraph::opset8::GatherND>(params[0], indexes_node, batchDims);
+        ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(gather_nd)};
+        function = std::make_shared<ngraph::Function>(results, params, "gatherND");
+    }
+};
+
+TEST_P(GatherNDLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+}
+
+TEST_P(GatherND8LayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+}
+
+namespace {
+
+const std::vector<ElementType> inputPrecisions = {
+        ElementType::f32,
+        ElementType::bf16,
+        ElementType::i8
+};
+
+const std::vector<ElementType> indexesPrecisions = {
+        ElementType::i32
+};
+
+const std::vector<InputShape> inputShapesDynamicBD_0 = {
+        {{-1, -1, -1},                                       // dynamic
+         {{5, 10, 5}, {4, 12, 4}, {4, 12, 4}, {5, 5, 5}}},   // target
+
+        {{-1, 5, -1, -1},                                    // dynamic
+         {{8, 5, 5, 5}, {5, 5, 8, 4}, {4, 5, 4, 5}}},        // target
+
+        {{{4, 10}, {5, 10}, {5, 10}, {5, 10}, {5, 10}},           // dynamic
+         {{4, 5, 5, 5, 5}, {4, 5, 5, 8, 5}, {10, 8, 5, 5, 5}}},   // target
+};
+
+const std::vector<std::pair<Shape, std::vector<int>>> indexesShapesBD_0 = {
+        std::pair<Shape, std::vector<int>>{{2, 2}, {3, 3, 2, 1}},
+        std::pair<Shape, std::vector<int>>{{1, 2, 3}, {0, 1, 1, 1, 0, 2}},
+        std::pair<Shape, std::vector<int>>{{2, 1, 1, 2}, {0, 2, 1, 1}},
+};
+
+const auto subset_BD0 = ::testing::Combine(
+        ::testing::ValuesIn(inputShapesDynamicBD_0),
+        ::testing::ValuesIn(indexesShapesBD_0),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::ValuesIn(indexesPrecisions),
+        ::testing::Values(0));
+
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND5DynamicBD_0, GatherNDLayerCPUTest, subset_BD0, GatherNDLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND8DynamicBD_0, GatherND8LayerCPUTest, subset_BD0, GatherNDLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapesDynamicBD_1 = {
+        {{3, -1, -1},                                            // dynamic
+         {{3, 10, 5}, {3, 10, 5}, {3, 12, 8}, {3, 8, 8}}},       // target
+
+        {{3, {5, 10}, {5, 10}, {5, 10}, {5, 10}},                 // dynamic
+         {{3, 5, 5, 5, 5}, {3, 8, 10, 10, 10}, {3, 8, 6, 8, 7}}}, // target
+};
+
+const std::vector<std::pair<Shape, std::vector<int>>> indexesShapesBD_1 = {
+        std::pair<Shape, std::vector<int>>{{3, 2}, {0, 1, 2, 1, 0, 0}},
+        std::pair<Shape, std::vector<int>>{{3, 2, 2}, {0, 1, 1, 1, 0, 2, 0, 1, 1, 1, 0, 2}},
+        std::pair<Shape, std::vector<int>>{{3, 1, 1, 2}, {0, 2, 1, 1, 0, 2}},
+};
+
+const auto subset_BD1 = ::testing::Combine(
+        ::testing::ValuesIn(inputShapesDynamicBD_1),
+        ::testing::ValuesIn(indexesShapesBD_1),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::ValuesIn(indexesPrecisions),
+        ::testing::Values(0));
+
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND5DynamicBD_1, GatherNDLayerCPUTest, subset_BD1, GatherNDLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND8DynamicBD_1, GatherND8LayerCPUTest, subset_BD1, GatherNDLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapesDynamicBD_2 = {
+        {{2, 2, -1, -1, -1},                                                       // dynamic
+         {{2, 2, 5, 6, 5}, {2, 2, 2, 3, 3}, {2, 2, 2, 3, 3}, {2, 2, 7, 2, 3}}},    // target
+
+        {{2, 2, {5, 10}, {5, 10}, {5, 10}},                                        // dynamic
+         {{2, 2, 5, 5, 5}, {2, 2, 10, 10, 5}, {2, 2, 7, 8, 7}}},                   // target
+};
+
+const std::vector<std::pair<Shape, std::vector<int>>> indexesShapesBD_2 = {
+        std::pair<Shape, std::vector<int>>{{2, 2, 3}, {0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0}},
+        std::pair<Shape, std::vector<int>>{{2, 2, 2, 3}, {0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,
+                                                                0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0}},
+};
+
+const auto subset_BD2 = ::testing::Combine(
+        ::testing::ValuesIn(inputShapesDynamicBD_2),
+        ::testing::ValuesIn(indexesShapesBD_2),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::ValuesIn(indexesPrecisions),
+        ::testing::Values(0));
+
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND5DynamicBD_2, GatherNDLayerCPUTest, subset_BD2, GatherNDLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_GatherND8DynamicBD_2, GatherND8LayerCPUTest, subset_BD2, GatherNDLayerCPUTest::getTestCaseName);
+
+
+}  // namespace
+} // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/group_convolution_backprop_data.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/group_convolution_backprop_data.cpp
index 1f50a43b80d178..0a18a2bc74abc2 100755
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/group_convolution_backprop_data.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/group_convolution_backprop_data.cpp
@@ -49,6 +49,7 @@ class GroupDeconvolutionLayerCPUTest : public testing::WithParamInterface<groupD
     }
 
 protected:
+    InferenceEngine::SizeVector kernel, stride;
     void SetUp() override {
         groupConvBackpropDataLayerTestParamsSet basicParamsSet;
         CPUSpecificParams cpuParams;
@@ -73,7 +74,7 @@ class GroupDeconvolutionLayerCPUTest : public testing::WithParamInterface<groupD
         }
 
         ngraph::op::PadType padType;
-        InferenceEngine::SizeVector kernel, stride, dilation;
+        InferenceEngine::SizeVector dilation;
         std::vector<ptrdiff_t> padBegin, padEnd, outputPadding;
         size_t convOutChannels, numGroups;
         std::tie(kernel, stride, padBegin, padEnd, dilation, convOutChannels, numGroups, padType, outputPadding) = groupConvParams;
@@ -101,7 +102,14 @@ TEST_P(GroupDeconvolutionLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
     Run();
-    CheckPluginRelatedResults(executableNetwork, "Deconvolution");
+    CPUTestsBase::CheckPluginRelatedResults(executableNetwork, "Deconvolution");
+    bool isSupportedParams = stride[stride.size() - 1] <= kernel[kernel.size() - 1];
+    if (stride.size() > 1)
+        isSupportedParams &= stride[stride.size() - 2] <= kernel[kernel.size() - 2];
+    if (stride.size() > 2)
+        isSupportedParams &= stride[stride.size() - 3] <= kernel[kernel.size() - 3];
+    if (isSupportedParams)
+        CheckFusingResults(executableNetwork, "Deconvolution");
 }
 
 namespace {
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/log_softmax.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/log_softmax.cpp
new file mode 100644
index 00000000000000..b3248d5aa2b299
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/log_softmax.cpp
@@ -0,0 +1,127 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils/cpu_test_utils.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+using logSoftmaxLayerTestParams = std::tuple<
+        std::vector<InputShape>,               // inputShape
+        Precision,                             // netPrecision
+        int64_t>;                              // axis
+
+class LogSoftmaxLayerCPUTest
+        : public testing::WithParamInterface<logSoftmaxLayerTestParams>,
+          public SubgraphBaseTest,
+          public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<logSoftmaxLayerTestParams> obj) {
+        std::vector<InputShape> inputShapes;
+        Precision netPrecision;
+        int64_t axis;
+        std::tie(inputShapes, netPrecision, axis) = obj.param;
+
+        std::ostringstream result;
+        if (inputShapes.front().first.size() != 0) {
+            result << "IS=(";
+            for (const auto &shape : inputShapes) {
+                result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+            }
+            result.seekp(-1, result.cur);
+            result << ")_";
+        }
+        result << "TS=";
+        for (const auto &shape : inputShapes) {
+            for (const auto &item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "netPRC=" << netPrecision.name();
+        result << "Axis=" << axis;
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        std::vector<InputShape> inputShapes;
+        Precision netPrecision;
+        int64_t axis;
+        std::tie(inputShapes, netPrecision, axis) = this->GetParam();
+
+        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+        inType = outType = ngPrc;
+
+        selectedType = std::string("unknown_") + netPrecision.name();
+        init_input_shapes(inputShapes);
+
+        const auto params = ngraph::builder::makeDynamicParams(ngPrc, {inputDynamicShapes.front()});
+        const auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+        const auto logSoftmax = std::make_shared<ngraph::op::v5::LogSoftmax>(paramOuts[0], axis);
+        const ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(logSoftmax)};
+        function = std::make_shared<ngraph::Function>(results, params, "logSoftmax");
+    }
+};
+
+TEST_P(LogSoftmaxLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    // TODO: Should be uncommented after updating the CheckPluginRelatedResults() method
+    // CheckPluginRelatedResults(executableNetwork, "logSoftmax");
+}
+
+namespace {
+const std::vector<InferenceEngine::Precision> netPrecisions = {
+        Precision::FP32
+};
+
+const std::vector<std::vector<InputShape>> inputShapes2D = {
+        {
+                {{{-1, -1}, {{1, 100}, {100, 1}, {10, 10}}}},
+                {{{-1, {1}}, {{1, 1}, {100, 1}, {10, 1}}}}
+        }
+};
+
+const std::vector<int64_t> axis2D = {
+        -2, -1, 0, 1
+};
+
+const auto params2D = testing::Combine(
+        testing::ValuesIn(inputShapes2D),
+        testing::ValuesIn(netPrecisions),
+        testing::ValuesIn(axis2D));
+
+INSTANTIATE_TEST_SUITE_P(smoke_LogSoftmax2D_dynamic, LogSoftmaxLayerCPUTest, params2D,
+                         LogSoftmaxLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<InputShape>> inputShapes4D = {
+        {
+                {{{-1, -1, -1, -1}, {{1, 100, 1, 1}, {1, 3, 4, 3}, {2, 3, 4, 5}}}},
+                {{{{1, 2}, -1, {1, 5}, -1}, {{1, 100, 1, 1}, {1, 3, 5, 3}, {2, 3, 4, 5}}}}
+        }
+};
+
+const std::vector<int64_t> axis4D = {
+        -4, -3, -2, -1, 0, 1, 2, 3
+};
+
+const auto params4D = testing::Combine(
+        testing::ValuesIn(inputShapes4D),
+        testing::ValuesIn(netPrecisions),
+        testing::ValuesIn(axis4D));
+
+INSTANTIATE_TEST_SUITE_P(smoke_LogSoftmax4D_dynamic, LogSoftmaxLayerCPUTest, params4D,
+                         LogSoftmaxLayerCPUTest::getTestCaseName);
+
+} // namespace
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp
index 8b87079f3b2479..0eccdbdce1cc31 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp
@@ -188,6 +188,8 @@ const std::vector<ShapeRelatedParams> IS3D = {
     {{{7, 32, 120}, true}, {{120, 50}, false}},
     {{{7, 32, 120}, false}, {{120, 50}, true}},
     {{{7, 32, 120}, true}, {{120, 50}, true}},
+
+    {{{1, 429}, false}, {{1, 429, 1}, true}},
 };
 
 std::vector<fusingSpecificParams> fusingParamsSet3D {
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/non_max_suppression.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/non_max_suppression.cpp
new file mode 100644
index 00000000000000..aad65a57160496
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/non_max_suppression.cpp
@@ -0,0 +1,446 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <tuple>
+#include <string>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace ov::test;
+using namespace ngraph;
+using namespace CPUTestUtils;
+
+namespace CPULayerTestsDefinitions {
+
+enum {
+    BATCHES,
+    BOXES,
+    CLASSES
+};
+
+using TargetShapeParams = std::tuple<size_t,   // Number of batches
+                                     size_t,   // Number of boxes
+                                     size_t>;  // Number of classes
+
+using InputShapeParams = std::tuple<std::vector<ov::Dimension>,       // bounds for input dynamic shape
+                                    std::vector<TargetShapeParams>>;  // target input dimensions
+
+using InputPrecisions = std::tuple<ElementType,  // boxes and scores precisions
+                                   ElementType,  // max_output_boxes_per_class precision
+                                   ElementType>; // iou_threshold, score_threshold, soft_nms_sigma precisions
+
+using ThresholdValues = std::tuple<float,  // IOU threshold
+                                   float,  // Score threshold
+                                   float>; // Soft NMS sigma
+
+using NmsParams = std::tuple<InputShapeParams,                                   // Params using to create 1st and 2nd inputs
+                             InputPrecisions,                                    // Input precisions
+                             int32_t,                                            // Max output boxes per class
+                             ThresholdValues,                                    // IOU, Score, Soft NMS sigma
+                             ngraph::helpers::InputLayerType,                    // max_output_boxes_per_class input type
+                             ngraph::op::v5::NonMaxSuppression::BoxEncodingType, // Box encoding
+                             bool,                                               // Sort result descending
+                             ngraph::element::Type,                              // Output type
+                             std::string>;                                       // Device name
+
+class NmsLayerCPUTest : public testing::WithParamInterface<NmsParams>, virtual public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<NmsParams>& obj) {
+        InputShapeParams inShapeParams;
+        InputPrecisions inPrecisions;
+        int32_t maxOutBoxesPerClass;
+        ngraph::helpers::InputLayerType maxOutBoxesType;
+        ThresholdValues thrValues;
+        float iouThr, scoreThr, softNmsSigma;
+        op::v5::NonMaxSuppression::BoxEncodingType boxEncoding;
+        bool sortResDescend;
+        element::Type outType;
+        std::string targetDevice;
+        std::tie(inShapeParams, inPrecisions, maxOutBoxesPerClass, thrValues, maxOutBoxesType, boxEncoding, sortResDescend, outType, targetDevice) = obj.param;
+
+        std::tie(iouThr, scoreThr, softNmsSigma) = thrValues;
+
+        ElementType paramsPrec, maxBoxPrec, thrPrec;
+        std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
+
+        std::vector<ov::Dimension> bounds;
+        std::vector<TargetShapeParams> targetShapes;
+        std::tie(bounds, targetShapes) = inShapeParams;
+
+        std::ostringstream result;
+        if (!bounds.empty()) {
+            IE_ASSERT(bounds.size() == 3);
+            result << "BatchesBounds=" << bounds[BATCHES] << "_BoxesBounds=" << bounds[BOXES] << "_ClassesBounds=" << bounds[CLASSES] << "_";
+        }
+        for (const auto &ts : targetShapes) {
+            size_t numBatches, numBoxes, numClasses;
+            std::tie(numBatches, numBoxes, numClasses) = ts;
+            result << "(nB=" << numBatches << "_nBox=" << numBoxes << "_nC=" << numClasses << ")_";
+        }
+        result << "paramsPrec=" << paramsPrec << "_maxBoxPrec=" << maxBoxPrec << "_thrPrec=" << thrPrec << "_";
+        result << "maxOutBoxesPerClass=" << maxOutBoxesPerClass << "_";
+        result << "iouThr=" << iouThr << "_scoreThr=" << scoreThr << "_softNmsSigma=" << softNmsSigma << "_";
+        result << "maxOutBoxesType=" << maxOutBoxesType << "_";
+        result << "boxEncoding=" << boxEncoding << "_sortResDescend=" << sortResDescend << "_outType=" << outType << "_";
+        result << "TargetDevice=" << targetDevice;
+        return result.str();
+    }
+
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+
+            if (i == 1) {
+                tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+
+                const size_t range = 1;
+                const size_t startFrom = 0;
+                const size_t k = 1000;
+                const int seed = 1;
+                std::default_random_engine random(seed);
+                std::uniform_int_distribution<int32_t> distribution(k * startFrom, k * (startFrom + range));
+
+                auto *dataPtr = tensor.data<float>();
+                for (size_t i = 0; i < tensor.get_size(); i++) {
+                    auto value = static_cast<float>(distribution(random));
+                    dataPtr[i] = value / static_cast<float>(k);
+                }
+            } else if (i == 2) {
+                tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i], &maxOutBoxesPerClass);
+            } else {
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+            }
+
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override {
+        CompareBBoxes(expected, actual);
+        inferRequestNum++;
+    }
+
+protected:
+    void SetUp() override {
+        InputShapeParams inShapeParams;
+        InputPrecisions inPrecisions;
+        ThresholdValues thrValues;
+        ngraph::helpers::InputLayerType maxOutBoxesType;
+        float iouThr, scoreThr, softNmsSigma;
+        op::v5::NonMaxSuppression::BoxEncodingType boxEncoding;
+        bool sortResDescend;
+        element::Type outType;
+        std::tie(inShapeParams, inPrecisions, maxOutBoxesPerClass, thrValues, maxOutBoxesType, boxEncoding, sortResDescend, outType,
+                 targetDevice) = this->GetParam();
+
+        element::Type paramsPrec, maxBoxPrec, thrPrec;
+        std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
+
+        std::tie(iouThr, scoreThr, softNmsSigma) = thrValues;
+
+        std::vector<ov::Dimension> bounds;
+        std::tie(bounds, targetInDims) = inShapeParams;
+
+        if (!bounds.empty()) {
+            inputDynamicShapes = std::vector<ngraph::PartialShape>{{bounds[BATCHES], bounds[BOXES], 4}, {bounds[BATCHES], bounds[CLASSES], bounds[BOXES]}};
+        } else {
+            size_t batches, boxes, classes;
+            std::tie(batches, boxes, classes) = targetInDims.front();
+            ov::Dimension numBatches(batches), numBoxes(boxes), numClasses(classes);
+            inputDynamicShapes = std::vector<ngraph::PartialShape>{{numBatches, numBoxes, 4}, {numBatches, numClasses, numBoxes}};
+        }
+
+        for (const auto &ts : targetInDims) {
+            size_t numBatches, numBoxes, numClasses;
+            std::tie(numBatches, numBoxes, numClasses) = ts;
+            targetStaticShapes.push_back(std::vector<ngraph::Shape>{{numBatches, numBoxes, 4}, {numBatches, numClasses, numBoxes}});
+            if (maxOutBoxesType == ngraph::helpers::InputLayerType::PARAMETER) {
+                targetStaticShapes.back().push_back(ngraph::Shape{1});
+            }
+        }
+
+        std::shared_ptr<ngraph::Node> maxOutBoxesPerClassNode;
+        auto params = ngraph::builder::makeDynamicParams(paramsPrec, inputDynamicShapes);
+        params[0]->set_friendly_name("param_1");
+        params[1]->set_friendly_name("param_2");
+
+        if (maxOutBoxesType == ngraph::helpers::InputLayerType::PARAMETER) {
+            inputDynamicShapes.push_back(ngraph::PartialShape{1});
+            params.push_back(std::make_shared<ngraph::opset1::Parameter>(element::Type_t::i32, inputDynamicShapes.back()));
+            params[1]->set_friendly_name("param_3");
+            maxOutBoxesPerClassNode = params.back();
+        } else {
+            maxOutBoxesPerClassNode = builder::makeConstant(maxBoxPrec, ngraph::Shape{}, std::vector<int32_t>{maxOutBoxesPerClass});
+        }
+
+        auto iouThrNode = builder::makeConstant(thrPrec, ngraph::Shape{}, std::vector<float>{iouThr})->output(0);
+        auto scoreThrNode = builder::makeConstant(thrPrec, ngraph::Shape{}, std::vector<float>{scoreThr})->output(0);
+        auto softNmsSigmaNode = builder::makeConstant(thrPrec, ngraph::Shape{}, std::vector<float>{softNmsSigma})->output(0);
+        auto nms = std::make_shared<ngraph::op::v5::NonMaxSuppression>(params[0], params[1], maxOutBoxesPerClassNode, iouThrNode, scoreThrNode,
+                                                                       softNmsSigmaNode, boxEncoding, sortResDescend, outType);
+
+        function = makeNgraphFunction(paramsPrec, params, nms, "NMS");
+    }
+
+private:
+    typedef struct Rect {
+        int32_t x1;
+        int32_t y1;
+        int32_t x2;
+        int32_t y2;
+    } Rect;
+
+    class Box {
+    public:
+        Box() = default;
+
+        Box(int32_t batchId, int32_t classId, int32_t boxId, Rect rect, float score) {
+            this->batchId = batchId;
+            this->classId = classId;
+            this->boxId = boxId;
+            this->rect = rect;
+            this->score = score;
+        }
+
+        int32_t batchId;
+        int32_t classId;
+        int32_t boxId;
+        Rect rect;
+        float score;
+    };
+
+    /*
+     * 1: selected_indices - tensor of type T_IND and shape [number of selected boxes, 3] containing information about selected boxes as triplets
+     *    [batch_index, class_index, box_index].
+     * 2: selected_scores - tensor of type T_THRESHOLDS and shape [number of selected boxes, 3] containing information about scores for each selected box as triplets
+     *    [batch_index, class_index, box_score].
+     * 3: valid_outputs - 1D tensor with 1 element of type T_IND representing the total number of selected boxes.
+     */
+    void CompareBBoxes(const std::vector<ov::runtime::Tensor> &expectedOutputs, const std::vector<ov::runtime::Tensor> &actualOutputs) {
+        size_t numBatches, numBoxes, numClasses;
+        std::tie(numBatches, numBoxes, numClasses) = targetInDims[inferRequestNum];
+
+        auto iouFunc = [](const Box& boxI, const Box& boxJ) {
+            const Rect& rectI = boxI.rect;
+            const Rect& rectJ = boxJ.rect;
+
+            float areaI = (rectI.y2 - rectI.y1) * (rectI.x2 - rectI.x1);
+            float areaJ = (rectJ.y2 - rectJ.y1) * (rectJ.x2 - rectJ.x1);
+
+            if (areaI <= 0.0f || areaJ <= 0.0f) {
+                return 0.0f;
+            }
+
+            float intersection_ymin = std::max(rectI.y1, rectJ.y1);
+            float intersection_xmin = std::max(rectI.x1, rectJ.x1);
+            float intersection_ymax = std::min(rectI.y2, rectJ.y2);
+            float intersection_xmax = std::min(rectI.x2, rectJ.x2);
+
+            float intersection_area =
+                std::max(intersection_ymax - intersection_ymin, 0.0f) *
+                std::max(intersection_xmax - intersection_xmin, 0.0f);
+
+            return intersection_area / (areaI + areaJ - intersection_area);
+        };
+
+        // Get input bboxes' coords
+        std::vector<std::vector<Rect>> coordList(numBatches, std::vector<Rect>(numBoxes));
+        {
+            std::pair<std::shared_ptr<ov::Node>, ov::runtime::Tensor> bboxes = *inputs.begin();
+            for (const auto &input : inputs) {
+                if (input.first->get_name() < bboxes.first->get_name()) {
+                    bboxes = input;
+                }
+            }
+
+            const auto buffer = bboxes.second.data<float>();
+            for (size_t i = 0; i < numBatches; ++i) {
+                for (size_t j = 0; j < numBoxes; ++j) {
+                    const int32_t y1 = static_cast<int32_t>(buffer[(i*numBoxes+j)*4+0]);
+                    const int32_t x1 = static_cast<int32_t>(buffer[(i*numBoxes+j)*4+1]);
+                    const int32_t y2 = static_cast<int32_t>(buffer[(i*numBoxes+j)*4+2]);
+                    const int32_t x2 = static_cast<int32_t>(buffer[(i*numBoxes+j)*4+3]);
+
+                    coordList[i][j] = { std::min(y1, y2),
+                                        std::min(x1, x2),
+                                        std::max(y1, y2),
+                                        std::max(x1, x2) };
+                }
+            }
+        }
+
+        auto compareBox = [](const Box& boxA, const Box& boxB) {
+            return (boxA.batchId < boxB.batchId) ||
+                    (boxA.batchId == boxB.batchId && boxA.classId < boxB.classId) ||
+                    (boxA.batchId == boxB.batchId && boxA.classId == boxB.classId && boxA.boxId < boxB.boxId);
+        };
+
+        // Get expected bboxes' index/score
+        std::vector<Box> expectedList;
+        {
+            const auto indeces_iter = expectedOutputs.begin();
+            const auto scores_iter = expectedOutputs.begin() + 1;
+            size_t selected_indices_size = indeces_iter->get_size();
+            size_t selected_scores_size = scores_iter->get_size();
+            ASSERT_TRUE(selected_indices_size == selected_scores_size);
+
+            expectedList.resize(selected_indices_size);
+
+            if (indeces_iter->get_element_type() == ov::element::i32) {
+                auto selected_indices_data = indeces_iter->data<int32_t>();
+
+                for (size_t i = 0; i < selected_indices_size; i += 3) {
+                    expectedList[i/3].batchId = selected_indices_data[i+0];
+                    expectedList[i/3].classId = selected_indices_data[i+1];
+                    expectedList[i/3].boxId   = selected_indices_data[i+2];
+                    expectedList[i/3].rect    = coordList[expectedList[i/3].batchId][expectedList[i/3].boxId];
+                }
+            } else {
+                auto selected_indices_data = indeces_iter->data<int64_t>();
+
+                for (size_t i = 0; i < selected_indices_size; i += 3) {
+                    expectedList[i/3].batchId = static_cast<int32_t>(selected_indices_data[i+0]);
+                    expectedList[i/3].classId = static_cast<int32_t>(selected_indices_data[i+1]);
+                    expectedList[i/3].boxId   = static_cast<int32_t>(selected_indices_data[i+2]);
+                    expectedList[i/3].rect    = coordList[expectedList[i/3].batchId][expectedList[i/3].boxId];
+                }
+            }
+
+            if (scores_iter->get_element_type() == ov::element::f32) {
+                auto selected_scores_data = scores_iter->data<float>();
+                for (size_t i = 0; i < selected_scores_size; i += 3) {
+                    expectedList[i/3].score = selected_scores_data[i+2];
+                }
+            } else {
+                auto selected_scores_data = scores_iter->data<double>();
+                for (size_t i = 0; i < selected_scores_size; i += 3) {
+                    expectedList[i/3].score = static_cast<float>(selected_scores_data[i+2]);
+                }
+            }
+
+            std::sort(expectedList.begin(), expectedList.end(), compareBox);
+        }
+
+        // Get actual bboxes' index/score
+        std::vector<Box> actualList;
+        {
+            const auto indeces_iter = actualOutputs.begin();
+            const auto scores_iter = actualOutputs.begin() + 1;
+            size_t selected_indices_size = indeces_iter->get_size();
+            const auto selected_indices_data = indeces_iter->data<int32_t>();
+
+            const auto selected_scores_data = scores_iter->data<float>();
+
+            for (size_t i = 0; i < selected_indices_size; i += 3) {
+                const int32_t batchId = selected_indices_data[i+0];
+                const int32_t classId = selected_indices_data[i+1];
+                const int32_t boxId   = selected_indices_data[i+2];
+                const float score = selected_scores_data[i+2];
+                if (batchId == -1 || classId == -1 || boxId == -1)
+                    break;
+
+                actualList.emplace_back(batchId, classId, boxId, coordList[batchId][boxId], score);
+            }
+            std::sort(actualList.begin(), actualList.end(), compareBox);
+        }
+
+        std::vector<Box> intersectionList;
+        std::vector<Box> differenceList;
+        {
+            std::list<Box> tempExpectedList(expectedList.size()), tempActualList(actualList.size());
+            std::copy(expectedList.begin(), expectedList.end(), tempExpectedList.begin());
+            std::copy(actualList.begin(), actualList.end(), tempActualList.begin());
+            auto sameBox = [](const Box& boxA, const Box& boxB) {
+                return (boxA.batchId == boxB.batchId) && (boxA.classId == boxB.classId) && (boxA.boxId == boxB.boxId);
+            };
+
+            for (auto itA = tempActualList.begin(); itA != tempActualList.end(); ++itA) {
+                bool found = false;
+                for (auto itB = tempExpectedList.begin(); itB != tempExpectedList.end(); ++itB) {
+                    if (sameBox(*itA, *itB)) {
+                        intersectionList.emplace_back(*itB);
+                        tempExpectedList.erase(itB);
+                        found = true;
+                        break;
+                    }
+                }
+
+                if (!found) {
+                    differenceList.emplace_back(*itA);
+                }
+            }
+            differenceList.insert(differenceList.end(), tempExpectedList.begin(), tempExpectedList.end());
+
+            for (auto& item : differenceList) {
+                if ((item.rect.x1 == item.rect.x2) || (item.rect.y1 == item.rect.y2))
+                    continue;
+
+                float maxIou = 0.f;
+                for (auto& refItem : intersectionList) {
+                    maxIou = std::max(maxIou, iouFunc(item, refItem));
+
+                    if (maxIou > 0.3f) break;
+                }
+
+                ASSERT_TRUE(maxIou > 0.3f) << "MaxIOU: " << maxIou
+                    << ", expectedList.size(): " << expectedList.size() << ", actualList.size(): " << actualList.size()
+                    << ", intersectionList.size(): " << intersectionList.size() << ", diffList.size(): " << differenceList.size()
+                    << ", batchId: " << item.batchId << ", classId: " << item.classId << ", boxId: " << item.boxId
+                    << ", score: " << item.score << ", coord: " << item.rect.x1 << ", " << item.rect.y1 << ", " << item.rect.x2 << ", " << item.rect.y2;
+            }
+        }
+    }
+    std::vector<TargetShapeParams> targetInDims;
+    size_t inferRequestNum = 0;
+    int32_t maxOutBoxesPerClass;
+};
+
+TEST_P(NmsLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    // CheckPluginRelatedResults(executableNetwork, "NonMaxSuppression");
+};
+
+const std::vector<InputShapeParams> inShapeParams = {
+    InputShapeParams{std::vector<ov::Dimension>{-1, -1, -1}, std::vector<TargetShapeParams>{TargetShapeParams{2, 50, 50},
+                                                                                            TargetShapeParams{3, 100, 5},
+                                                                                            TargetShapeParams{1, 10, 50}}},
+    InputShapeParams{std::vector<ov::Dimension>{{1, 5}, {1, 100}, {10, 75}}, std::vector<TargetShapeParams>{TargetShapeParams{4, 15, 10},
+                                                                                                            TargetShapeParams{5, 5, 12},
+                                                                                                            TargetShapeParams{1, 35, 15}}}
+};
+
+const std::vector<int32_t> maxOutBoxPerClass = {5, 20};
+const std::vector<float> threshold = {0.3f, 0.7f};
+const std::vector<float> sigmaThreshold = {0.0f, 0.5f};
+const std::vector<op::v5::NonMaxSuppression::BoxEncodingType> encodType = {op::v5::NonMaxSuppression::BoxEncodingType::CENTER,
+                                                                           op::v5::NonMaxSuppression::BoxEncodingType::CORNER};
+const std::vector<bool> sortResDesc = {true, false};
+const std::vector<element::Type> outType = {element::i32, element::i64};
+const std::vector<ngraph::helpers::InputLayerType> maxBoxInputTypes = {ngraph::helpers::InputLayerType::PARAMETER, ngraph::helpers::InputLayerType::CONSTANT};
+
+const auto nmsParams = ::testing::Combine(::testing::ValuesIn(inShapeParams),
+                                          ::testing::Combine(::testing::Values(ElementType::f32),
+                                                             ::testing::Values(ElementType::i32),
+                                                             ::testing::Values(ElementType::f32)),
+                                          ::testing::ValuesIn(maxOutBoxPerClass),
+                                          ::testing::Combine(::testing::ValuesIn(threshold),
+                                                             ::testing::ValuesIn(threshold),
+                                                             ::testing::ValuesIn(sigmaThreshold)),
+                                          ::testing::ValuesIn(maxBoxInputTypes),
+                                          ::testing::ValuesIn(encodType),
+                                          ::testing::ValuesIn(sortResDesc),
+                                          ::testing::ValuesIn(outType),
+                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_NmsLayerCPUTest, NmsLayerCPUTest, nmsParams, NmsLayerCPUTest::getTestCaseName);
+
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pad.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pad.cpp
index d0838784ad18f9..9ce20e53bc3c1e 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pad.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pad.cpp
@@ -4,57 +4,75 @@
 
 #include <shared_test_classes/single_layer/pad.hpp>
 #include "test_utils/cpu_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
 using namespace InferenceEngine;
 using namespace CPUTestUtils;
+using namespace ov;
+using namespace test;
 
 namespace CPULayerTestsDefinitions {
 
-typedef std::tuple<
-        LayerTestsDefinitions::padLayerTestParamsSet,
+using PadLayerCPUTestParamSet = std::tuple<
+        InputShape,                                     // Input shape
+        ElementType,                                    // Input element type
+        std::vector<int64_t>,                           // padsBegin
+        std::vector<int64_t>,                           // padsEnd
+        float,                                          // argPadValue
+        ngraph::helpers::PadMode,                       // padMode
         CPUSpecificParams
-> padLayerCPUTestParamsSet;
+>;
 
-class PadLayerCPUTest : public testing::WithParamInterface<padLayerCPUTestParamsSet>,
-                        virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+class PadLayerCPUTest : public testing::WithParamInterface<PadLayerCPUTestParamSet>,
+                        virtual public SubgraphBaseTest, public CPUTestsBase {
 public:
-    static std::string getTestCaseName(testing::TestParamInfo<padLayerCPUTestParamsSet> obj) {
-        LayerTestsDefinitions::padLayerTestParamsSet basicParamsSet;
+    static std::string getTestCaseName(testing::TestParamInfo<PadLayerCPUTestParamSet> obj) {
+        InputShape shapes;
+        ElementType elementType;
+        std::vector<int64_t> padsBegin, padsEnd;
+        ngraph::helpers::PadMode padMode;
+        float argPadValue;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = obj.param;
-
-        std::ostringstream result;
-        result << LayerTestsDefinitions::PadLayerTest::getTestCaseName(testing::TestParamInfo<LayerTestsDefinitions::padLayerTestParamsSet>(
-                basicParamsSet, 0));
-
-        result << CPUTestsBase::getTestCaseName(cpuParams);
-
-        return result.str();
+        std::tie(shapes, elementType, padsBegin, padsEnd, argPadValue, padMode, cpuParams) = obj.param;
+
+        std::ostringstream results;
+        results << "IS=" << CommonTestUtils::partialShape2str({shapes.first}) << "_";
+        results << "TS=";
+        for (const auto& item : shapes.second) {
+            results << CommonTestUtils::vec2str(item) << "_";
+        }
+        results << "Prc=" << elementType << "_";
+        results << "padsBegin=" << CommonTestUtils::vec2str(padsBegin) << "_";
+        results << "padsEnd=" << CommonTestUtils::vec2str(padsEnd) << "_";
+        if (padMode == ngraph::helpers::PadMode::CONSTANT) {
+            results << "Value=" << argPadValue << "_";
+        }
+        results << "PadMode=" << padMode << "_";
+        results << CPUTestsBase::getTestCaseName(cpuParams);
+
+        return results.str();
     }
 
 protected:
     void SetUp() override {
-        LayerTestsDefinitions::padLayerTestParamsSet basicParamsSet;
+        InputShape shapes;
+        ElementType elementType;
+        std::vector<int64_t> padsBegin, padsEnd;
+        ngraph::helpers::PadMode padMode;
+        float argPadValue;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+        std::tie(shapes, elementType, padsBegin, padsEnd, argPadValue, padMode, cpuParams) = this->GetParam();
 
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-
-        InferenceEngine::SizeVector inputShape;
-        std::vector<int64_t> padsBegin, padsEnd;
-        float argPadValue;
-        ngraph::helpers::PadMode padMode;
-        InferenceEngine::Precision netPrecision;
-        std::tie(padsBegin, padsEnd, argPadValue, padMode, netPrecision, inPrc, outPrc, inLayout, inputShape, targetDevice) =
-                basicParamsSet;
-
-        inPrc = outPrc = netPrecision;
-        selectedType = std::string("ref_") + netPrecision.name();
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
-        auto paramOuts = ngraph::helpers::convert2OutputVector(
-                ngraph::helpers::castOps2Nodes<ngraph::opset3::Parameter>(params));
-        auto pad = ngraph::builder::makePad(paramOuts[0], padsBegin, padsEnd, argPadValue, padMode);
+        if (selectedType.empty()) {
+            selectedType = getPrimitiveType();
+        }
+        selectedType = selectedType + "_" + InferenceEngine::details::convertPrecision(inType).name();
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        init_input_shapes({shapes});
+
+        auto params = ngraph::builder::makeDynamicParams(elementType, inputDynamicShapes);
+        auto pad = ngraph::builder::makePad(params[0], padsBegin, padsEnd, argPadValue, padMode);
         pad->get_rt_info() = getCPUInfo();
         ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(pad)};
         function = std::make_shared<ngraph::Function>(results, params, "pad");
@@ -64,8 +82,10 @@ class PadLayerCPUTest : public testing::WithParamInterface<padLayerCPUTestParams
 TEST_P(PadLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
-    CheckPluginRelatedResults(executableNetwork, "Pad");
+    run();
+
+    // TODO: need to uncomment when this method will be updated
+    //CheckPluginRelatedResults(executableNetwork, "Pad");
 }
 
 namespace {
@@ -80,11 +100,13 @@ const auto cpuParams_nCdhw8c = CPUSpecificParams {{nCdhw8c}, {nCdhw8c}, {}, {}};
 const auto cpuParams_nhwc = CPUSpecificParams {{nhwc}, {nhwc}, {}, {}};
 const auto cpuParams_ndhwc = CPUSpecificParams {{ndhwc}, {ndhwc}, {}, {}};
 
+const auto cpuParams_nchw = CPUSpecificParams {{nchw}, {nchw}, {}, {}};
+const auto cpuParams_ncdhw = CPUSpecificParams {{ncdhw}, {ncdhw}, {}, {}};
 
-const std::vector<InferenceEngine::Precision> inputPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::BF16,
-        InferenceEngine::Precision::I8
+const std::vector<ElementType> inputPrecisions = {
+        ElementType::f32,
+        ElementType::bf16,
+        ElementType::i8
 };
 
 const std::vector<float> argPadValue = {0.f, 1.f, 2.5f, -1.f};
@@ -95,6 +117,8 @@ const std::vector<ngraph::helpers::PadMode> padMode = {
         ngraph::helpers::PadMode::SYMMETRIC
 };
 
+/* *======================* Static Shapes Tests 4D *======================* */
+
 const std::vector<std::vector<int64_t>> padsBegin4DConstBlocked = {{0, 0, 0, 0}, {0, 0, 1, 3}, {2, 16, 1, 0}, {0, 0, 2, 0}};
 const std::vector<std::vector<int64_t>> padsEnd4DConstBlocked   = {{0, 0, 0, 0}, {0, 0, 2, 1}, {2, 0, 0, 1}, {1, 32, 2, 0}};
 
@@ -109,95 +133,142 @@ const std::vector<CPUSpecificParams> CPUParams4DBlocked = {
         cpuParams_nChw8c,
 };
 
-const auto pad4DConstParamsBlocked = testing::Combine(
-        testing::ValuesIn(padsBegin4DConstBlocked),
-        testing::ValuesIn(padsEnd4DConstBlocked),
-        testing::ValuesIn(argPadValue),
-        testing::Values(ngraph::helpers::PadMode::CONSTANT),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 5, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
-);
-
 INSTANTIATE_TEST_SUITE_P(
         smoke_CPUPad4DConstBlocked,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad4DConstParamsBlocked,
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DConstBlocked),
+                ::testing::ValuesIn(padsEnd4DConstBlocked),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams4DBlocked)),
         PadLayerCPUTest::getTestCaseName
 );
 
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPad4DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4D),
+                ::testing::ValuesIn(padsEnd4D),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::Values(cpuParams_nhwc)),
+        PadLayerCPUTest::getTestCaseName
+);
 
-const auto pad4DConstParams = testing::Combine(
-        testing::ValuesIn(padsBegin4D),
-        testing::ValuesIn(padsEnd4D),
-        testing::ValuesIn(argPadValue),
-        testing::Values(ngraph::helpers::PadMode::CONSTANT),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 5, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPad4DBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DBlocked),
+                ::testing::ValuesIn(padsEnd4DBlocked),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams4DBlocked)),
+        PadLayerCPUTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_CPUPad4DConst,
+        smoke_CPUPad4D,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad4DConstParams,
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DBlocked),
+                ::testing::ValuesIn(padsEnd4DBlocked),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
                 ::testing::Values(cpuParams_nhwc)),
         PadLayerCPUTest::getTestCaseName
 );
 
-const auto pad4DParamsBlocked = testing::Combine(
-        testing::ValuesIn(padsBegin4DBlocked),
-        testing::ValuesIn(padsEnd4DBlocked),
-        testing::Values(0),
-        testing::ValuesIn(padMode),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 10, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
+/* *======================* *=====================* *======================* */
+
+/* *======================* Dynamic Shapes Tests 4D *======================* */
+
+const std::vector<InputShape> inputShapesDynamic4D = {
+        {{-1, -1, -1, -1},                                    // dynamic
+         {{5, 36, 5, 5}, {3, 16, 10, 5}, {3, 24, 10, 10}}},   // target
+
+        {{-1, 32, -1, -1},                                    // dynamic
+         {{5, 32, 5, 5}, {5, 32, 5, 8}, {3, 32, 8, 8}}},      // target
+
+        {{{1, 5}, {16, 32}, {1, 16}, {1, 16}},                // dynamic
+         {{3, 16, 5, 5}, {5, 24, 5, 8}, {3, 32, 8, 8}}},      // target
+};
+
+const std::vector<CPUSpecificParams> CPUParams4DDynamic = {
+        cpuParams_nhwc,
+        cpuParams_nchw
+};
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPadDynamic4DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic4D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4D),
+                ::testing::ValuesIn(padsEnd4D),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams4DDynamic)),
+        PadLayerCPUTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_CPUPad4DBlocked,
+        smoke_CPUPadDynamic4DConstBlocked,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad4DParamsBlocked,
+                ::testing::Values(inputShapesDynamic4D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DConstBlocked),
+                ::testing::ValuesIn(padsEnd4DConstBlocked),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams4DBlocked)),
         PadLayerCPUTest::getTestCaseName
 );
 
-const auto pad4DParams = testing::Combine(
-        testing::ValuesIn(padsBegin4D),
-        testing::ValuesIn(padsEnd4D),
-        testing::Values(0),
-        testing::ValuesIn(padMode),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 10, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPadDynamic4D,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic4D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4D),
+                ::testing::ValuesIn(padsEnd4D),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams4DDynamic)),
+        PadLayerCPUTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_CPUPad4D,
+        smoke_CPUPadDynamic4DBlocked,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad4DParams,
-                ::testing::Values(cpuParams_nhwc)),
+                ::testing::Values(inputShapesDynamic4D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DBlocked),
+                ::testing::ValuesIn(padsEnd4DBlocked),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams4DBlocked)),
         PadLayerCPUTest::getTestCaseName
 );
 
+/* *======================* *=====================* *======================* */
+
+/* *======================* Static Shapes Tests 5D *======================* */
+
 const std::vector<std::vector<int64_t>> padsBegin5DConstBlocked = {{0, 0, 0, 0, 0}, {0, 0, 1, 1, 0}, {2, 32, 1, 1, 0}, {0, 0, 1, 3, 1}, {0, 0, 0, 1, 0}};
 const std::vector<std::vector<int64_t>> padsEnd5DConstBlocked   = {{0, 0, 0, 0, 0}, {1, 16, 1, 1, 0}, {0, 0, 0, 1, 0}, {0, 0, 0, 1, 1}, {0, 0, 1, 0, 1}};
 
@@ -212,94 +283,139 @@ const std::vector<CPUSpecificParams> CPUParams5DBlocked = {
         cpuParams_nCdhw8c,
 };
 
-const auto pad5DConstParamsBlocked = testing::Combine(
-        testing::ValuesIn(padsBegin5DConstBlocked),
-        testing::ValuesIn(padsEnd5DConstBlocked),
-        testing::ValuesIn(argPadValue),
-        testing::Values(ngraph::helpers::PadMode::CONSTANT),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 5, 5, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
-);
-
 INSTANTIATE_TEST_SUITE_P(
         smoke_CPUPad5DConstBlocked,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad5DConstParamsBlocked,
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DConstBlocked),
+                ::testing::ValuesIn(padsEnd5DConstBlocked),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams5DBlocked)),
         PadLayerCPUTest::getTestCaseName
 );
 
-const auto pad5DConstParams = testing::Combine(
-        testing::ValuesIn(padsBegin5D),
-        testing::ValuesIn(padsEnd5D),
-        testing::ValuesIn(argPadValue),
-        testing::Values(ngraph::helpers::PadMode::CONSTANT),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 10, 5, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPad5DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D),
+                ::testing::ValuesIn(padsEnd5D),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::Values(cpuParams_ndhwc)),
+        PadLayerCPUTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_CPUPad5DConst,
+        smoke_CPUPad5DBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DBlocked),
+                ::testing::ValuesIn(padsEnd5DBlocked),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams5DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPad5D,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad5DConstParams,
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D),
+                ::testing::ValuesIn(padsEnd5D),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
                 ::testing::Values(cpuParams_ndhwc)),
         PadLayerCPUTest::getTestCaseName
 );
 
-const auto pad5DParamsBlocked = testing::Combine(
-        testing::ValuesIn(padsBegin5DBlocked),
-        testing::ValuesIn(padsEnd5DBlocked),
-        testing::Values(0),
-        testing::ValuesIn(padMode),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 5, 5, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
+/* *======================* *=====================* *======================* */
+
+/* *======================* Dynamic Shapes Tests 5D *======================* */
+
+const std::vector<InputShape> inputShapesDynamic5D = {
+        {{-1, -1, -1, -1, -1},                                            // dynamic
+         {{5, 36, 5, 5, 5}, {3, 16, 8, 5, 7}, {3, 24, 10, 10, 10}}},      // target
+
+        {{-1, 32, -1, -1, -1},                                            // dynamic
+         {{5, 32, 5, 5, 5}, {3, 32, 8, 5, 7}, {3, 32, 10, 10, 10}}},      // target
+
+        {{{1, 5}, {16, 32}, {1, 16}, {1, 16}, {1, 16}},                   // dynamic
+         {{3, 16, 5, 5, 5}, {3, 24, 8, 5, 7}, {4, 32, 10, 10, 10}}},      // target
+};
+
+const std::vector<CPUSpecificParams> CPUParams5DDynamic = {
+        cpuParams_ndhwc,
+        cpuParams_ncdhw
+};
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPadDynamic5DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic5D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D),
+                ::testing::ValuesIn(padsEnd5D),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams5DDynamic)),
+        PadLayerCPUTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_CPUPad5DBlocked,
+        smoke_CPUPadDynamic5DConstBlocked,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad5DParamsBlocked,
+                ::testing::Values(inputShapesDynamic5D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DConstBlocked),
+                ::testing::ValuesIn(padsEnd5DConstBlocked),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams5DBlocked)),
         PadLayerCPUTest::getTestCaseName
 );
 
-const auto pad5DParams = testing::Combine(
-        testing::ValuesIn(padsBegin5D),
-        testing::ValuesIn(padsEnd5D),
-        testing::Values(0),
-        testing::ValuesIn(padMode),
-        testing::ValuesIn(inputPrecisions),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        testing::Values(InferenceEngine::Layout::ANY),
-        testing::Values(std::vector<size_t>{3, 16, 5, 5, 5}),
-        testing::Values(CommonTestUtils::DEVICE_CPU)
+INSTANTIATE_TEST_SUITE_P(
+        smoke_CPUPadDynamic5D,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic5D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D),
+                ::testing::ValuesIn(padsEnd5D),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams5DDynamic)),
+        PadLayerCPUTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_CPUPad5D,
+        smoke_CPUPadDynamic5DBlocked,
         PadLayerCPUTest,
         ::testing::Combine(
-                pad5DParams,
-                ::testing::Values(cpuParams_ndhwc)),
+                ::testing::Values(inputShapesDynamic5D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DBlocked),
+                ::testing::ValuesIn(padsEnd5DBlocked),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams5DBlocked)),
         PadLayerCPUTest::getTestCaseName
 );
 
+/* *======================* *=====================* *======================* */
 
 } // namespace
 } // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp
index 70e732fdb3b2d5..564ecef0348322 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp
@@ -4,53 +4,93 @@
 
 #include "ngraph_functions/builders.hpp"
 #include "test_utils/cpu_test_utils.hpp"
-#include "shared_test_classes/single_layer/pooling.hpp"
 #include "test_utils/fusing_test_utils.hpp"
+#include "shared_test_classes/single_layer/pooling.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-using namespace InferenceEngine;
+using namespace ov::test;
 using namespace CPUTestUtils;
-using namespace LayerTestsDefinitions;
 
 namespace CPULayerTestsDefinitions {
-typedef std::tuple<
-        poolLayerTestParamsSet,
-        CPUSpecificParams,
-        fusingSpecificParams
-> poolLayerCpuTestParamsSet;
+
+using poolLayerCpuTestParamsSet = std::tuple<LayerTestsDefinitions::poolSpecificParams,
+                                             InputShape,
+                                             ElementType,
+                                             bool,
+                                             CPUSpecificParams,
+                                             fusingSpecificParams>;
 
 class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestParamsSet>,
-                            virtual public LayerTestsUtils::LayerTestsCommon, public CpuTestWithFusing {
+                            virtual public SubgraphBaseTest, public CpuTestWithFusing {
 public:
     static std::string getTestCaseName(const testing::TestParamInfo<poolLayerCpuTestParamsSet>& obj) {
-        poolLayerTestParamsSet basicParamsSet;
+        LayerTestsDefinitions::poolSpecificParams basicParamsSet;
+        InputShape inputShapes;
+        ElementType inPrc;
+        bool isInt8;
         CPUSpecificParams cpuParams;
         fusingSpecificParams fusingParams;
-        std::tie(basicParamsSet, cpuParams, fusingParams) = obj.param;
-
-        std::ostringstream result;
-        result << PoolingLayerTest::getTestCaseName(testing::TestParamInfo<poolLayerTestParamsSet>(
-                basicParamsSet, 0));
-        result << CPUTestsBase::getTestCaseName(cpuParams);
-        result << CpuTestWithFusing::getTestCaseName(fusingParams);
+        std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = obj.param;
 
-        return result.str();
+        ngraph::helpers::PoolingTypes poolType;
+        std::vector<size_t> kernel, stride;
+        std::vector<size_t> padBegin, padEnd;
+        ngraph::op::PadType padType;
+        ngraph::op::RoundingType roundingType;
+        bool excludePad;
+        std::tie(poolType, kernel, stride, padBegin, padEnd, roundingType, padType, excludePad) = basicParamsSet;
+
+        std::ostringstream results;
+        results << "IS=(";
+        results << CommonTestUtils::partialShape2str({inputShapes.first}) << ")_";
+        results << "TS=";
+        for (const auto& shape : inputShapes.second) {
+            results << CommonTestUtils::vec2str(shape) << "_";
+        }
+        results << "Prc=" << inPrc << "_";
+        switch (poolType) {
+            case ngraph::helpers::PoolingTypes::MAX:
+                results << "MaxPool_";
+                break;
+            case ngraph::helpers::PoolingTypes::AVG:
+                results << "AvgPool_";
+                results << "ExcludePad=" << excludePad << "_";
+                break;
+        }
+        results << "K" << CommonTestUtils::vec2str(kernel) << "_";
+        results << "S" << CommonTestUtils::vec2str(stride) << "_";
+        results << "PB" << CommonTestUtils::vec2str(padBegin) << "_";
+        results << "PE" << CommonTestUtils::vec2str(padEnd) << "_";
+        results << "Rounding=" << roundingType << "_";
+        results << "AutoPad=" << padType << "_";
+        results << "INT8=" << isInt8 << "_";
+
+        results << CPUTestsBase::getTestCaseName(cpuParams);
+        results << CpuTestWithFusing::getTestCaseName(fusingParams);
+        return results.str();
+
+        return results.str();
     }
 
 protected:
     void SetUp() override {
-        poolLayerTestParamsSet basicParamsSet;
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        LayerTestsDefinitions::poolSpecificParams basicParamsSet;
+        InputShape inputShapes;
+        ElementType inPrc;
+        bool isInt8;
         CPUSpecificParams cpuParams;
         fusingSpecificParams fusingParams;
-        std::tie(basicParamsSet, cpuParams, fusingParams) = this->GetParam();
-
-        poolSpecificParams poolParams;
-        std::vector<size_t> inputShape;
-        InferenceEngine::Precision netPrecision;
-        std::tie(poolParams, netPrecision, inPrc, outPrc, inLayout, outLayout, inputShape, targetDevice) = basicParamsSet;
+        std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = this->GetParam();
 
-        if (outPrc == Precision::UNSPECIFIED) {
-            outPrc = inPrc;
-        }
+        ngraph::helpers::PoolingTypes poolType;
+        std::vector<size_t> kernel, stride;
+        std::vector<size_t> padBegin, padEnd;
+        ngraph::op::PadType padType;
+        ngraph::op::RoundingType roundingType;
+        bool excludePad;
+        std::tie(poolType, kernel, stride, padBegin, padEnd, roundingType, padType, excludePad) = basicParamsSet;
 
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
         std::tie(postOpMgrPtr, fusedOps) = fusingParams;
@@ -58,23 +98,19 @@ class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestP
         if (selectedType.empty()) {
             selectedType = getPrimitiveType();
         }
+        selectedType = selectedType + "_" + InferenceEngine::details::convertPrecision(inPrc).name();
 
-        selectedType = selectedType + "_" + inPrc.name();
+        init_input_shapes({inputShapes});
 
-        ngraph::helpers::PoolingTypes poolType;
-        std::vector<size_t> kernel, stride;
-        std::vector<size_t> padBegin, padEnd;
-        ngraph::op::PadType padType;
-        ngraph::op::RoundingType roundingType;
-        bool excludePad;
-        std::tie(poolType, kernel, stride, padBegin, padEnd, roundingType, padType, excludePad) = poolParams;
+        auto params = ngraph::builder::makeDynamicParams(inPrc, inputDynamicShapes);
 
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
-        auto paramOuts = ngraph::helpers::convert2OutputVector(
-                ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+        std::shared_ptr<ngraph::Node> poolInput = params[0];
+        if (isInt8) {
+            ov::Shape newShape(poolInput->get_output_partial_shape(0).size(), 1);
+            poolInput = ngraph::builder::makeFakeQuantize(poolInput, inPrc, 256, newShape);
+        }
 
-        std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makePooling(paramOuts[0],
+        std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makePooling(poolInput,
                                                                              stride,
                                                                              padBegin,
                                                                              padEnd,
@@ -84,180 +120,206 @@ class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestP
                                                                              excludePad,
                                                                              poolType);
 
-
-        function = makeNgraphFunction(ngPrc, params, pooling, "Pooling");
+        function = makeNgraphFunction(inPrc, params, pooling, "PoolingCPU");
     }
 };
 
 TEST_P(PoolingLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
-    CheckPluginRelatedResults(executableNetwork, "Pooling");
+    run();
+//     CheckPluginRelatedResults(executableNetwork, "Pooling");
 }
 
 namespace {
+
 const auto avx512 = CPUSpecificParams{{}, {}, {"jit_avx512"}, "jit_avx512"};
 const auto avx = CPUSpecificParams{{}, {}, {"jit_avx"}, "jit_avx"};
 const auto sse42 = CPUSpecificParams{{}, {}, {"jit_sse42"}, "jit_sse42"};
 const auto ref = CPUSpecificParams{{}, {}, {"ref_any"}, "ref_any"};
 
 const std::vector<CPUSpecificParams> vecCpuConfigs = {ref, sse42, avx, avx512};
-const std::vector<Precision> inpOutPrecision = {Precision::FP32, Precision::BF16};
-
-const std::vector<std::vector<size_t>> inputShapes4D = {
-        std::vector<size_t>{3, 4, 64, 64},
-        std::vector<size_t>{2, 8, 8, 12},
-        std::vector<size_t>{1, 16, 16, 12},
-        std::vector<size_t>{1, 21, 8, 4},
-        std::vector<size_t>{1, 32, 8, 8},
+const std::vector<ElementType> inpOutPrecision = {ElementType::f32/*, ElementType::bf16*/};
+
+const std::vector<InputShape> inputShapes4D = {
+        { {}, {{3, 4, 64, 64}} },
+        { {}, {{2, 8, 8, 12}} },
+        { {}, {{1, 16, 16, 12}} },
+        { {}, {{1, 21, 8, 4}} },
+        { {}, {{1, 32, 8, 8}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1},
+            // target
+            {
+                {1, 32, 8, 8},
+                {1, 21, 8, 4},
+                {2, 8, 8, 12}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {4, 32}, {1, 64}, {1, 64}},
+            // target
+            {
+                {3, 4, 64, 64},
+                {1, 16, 16, 12},
+                {1, 32, 8, 8}
+            }
+        }
 };
 
-const std::vector<std::vector<size_t>> inputShapes5D = {
-        std::vector<size_t>{1, 4, 16, 16, 16},
-        std::vector<size_t>{2, 8, 8, 8, 8},
-        std::vector<size_t>{2, 16, 12, 16, 20},
-        std::vector<size_t>{1, 19, 16, 20, 8},
-        std::vector<size_t>{1, 32, 16, 8, 12},
+const std::vector<InputShape> inputShapes5D = {
+        { {}, {{1, 4, 16, 16, 16}} },
+        { {}, {{2, 8, 8, 8, 8}} },
+        { {}, {{2, 16, 12, 16, 20}} },
+        { {}, {{1, 19, 16, 20, 8}} },
+        { {}, {{1, 32, 16, 8, 12}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1, -1},
+            // target
+            {
+                {2, 8, 8, 8, 8},
+                {1, 19, 16, 20, 8},
+                {1, 4, 16, 16, 16}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {4, 32}, {1, 64}, {1, 64}, {1, 25}},
+            // target
+            {
+                {1, 4, 16, 16, 16},
+                {1, 32, 16, 8, 12},
+                {3, 16, 4, 8, 3}
+            }
+        }
 };
 
-const std::vector<poolSpecificParams> paramsMax4D = {
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2}, {2, 2}, {0, 0}, {0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 2}, {0, 0}, {0, 0},
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax4D = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 2}, {0, 0}, {0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 1}, {0, 0}, {0, 0},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {4, 2}, {2, 1}, {0, 0}, {0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
 };
 
-const std::vector<poolSpecificParams> paramsAvg4D = {
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, true },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, true },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {0, 0}, {0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4}, {4, 4}, {2, 2}, {2, 2},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4}, {4, 4}, {2, 2}, {2, 2},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
 };
 
-const std::vector<poolSpecificParams> paramsAvg4D_RefOnly = {
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {2, 2}, {2, 2},
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D_RefOnly = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {2, 2}, {2, 2},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_4D, PoolingLayerCPUTest,
-                        ::testing::Combine(
                             ::testing::Combine(
-                                ::testing::ValuesIn(paramsMax4D),
-                                ::testing::Values(Precision::FP32),
-                                ::testing::ValuesIn(inpOutPrecision),
-                                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::Values(InferenceEngine::Layout::ANY),
-                                ::testing::ValuesIn(inputShapes4D),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                        ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                        ::testing::Values(emptyFusingSpec)),
+                            ::testing::ValuesIn(paramsMax4D),
+                            ::testing::ValuesIn(inputShapes4D),
+                            ::testing::ValuesIn(inpOutPrecision),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
+                            ::testing::Values(emptyFusingSpec)),
                         PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D, PoolingLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Combine(
-                                        ::testing::ValuesIn(paramsAvg4D),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::ValuesIn(inpOutPrecision),
-                                        ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::ValuesIn(inputShapes4D),
-                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                                ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                                ::testing::Values(emptyFusingSpec)),
+                            ::testing::ValuesIn(paramsAvg4D),
+                            ::testing::ValuesIn(inputShapes4D),
+                            ::testing::ValuesIn(inpOutPrecision),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
+                            ::testing::Values(emptyFusingSpec)),
                         PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_NotOptimized, PoolingLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Combine(
-                                        ::testing::ValuesIn(paramsAvg4D_RefOnly),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::ValuesIn(inpOutPrecision),
-                                        ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::ValuesIn(inputShapes4D),
-                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                                ::testing::Values(ref),
-                                ::testing::Values(emptyFusingSpec)),
+                            ::testing::ValuesIn(paramsAvg4D_RefOnly),
+                            ::testing::ValuesIn(inputShapes4D),
+                            ::testing::ValuesIn(inpOutPrecision),
+                            ::testing::Values(false),
+                            ::testing::Values(ref),
+                            ::testing::Values(emptyFusingSpec)),
                         PoolingLayerCPUTest::getTestCaseName);
 
-const std::vector<poolSpecificParams> paramsMax5D = {
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
-                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1},
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax5D = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 3, 4}, {2, 2, 2}, {1, 1, 1}, {1, 2, 3},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 3, 4}, {2, 2, 2}, {1, 1, 1}, {1, 2, 3},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
 };
 
-const std::vector<poolSpecificParams> paramsAvg5D = {
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, true },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, true },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
+                            ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, false },
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3, 3, 3}, {3, 3, 3}, {1, 1, 1}, {0, 0, 0},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3, 3, 3}, {3, 3, 3}, {1, 1, 1}, {0, 0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4, 4}, {4, 4, 4}, {2, 2, 2}, {2, 2, 2},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4, 4}, {4, 4, 4}, {2, 2, 2}, {2, 2, 2},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
 };
 
-const std::vector<poolSpecificParams> paramsAvg5D_RefOnly = {
-        poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2},
+const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D_RefOnly = {
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                                ::testing::Combine(
-                                        ::testing::ValuesIn(paramsMax5D),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::ValuesIn(inpOutPrecision),
-                                        ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::ValuesIn(inputShapes5D),
-                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                                ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                                ::testing::Values(emptyFusingSpec)),
-                        PoolingLayerCPUTest::getTestCaseName);
+                         ::testing::Combine(
+                             ::testing::ValuesIn(paramsMax5D),
+                             ::testing::ValuesIn(inputShapes5D),
+                             ::testing::ValuesIn(inpOutPrecision),
+                             ::testing::Values(false),
+                             ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
+                             ::testing::Values(emptyFusingSpec)),
+                         PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                                ::testing::Combine(
-                                        ::testing::ValuesIn(paramsAvg5D),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::ValuesIn(inpOutPrecision),
-                                        ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::ValuesIn(inputShapes5D),
-                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                                ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
-                                ::testing::Values(emptyFusingSpec)),
-                        PoolingLayerCPUTest::getTestCaseName);
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg5D),
+                              ::testing::ValuesIn(inputShapes5D),
+                              ::testing::ValuesIn(inpOutPrecision),
+                              ::testing::Values(false),
+                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs)),
+                              ::testing::Values(emptyFusingSpec)),
+                          PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_NotOptimized, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                                ::testing::Combine(
-                                        ::testing::ValuesIn(paramsAvg5D_RefOnly),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::ValuesIn(inpOutPrecision),
-                                        ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::ValuesIn(inputShapes5D),
-                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                                ::testing::Values(ref),
-                                ::testing::Values(emptyFusingSpec)),
-                        PoolingLayerCPUTest::getTestCaseName);
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg5D_RefOnly),
+                              ::testing::ValuesIn(inputShapes5D),
+                              ::testing::ValuesIn(inpOutPrecision),
+                              ::testing::Values(false),
+                              ::testing::Values(ref),
+                              ::testing::Values(emptyFusingSpec)),
+                          PoolingLayerCPUTest::getTestCaseName);
 
 /* === Fusing === */
 
@@ -279,35 +341,82 @@ std::vector<fusingSpecificParams> fusingParamsSet {
     fusingFakeQuantizePerChannel,
 };
 
+const std::vector<InputShape> inputShapes4D_int8 = {
+        { {}, {{3, 4, 64, 64}} },
+        { {}, {{2, 8, 8, 12}} },
+        { {}, {{1, 16, 16, 12}} },
+        { {}, {{1, 21, 8, 4}} },
+        { {}, {{1, 32, 8, 8}} },
+        {
+            // dynamic
+            {-1, 32, -1, -1},
+            // target
+            {
+                {1, 32, 8, 8},
+                {1, 32, 8, 4},
+                {2, 32, 8, 12}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 64}, {1, 64}},
+            // target
+            {
+                {3, 16, 32, 32},
+                {1, 16, 16, 12},
+                {1, 16, 8, 8}
+            }
+        }
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_I8, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                                ::testing::Combine(
-                                        ::testing::ValuesIn(paramsAvg4D),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::Values(Precision::I8),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::ValuesIn(inputShapes4D),
-                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                                ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_4D)),
-                                ::testing::ValuesIn(fusingParamsSet)),
-                        PoolingLayerCPUTest::getTestCaseName);
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg4D),
+                              ::testing::ValuesIn(inputShapes4D_int8),
+                              ::testing::Values(ElementType::f32),
+                              ::testing::Values(true),
+                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_4D)),
+                              ::testing::ValuesIn(fusingParamsSet)),
+                          PoolingLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes5D_int8 = {
+        { {}, {{1, 4, 16, 16, 16}} },
+        { {}, {{2, 8, 8, 8, 8}} },
+        { {}, {{2, 16, 12, 16, 20}} },
+        { {}, {{1, 19, 16, 20, 8}} },
+        { {}, {{1, 32, 16, 8, 12}} },
+        {
+            // dynamic
+            {-1, 32, -1, -1, -1},
+            // target
+            {
+                {2, 32, 8, 8, 8},
+                {1, 32, 16, 20, 8},
+                {1, 32, 16, 16, 16}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 64}, {1, 64}, {1, 25}},
+            // target
+            {
+                {1, 16, 16, 16, 16},
+                {1, 16, 16, 8, 12},
+                {2, 16, 8, 8, 8}
+            }
+        }
+};
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_I8, PoolingLayerCPUTest,
-                        ::testing::Combine(
-                                ::testing::Combine(
-                                        ::testing::ValuesIn(paramsAvg5D),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::Values(Precision::I8),
-                                        ::testing::Values(Precision::FP32),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::Values(InferenceEngine::Layout::ANY),
-                                        ::testing::ValuesIn(inputShapes5D),
-                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
-                                ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D)),
-                                ::testing::ValuesIn(fusingParamsSet)),
-                        PoolingLayerCPUTest::getTestCaseName);
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg5D),
+                              ::testing::ValuesIn(inputShapes5D_int8),
+                              ::testing::Values(ElementType::f32),
+                              ::testing::Values(true),
+                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D)),
+                              ::testing::ValuesIn(fusingParamsSet)),
+                          PoolingLayerCPUTest::getTestCaseName);
 
 } // namespace
-} // namespace CPULayerTestsDefinitions
\ No newline at end of file
+
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/proposal.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/proposal.cpp
new file mode 100644
index 00000000000000..85099c1cdfede0
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/proposal.cpp
@@ -0,0 +1,341 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <functional_test_utils/ov_tensor_utils.hpp>
+#include "test_utils/cpu_test_utils.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+namespace proposalTypes {
+
+    typedef size_t base_size_type;
+    typedef float box_coordinate_scale_type;
+    typedef float box_size_scale_type;
+    typedef bool clip_after_nms_type;
+    typedef bool clip_before_nms_type;
+    typedef size_t feat_stride_type;
+    typedef std::string framework_type;
+    typedef size_t min_size_type;
+    typedef float nms_thresh_type;
+    typedef bool normalize_type;
+    typedef size_t post_nms_topn_type;
+    typedef size_t pre_nms_topn_type;
+    typedef std::vector<float> ratio_type;
+    typedef std::vector<float> scale_type;
+
+};  // namespace proposalTypes
+
+using namespace proposalTypes;
+
+using proposalSpecificParams = std::tuple<
+    base_size_type,
+    box_coordinate_scale_type,
+    box_size_scale_type,
+    clip_after_nms_type,
+    clip_before_nms_type,
+    feat_stride_type,
+    framework_type,
+    min_size_type,
+    nms_thresh_type,
+    normalize_type,
+    post_nms_topn_type,
+    pre_nms_topn_type,
+    ratio_type,
+    scale_type>;
+
+using proposalLayerTestCPUParams = std::tuple<
+        std::vector<InputShape>,              // Input shapes
+        proposalSpecificParams,               // Node attributes
+        Precision>;                           // Network precision
+
+class ProposalLayerCPUTest : public testing::WithParamInterface<proposalLayerTestCPUParams>,
+                             public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<proposalLayerTestCPUParams> obj) {
+        std::vector<InputShape> inputShapes;
+        proposalSpecificParams proposalParams;
+        Precision netPrecision;
+        std::tie(inputShapes, proposalParams, netPrecision) = obj.param;
+
+        base_size_type base_size;
+        box_coordinate_scale_type box_coordinate_scale;
+        box_size_scale_type box_size_scale;
+        clip_after_nms_type clip_after_nms;
+        clip_before_nms_type clip_before_nms;
+        feat_stride_type feat_stride;
+        framework_type framework;
+        min_size_type min_size;
+        nms_thresh_type nms_thresh;
+        normalize_type normalize;
+        post_nms_topn_type post_nms_topn;
+        pre_nms_topn_type pre_nms_topn;
+        ratio_type ratio;
+        scale_type scale;
+        std::tie(base_size, box_coordinate_scale, box_size_scale,
+                 clip_after_nms, clip_before_nms, feat_stride,
+                 framework, min_size, nms_thresh, normalize,
+                 post_nms_topn, pre_nms_topn, ratio, scale) = proposalParams;
+
+        std::ostringstream result;
+        if (inputShapes.front().first.size() != 0) {
+            result << "IS=(";
+            for (const auto &shape : inputShapes) {
+                result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+            }
+            result.seekp(-1, result.cur);
+            result << ")_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            for (const auto& item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "base_size=" << base_size << "_";
+        result << "framework=" << framework << "_";
+        result << "ratio=" << CommonTestUtils::vec2str(ratio) << "_";
+        result << "scale=" << CommonTestUtils::vec2str(scale) << "_";
+        result << "netPRC=" << netPrecision.name();
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        std::vector<InputShape> inputShapes;
+        proposalSpecificParams proposalParams;
+        Precision netPrecision;
+        std::tie(inputShapes, proposalParams, netPrecision) = this->GetParam();
+
+        base_size_type base_size;
+        box_coordinate_scale_type box_coordinate_scale;
+        box_size_scale_type box_size_scale;
+        clip_after_nms_type clip_after_nms;
+        clip_before_nms_type clip_before_nms;
+        feat_stride_type feat_stride;
+        framework_type framework;
+        min_size_type min_size;
+        nms_thresh_type nms_thresh;
+        normalize_type normalize;
+        post_nms_topn_type post_nms_topn;
+        pre_nms_topn_type pre_nms_topn;
+        ratio_type ratio;
+        scale_type scale;
+        std::tie(base_size, box_coordinate_scale, box_size_scale,
+                 clip_after_nms, clip_before_nms, feat_stride,
+                 framework, min_size, nms_thresh, normalize,
+                 post_nms_topn, pre_nms_topn, ratio, scale) = proposalParams;
+
+        init_input_shapes(inputShapes);
+
+        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+        auto params = ngraph::builder::makeDynamicParams(ngPrc, {inputDynamicShapes[0], inputDynamicShapes[1], inputDynamicShapes[2]});
+        auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+
+        ngraph::op::ProposalAttrs attrs;
+        attrs.base_size = base_size;
+        attrs.pre_nms_topn = pre_nms_topn;
+        attrs.post_nms_topn = post_nms_topn;
+        attrs.nms_thresh = nms_thresh;
+        attrs.feat_stride = feat_stride;
+        attrs.min_size = min_size;
+        attrs.ratio = ratio;
+        attrs.scale = scale;
+        attrs.clip_before_nms = clip_before_nms;
+        attrs.clip_after_nms = clip_after_nms;
+        attrs.normalize = normalize;
+        attrs.box_size_scale = box_size_scale;
+        attrs.box_coordinate_scale = box_coordinate_scale;
+        attrs.framework = framework;
+        attrs.infer_probs = true;
+
+        auto proposal = std::make_shared<opset4::Proposal>(paramOuts[0], paramOuts[1], paramOuts[2], attrs);
+
+        ngraph::ResultVector results{
+                std::make_shared<ngraph::opset1::Result>(proposal->output(0)),
+                std::make_shared<ngraph::opset1::Result>(proposal->output(1))
+        };
+
+        function = std::make_shared<ngraph::Function>(results, params, "Proposal");
+    }
+    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+
+            if (i == 2) {
+                tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+
+                auto *dataPtr = tensor.data<float>();
+                dataPtr[0] = dataPtr[1] = 225.0f;
+                dataPtr[2] = 1.0f;
+                if (tensor.get_size() == 4) dataPtr[3] = 1.0f;
+            } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], 10, 0, 1000);
+            }
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+};
+
+TEST_P(ProposalLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    // TODO: Should be uncommented after updating the CheckPluginRelatedResults() method
+    // CheckPluginRelatedResults(executableNetwork, "Proposal");
+}
+
+namespace {
+
+const std::vector<Precision> netPrecision = {
+        Precision::FP32
+};
+
+std::vector<std::vector<ov::Shape>> staticInputShapesCase1 = {
+        {{2, 30, 18, 22}, {2, 60, 18, 22}, {3}},
+        {{1, 30, 18, 22}, {1, 60, 18, 22}, {3}},
+        {{1, 30, 50, 80}, {1, 60, 50, 80}, {4}}
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapesCase1 = {
+        {
+                {
+                        {{-1, 30, -1, -1}, {{2, 30, 75, 75}, {1, 30, 50, 80}, {3, 30, 80, 80}}},
+                        {{-1, 60, -1, -1}, {{2, 60, 75, 75}, {1, 60, 50, 80}, {3, 60, 80, 80}}},
+                        {{{3, 4}}, {{3}, {4}, {3}}}
+                }
+        },
+        {
+                {
+                        {{-1, 30, {20, 40}, {10, 50}}, {{1, 30, 20, 10}, {1, 30, 20, 30}, {1, 30, 40, 35}}},
+                        {{-1, 60, {20, 40}, {10, 50}}, {{1, 60, 20, 10}, {1, 60, 20, 30}, {1, 60, 40, 35}}},
+                        {{3}, {{3}, {3}, {3}}}
+                }
+        }
+};
+
+const std::vector<base_size_type> base_size_1 = {16};
+const std::vector<box_coordinate_scale_type> box_coordinate_scale_1 = {1};
+const std::vector<box_size_scale_type> box_size_scale_1 = {1};
+const std::vector<clip_after_nms_type> clip_after_nms_1 = {false};
+const std::vector<clip_before_nms_type> clip_before_nms_1 = {true};
+const std::vector<feat_stride_type> feat_stride_1 = {16};
+const std::vector<framework_type> framework_1 = {""};
+const std::vector<min_size_type> min_size_1 = {12};
+const std::vector<nms_thresh_type> nms_thresh_1 = {0.699999988079071};
+const std::vector<normalize_type> normalize_1 = {true};
+const std::vector<post_nms_topn_type> post_nms_topn_1 = {300};
+const std::vector<pre_nms_topn_type> pre_nms_topn_1 = {6000};
+const std::vector<ratio_type> ratio_1 = {{0.5, 1.0, 2.0}};
+const std::vector<scale_type> scale_1 = {{2.0, 4.0, 8.0, 16.0, 32.0}};
+
+const auto proposalParamsCase1 = ::testing::Combine(
+        ::testing::ValuesIn(base_size_1),
+        ::testing::ValuesIn(box_coordinate_scale_1),
+        ::testing::ValuesIn(box_size_scale_1),
+        ::testing::ValuesIn(clip_after_nms_1),
+        ::testing::ValuesIn(clip_before_nms_1),
+        ::testing::ValuesIn(feat_stride_1),
+        ::testing::ValuesIn(framework_1),
+        ::testing::ValuesIn(min_size_1),
+        ::testing::ValuesIn(nms_thresh_1),
+        ::testing::ValuesIn(normalize_1),
+        ::testing::ValuesIn(post_nms_topn_1),
+        ::testing::ValuesIn(pre_nms_topn_1),
+        ::testing::ValuesIn(ratio_1),
+        ::testing::ValuesIn(scale_1)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Proposal_Static_Test_Case1, ProposalLayerCPUTest,
+                         ::testing::Combine(::testing::ValuesIn(static_shapes_to_test_representation(staticInputShapesCase1)),
+                                            proposalParamsCase1,
+                                            ::testing::ValuesIn(netPrecision)),
+                         ProposalLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Proposal_Dynamic_Test_Case1, ProposalLayerCPUTest,
+                         ::testing::Combine(::testing::ValuesIn(dynamicInputShapesCase1),
+                                            proposalParamsCase1,
+                                            ::testing::ValuesIn(netPrecision)),
+                         ProposalLayerCPUTest::getTestCaseName);
+
+
+std::vector<std::vector<ov::Shape>> staticInputShapesCase2 = {
+        {{1, 24, 24, 30}, {1, 48, 24, 30}, {3}},
+        {{1, 24, 38, 38}, {1, 48, 38, 38}, {4}}
+};
+
+std::vector<std::vector<InputShape>> dynamicInputShapesCase2 = {
+        {
+                {
+                        {{1, 24, -1, -1}, {{1, 24, 38, 38}, {1, 24, 20, 12}, {1, 24, 15, 15}}},
+                        {{1, 48, -1, -1}, {{1, 48, 38, 38}, {1, 48, 20, 12}, {1, 48, 15, 15}}},
+                        {{{3, 4}}, {{4}, {3}, {4}}}
+                }
+        },
+        {
+                {
+                        {{1, 24, {11, 38}, {11, 38}}, {{1, 24, 19, 11}, {1, 24, 15, 30}, {1, 24, 18, 17}}},
+                        {{1, 48, {11, 38}, {11, 38}}, {{1, 48, 19, 11}, {1, 48, 15, 30}, {1, 48, 18, 17}}},
+                        {{4}, {{4}, {4}, {4}}}
+                }
+        }
+};
+
+const std::vector<base_size_type> base_size_2 = {256};
+const std::vector<box_coordinate_scale_type> box_coordinate_scale_2 = {10};
+const std::vector<box_size_scale_type> box_size_scale_2 = {5};
+const std::vector<clip_after_nms_type> clip_after_nms_2 = {false};
+const std::vector<clip_before_nms_type> clip_before_nms_2 = {true};
+const std::vector<feat_stride_type> feat_stride_2 = {16};
+const std::vector<framework_type> framework_2 = {"tensorflow"};
+const std::vector<min_size_type> min_size_2 = {1};
+const std::vector<nms_thresh_type> nms_thresh_2 = {0.699999988079071};
+const std::vector<normalize_type> normalize_2 = {true};
+const std::vector<post_nms_topn_type> post_nms_topn_2 = {100};
+const std::vector<pre_nms_topn_type> pre_nms_topn_2 = {2147483647};
+const std::vector<ratio_type> ratio_2 = {{0.5, 1.0, 2.0}};
+const std::vector<scale_type> scale_2 = {{0.25, 0.5, 1.0, 2.0}};
+
+const auto proposalParamsCase2 = ::testing::Combine(
+        ::testing::ValuesIn(base_size_2),
+        ::testing::ValuesIn(box_coordinate_scale_2),
+        ::testing::ValuesIn(box_size_scale_2),
+        ::testing::ValuesIn(clip_after_nms_2),
+        ::testing::ValuesIn(clip_before_nms_2),
+        ::testing::ValuesIn(feat_stride_2),
+        ::testing::ValuesIn(framework_2),
+        ::testing::ValuesIn(min_size_2),
+        ::testing::ValuesIn(nms_thresh_2),
+        ::testing::ValuesIn(normalize_2),
+        ::testing::ValuesIn(post_nms_topn_2),
+        ::testing::ValuesIn(pre_nms_topn_2),
+        ::testing::ValuesIn(ratio_2),
+        ::testing::ValuesIn(scale_2)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Proposal_Static_Test_Case2, ProposalLayerCPUTest,
+                         ::testing::Combine(::testing::ValuesIn(static_shapes_to_test_representation(staticInputShapesCase2)),
+                                            proposalParamsCase2,
+                                            ::testing::ValuesIn(netPrecision)),
+                         ProposalLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Proposal_Dynamic_Test_Case2, ProposalLayerCPUTest,
+                         ::testing::Combine(::testing::ValuesIn(dynamicInputShapesCase2),
+                                            proposalParamsCase2,
+                                            ::testing::ValuesIn(netPrecision)),
+                         ProposalLayerCPUTest::getTestCaseName);
+
+}  // namespace
+}  // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/split.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/split.cpp
index 14b83c08ae07a6..68b18870b340bc 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/split.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/split.cpp
@@ -2,56 +2,63 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "ngraph_functions/builders.hpp"
 #include "test_utils/cpu_test_utils.hpp"
 
-using namespace InferenceEngine;
+using namespace ov::test;
 using namespace CPUTestUtils;
 
 namespace CPULayerTestsDefinitions {
 
 typedef std::tuple<
-        size_t,                         // Num splits
-        int64_t,                        // Axis
-        InferenceEngine::Precision,     // Net precision
-        std::vector<size_t>,            // Input shapes
-        std::vector<size_t>,            // Used outputs indices
-        std::string,                    // Target device name
+        size_t,                    // Num splits
+        int64_t,                   // Axis
+        ElementType,               // Net precision
+        InputShape,                // Input shapes
+        std::vector<size_t>,       // Used outputs indices
         CPUSpecificParams
 > splitCPUTestParams;
 
 class SplitLayerCPUTest : public testing::WithParamInterface<splitCPUTestParams>,
-                          virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+                          virtual public SubgraphBaseTest, public CPUTestsBase {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<splitCPUTestParams> obj) {
         size_t numSplits;
         int64_t axis;
-        InferenceEngine::Precision netPrecision;
-        InferenceEngine::SizeVector inputShape, outIndices;
-        std::string targetDevice;
+        ElementType netPrecision;
+        InputShape inputShapes;
+        InferenceEngine::SizeVector outIndices;
         CPUSpecificParams cpuParams;
-        std::tie(numSplits, axis, netPrecision, inputShape, outIndices, targetDevice, cpuParams) = obj.param;
+        std::tie(numSplits, axis, netPrecision, inputShapes, outIndices, cpuParams) = obj.param;
 
         std::ostringstream result;
-        result << "IS=" << CommonTestUtils::vec2str(inputShape) << "_";
+        result << "IS=";
+        result << CommonTestUtils::partialShape2str({inputShapes.first}) << "_";
+        result << "TS=";
+        for (const auto& shape : inputShapes.second) {
+            result << CommonTestUtils::vec2str(shape) << "_";
+        }
         result << "numSplits=" << numSplits << "_";
         result << "axis=" << axis << "_";
         if (!outIndices.empty()) {
             result << "outIndices" << CommonTestUtils::vec2str(outIndices) << "_";
         }
-        result << "netPRC=" << netPrecision.name() << "_";
-        result << "trgDev=" << targetDevice;
+        result << "netPRC=" << netPrecision << "_";
         result << CPUTestsBase::getTestCaseName(cpuParams);
         return result.str();
     }
+
 protected:
     void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
         size_t axis, numSplits;
-        std::vector<size_t> inputShape, outIndices;
-        InferenceEngine::Precision netPrecision;
+        ElementType netPrecision;
+        InputShape inputShapes;
+        InferenceEngine::SizeVector outIndices;
         CPUSpecificParams cpuParams;
-        std::tie(numSplits, axis, netPrecision, inputShape, outIndices, targetDevice, cpuParams) = this->GetParam();
-        inPrc = outPrc = netPrecision;
+        std::tie(numSplits, axis, netPrecision, inputShapes, outIndices, cpuParams) = this->GetParam();
         if (outIndices.empty()) {
             for (int i = 0; i < numSplits; ++i) {
                 outIndices.push_back(i);
@@ -59,14 +66,15 @@ class SplitLayerCPUTest : public testing::WithParamInterface<splitCPUTestParams>
         }
 
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-        selectedType += std::string("_") + inPrc.name();
+        selectedType += std::string("_") + InferenceEngine::details::convertPrecision(netPrecision).name();
+
+        init_input_shapes({inputShapes});
 
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+        auto params = ngraph::builder::makeDynamicParams(netPrecision, inputDynamicShapes);
         auto paramOuts = ngraph::helpers::convert2OutputVector(
                 ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
         auto split = std::dynamic_pointer_cast<ngraph::opset5::Split>(ngraph::builder::makeSplit(paramOuts[0],
-                                                                                                 ngPrc, numSplits, axis));
+                                                                                                 netPrecision, numSplits, axis));
         ngraph::ResultVector results;
 
         for (int i = 0; i < outIndices.size(); i++) {
@@ -85,8 +93,8 @@ class SplitLayerCPUTest : public testing::WithParamInterface<splitCPUTestParams>
 TEST_P(SplitLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
-    CheckPluginRelatedResults(executableNetwork, "Split");
+    run();
+//     CheckPluginRelatedResults(executableNetwork, "Split");
 }
 
 namespace {
@@ -115,70 +123,150 @@ const auto blocked16_4D_ref = CPUSpecificParams{{nChw16c}, {nChw16c}, {}, "ref"}
 const auto blocked16_5D_ref = CPUSpecificParams{{nCdhw16c}, {nCdhw16c}, {}, "ref"};
 
 // List of precisions natively supported by mkldnn.
-const std::vector<Precision> netPrecisions = {
-        Precision::I8,
-        Precision::I32,
-        Precision::FP32,
-        Precision::BF16
+const std::vector<ElementType> netPrecisions = {
+        ElementType::i8,
+        ElementType::i32,
+        ElementType::f32,
+        ElementType::bf16
 };
 
 const std::vector<std::vector<size_t>> outIndices3 = {{0, 1, 2}, {0, 1, 1, 0, 2}, {0, 0, 0, 2}};
 const std::vector<std::vector<size_t>> outIndices4 = {{0, 1, 2, 3}, {0, 1, 1, 0, 2, 3}, {0, 0, 0, 2, 3}};
 
+const std::vector<InputShape> inputShapes4D_Nspc2NcspSpecial = {
+        { {}, {{3, 8, 11, 9}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1},
+            // target
+            {
+                {1, 4, 5, 7},
+                {3, 8, 5, 9},
+                {5, 16, 1, 8}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 25}, {2, 10}},
+            // target
+            {
+                {2, 8, 5, 7},
+                {1, 4, 10, 2},
+                {3, 16, 5, 9}
+            }
+        },
+};
 
 INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Nspc2NcspSpecial, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(4),
                                 ::testing::Values(1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({3, 28, 24, 9})),
+                                ::testing::ValuesIn(inputShapes4D_Nspc2NcspSpecial),
                                 ::testing::ValuesIn(outIndices4),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(perChannelsToPlanar_4D)),
                         SplitLayerCPUTest::getTestCaseName);
 
+const std::vector<InputShape> inputShapes5D_Nspc2NcspSpecial = {
+        { {}, {{3, 9, 5, 9, 11}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1, -1},
+            // target
+            {
+                {1, 12, 5, 7, 5},
+                {3, 6, 8, 9, 1},
+                {5, 9, 1, 8, 2}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 25}, {2, 10}, {1, 64}},
+            // target
+            {
+                {2, 6, 5, 7, 7},
+                {1, 3, 10, 2, 11},
+                {3, 9, 4, 9, 8}
+            }
+        },
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Nspc2NcspSpecial, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(3),
                                 ::testing::Values(1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({3, 21, 24, 9, 15})),
+                                ::testing::ValuesIn(inputShapes5D_Nspc2NcspSpecial),
                                 ::testing::ValuesIn(outIndices3),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(perChannelsToPlanar_5D)),
                         SplitLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block8inPlace, SplitLayerCPUTest,
-                    ::testing::Combine(
-                            ::testing::Values(3),
-                            ::testing::Values(0, 1),
-                            ::testing::ValuesIn(netPrecisions),
-                            ::testing::Values(std::vector<size_t>({3, 24, 24, 9})),
-                            ::testing::ValuesIn(outIndices3),
-                            ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                            ::testing::Values(planar_4D, planar_4D_ref, perChannels_4D, blocked8_4D)),
-                    SplitLayerCPUTest::getTestCaseName);
+const std::vector<InputShape> inputShapes4D_planar = {
+        { {}, {{3, 24, 24, 9}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1},
+            // target
+            {
+                {1, 15, 12, 9},
+                {3, 1, 9, 12},
+                {5, 5, 6, 6}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 48}, {2, 48}},
+            // target
+            {
+                {2, 5, 6, 9},
+                {1, 7, 12, 6},
+                {3, 11, 9, 3}
+            }
+        },
+};
 
-INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block8, SplitLayerCPUTest,
+INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_planar, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(3),
                                 ::testing::Values(2, 3),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({3, 24, 24, 9})),
+                                ::testing::ValuesIn(inputShapes4D_planar),
                                 ::testing::ValuesIn(outIndices3),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(planar_4D, planar_4D_ref, perChannels_4D, blocked8_4D_ref)),
+                                ::testing::Values(planar_4D, planar_4D_ref, perChannels_4D)),
                         SplitLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block16inPlace, SplitLayerCPUTest,
+const std::vector<InputShape> inputShapes4D_block = {
+        { {}, {{3, 16, 12, 12}} },
+        {
+            // dynamic
+            {-1, 16, -1, -1},
+            // target
+            {
+                {1, 16, 12, 12},
+                {3, 16, 12, 12},
+                {5, 16, 12, 12}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 48}, {2, 24}},
+            // target
+            {
+                {2, 16, 12, 12},
+                {1, 16, 12, 12},
+                {3, 16, 12, 12}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block8, SplitLayerCPUTest,
                         ::testing::Combine(
-                                ::testing::Values(4),
-                                ::testing::Values(0, 1),
+                                ::testing::Values(3),
+                                ::testing::Values(2, 3),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({4, 64, 32, 12})),
+                                ::testing::ValuesIn(inputShapes4D_block),
                                 ::testing::ValuesIn(outIndices3),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(blocked16_4D)),
+                                ::testing::Values(blocked8_4D_ref)),
                         SplitLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block16, SplitLayerCPUTest,
@@ -186,43 +274,77 @@ INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block16, SplitLayerCPUTest,
                                 ::testing::Values(4),
                                 ::testing::Values(2, 3),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({4, 64, 32, 12})),
+                                ::testing::ValuesIn(inputShapes4D_block),
                                 ::testing::ValuesIn(outIndices4),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(blocked16_4D_ref)),
                         SplitLayerCPUTest::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Block8inPlace, SplitLayerCPUTest,
+const std::vector<InputShape> inputShapes5D_planar = {
+        { {}, {{3, 5, 3, 6, 12}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1, -1},
+            // target
+            {
+                {1, 15, 12, 3, 9},
+                {3, 1, 6, 12, 3},
+                {5, 5, 6, 6, 6}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 48}, {2, 48}, {1, 40}},
+            // target
+            {
+                {2, 5, 12, 3, 6},
+                {1, 7, 12, 6, 9},
+                {3, 11, 9, 3, 30}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_planar, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(3),
-                                ::testing::Values(0, 1),
+                                ::testing::Values(2, 3, 4),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({3, 24, 24, 9, 15})),
+                                ::testing::ValuesIn(inputShapes5D_planar),
                                 ::testing::ValuesIn(outIndices3),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(planar_5D, planar_5D_ref, perChannels_5D, blocked8_5D)),
+                                ::testing::Values(planar_5D, planar_5D_ref, perChannels_5D)),
                         SplitLayerCPUTest::getTestCaseName);
 
+const std::vector<InputShape> inputShapes5D_block = {
+        { {}, {{3, 16, 24, 12, 36}} },
+        {
+            // dynamic
+            {-1, 16, -1, -1, -1},
+            // target
+            {
+                {1, 16, 12, 24, 24},
+                {3, 16, 12, 12, 12},
+                {5, 16, 12, 12, 24}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 48}, {2, 24}, {3, 64}},
+            // target
+            {
+                {2, 16, 12, 12, 24},
+                {1, 16, 12, 12, 24},
+                {3, 16, 12, 12, 12}
+            }
+        },
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Block8, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(3),
                                 ::testing::Values(2, 3, 4),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({3, 24, 24, 9, 15})),
+                                ::testing::ValuesIn(inputShapes5D_block),
                                 ::testing::ValuesIn(outIndices3),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(planar_5D, planar_5D_ref, perChannels_5D, blocked8_5D_ref)),
-                        SplitLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Block16inPlace, SplitLayerCPUTest,
-                        ::testing::Combine(
-                                ::testing::Values(4),
-                                ::testing::Values(0, 1),
-                                ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({4, 64, 32, 12, 20})),
-                                ::testing::ValuesIn(outIndices4),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                                ::testing::Values(blocked16_5D)),
+                                ::testing::Values(blocked8_5D_ref)),
                         SplitLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Block16, SplitLayerCPUTest,
@@ -230,43 +352,154 @@ INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Block16, SplitLayerCPUTest,
                                 ::testing::Values(4),
                                 ::testing::Values(2, 3, 4),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({4, 64, 32, 12, 20})),
+                                ::testing::ValuesIn(inputShapes5D_block),
                                 ::testing::ValuesIn(outIndices4),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(blocked16_5D_ref)),
                         SplitLayerCPUTest::getTestCaseName);
 
+const std::vector<InputShape> inputShapes3D = {
+        { {}, {{14, 28, 21}} },
+        {
+            // dynamic
+            {-1, -1, -1},
+            // target
+            {
+                {7, 21, 14},
+                {21, 7, 14},
+                {21, 14, 7},
+            }
+        },
+        {
+            // dynamic
+            {{1, 60}, {1, 50}, {1, 48}},
+            // target
+            {
+                {14, 21, 7},
+                {21, 7, 14},
+                {7, 14, 21},
+            }
+        },
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_Split3D, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(7),
                                 ::testing::Values(0, 1, 2),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({14, 42, 21})),
+                                ::testing::ValuesIn(inputShapes3D),
                                 ::testing::Values(std::vector<size_t>({})),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
                                 SplitLayerCPUTest::getTestCaseName);
 
+const std::vector<InputShape> inputShapes2D = {
+        { {}, {{6, 12}} },
+        {
+            // dynamic
+            {-1, -1},
+            // target
+            {
+                {2, 8},
+                {10, 4},
+                {2, 6},
+            }
+        },
+        {
+            // dynamic
+            {{1, 60}, {1, 50}},
+            // target
+            {
+                {2, 4},
+                {4, 4},
+                {6, 12},
+            }
+        },
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_Split2D, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(2),
                                 ::testing::Values(0, 1),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({6, 12})),
+                                ::testing::ValuesIn(inputShapes2D),
                                 ::testing::Values(std::vector<size_t>({})),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
                         SplitLayerCPUTest::getTestCaseName);
 
+const std::vector<InputShape> inputShapes1D = {
+        { {}, {{10}} },
+        {
+            // dynamic
+            {-1},
+            // target
+            {
+                {5},
+                {15},
+                {10},
+            }
+        },
+        {
+            // dynamic
+            {{1, 60}},
+            // target
+            {
+                {15},
+                {5},
+                {10},
+            }
+        },
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_Split1D, SplitLayerCPUTest,
                         ::testing::Combine(
                                 ::testing::Values(5),
                                 ::testing::Values(0),
                                 ::testing::ValuesIn(netPrecisions),
-                                ::testing::Values(std::vector<size_t>({10})),
+                                ::testing::ValuesIn(inputShapes1D),
                                 ::testing::Values(std::vector<size_t>({})),
-                                ::testing::Values(CommonTestUtils::DEVICE_CPU),
                                 ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
                             SplitLayerCPUTest::getTestCaseName);
+
+// ============================================== inPlace cases ============================================
+INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block8inPlace, SplitLayerCPUTest,
+                    ::testing::Combine(
+                            ::testing::Values(3),
+                            ::testing::Values(0, 1),
+                            ::testing::ValuesIn(netPrecisions),
+                            ::testing::Values(InputShape{ {}, {{3, 24, 24, 9}} }),
+                            ::testing::ValuesIn(outIndices3),
+                            ::testing::Values(planar_4D, planar_4D_ref, perChannels_4D, blocked8_4D)),
+                    SplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Split4D_CPU_Block16inPlace, SplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(4),
+                                ::testing::Values(0, 1),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(InputShape{ {}, {{4, 64, 32, 12}} }),
+                                ::testing::ValuesIn(outIndices3),
+                                ::testing::Values(blocked16_4D)),
+                        SplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Block8inPlace, SplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(3),
+                                ::testing::Values(0, 1),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(InputShape{ {}, {{3, 24, 24, 9, 15}} }),
+                                ::testing::ValuesIn(outIndices3),
+                                ::testing::Values(planar_5D, planar_5D_ref, perChannels_5D, blocked8_5D)),
+                        SplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Split5D_CPU_Block16inPlace, SplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Values(4),
+                                ::testing::Values(0, 1),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(InputShape{ {}, {{4, 64, 32, 12, 20}} }),
+                                ::testing::ValuesIn(outIndices4),
+                                ::testing::Values(blocked16_5D)),
+                        SplitLayerCPUTest::getTestCaseName);
+
 } // namespace
+
 } // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/variadic_split.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/variadic_split.cpp
new file mode 100644
index 00000000000000..d7395f315977ad
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/variadic_split.cpp
@@ -0,0 +1,434 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace ov::test;
+using namespace CPUTestUtils;
+
+namespace CPULayerTestsDefinitions {
+
+typedef std::tuple<
+        InputShape,
+        int64_t,              // Axis
+        std::vector<int>,     // Split lengths
+        ElementType,          // Net precision
+        CPUSpecificParams
+> varSplitCPUTestParams;
+
+class VariadicSplitLayerCPUTest : public testing::WithParamInterface<varSplitCPUTestParams>,
+                                  virtual public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<varSplitCPUTestParams> obj) {
+        InputShape shapes;
+        int64_t axis;
+        std::vector<int> splitLenght;
+        ElementType netPrecision;
+        CPUSpecificParams cpuParams;
+        std::tie(shapes, axis, splitLenght, netPrecision, cpuParams) = obj.param;
+
+        std::ostringstream result;
+        result << "IS=";
+        result << CommonTestUtils::partialShape2str({shapes.first}) << "_";
+        result << "TS=";
+        for (const auto& shape : shapes.second) {
+            result << CommonTestUtils::vec2str(shape) << "_";
+        }
+        result << "axis=" << axis << "_";
+        result << "splitLenght=" << CommonTestUtils::vec2str(splitLenght) << "_";
+        result << "netPRC=" << netPrecision << "_";
+        result << CPUTestsBase::getTestCaseName(cpuParams);
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        InputShape inputShapes;
+        int64_t axis;
+        std::vector<int> splitLenght;
+        ElementType netPrecision;
+        CPUSpecificParams cpuParams;
+        std::tie(inputShapes, axis, splitLenght, netPrecision, cpuParams) = this->GetParam();
+
+        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+        selectedType += std::string("_") + InferenceEngine::details::convertPrecision(netPrecision).name();
+
+        init_input_shapes({inputShapes});
+
+        auto params = ngraph::builder::makeDynamicParams(netPrecision, inputDynamicShapes);
+        auto paramOuts = ngraph::helpers::convert2OutputVector(
+                ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+
+        auto splitAxisOp = std::make_shared<ngraph::opset3::Constant>(ngraph::element::i64, ngraph::Shape{}, std::vector<int64_t>{axis});
+        auto splitLengthsOp = std::make_shared<ngraph::opset3::Constant>(ngraph::element::i32, ngraph::Shape{splitLenght.size()}, splitLenght);
+        auto varSplit = std::make_shared<ngraph::opset3::VariadicSplit>(paramOuts[0], splitAxisOp, splitLengthsOp);
+
+        varSplit->get_rt_info() = getCPUInfo();
+
+        function = std::make_shared<ngraph::Function>(varSplit, params, "VariadicSplitCPU");
+    }
+};
+
+TEST_P(VariadicSplitLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+//     CheckPluginRelatedResults(executableNetwork, "Split");
+}
+
+namespace {
+const auto planar_4D_ref = CPUSpecificParams{{nchw}, {nchw}, {"ref"}, "ref"};
+const auto planar_5D_ref = CPUSpecificParams{{ncdhw}, {ncdhw}, {"ref"}, "ref"};
+
+const auto planar_4D = CPUSpecificParams{{nchw}, {nchw}, {}, "unknown"};
+const auto planar_5D = CPUSpecificParams{{ncdhw}, {ncdhw}, {}, "unknown"};
+
+const auto perChannels_4D = CPUSpecificParams{{nhwc}, {nhwc}, {}, "ref"};
+const auto perChannels_5D = CPUSpecificParams{{ndhwc}, {ndhwc}, {}, "ref"};
+
+const auto perChannelsToPlanar_4D = CPUSpecificParams{{nhwc}, {nchw}, {}, "ref"};
+const auto perChannelsToPlanar_5D = CPUSpecificParams{{ndhwc}, {ncdhw}, {}, "ref"};
+
+const auto blocked8_4D = CPUSpecificParams{{nChw8c}, {nChw8c}, {}, "unknown"};
+const auto blocked8_5D = CPUSpecificParams{{nCdhw8c}, {nCdhw8c}, {}, "unknown"};
+
+const auto blocked8_4D_ref = CPUSpecificParams{{nChw8c}, {nChw8c}, {}, "ref"};
+const auto blocked8_5D_ref = CPUSpecificParams{{nCdhw8c}, {nCdhw8c}, {}, "ref"};
+
+const auto blocked16_4D = CPUSpecificParams{{nChw16c}, {nChw16c}, {}, "unknown"};
+const auto blocked16_5D = CPUSpecificParams{{nCdhw16c}, {nCdhw16c}, {}, "unknown"};
+
+const auto blocked16_4D_ref = CPUSpecificParams{{nChw16c}, {nChw16c}, {}, "ref"};
+const auto blocked16_5D_ref = CPUSpecificParams{{nCdhw16c}, {nCdhw16c}, {}, "ref"};
+
+// List of precisions natively supported by mkldnn.
+const std::vector<ElementType> netPrecisions = {
+        ElementType::i8,
+        ElementType::i32,
+        ElementType::f32,
+        ElementType::bf16
+};
+
+const std::vector<InputShape> inputShapes4D_Nspc2NcspSpecial = {
+        { {}, {{3, 5, 24, 9}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1},
+            // target
+            {
+                {1, 8, 5, 7},
+                {3, 9, 7, 9},
+                {5, 6, 1, 8}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 25}, {2, 10}},
+            // target
+            {
+                {2, 7, 5, 7},
+                {1, 10, 10, 2},
+                {3, 5, 6, 9}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_Nspc2NcspSpecial, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes4D_Nspc2NcspSpecial),
+                                ::testing::Values(1),
+                                ::testing::Values(std::vector<int>{1, 2, -1, 1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(perChannelsToPlanar_4D)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes5D_Nspc2NcspSpecial = {
+        { {}, {{3, 4, 7, 9, 3}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1, -1},
+            // target
+            {
+                {1, 6, 5, 7, 5},
+                {3, 8, 6, 9, 1},
+                {5, 9, 1, 8, 2}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 25}, {2, 10}, {1, 64}},
+            // target
+            {
+                {2, 5, 5, 7, 7},
+                {1, 4, 10, 2, 11},
+                {3, 7, 5, 9, 8}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_Nspc2NcspSpecial, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes5D_Nspc2NcspSpecial),
+                                ::testing::Values(1),
+                                ::testing::Values(std::vector<int>{2, 1, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(perChannelsToPlanar_5D)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes4D_planar = {
+        { {}, {{3, 6, 5, 6}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1},
+            // target
+            {
+                {1, 9, 8, 7},
+                {3, 8, 6, 5},
+                {5, 3, 7, 6}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 48}, {2, 48}},
+            // target
+            {
+                {2, 9, 5, 6},
+                {1, 6, 9, 8},
+                {3, 1, 6, 7}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_planar, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes4D_planar),
+                                ::testing::Values(2, 3),
+                                ::testing::Values(std::vector<int>{1, 3, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_4D, planar_4D_ref, perChannels_4D)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes4D_block = {
+        { {}, {{3, 16, 6, 7}} },
+        {
+            // dynamic
+            {-1, 16, -1, -1},
+            // target
+            {
+                {1, 16, 8, 7},
+                {3, 16, 7, 8},
+                {5, 16, 9, 8}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 48}, {2, 24}},
+            // target
+            {
+                {2, 16, 12, 6},
+                {1, 16, 6, 9},
+                {3, 16, 7, 6}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_Block8, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes4D_block),
+                                ::testing::Values(2, 3),
+                                ::testing::Values(std::vector<int>{2, 2, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked8_4D_ref)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit4D_CPU_Block16, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes4D_block),
+                                ::testing::Values(2, 3),
+                                ::testing::Values(std::vector<int>{2, 2, -1, 1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked16_4D_ref)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes5D_planar = {
+        { {}, {{3, 24, 4, 5, 6}} },
+        {
+            // dynamic
+            {-1, -1, -1, -1, -1},
+            // target
+            {
+                {1, 2, 4, 6, 5},
+                {3, 1, 6, 4, 5},
+                {5, 6, 5, 7, 4}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, {1, 64}, {1, 48}, {2, 48}, {2, 40}},
+            // target
+            {
+                {2, 5, 4, 5, 6},
+                {1, 7, 5, 4, 7},
+                {3, 3, 5, 6, 4}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_planar, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes5D_planar),
+                                ::testing::Values(2, 3, 4),
+                                ::testing::Values(std::vector<int>{2, 1, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(planar_5D, planar_5D_ref, perChannels_5D)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes5D_block = {
+        { {}, {{3, 16, 8, 5, 6}} },
+        {
+            // dynamic
+            {-1, 16, -1, -1, -1},
+            // target
+            {
+                {1, 16, 5, 6, 7},
+                {3, 16, 24, 5, 8},
+                {5, 16, 6, 7, 5}
+            }
+        },
+        {
+            // dynamic
+            {{1, 5}, 16, {1, 48}, {2, 24}, {2, 64}},
+            // target
+            {
+                {2, 16, 7, 6, 5},
+                {1, 16, 6, 5, 7},
+                {3, 16, 5, 7, 6}
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_Block8, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes5D_block),
+                                ::testing::Values(2, 3, 4),
+                                ::testing::Values(std::vector<int>{1, 2, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked8_5D_ref)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit5D_CPU_Block16, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes5D_block),
+                                ::testing::Values(2, 3, 4),
+                                ::testing::Values(std::vector<int>{2, 1, -1, 1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(blocked16_5D_ref)),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes3D = {
+        { {}, {{14, 7, 21}} },
+        {
+            // dynamic
+            {-1, -1, -1},
+            // target
+            {
+                {7, 21, 14},
+                {21, 7, 14},
+                {21, 14, 7},
+            }
+        },
+        {
+            // dynamic
+            {{1, 60}, {1, 50}, {1, 48}},
+            // target
+            {
+                {14, 21, 7},
+                {21, 7, 14},
+                {7, 14, 21},
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit3D, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes3D),
+                                ::testing::Values(0, 1, 2),
+                                ::testing::Values(std::vector<int>{2, 4, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes2D = {
+        { {}, {{6, 12}} },
+        {
+            // dynamic
+            {-1, -1},
+            // target
+            {
+                {3, 8},
+                {10, 4},
+                {3, 6},
+            }
+        },
+        {
+            // dynamic
+            {{1, 60}, {1, 50}},
+            // target
+            {
+                {3, 4},
+                {4, 4},
+                {6, 12},
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit2D, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes2D),
+                                ::testing::Values(0, 1),
+                                ::testing::Values(std::vector<int>{2, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+const std::vector<InputShape> inputShapes1D = {
+        { {}, {{10}} },
+        {
+            // dynamic
+            {-1},
+            // target
+            {
+                {5},
+                {15},
+                {10},
+            }
+        },
+        {
+            // dynamic
+            {{1, 60}},
+            // target
+            {
+                {15},
+                {5},
+                {10},
+            }
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_VariadicSplit1D, VariadicSplitLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(inputShapes1D),
+                                ::testing::Values(0),
+                                ::testing::Values(std::vector<int>{2, 1, 1, -1}),
+                                ::testing::ValuesIn(netPrecisions),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "unknown"}, CPUSpecificParams{{}, {}, {"ref"}, "ref"})),
+                        VariadicSplitLayerCPUTest::getTestCaseName);
+
+} // namespace
+
+} // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/align_mamtul_input_ranks.cpp b/inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/align_matmul_input_ranks.cpp
similarity index 100%
rename from inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/align_mamtul_input_ranks.cpp
rename to inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/align_matmul_input_ranks.cpp
diff --git a/inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/concat_const_inplace.cpp b/inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/concat_const_inplace.cpp
index ca99be789a9006..4704becf624649 100644
--- a/inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/concat_const_inplace.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/subgraph_tests/src/concat_const_inplace.cpp
@@ -13,43 +13,74 @@ using namespace InferenceEngine;
 namespace SubgraphTestsDefinitions {
 // Subgraph:
 /*
- *          Parameter    Constant
+ *          Parameter    Constant[FP32/BF16]
  *                  \    /
  *                   \  /
- *               Transpose
- *  Constant      /
- *        \      /
+ *               Transpose[FP32/BF16]
+ *  Constant[FP32] /
+ *        \      X  No Reorder
  *         \    /
- *        Concat (inPlace)
+ *        Concat (inPlace)[FP32/BF16]
  *           |
+ *      Convolution [FP32/BF16]
  *           |
- *        Result
+ *        Result[FP32/BF16]
  */
 
-class ConcatConstantInPlaceTest : virtual public LayerTestsUtils::LayerTestsCommon {
+class ConcatConstantInPlaceTest : public testing::WithParamInterface<InferenceEngine::Precision>, virtual public LayerTestsUtils::LayerTestsCommon {
 public:
+    static std::string getTestCaseName(testing::TestParamInfo<InferenceEngine::Precision> obj) {
+        std::ostringstream result;
+        result << "ConcatConstantInPlaceTest" << obj.param.name();
+        return result.str();
+    }
+
     void SetUp() override {
         targetDevice = CommonTestUtils::DEVICE_CPU;
-        inPrc = outPrc = Precision::FP32;
-        const std::vector<size_t> inputShape = {1, 384, 196};
-        auto inputParams = ngraph::builder::makeParams(ngraph::element::f32, {inputShape, inputShape});
+        if (Precision::BF16 == (inPrc = outPrc = this->GetParam()))
+            configuration.insert({ PluginConfigParams::KEY_ENFORCE_BF16, PluginConfigParams::YES });
+        else
+            configuration.insert({ PluginConfigParams::KEY_ENFORCE_BF16, PluginConfigParams::NO });
+
+        const std::vector<size_t> inputShape = {1, 3, 3, 11};
+        auto inputParams = ngraph::builder::makeParams(ngraph::element::f32, {inputShape});
 
-        auto transposeOrder = ngraph::opset8::Constant::create(ngraph::element::i32, {3}, {0, 2, 1});
+        auto transposeOrder = ngraph::opset8::Constant::create(ngraph::element::i32, {4}, {0, 3, 2, 1});
         auto transpose = std::make_shared<ngraph::opset8::Transpose>(inputParams[0], transposeOrder);
 
-        auto concatConstantInput = ngraph::opset8::Constant::create(ngraph::element::f32, {1, 1, 384}, {10.0f});
+        auto concatConstantInput = ngraph::opset8::Constant::create(ngraph::element::f32, {1, 1, 3, 3}, {10.0f});
         auto concat = ngraph::builder::makeConcat({concatConstantInput, transpose}, 1);
 
-        ngraph::ResultVector results{std::make_shared<ngraph::opset8::Result>(concat)};
+        // convolution
+        std::vector<float> weightValuesFP32(12);
+        ngraph::Shape convFilterShape = { 1, 12, 1, 1 };
+//        weightValuesFP32.resize(12);
+        FuncTestUtils::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size());
+        auto weightsNode = std::make_shared<ngraph::opset1::Constant>(ngraph::element::f32, convFilterShape, weightValuesFP32);
+        std::shared_ptr<ngraph::Node> conv = std::make_shared<ngraph::opset1::Convolution>(
+            concat, weightsNode, ngraph::Strides({ 1, 1 }), ngraph::CoordinateDiff({ 0, 0 }),
+            ngraph::CoordinateDiff({ 0, 0 }), ngraph::Strides({ 1, 1 }), ngraph::op::PadType::EXPLICIT);
+            conv->set_friendly_name("CONV");
+
+        ngraph::ResultVector results{std::make_shared<ngraph::opset8::Result>(conv)};
         function = std::make_shared<ngraph::Function>(results, inputParams, "ConcatConstantInPlace");
     }
 };
 
 namespace {
-    TEST_F(ConcatConstantInPlaceTest, smoke_ConcatConstantInPlaceTest_CPU) {
+    TEST_P(ConcatConstantInPlaceTest, smoke_ConcatConstantInPlaceTest_CPU) {
         SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
         Run();
+        if (this->GetParam() == Precision::BF16)
+            CheckNodeOfTypeCount(executableNetwork, "Reorder", 4);
+        else
+            CheckNodeOfTypeCount(executableNetwork, "Reorder", 3);
     }
+
+INSTANTIATE_TEST_SUITE_P(smoke_ConcatConstantInPlaceTest_CPU, ConcatConstantInPlaceTest,
+    testing::Values(Precision::FP32, Precision::BF16),
+    ConcatConstantInPlaceTest::getTestCaseName);
+
 } // namespace
-} // namespace SubgraphTestsDefinitions
\ No newline at end of file
+} // namespace SubgraphTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/test_utils/fusing_test_utils.hpp b/inference-engine/tests/functional/plugin/cpu/test_utils/fusing_test_utils.hpp
index d55f2a988593b2..7d1dc60109ae2c 100644
--- a/inference-engine/tests/functional/plugin/cpu/test_utils/fusing_test_utils.hpp
+++ b/inference-engine/tests/functional/plugin/cpu/test_utils/fusing_test_utils.hpp
@@ -219,18 +219,18 @@ const auto fusingScaleShift = fusingSpecificParams{ std::make_shared<postNodesMg
 const auto fusingFakeQuantizePerTensor = fusingSpecificParams{ std::make_shared<postNodesMgr>(std::vector<postNodeBuilder>{
             {[](std::shared_ptr<ngraph::Node> inpNode, const ngraph::element::Type& ngPrc, ngraph::ParameterVector& params){
                 auto localPrc = inpNode->get_element_type();
-                ngraph::Shape newShape(inpNode->get_shape().size(), 1);
+                ngraph::Shape newShape(inpNode->get_output_partial_shape(0).size(), 1);
                 return ngraph::builder::makeFakeQuantize(inpNode, localPrc, 256, newShape);
             }, "FakeQuantize(PerTensor)"}}), {"FakeQuantize"} };
 
 const auto fusingFakeQuantizePerChannel = fusingSpecificParams{std::make_shared<postNodesMgr>(std::vector<postNodeBuilder>{
             {[](std::shared_ptr<ngraph::Node> inpNode, const ngraph::element::Type& ngPrc, ngraph::ParameterVector& params){
                 auto localPrc = inpNode->get_element_type();
-                auto shape = inpNode->get_shape();
+                auto shape = inpNode->get_output_partial_shape(0);
                 if (shape.size() == 1)
                     IE_THROW() << "If shape.size() == 1 then Granularity can be PerTensor only";
                 ngraph::Shape newShape(shape.size(), 1);
-                newShape[1] = shape[1];
+                newShape[1] = shape[1].get_length();
                 return ngraph::builder::makeFakeQuantize(inpNode, localPrc, 256, newShape);
             }, "FakeQuantize(PerChannel)"}}), {"FakeQuantize"}};
 
diff --git a/inference-engine/tests/functional/plugin/gna/Import_export_tests/import_export_memory_layer.cpp b/inference-engine/tests/functional/plugin/gna/Import_export_tests/import_export_memory_layer.cpp
index f0deafaf07140d..91d28dc577a947 100644
--- a/inference-engine/tests/functional/plugin/gna/Import_export_tests/import_export_memory_layer.cpp
+++ b/inference-engine/tests/functional/plugin/gna/Import_export_tests/import_export_memory_layer.cpp
@@ -65,16 +65,14 @@ class ImportMemoryTest : public testing::WithParamInterface<exportImportNetworkP
         }
         auto importedNetwork = core->ImportNetwork(inputStream, targetDevice, configuration);
         std::vector<std::string> queryToState;
-        IE_SUPPRESS_DEPRECATED_START
-        for (const auto &query_state : executableNetwork.QueryState()) {
+        InferenceEngine::InferRequest importInfer = importedNetwork.CreateInferRequest();
+        for (const auto &query_state : importInfer.QueryState()) {
             queryToState.push_back(query_state.GetName());
         }
-        for (const auto &next_memory : importedNetwork.QueryState()) {
+        for (const auto &next_memory : importInfer.QueryState()) {
             ASSERT_TRUE(std::find(queryToState.begin(), queryToState.end(), next_memory.GetName()) != queryToState.end())
                                         << "State " << next_memory.GetName() << " expected to be in memory states but it is not!";
         }
-        IE_SUPPRESS_DEPRECATED_END
-        InferenceEngine::InferRequest importInfer = importedNetwork.CreateInferRequest();
         importInfer.Infer();
     }
 
diff --git a/inference-engine/tests/functional/plugin/gna/scale_factors_tests/eltwise_act_fq.cpp b/inference-engine/tests/functional/plugin/gna/scale_factors_tests/eltwise_act_fq.cpp
index b9d54f1308b818..a8d49970d8dd71 100644
--- a/inference-engine/tests/functional/plugin/gna/scale_factors_tests/eltwise_act_fq.cpp
+++ b/inference-engine/tests/functional/plugin/gna/scale_factors_tests/eltwise_act_fq.cpp
@@ -85,6 +85,8 @@ class EltwiseActFqTest : public testing::WithParamInterface<eltwiseActFqParams>,
         if (act == ngraph::helpers::ActivationTypes::Log) {
             // clamp not positive values
             inputDataMin = 1.0e-3;
+            // get error threshold value from PWL error
+            threshold = std::stof(configuration["GNA_PWL_MAX_ERROR_PERCENT"]);
         }
         auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
 
@@ -136,6 +138,7 @@ const std::vector<InferenceEngine::Precision> netPrecisions = {
 const std::vector<std::map<std::string, std::string>> configs = {
     {
         {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
+        {"GNA_PWL_MAX_ERROR_PERCENT", "0.025"}
     }
 };
 
diff --git a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/activation.cpp b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/activation.cpp
index 71c0cfc3d70766..584f31ed76913d 100644
--- a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/activation.cpp
+++ b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/activation.cpp
@@ -6,6 +6,66 @@
 #include "single_layer_tests/activation.hpp"
 #include "common_test_utils/test_constants.hpp"
 
+namespace LayerTestsDefinitions {
+
+class ActivationLayerGNATest : public ActivationLayerTest {
+protected:
+    void SetUp() override {
+        ActivationLayerTest::SetUp();
+        // TODO: remove after integer inference output support
+        auto ngPrc = function->get_parameters()[0]->get_element_type().get_type_name();
+        if (ngPrc == "u8" || ngPrc == "i16") {
+            threshold = 1.0;
+        }
+    }
+
+    InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo &info) const override {
+        bool inPrcSigned = function->get_parameters()[0]->get_element_type().is_signed();
+        int32_t data_start_from = -10;
+        uint32_t data_range = 20;
+        int32_t resolution = 32768;
+        switch (activationType) {
+            case ngraph::helpers::ActivationTypes::Log: {
+                data_start_from = 1;
+                data_range = 20;
+                resolution = 32768;
+                break;
+            }
+            case ngraph::helpers::ActivationTypes::Sign: {
+                data_start_from = -10;
+                data_range = 20;
+                resolution = 3072;
+                break;
+            }
+            case ngraph::helpers::ActivationTypes::Exp: {
+                const double max_result_on_GNA = 15.9;
+                const double exp_inverse = std::round(std::log(max_result_on_GNA));
+                if (inPrcSigned) {
+                    data_range = exp_inverse * 2.0;
+                    data_start_from = -exp_inverse;
+                } else {
+                    data_range = exp_inverse;
+                    data_start_from = 0;
+                }
+                break;
+            }
+        }
+        if (!inPrcSigned) {
+            data_range = 15;
+            data_start_from = 0;
+        }
+
+        return FuncTestUtils::createAndFillBlob(info.getTensorDesc(), data_range,
+                                                data_start_from, resolution);
+    }
+};
+
+TEST_P(ActivationLayerGNATest, CompareWithRefs) {
+        Run();
+}
+
+}  //  namespace LayerTestsDefinitions
+
 using namespace LayerTestsDefinitions;
 using namespace ngraph::helpers;
 namespace {
@@ -16,13 +76,12 @@ const std::vector<InferenceEngine::Precision> inputPrecisions = {
         InferenceEngine::Precision::U8
 };
 
+
 const std::vector<InferenceEngine::Precision> netPrecisions = {
-        // TODO: Issue:27391
-        // InferenceEngine::Precision::FP32,
-        // TODO: Issue:28036
-        // InferenceEngine::Precision::FP16,
+        InferenceEngine::Precision::FP32,
+        InferenceEngine::Precision::FP16,
         InferenceEngine::Precision::I16,
-        InferenceEngine::Precision::U8
+        InferenceEngine::Precision::U8,
 };
 
 const std::map<ActivationTypes, std::vector<std::vector<float>>> activationTypes = {
@@ -62,6 +121,6 @@ const auto basicCases = ::testing::Combine(
 );
 
 
-INSTANTIATE_TEST_SUITE_P(smoke_Activation_Basic, ActivationLayerTest, basicCases, ActivationLayerTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_Activation_Basic, ActivationLayerGNATest, basicCases, ActivationLayerTest::getTestCaseName);
 
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/eltwise.cpp b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/eltwise.cpp
index e065d8597f664c..50153264efbbe5 100644
--- a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/eltwise.cpp
+++ b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/single_layer_tests/eltwise.cpp
@@ -41,10 +41,17 @@ std::vector<ngraph::helpers::EltwiseTypes> eltwiseOpTypes = {
         ngraph::helpers::EltwiseTypes::ADD
 };
 
-std::map<std::string, std::string> additional_config = {
-        {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
-        {"GNA_SCALE_FACTOR_0", "1638.4"},
-        {"GNA_SCALE_FACTOR_1", "1638.4"}
+std::vector<std::map<std::string, std::string>> additional_config = {
+        {
+                {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
+                {"GNA_SCALE_FACTOR_0", "1638.4"},
+                {"GNA_SCALE_FACTOR_1", "1638.4"}
+        },
+        {
+                {"GNA_DEVICE_MODE", "GNA_SW_FP32"},
+                {"GNA_SCALE_FACTOR_0", "1638.4"},
+                {"GNA_SCALE_FACTOR_1", "1638.4"}
+        }
 };
 
 const auto multiply_params = ::testing::Combine(
@@ -56,7 +63,7 @@ const auto multiply_params = ::testing::Combine(
         ::testing::Values(ov::element::undefined),
         ::testing::Values(ov::element::undefined),
         ::testing::Values(CommonTestUtils::DEVICE_GNA),
-        ::testing::Values(additional_config));
+        ::testing::ValuesIn(additional_config));
 
 INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs, EltwiseLayerTest, multiply_params, EltwiseLayerTest::getTestCaseName);
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp
index c727248cbbd176..082303bddc16a0 100644
--- a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/skip_tests_config.cpp
@@ -17,10 +17,7 @@ std::vector<std::string> disabledTestPatterns() {
         // TODO: FIX BUG 59041
         ".*Behavior.*CallbackThrowException.*",
         // TODO: FIX BUG 32210
-        R"(.*ActivationLayerTest.CompareWithRefs/(Sigmoid|Tanh|Exp|Log).*)",
         R"(.*ActivationFQSubgraph.*activation=(Exp|Log).*)",
-        // TODO: Issue 68586
-        R"(.*EltwiseActFqTest.*act=Log.*)",
         // TODO: Issue 32542
         R"(.*(EltwiseLayerTest).*eltwiseOpType=(Sum|Sub).*opType=SCALAR.*)",
         R"(.*(EltwiseLayerTest).*eltwiseOpType=Prod.*secondaryInputType=PARAMETER.*opType=SCALAR.*)",
@@ -76,5 +73,8 @@ std::vector<std::string> disabledTestPatterns() {
         // TODO: Issue: CVS-69639
         R"(.*EltwiseLayerTest.*OpType=Prod.*)",
         R"(.*EltwiseLayerTest.*OpType=Sum.*PARAMETER.*VECTOR.*)",
+        // TODO: Issue:27391
+        // TODO: Issue:28036
+        R"(.*ActivationLayerGNATest.*(Log|Exp).*netPRC=(FP16|FP32).*)",
     };
 }
diff --git a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/concat_quantization_during_memory_requantization.cpp b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/concat_quantization_during_memory_requantization.cpp
index 0357c545f57872..5e5618e498a8a6 100644
--- a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/concat_quantization_during_memory_requantization.cpp
+++ b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/concat_quantization_during_memory_requantization.cpp
@@ -15,8 +15,10 @@ namespace {
         InferenceEngine::Precision::FP32
     };
 
-    std::map<std::string, std::string> config = {
-            {"GNA_COMPACT_MODE", "NO"}
+    std::vector<std::map<std::string, std::string>> additionalConfig = {
+            { {"GNA_COMPACT_MODE", "NO"} },
+            { {"GNA_COMPACT_MODE", "NO"},
+              {"GNA_DEVICE_MODE", "GNA_SW_FP32"}}
     };
 
     std::vector<size_t> inputSizes = {
@@ -37,6 +39,6 @@ namespace {
             ::testing::Values(CommonTestUtils::DEVICE_GNA),
             ::testing::ValuesIn(inputSizes),
             ::testing::ValuesIn(hiddenSizes),
-            ::testing::Values(config)),
+            ::testing::ValuesIn(additionalConfig)),
         ConcatQuantDuringMemoryRequantTest::getTestCaseName);
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/first_connect_input_concat.cpp b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/first_connect_input_concat.cpp
index e62bdd99cfcb72..1d9f23edfb4d51 100644
--- a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/first_connect_input_concat.cpp
+++ b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/first_connect_input_concat.cpp
@@ -22,9 +22,15 @@ const std::vector<InferenceEngine::Precision> netPrecisions = {
         InferenceEngine::Precision::FP16,
 };
 
-std::map<std::string, std::string> additional_config = {
-        {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
-        {"GNA_COMPACT_MODE", "NO"},
+std::vector<std::map<std::string, std::string>> additional_config = {
+        {
+                {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
+                {"GNA_COMPACT_MODE", "NO"}
+        },
+        {
+                {"GNA_DEVICE_MODE", "GNA_SW_FP32"},
+                {"GNA_COMPACT_MODE", "NO"}
+        }
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_concat_first_input, ConcatFirstInputTest,
@@ -32,7 +38,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_concat_first_input, ConcatFirstInputTest,
                                 ::testing::ValuesIn(inShapes),
                                 ::testing::ValuesIn(netPrecisions),
                                 ::testing::Values(CommonTestUtils::DEVICE_GNA),
-                                ::testing::Values(additional_config)),
+                                ::testing::ValuesIn(additional_config)),
                         ConcatFirstInputTest::getTestCaseName);
 
 } //namespace
diff --git a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/multiple_concat.cpp b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/multiple_concat.cpp
index 2cd3e96ee992e3..52b85236db3222 100644
--- a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/multiple_concat.cpp
+++ b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/multiple_concat.cpp
@@ -27,10 +27,17 @@ std::vector<size_t> constant_sizes_unaligned = {
     99
 };
 
-std::map<std::string, std::string> additional_config = {
-    {"GNA_COMPACT_MODE", "NO"},
-    {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
-    {"GNA_SCALE_FACTOR_0", "3276.8"},
+std::vector<std::map<std::string, std::string>> additional_config = {
+    {
+        {"GNA_COMPACT_MODE", "NO"},
+        {"GNA_DEVICE_MODE", "GNA_SW_EXACT"},
+        {"GNA_SCALE_FACTOR_0", "3276.8"}
+    },
+    {
+        {"GNA_COMPACT_MODE", "NO"},
+        {"GNA_DEVICE_MODE", "GNA_SW_FP32"},
+        {"GNA_SCALE_FACTOR_0", "3276.8"}
+    },
 };
 } // namespace
 
@@ -40,7 +47,7 @@ INSTANTIATE_TEST_SUITE_P(I_aligned_C_aligned, MultipleConcatTest,
         ::testing::Values(InferenceEngine::Precision::FP32),
         ::testing::ValuesIn(input_sizes_aligned),
         ::testing::ValuesIn(constant_sizes_aligned),
-        ::testing::Values(additional_config)),
+        ::testing::ValuesIn(additional_config)),
     MultipleConcatTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(I_aligned_C_unaligned, MultipleConcatTest,
@@ -49,7 +56,7 @@ INSTANTIATE_TEST_SUITE_P(I_aligned_C_unaligned, MultipleConcatTest,
         ::testing::Values(InferenceEngine::Precision::FP32),
         ::testing::ValuesIn(input_sizes_aligned),
         ::testing::ValuesIn(constant_sizes_unaligned),
-        ::testing::Values(additional_config)),
+        ::testing::ValuesIn(additional_config)),
     MultipleConcatTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(I_unaligned_C_aligned, MultipleConcatTest,
@@ -58,7 +65,7 @@ INSTANTIATE_TEST_SUITE_P(I_unaligned_C_aligned, MultipleConcatTest,
         ::testing::Values(InferenceEngine::Precision::FP32),
         ::testing::ValuesIn(input_sizes_unaligned),
         ::testing::ValuesIn(constant_sizes_aligned),
-        ::testing::Values(additional_config)),
+        ::testing::ValuesIn(additional_config)),
     MultipleConcatTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(I_unaligned_C_unaligned, MultipleConcatTest,
@@ -67,6 +74,6 @@ INSTANTIATE_TEST_SUITE_P(I_unaligned_C_unaligned, MultipleConcatTest,
         ::testing::Values(InferenceEngine::Precision::FP32),
         ::testing::ValuesIn(input_sizes_unaligned),
         ::testing::ValuesIn(constant_sizes_unaligned),
-        ::testing::Values(additional_config)),
+        ::testing::ValuesIn(additional_config)),
     MultipleConcatTest::getTestCaseName);
 } // namespace SubgraphTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/gpu/concurrency/gpu_concurrency_tests.cpp b/inference-engine/tests/functional/plugin/gpu/concurrency/gpu_concurrency_tests.cpp
new file mode 100644
index 00000000000000..52296a3f42f1d6
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/concurrency/gpu_concurrency_tests.cpp
@@ -0,0 +1,110 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <string>
+#include <utility>
+#include <vector>
+#include <memory>
+
+#include "openvino/runtime/core.hpp"
+
+#include <gpu/gpu_config.hpp>
+#include <common_test_utils/test_common.hpp>
+#include <functional_test_utils/plugin_cache.hpp>
+#include "ngraph_functions/subgraph_builders.hpp"
+#include "functional_test_utils/blob_utils.hpp"
+#include "openvino/core/preprocess/pre_post_process.hpp"
+#include "transformations/utils/utils.hpp"
+
+using namespace ::testing;
+
+using ConcurrencyTestParams = std::tuple<size_t,   // number of streams
+                                         size_t>;  // number of requests
+
+class OVConcurrencyTest : public CommonTestUtils::TestsCommon,
+    public testing::WithParamInterface<ConcurrencyTestParams> {
+    void SetUp() override {
+        std::tie(num_streams, num_requests) = this->GetParam();
+        fn_ptrs = {ngraph::builder::subgraph::makeSplitMultiConvConcat(),
+                   ngraph::builder::subgraph::makeMultiSingleConv()};
+    };
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ConcurrencyTestParams>& obj) {
+        size_t streams, requests;
+        std::tie(streams, requests) = obj.param;
+        return "_num_streams_" + std::to_string(streams) + "_num_req_" +
+            std::to_string(requests);
+    }
+
+protected:
+    size_t num_streams;
+    size_t num_requests;
+    std::vector<std::shared_ptr<ngraph::Function>> fn_ptrs;
+};
+
+TEST_P(OVConcurrencyTest, canInferTwoExecNets) {
+    auto ie = ov::runtime::Core();
+
+    ov::ResultVector outputs;
+    std::vector<ov::runtime::InferRequest> irs;
+    std::vector<std::vector<uint8_t>> ref;
+    std::vector<int> outElementsCount;
+
+    for (size_t i = 0; i < fn_ptrs.size(); ++i) {
+        auto fn = fn_ptrs[i];
+
+        auto exec_net = ie.compile_model(fn_ptrs[i], CommonTestUtils::DEVICE_GPU,
+                                         {{ov::ie::PluginConfigParams::KEY_GPU_THROUGHPUT_STREAMS, std::to_string(num_streams)}});
+
+        auto input = fn_ptrs[i]->get_parameters().at(0);
+        auto output = fn_ptrs[i]->get_results().at(0);
+
+        for (int j = 0; j < num_streams * num_requests; j++) {
+            outputs.push_back(output);
+
+            auto inf_req = exec_net.create_infer_request();
+            irs.push_back(inf_req);
+
+            auto tensor = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
+            inf_req.set_tensor(input, tensor);
+
+            outElementsCount.push_back(ov::shape_size(fn_ptrs[i]->get_output_shape(0)));
+            const auto in_tensor = inf_req.get_tensor(input);
+            const auto tensorSize = in_tensor.get_byte_size();
+            const auto inBlobBuf = static_cast<uint8_t*>(in_tensor.data());
+            std::vector<uint8_t> inData(inBlobBuf, inBlobBuf + tensorSize);
+            auto reOutData = ngraph::helpers::interpreterFunction(fn_ptrs[i], {inData}).front().second;
+            ref.push_back(reOutData);
+        }
+    }
+
+    const int niter = 10;
+    for (int i = 0; i < niter; i++) {
+        for (auto ir : irs) {
+            ir.start_async();
+        }
+
+        for (auto ir : irs) {
+            ir.wait();
+        }
+    }
+
+    auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
+    for (size_t i = 0; i < irs.size(); ++i) {
+        const auto &refBuffer = ref[i].data();
+        ASSERT_EQ(outElementsCount[i], irs[i].get_tensor(outputs[i]).get_size());
+        FuncTestUtils::compareRawBuffers(irs[i].get_tensor(outputs[i]).data<float>(),
+                                         reinterpret_cast<const float *>(refBuffer), outElementsCount[i],
+                                         outElementsCount[i],
+                                         thr);
+    }
+}
+
+const std::vector<size_t> num_streams{ 1, 2 };
+const std::vector<size_t> num_requests{ 1, 4 };
+
+INSTANTIATE_TEST_SUITE_P(smoke_RemoteTensor, OVConcurrencyTest,
+    ::testing::Combine(::testing::ValuesIn(num_streams),
+        ::testing::ValuesIn(num_requests)),
+    OVConcurrencyTest::getTestCaseName);
diff --git a/inference-engine/tests/functional/plugin/gpu/remote_blob_tests/cldnn_remote_tensor_tests.cpp b/inference-engine/tests/functional/plugin/gpu/remote_blob_tests/cldnn_remote_tensor_tests.cpp
deleted file mode 100644
index 21bf8d8988cf43..00000000000000
--- a/inference-engine/tests/functional/plugin/gpu/remote_blob_tests/cldnn_remote_tensor_tests.cpp
+++ /dev/null
@@ -1,367 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <string>
-#include <utility>
-#include <vector>
-#include <memory>
-
-#include "openvino/runtime/gpu/ocl.hpp"
-#include "openvino/runtime/core.hpp"
-
-#include <gpu/gpu_config.hpp>
-#include <remote_blob_tests/remote_blob_helpers.hpp>
-#include <common_test_utils/test_common.hpp>
-#include <functional_test_utils/plugin_cache.hpp>
-#include "ngraph_functions/subgraph_builders.hpp"
-#include "functional_test_utils/blob_utils.hpp"
-#include "openvino/core/preprocess/pre_post_process.hpp"
-#include "transformations/utils/utils.hpp"
-
-using namespace ::testing;
-
-class OVRemoteTensor_Test : public CommonTestUtils::TestsCommon {
-protected:
-    std::shared_ptr<ngraph::Function> fn_ptr;
-
-    void SetUp() override {
-        fn_ptr = ngraph::builder::subgraph::makeSplitMultiConvConcat();
-    }
-};
-
-TEST_F(OVRemoteTensor_Test, DISABLED_smoke_canInputUserTensor) {
-#if defined(ANDROID)
-    GTEST_SKIP();
-#endif
-    auto ie = ov::runtime::Core();
-
-    using namespace ov::preprocess;
-    auto function = PrePostProcessor()
-            .input(InputInfo()
-                           .tensor(InputTensorInfo().set_element_type(ov::element::i8))
-                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
-            .build(fn_ptr);
-
-    auto exec_net = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
-
-    // regular inference
-    auto inf_req_regular = exec_net.create_infer_request();
-    auto input = function->get_parameters().at(0);
-    auto output = function->get_results().at(0);
-    auto fakeImageData = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
-
-    inf_req_regular.set_tensor(input->get_friendly_name(), fakeImageData);
-
-    inf_req_regular.infer();
-    auto output_tensor_regular = inf_req_regular.get_tensor(ngraph::op::util::create_ie_output_name(output->input_value(0)));
-
-    // inference using remote tensor
-    auto inf_req_shared = exec_net.create_infer_request();
-    auto cldnn_context = exec_net.get_context().as<ov::runtime::gpu::ClContext>();
-    cl_context ctx = cldnn_context;
-    auto ocl_instance = std::make_shared<OpenCL>(ctx);
-    cl_int err;
-
-    auto imSize = ov::shape_size(input->get_shape());
-
-    cl::Buffer shared_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, imSize, NULL, &err);
-    {
-        void* buffer = fakeImageData.data();
-        ocl_instance->_queue.enqueueWriteBuffer(shared_buffer, true, 0, imSize, buffer);
-    }
-
-    auto cldnn_tensor = cldnn_context.create_tensor(input->get_element_type(), input->get_shape(), shared_buffer);
-    inf_req_shared.set_tensor(input->get_friendly_name(), cldnn_tensor);
-
-    inf_req_shared.infer();
-    auto output_tensor_shared = inf_req_shared.get_tensor(ngraph::op::util::create_ie_output_name(output->input_value(0)));
-
-    // compare results
-    {
-        ASSERT_EQ(output->get_element_type(), ov::element::f32);
-        ASSERT_EQ(output_tensor_regular.get_size(), output_tensor_shared.get_size());
-        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
-        ASSERT_NO_THROW(output_tensor_regular.data());
-        ASSERT_NO_THROW(output_tensor_shared.data());
-        FuncTestUtils::compare_tensor(output_tensor_regular, output_tensor_shared, thr);
-    }
-}
-
-TEST_F(OVRemoteTensor_Test, DISABLED_smoke_canInferOnUserContext) {
-    auto ie = ov::runtime::Core();
-
-    using namespace ov::preprocess;
-    auto function = PrePostProcessor()
-            .input(InputInfo()
-                           .tensor(InputTensorInfo().set_element_type(ov::element::i8))
-                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
-            .build(fn_ptr);
-
-    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
-    auto input = function->get_parameters().at(0);
-    auto output = function->get_results().at(0);
-
-    // regular inference
-    auto inf_req_regular = exec_net_regular.create_infer_request();
-    auto fakeImageData = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
-    inf_req_regular.set_tensor(input->get_friendly_name(), fakeImageData);
-
-    inf_req_regular.infer();
-    auto output_tensor_regular = inf_req_regular.get_tensor(ngraph::op::util::create_ie_output_name(output->input_value(0)));
-
-    // inference using remote tensor
-    auto ocl_instance = std::make_shared<OpenCL>();
-
-    auto remote_context = ov::runtime::gpu::ClContext(ie, ocl_instance->_context.get());
-    auto exec_net_shared = ie.compile_model(function, remote_context);
-    auto inf_req_shared = exec_net_shared.create_infer_request();
-    inf_req_shared.set_tensor(input->get_friendly_name(), fakeImageData);
-
-    inf_req_shared.infer();
-    auto output_tensor_shared = inf_req_shared.get_tensor(ngraph::op::util::create_ie_output_name(output->input_value(0)));
-
-    // compare results
-    {
-        ASSERT_EQ(output->get_element_type(), ov::element::f32);
-        ASSERT_EQ(output_tensor_regular.get_size(), output_tensor_shared.get_size());
-        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
-        ASSERT_NO_THROW(output_tensor_regular.data());
-        ASSERT_NO_THROW(output_tensor_shared.data());
-        FuncTestUtils::compare_tensor(output_tensor_regular, output_tensor_shared, thr);
-    }
-}
-
-class OVRemoteTensorBatched_Test : public CommonTestUtils::TestsCommon, public testing::WithParamInterface<size_t> {
-    void SetUp() override {
-        num_batch = this->GetParam();
-    };
-public:
-    static std::string getTestCaseName(const testing::TestParamInfo<std::size_t> &obj) {
-        return "num_batch_" + std::to_string(obj.param);
-    }
-
-protected:
-    size_t num_batch;
-    std::vector<std::shared_ptr<ngraph::Function>> fn_ptrs;
-};
-
-TEST_P(OVRemoteTensorBatched_Test, DISABLED_canInputNV12) {
-#if defined(ANDROID)
-    GTEST_SKIP();
-#endif
-    const int height = 16;
-    const int width = 16;
-
-    // ------------------------------------------------------
-    // Prepare input data
-    std::vector<ov::runtime::Tensor> fake_image_data_y;
-    std::vector<ov::runtime::Tensor> fake_image_data_uv;
-
-    for (int i = 0; i < num_batch; i++) {
-        fake_image_data_y.push_back(FuncTestUtils::create_and_fill_tensor(ov::element::u8, {1, 1, height, width}, 50, 0, 1, i));
-        fake_image_data_uv.push_back(FuncTestUtils::create_and_fill_tensor(ov::element::u8, {1, 2, height / 2, width / 2}, 256, 0, 1, i));
-    }
-
-    auto ie = ov::runtime::Core();
-
-    // ------------------------------------------------------
-    // inference using remote tensor with batch
-    auto fn_ptr_remote = ngraph::builder::subgraph::makeConvPoolRelu({num_batch, 3, height, width});
-
-    // TODO: Add preprocessing!
-    // CNNNetwork net_remote(fn_ptr_remote);
-    // net_remote.getInputsInfo().begin()->second->setLayout(Layout::NCHW);
-    // net_remote.getInputsInfo().begin()->second->setPrecision(Precision::U8);
-    // net_remote.getInputsInfo().begin()->second->getPreProcess().setColorFormat(ColorFormat::NV12);
-
-    /* XXX: is it correct to set KEY_CLDNN_NV12_TWO_INPUTS in case of remote tensor? */
-    auto exec_net_b = ie.compile_model(fn_ptr_remote, CommonTestUtils::DEVICE_GPU,
-                { { ov::ie::GPUConfigParams::KEY_GPU_NV12_TWO_INPUTS, ov::ie::PluginConfigParams::YES} });
-    auto inf_req_remote = exec_net_b.create_infer_request();
-    auto cldnn_context = exec_net_b.get_context().as<ov::runtime::gpu::ClContext>();
-    cl_context ctx = cldnn_context.get();
-    auto ocl_instance = std::make_shared<OpenCL>(ctx);
-    cl_int err;
-
-    std::vector<cl_mem> nv12_image_plane_y, nv12_image_plane_uv;
-    std::vector<cl::Image2D> img_y, img_uv;
-    std::vector<std::pair<ov::runtime::RemoteTensor, ov::runtime::RemoteTensor>> tensor_remote;
-
-    for (int i = 0; i < num_batch; i++) {
-        cl_image_format image_format;
-        cl_image_desc image_desc = { 0 };
-        image_format.image_channel_order = CL_R;
-        image_format.image_channel_data_type = CL_UNORM_INT8;
-        image_desc.image_type = CL_MEM_OBJECT_IMAGE2D;
-        image_desc.image_width = width;
-        image_desc.image_height = height;
-
-        nv12_image_plane_y.push_back(clCreateImage(ocl_instance->_context.get(), CL_MEM_READ_WRITE, &image_format, &image_desc, NULL, &err));
-        ASSERT_EQ(err, 0);
-
-        image_format.image_channel_order = CL_RG;
-        image_desc.image_width = width / 2;
-        image_desc.image_height = height / 2;
-
-        nv12_image_plane_uv.push_back(clCreateImage(ocl_instance->_context.get(), CL_MEM_READ_WRITE, &image_format, &image_desc, NULL, &err));
-        ASSERT_EQ(err, 0);
-
-        size_t origin[3] = { 0, 0, 0 };
-        size_t y_region[3] = { (size_t)width, (size_t)height, 1 };
-        size_t uv_region[3] = { (size_t)width / 2, (size_t)height / 2, 1 };
-
-        err = clEnqueueWriteImage(ocl_instance->_queue.get(), nv12_image_plane_y[i],
-            true, origin, y_region, 0, 0, fake_image_data_y[i].data(), 0, NULL, NULL);
-        ASSERT_EQ(err, 0);
-
-        err = clEnqueueWriteImage(ocl_instance->_queue.get(), nv12_image_plane_uv[i],
-            true, origin, uv_region, 0, 0, fake_image_data_uv[i].data(), 0, NULL, NULL);
-        ASSERT_EQ(err, 0);
-
-        img_y.push_back(cl::Image2D(nv12_image_plane_y[i]));
-        img_uv.push_back(cl::Image2D(nv12_image_plane_uv[i]));
-
-        tensor_remote.push_back(cldnn_context.create_tensor_nv12(img_y[i], img_uv[i]));
-    }
-
-    if (num_batch == 1) {
-        inf_req_remote.set_tensor(fn_ptr_remote->get_parameters().front()->get_friendly_name() + "/y", tensor_remote[0].first);
-        inf_req_remote.set_tensor(fn_ptr_remote->get_parameters().front()->get_friendly_name() + "/uv", tensor_remote[0].second);
-    } else {
-        GTEST_SKIP() << "Not implemented test";
-    }
-
-    inf_req_remote.infer();
-
-    auto outputTensor_shared = inf_req_remote.get_tensor(
-        ngraph::op::util::create_ie_output_name(fn_ptr_remote->get_results().front()->input_value(0)));
-
-    // ------------------------------------------------------
-    // Setup to inference using local tensor with batch=1
-    auto fn_ptr_local = ngraph::builder::subgraph::makeConvPoolRelu({1, 3, height, width});
-
-    // net_local.getInputsInfo().begin()->second->setLayout(Layout::NCHW);
-    // net_local.getInputsInfo().begin()->second->setPrecision(Precision::U8);
-    // net_local.getInputsInfo().begin()->second->getPreProcess().setColorFormat(ColorFormat::NV12);
-
-    auto exec_net_b1 = ie.compile_model(fn_ptr_local, CommonTestUtils::DEVICE_GPU);
-
-    auto inf_req_local = exec_net_b1.create_infer_request();
-
-    // Run regular input for each image and compare against batched tensor
-    for (int i = 0; i < num_batch; i++) {
-        auto y_tensor = ov::runtime::Tensor{ov::element::u8, {1, 1, height, width}};
-        auto uv_tensor = ov::runtime::Tensor{ov::element::u8, {1, 2, height / 2, width / 2}};
-        inf_req_local.set_tensor(fn_ptr_local->get_parameters().front()->get_friendly_name() + "/y", y_tensor);
-        inf_req_local.set_tensor(fn_ptr_local->get_parameters().front()->get_friendly_name() + "/uv", uv_tensor);
-        inf_req_local.infer();
-        auto output_tensor_local = inf_req_local.get_tensor(
-            ngraph::op::util::create_ie_output_name(fn_ptr_local->get_results().front()->input_value(0)));
-
-        // This network generates [1, size] tensor whether batch=1 or 2. So need to split
-        auto split_shared_tensor = ov::runtime::Tensor{output_tensor_local.get_element_type(),
-                                        output_tensor_local.get_shape(),
-                                        outputTensor_shared.data<float_t>() + output_tensor_local.get_size() * i};
-        ASSERT_EQ(output_tensor_local.get_size(), split_shared_tensor.get_size());
-        float thr = 0.1;
-
-        FuncTestUtils::compare_tensor(output_tensor_local, split_shared_tensor, thr, "", false);
-    }
-}
-
-const std::vector<size_t> num_batches{1, 2, 4};
-
-INSTANTIATE_TEST_SUITE_P(smoke_RemoteTensor, OVRemoteTensorBatched_Test, ::testing::ValuesIn(num_batches), OVRemoteTensorBatched_Test::getTestCaseName);
-
-using TwoNetsParams = std::tuple<size_t,   // number of streams
-                                 size_t>;  // number of requests
-
-class OVRemoteTensorTwoNets_Test : public CommonTestUtils::TestsCommon,
-    public testing::WithParamInterface<TwoNetsParams> {
-    void SetUp() override {
-        std::tie(num_streams, num_requests) = this->GetParam();
-        fn_ptrs = {ngraph::builder::subgraph::makeSplitMultiConvConcat(),
-                   ngraph::builder::subgraph::makeMultiSingleConv()};
-    };
-public:
-    static std::string getTestCaseName(const testing::TestParamInfo<TwoNetsParams>& obj) {
-        size_t streams, requests;
-        std::tie(streams, requests) = obj.param;
-        return "_num_streams_" + std::to_string(streams) + "_num_req_" +
-            std::to_string(requests);
-    }
-
-protected:
-    size_t num_streams;
-    size_t num_requests;
-    std::vector<std::shared_ptr<ngraph::Function>> fn_ptrs;
-};
-
-TEST_P(OVRemoteTensorTwoNets_Test, DISABLED_canInferTwoExecNets) {
-    auto ie = ov::runtime::Core();
-
-    std::vector<std::string> outputs;
-    std::vector<ov::runtime::InferRequest> irs;
-    std::vector<std::vector<uint8_t>> ref;
-    std::vector<int> outElementsCount;
-
-    for (size_t i = 0; i < fn_ptrs.size(); ++i) {
-        auto fn = fn_ptrs[i];
-
-        auto exec_net = ie.compile_model(fn_ptrs[i], CommonTestUtils::DEVICE_GPU,
-                                         {{ov::ie::PluginConfigParams::KEY_GPU_THROUGHPUT_STREAMS, std::to_string(num_streams)}});
-
-        auto input = fn_ptrs[i]->get_parameters().at(0);
-        auto output = fn_ptrs[i]->get_results().at(0);
-
-        for (int j = 0; j < num_streams * num_requests; j++) {
-            outputs.push_back(ngraph::op::util::create_ie_output_name(output->input_value(0)));
-
-            auto inf_req = exec_net.create_infer_request();
-            irs.push_back(inf_req);
-
-            auto tensor = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
-            inf_req.set_tensor(input->get_friendly_name(), tensor);
-
-            outElementsCount.push_back(
-                    std::accumulate(begin(fn_ptrs[i]->get_output_shape(0)), end(fn_ptrs[i]->get_output_shape(0)), 1,
-                                    std::multiplies<size_t>()));
-            const auto in_tensor = inf_req.get_tensor(input->get_friendly_name());
-            const auto tensorSize = in_tensor.get_byte_size();
-            const auto inBlobBuf = static_cast<uint8_t*>(in_tensor.data());
-            std::vector<uint8_t> inData(inBlobBuf, inBlobBuf + tensorSize);
-            auto reOutData = ngraph::helpers::interpreterFunction(fn_ptrs[i], {inData}).front().second;
-            ref.push_back(reOutData);
-        }
-    }
-
-    const int niter = 10;
-    for (int i = 0; i < niter; i++) {
-        for (auto ir : irs) {
-            ir.start_async();
-        }
-
-        for (auto ir : irs) {
-            ir.wait();
-        }
-    }
-
-    auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
-    for (size_t i = 0; i < irs.size(); ++i) {
-        const auto &refBuffer = ref[i].data();
-        ASSERT_EQ(outElementsCount[i], irs[i].get_tensor(outputs[i]).get_size());
-        FuncTestUtils::compareRawBuffers(irs[i].get_tensor(outputs[i]).data<float>(),
-                                         reinterpret_cast<const float *>(refBuffer), outElementsCount[i],
-                                         outElementsCount[i],
-                                         thr);
-    }
-}
-
-const std::vector<size_t> num_streams{ 1, 2 };
-const std::vector<size_t> num_requests{ 1, 4 };
-
-INSTANTIATE_TEST_SUITE_P(smoke_RemoteTensor, OVRemoteTensorTwoNets_Test,
-    ::testing::Combine(::testing::ValuesIn(num_streams),
-        ::testing::ValuesIn(num_requests)),
-    OVRemoteTensorTwoNets_Test::getTestCaseName);
diff --git a/inference-engine/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp b/inference-engine/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
new file mode 100644
index 00000000000000..b593c29183dac8
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/remote_blob_tests/gpu_remote_tensor_tests.cpp
@@ -0,0 +1,479 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <string>
+#include <utility>
+#include <vector>
+#include <memory>
+
+#include "openvino/runtime/gpu/ocl/ocl.hpp"
+#include "openvino/runtime/core.hpp"
+
+#include <gpu/gpu_config.hpp>
+#include <remote_blob_tests/remote_blob_helpers.hpp>
+#include <common_test_utils/test_common.hpp>
+#include <functional_test_utils/plugin_cache.hpp>
+#include "ngraph_functions/subgraph_builders.hpp"
+#include "functional_test_utils/blob_utils.hpp"
+#include "openvino/core/preprocess/pre_post_process.hpp"
+#include "transformations/utils/utils.hpp"
+
+using namespace ::testing;
+
+class OVRemoteTensor_Test : public CommonTestUtils::TestsCommon {
+protected:
+    std::shared_ptr<ngraph::Function> fn_ptr;
+
+    void SetUp() override {
+        fn_ptr = ngraph::builder::subgraph::makeSplitMultiConvConcat();
+    }
+};
+
+TEST_F(OVRemoteTensor_Test, smoke_canInputUserTensor) {
+#if defined(ANDROID)
+    GTEST_SKIP();
+#endif
+    auto ie = ov::runtime::Core();
+
+    using namespace ov::preprocess;
+    auto function = PrePostProcessor(fn_ptr)
+            .input(InputInfo()
+                           .tensor(InputTensorInfo().set_element_type(ov::element::i8))
+                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
+            .build();
+
+    auto exec_net = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+
+    // regular inference
+    auto inf_req_regular = exec_net.create_infer_request();
+    auto input = function->get_parameters().at(0);
+    auto output = function->get_results().at(0);
+    auto fakeImageData = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
+
+    inf_req_regular.set_tensor(input, fakeImageData);
+
+    inf_req_regular.infer();
+    auto output_tensor_regular = inf_req_regular.get_tensor(output);
+
+    // inference using remote tensor
+    auto inf_req_shared = exec_net.create_infer_request();
+    auto cldnn_context = exec_net.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+    cl_context ctx = cldnn_context;
+    auto ocl_instance = std::make_shared<OpenCL>(ctx);
+    cl_int err;
+
+    auto imSize = ov::shape_size(input->get_shape());
+
+    cl::Buffer shared_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, imSize, NULL, &err);
+    {
+        void* buffer = fakeImageData.data();
+        ocl_instance->_queue.enqueueWriteBuffer(shared_buffer, true, 0, imSize, buffer);
+    }
+
+    auto cldnn_tensor = cldnn_context.create_tensor(input->get_element_type(), input->get_shape(), shared_buffer);
+    inf_req_shared.set_tensor(input, cldnn_tensor);
+
+    inf_req_shared.infer();
+    auto output_tensor_shared = inf_req_shared.get_tensor(output);
+
+    // compare results
+    {
+        ASSERT_EQ(output->get_element_type(), ov::element::f32);
+        ASSERT_EQ(output_tensor_regular.get_size(), output_tensor_shared.get_size());
+        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
+        ASSERT_NO_THROW(output_tensor_regular.data());
+        ASSERT_NO_THROW(output_tensor_shared.data());
+        FuncTestUtils::compare_tensor(output_tensor_regular, output_tensor_shared, thr);
+    }
+}
+
+TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContext) {
+    auto ie = ov::runtime::Core();
+
+    using namespace ov::preprocess;
+    auto function = PrePostProcessor(fn_ptr)
+            .input(InputInfo()
+                           .tensor(InputTensorInfo().set_element_type(ov::element::i8))
+                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
+            .build();
+
+    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto input = function->get_parameters().at(0);
+    auto output = function->get_results().at(0);
+
+    // regular inference
+    auto inf_req_regular = exec_net_regular.create_infer_request();
+    auto fakeImageData = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
+    inf_req_regular.set_tensor(input, fakeImageData);
+
+    inf_req_regular.infer();
+    auto output_tensor_regular = inf_req_regular.get_tensor(exec_net_regular.output());
+
+    // inference using remote tensor
+    auto ocl_instance = std::make_shared<OpenCL>();
+
+    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_context.get());
+    auto exec_net_shared = ie.compile_model(function, remote_context);
+    auto inf_req_shared = exec_net_shared.create_infer_request();
+    inf_req_shared.set_tensor(input, fakeImageData);
+
+    inf_req_shared.infer();
+    auto output_tensor_shared = inf_req_shared.get_tensor(output);
+
+    // compare results
+    {
+        ASSERT_EQ(output->get_element_type(), ov::element::f32);
+        ASSERT_EQ(output_tensor_regular.get_size(), output_tensor_shared.get_size());
+        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
+        ASSERT_NO_THROW(output_tensor_regular.data());
+        ASSERT_NO_THROW(output_tensor_shared.data());
+        FuncTestUtils::compare_tensor(output_tensor_regular, output_tensor_shared, thr);
+    }
+}
+
+TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserContextWithMultipleDevices) {
+    auto ie = ov::runtime::Core();
+
+    using namespace ov::preprocess;
+    auto function = PrePostProcessor(fn_ptr)
+            .input(InputInfo()
+                           .tensor(InputTensorInfo().set_element_type(ov::element::i8))
+                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
+            .build();
+
+    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto input = function->get_parameters().at(0);
+    auto output = function->get_results().at(0);
+
+    // regular inference
+    auto inf_req_regular = exec_net_regular.create_infer_request();
+    auto fakeImageData = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
+    inf_req_regular.set_tensor(input, fakeImageData);
+
+    inf_req_regular.infer();
+    auto output_tensor_regular = inf_req_regular.get_tensor(exec_net_regular.output());
+
+    // inference using remote tensor
+
+    auto ocl_instance_tmp = std::make_shared<OpenCL>();
+    cl::Context multi_device_ctx({ocl_instance_tmp->_device, ocl_instance_tmp->_device});
+    auto ocl_instance = std::make_shared<OpenCL>(multi_device_ctx.get());
+
+    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_context.get(), 1);
+
+    ASSERT_EQ(remote_context.get_device_name(), "GPU.0");
+    auto exec_net_shared = ie.compile_model(function, remote_context);
+    auto inf_req_shared = exec_net_shared.create_infer_request();
+    inf_req_shared.set_tensor(input, fakeImageData);
+
+    inf_req_shared.infer();
+    auto output_tensor_shared = inf_req_shared.get_tensor(output);
+
+    // compare results
+    {
+        ASSERT_EQ(output->get_element_type(), ov::element::f32);
+        ASSERT_EQ(output_tensor_regular.get_size(), output_tensor_shared.get_size());
+        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
+        ASSERT_NO_THROW(output_tensor_regular.data());
+        ASSERT_NO_THROW(output_tensor_shared.data());
+        FuncTestUtils::compare_tensor(output_tensor_regular, output_tensor_shared, thr);
+    }
+}
+
+TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_out_of_order) {
+    auto ie = ov::runtime::Core();
+
+    using namespace ov::preprocess;
+    auto function = PrePostProcessor(fn_ptr)
+            .input(InputInfo()
+                           .tensor(InputTensorInfo().set_element_type(ov::element::i8))
+                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
+            .build();
+
+    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto input = function->get_parameters().at(0);
+    auto output = function->get_results().at(0);
+
+    // regular inference
+    auto inf_req_regular = exec_net_regular.create_infer_request();
+    auto fakeImageData = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
+    inf_req_regular.set_tensor(input, fakeImageData);
+
+    inf_req_regular.infer();
+    auto output_tensor_regular = inf_req_regular.get_tensor(exec_net_regular.output());
+
+    auto in_size = ov::shape_size(input->get_output_shape(0)) * input->get_output_element_type(0).size();
+    auto out_size = ov::shape_size(output->get_output_shape(0)) * output->get_output_element_type(0).size();
+
+    // inference using remote tensor
+    auto ocl_instance = std::make_shared<OpenCL>();
+    cl_int err;
+
+    // Allocate shared buffers for input and output data which will be set to infer request
+    cl::Buffer shared_input_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, in_size, NULL, &err);
+    cl::Buffer shared_output_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, out_size, NULL, &err);
+
+    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
+    auto exec_net_shared = ie.compile_model(function, remote_context);
+    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+
+    auto gpu_in_tensor = gpu_context.create_tensor(input->get_output_element_type(0), input->get_output_shape(0), shared_input_buffer);
+    auto gpu_out_tensor = gpu_context.create_tensor(output->get_output_element_type(0), output->get_output_shape(0), shared_output_buffer);
+    auto out_tensor = FuncTestUtils::create_and_fill_tensor(output->get_output_element_type(0), output->get_output_shape(0));
+
+    auto inf_req_shared = exec_net_shared.create_infer_request();
+    inf_req_shared.set_tensor(input, gpu_in_tensor);
+    inf_req_shared.set_tensor(output, gpu_out_tensor);
+
+    // 1. Pre-processing. Enqueue non-blocking copy from host ptr to shared device input buffer and barrier to ensure that copy is finished before
+    // inference primitives starts execution
+    {
+        void* buffer = fakeImageData.data();
+        ocl_instance->_queue.enqueueWriteBuffer(shared_input_buffer, false, 0, in_size, buffer);
+        ocl_instance->_queue.enqueueBarrierWithWaitList(nullptr, nullptr);
+    }
+
+    // 2. Enqueue inference primitives. With shared queue this call ensures that all kernels are scheduled to the corresponding queue
+    // before giving the control back
+    inf_req_shared.start_async();
+
+    // 3. Post-processing. Enqueue copy from shared blob with inference result to another output blob
+    // Enqueue barrier with empty wait list is needed to ensure that previous kernels are finished before copying the data. It's needed here since we
+    // create OOO queue.
+    // Note: inf_req_shared.wait() can be dropped in some cases, but if plugin-side post-processing is required,
+    // then the result may be incorrect without Wait().
+    {
+        ocl_instance->_queue.enqueueBarrierWithWaitList(nullptr, nullptr);
+        ocl_instance->_queue.enqueueReadBuffer(shared_output_buffer, false, 0, out_size, out_tensor.data(), nullptr, nullptr);
+    }
+
+    // 4. Wait for infer request and post-processing completion
+    ocl_instance->_queue.finish();
+
+    // compare results
+    {
+        ASSERT_EQ(output->get_element_type(), ov::element::f32);
+        ASSERT_EQ(output_tensor_regular.get_size(), out_tensor.get_size());
+        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
+        ASSERT_NO_THROW(output_tensor_regular.data());
+        FuncTestUtils::compare_tensor(output_tensor_regular, out_tensor, thr);
+    }
+}
+
+TEST_F(OVRemoteTensor_Test, smoke_canInferOnUserQueue_in_order) {
+    auto ie = ov::runtime::Core();
+
+    using namespace ov::preprocess;
+    auto function = PrePostProcessor(fn_ptr)
+            .input(InputInfo()
+                           .tensor(InputTensorInfo().set_element_type(ov::element::i8))
+                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
+            .build();
+
+    auto exec_net_regular = ie.compile_model(function, CommonTestUtils::DEVICE_GPU);
+    auto input = function->get_parameters().at(0);
+    auto output = function->get_results().at(0);
+
+    // regular inference
+    auto inf_req_regular = exec_net_regular.create_infer_request();
+    auto fakeImageData = FuncTestUtils::create_and_fill_tensor(input->get_element_type(), input->get_shape());
+    inf_req_regular.set_tensor(input, fakeImageData);
+
+    inf_req_regular.infer();
+    auto output_tensor_regular = inf_req_regular.get_tensor(exec_net_regular.output());
+
+    auto in_size = ov::shape_size(input->get_output_shape(0)) * input->get_output_element_type(0).size();
+    auto out_size = ov::shape_size(output->get_output_shape(0)) * output->get_output_element_type(0).size();
+
+    // inference using remote tensor
+    auto ocl_instance = std::make_shared<OpenCL>();
+    ocl_instance->_queue = cl::CommandQueue(ocl_instance->_context, ocl_instance->_device);
+    cl_int err;
+
+    // Allocate shared buffers for input and output data which will be set to infer request
+    cl::Buffer shared_input_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, in_size, NULL, &err);
+    cl::Buffer shared_output_buffer(ocl_instance->_context, CL_MEM_READ_WRITE, out_size, NULL, &err);
+
+    auto remote_context = ov::runtime::gpu::ocl::ClContext(ie, ocl_instance->_queue.get());
+    auto exec_net_shared = ie.compile_model(function, remote_context);
+    auto gpu_context = exec_net_shared.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+
+    auto gpu_in_tensor = gpu_context.create_tensor(input->get_output_element_type(0), input->get_output_shape(0), shared_input_buffer);
+    auto gpu_out_tensor = gpu_context.create_tensor(output->get_output_element_type(0), output->get_output_shape(0), shared_output_buffer);
+    auto out_tensor = FuncTestUtils::create_and_fill_tensor(output->get_output_element_type(0), output->get_output_shape(0));
+
+    auto inf_req_shared = exec_net_shared.create_infer_request();
+    inf_req_shared.set_tensor(input, gpu_in_tensor);
+    inf_req_shared.set_tensor(output, gpu_out_tensor);
+
+    // 1. Pre-processing. Enqueue non-blocking copy from host ptr to shared device input buffer
+    {
+        void* buffer = fakeImageData.data();
+        ocl_instance->_queue.enqueueWriteBuffer(shared_input_buffer, false, 0, in_size, buffer);
+    }
+
+    // 2. Enqueue inference primitives. With shared queue this call ensures that all kernels are scheduled to the corresponding queue
+    // before giving the control back
+    inf_req_shared.start_async();
+
+    // 3. Post-processing. Enqueue copy from shared blob with inference result to another output blob
+    // Note: inf_req_shared.Wait() can be dropped in some cases, but if plugin-side post-processing is required,
+    // then the result may be incorrect without Wait().
+    {
+        ocl_instance->_queue.enqueueReadBuffer(shared_output_buffer, false, 0, out_size, out_tensor.data(), nullptr, nullptr);
+    }
+
+    // 4. Wait for infer request and post-processing completion
+    ocl_instance->_queue.finish();
+
+    // compare results
+    {
+        ASSERT_EQ(output->get_element_type(), ov::element::f32);
+        ASSERT_EQ(output_tensor_regular.get_size(), out_tensor.get_size());
+        auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32);
+        ASSERT_NO_THROW(output_tensor_regular.data());
+        FuncTestUtils::compare_tensor(output_tensor_regular, out_tensor, thr);
+    }
+}
+
+class OVRemoteTensorBatched_Test : public CommonTestUtils::TestsCommon, public testing::WithParamInterface<size_t> {
+    void SetUp() override {
+        num_batch = this->GetParam();
+    };
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<std::size_t> &obj) {
+        return "num_batch_" + std::to_string(obj.param);
+    }
+
+protected:
+    size_t num_batch;
+    std::vector<std::shared_ptr<ngraph::Function>> fn_ptrs;
+};
+
+TEST_P(OVRemoteTensorBatched_Test, DISABLED_canInputNV12) {
+#if defined(ANDROID)
+    GTEST_SKIP();
+#endif
+    const int height = 16;
+    const int width = 16;
+
+    // ------------------------------------------------------
+    // Prepare input data
+    std::vector<ov::runtime::Tensor> fake_image_data_y;
+    std::vector<ov::runtime::Tensor> fake_image_data_uv;
+
+    for (int i = 0; i < num_batch; i++) {
+        fake_image_data_y.push_back(FuncTestUtils::create_and_fill_tensor(ov::element::u8, {1, 1, height, width}, 50, 0, 1, i));
+        fake_image_data_uv.push_back(FuncTestUtils::create_and_fill_tensor(ov::element::u8, {1, 2, height / 2, width / 2}, 256, 0, 1, i));
+    }
+
+    auto ie = ov::runtime::Core();
+
+    // ------------------------------------------------------
+    // inference using remote tensor with batch
+    auto fn_ptr_remote = ngraph::builder::subgraph::makeConvPoolRelu({num_batch, 3, height, width});
+
+    using namespace ov::preprocess;
+    auto function = PrePostProcessor(fn_ptr_remote)
+            .input(InputInfo()
+                           .tensor(InputTensorInfo().set_element_type(ov::element::i8).set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES))
+                           .preprocess(PreProcessSteps().convert_element_type(ov::element::f32)))
+            .build();
+
+    auto exec_net_b = ie.compile_model(fn_ptr_remote, CommonTestUtils::DEVICE_GPU);
+    auto inf_req_remote = exec_net_b.create_infer_request();
+    auto cldnn_context = exec_net_b.get_context().as<ov::runtime::gpu::ocl::ClContext>();
+    cl_context ctx = cldnn_context.get();
+    auto ocl_instance = std::make_shared<OpenCL>(ctx);
+    cl_int err;
+
+    std::vector<cl_mem> nv12_image_plane_y, nv12_image_plane_uv;
+    std::vector<cl::Image2D> img_y, img_uv;
+    std::vector<std::pair<ov::runtime::RemoteTensor, ov::runtime::RemoteTensor>> tensor_remote;
+
+    for (int i = 0; i < num_batch; i++) {
+        cl_image_format image_format;
+        cl_image_desc image_desc = { 0 };
+        image_format.image_channel_order = CL_R;
+        image_format.image_channel_data_type = CL_UNORM_INT8;
+        image_desc.image_type = CL_MEM_OBJECT_IMAGE2D;
+        image_desc.image_width = width;
+        image_desc.image_height = height;
+
+        nv12_image_plane_y.push_back(clCreateImage(ocl_instance->_context.get(), CL_MEM_READ_WRITE, &image_format, &image_desc, NULL, &err));
+        ASSERT_EQ(err, 0);
+
+        image_format.image_channel_order = CL_RG;
+        image_desc.image_width = width / 2;
+        image_desc.image_height = height / 2;
+
+        nv12_image_plane_uv.push_back(clCreateImage(ocl_instance->_context.get(), CL_MEM_READ_WRITE, &image_format, &image_desc, NULL, &err));
+        ASSERT_EQ(err, 0);
+
+        size_t origin[3] = { 0, 0, 0 };
+        size_t y_region[3] = { (size_t)width, (size_t)height, 1 };
+        size_t uv_region[3] = { (size_t)width / 2, (size_t)height / 2, 1 };
+
+        err = clEnqueueWriteImage(ocl_instance->_queue.get(), nv12_image_plane_y[i],
+            true, origin, y_region, 0, 0, fake_image_data_y[i].data(), 0, NULL, NULL);
+        ASSERT_EQ(err, 0);
+
+        err = clEnqueueWriteImage(ocl_instance->_queue.get(), nv12_image_plane_uv[i],
+            true, origin, uv_region, 0, 0, fake_image_data_uv[i].data(), 0, NULL, NULL);
+        ASSERT_EQ(err, 0);
+
+        img_y.push_back(cl::Image2D(nv12_image_plane_y[i]));
+        img_uv.push_back(cl::Image2D(nv12_image_plane_uv[i]));
+
+        tensor_remote.push_back(cldnn_context.create_tensor_nv12(img_y[i], img_uv[i]));
+    }
+
+    if (num_batch == 1) {
+        inf_req_remote.set_tensor(fn_ptr_remote->get_parameters().front()->get_friendly_name() + "/y", tensor_remote[0].first);
+        inf_req_remote.set_tensor(fn_ptr_remote->get_parameters().front()->get_friendly_name() + "/uv", tensor_remote[0].second);
+    } else {
+        GTEST_SKIP() << "Not implemented test";
+    }
+
+    inf_req_remote.infer();
+
+    auto outputTensor_shared = inf_req_remote.get_tensor(
+        ngraph::op::util::create_ie_output_name(fn_ptr_remote->get_results().front()->input_value(0)));
+
+    // ------------------------------------------------------
+    // Setup to inference using local tensor with batch=1
+    auto fn_ptr_local = ngraph::builder::subgraph::makeConvPoolRelu({1, 3, height, width});
+
+    // net_local.getInputsInfo().begin()->second->setLayout(Layout::NCHW);
+    // net_local.getInputsInfo().begin()->second->setPrecision(Precision::U8);
+    // net_local.getInputsInfo().begin()->second->getPreProcess().setColorFormat(ColorFormat::NV12);
+
+    auto exec_net_b1 = ie.compile_model(fn_ptr_local, CommonTestUtils::DEVICE_GPU);
+
+    auto inf_req_local = exec_net_b1.create_infer_request();
+
+    // Run regular input for each image and compare against batched tensor
+    for (int i = 0; i < num_batch; i++) {
+        auto y_tensor = ov::runtime::Tensor{ov::element::u8, {1, 1, height, width}};
+        auto uv_tensor = ov::runtime::Tensor{ov::element::u8, {1, 2, height / 2, width / 2}};
+        inf_req_local.set_tensor(fn_ptr_local->get_parameters().front()->get_friendly_name() + "/y", y_tensor);
+        inf_req_local.set_tensor(fn_ptr_local->get_parameters().front()->get_friendly_name() + "/uv", uv_tensor);
+        inf_req_local.infer();
+        auto output_tensor_local = inf_req_local.get_tensor(
+            ngraph::op::util::create_ie_output_name(fn_ptr_local->get_results().front()->input_value(0)));
+
+        // This network generates [1, size] tensor whether batch=1 or 2. So need to split
+        auto split_shared_tensor = ov::runtime::Tensor{output_tensor_local.get_element_type(),
+                                        output_tensor_local.get_shape(),
+                                        outputTensor_shared.data<float_t>() + output_tensor_local.get_size() * i};
+        ASSERT_EQ(output_tensor_local.get_size(), split_shared_tensor.get_size());
+        float thr = 0.1;
+
+        FuncTestUtils::compare_tensor(output_tensor_local, split_shared_tensor, thr, "", false);
+    }
+}
+
+const std::vector<size_t> num_batches{1, 2, 4};
+
+INSTANTIATE_TEST_SUITE_P(smoke_RemoteTensor, OVRemoteTensorBatched_Test, ::testing::ValuesIn(num_batches), OVRemoteTensorBatched_Test::getTestCaseName);
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/callback.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/callback.cpp
new file mode 100644
index 00000000000000..1bc86291780dba
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/callback.cpp
@@ -0,0 +1,38 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "behavior/ov_infer_request/callback.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+const std::vector<std::map<std::string, std::string>> configs = {
+        {},
+        {{InferenceEngine::PluginConfigParams::KEY_GPU_THROUGHPUT_STREAMS, InferenceEngine::PluginConfigParams::GPU_THROUGHPUT_AUTO}},
+};
+
+const std::vector<std::map<std::string, std::string>> multiConfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestCallbackTests,
+        ::testing::Combine(
+            ::testing::Values(CommonTestUtils::DEVICE_GPU),
+            ::testing::ValuesIn(configs)),
+        OVInferRequestCallbackTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestCallbackTests,
+        ::testing::Combine(
+                ::testing::Values(CommonTestUtils::DEVICE_MULTI),
+                ::testing::ValuesIn(multiConfigs)),
+        OVInferRequestCallbackTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestCallbackTests,
+        ::testing::Combine(
+                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                ::testing::ValuesIn(multiConfigs)),
+        OVInferRequestCallbackTests::getTestCaseName);
+}  // namespace
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/cancellation.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/cancellation.cpp
new file mode 100644
index 00000000000000..7279f2b5e8657f
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/cancellation.cpp
@@ -0,0 +1,19 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "behavior/ov_infer_request/cancellation.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+const std::vector<std::map<std::string, std::string>> configs = {
+        {},
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestCancellationTests,
+        ::testing::Combine(
+            ::testing::Values(CommonTestUtils::DEVICE_GPU),
+            ::testing::ValuesIn(configs)),
+        OVInferRequestCancellationTests::getTestCaseName);
+}  // namespace
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/io_tensor.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/io_tensor.cpp
new file mode 100644
index 00000000000000..543ca49de058b5
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/io_tensor.cpp
@@ -0,0 +1,83 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "behavior/ov_infer_request/io_tensor.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+const std::vector<std::map<std::string, std::string>> configs = {
+        {},
+        {{InferenceEngine::PluginConfigParams::KEY_GPU_THROUGHPUT_STREAMS, InferenceEngine::PluginConfigParams::GPU_THROUGHPUT_AUTO}},
+};
+
+const std::vector<std::map<std::string, std::string>> Multiconfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+const std::vector<std::map<std::string, std::string>> Autoconfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestIOTensorTest,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_GPU),
+                                ::testing::ValuesIn(configs)),
+                        OVInferRequestIOTensorTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestIOTensorTest,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_MULTI),
+                                ::testing::ValuesIn(Multiconfigs)),
+                        OVInferRequestIOTensorTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestIOTensorTest,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(Autoconfigs)),
+                        OVInferRequestIOTensorTest::getTestCaseName);
+
+std::vector<ov::element::Type> prcs = {
+    ov::element::boolean,
+    ov::element::bf16,
+    ov::element::f16,
+    ov::element::f32,
+    ov::element::f64,
+    ov::element::i4,
+    ov::element::i8,
+    ov::element::i16,
+    ov::element::i32,
+    ov::element::i64,
+    ov::element::u1,
+    ov::element::u4,
+    ov::element::u8,
+    ov::element::u16,
+    ov::element::u32,
+    ov::element::u64,
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(prcs),
+                                 ::testing::Values(CommonTestUtils::DEVICE_GPU),
+                                 ::testing::ValuesIn(configs)),
+                         OVInferRequestIOTensorSetPrecisionTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(prcs),
+                                 ::testing::Values(CommonTestUtils::DEVICE_MULTI),
+                                 ::testing::ValuesIn(Multiconfigs)),
+                         OVInferRequestIOTensorSetPrecisionTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(prcs),
+                                 ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                 ::testing::ValuesIn(Autoconfigs)),
+                         OVInferRequestIOTensorSetPrecisionTest::getTestCaseName);
+
+}  // namespace
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/multithreading.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/multithreading.cpp
new file mode 100644
index 00000000000000..10ea6764576f7c
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/multithreading.cpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "behavior/ov_infer_request/multithreading.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+
+const std::vector<std::map<std::string, std::string>> configs = {
+        {},
+        {{InferenceEngine::PluginConfigParams::KEY_GPU_THROUGHPUT_STREAMS, InferenceEngine::PluginConfigParams::GPU_THROUGHPUT_AUTO}},
+};
+
+const std::vector<std::map<std::string, std::string>> Multiconfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestMultithreadingTests,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_GPU),
+                                ::testing::ValuesIn(configs)),
+                            OVInferRequestMultithreadingTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestMultithreadingTests,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_MULTI),
+                                ::testing::ValuesIn(Multiconfigs)),
+                            OVInferRequestMultithreadingTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestMultithreadingTests,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(Multiconfigs)),
+                            OVInferRequestMultithreadingTests::getTestCaseName);
+
+}  // namespace
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/perf_counters.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/perf_counters.cpp
new file mode 100644
index 00000000000000..02ccf0a3c05a78
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/perf_counters.cpp
@@ -0,0 +1,59 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "behavior/ov_infer_request/perf_counters.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+TEST_P(OVInferRequestPerfCountersTest, CheckOperationInProfilingInfo) {
+    req = execNet.create_infer_request();
+    ASSERT_NO_THROW(req.infer());
+
+    std::vector<ov::runtime::ProfilingInfo> profiling_info;
+    ASSERT_NO_THROW(profiling_info = req.get_profiling_info());
+
+    for (const auto& op : function->get_ops()) {
+        auto op_is_in_profiling_info = std::any_of(std::begin(profiling_info), std::end(profiling_info),
+            [&] (const ov::runtime::ProfilingInfo& info) {
+            if (info.node_name.find(op->get_friendly_name() + "_") != std::string::npos || info.node_name == op->get_friendly_name()) {
+                return true;
+            } else {
+                return false;
+            }
+        });
+        ASSERT_TRUE(op_is_in_profiling_info) << "For op: " << op;
+    }
+}
+
+const std::vector<std::map<std::string, std::string>> configs = {
+        {}
+};
+
+const std::vector<std::map<std::string, std::string>> Multiconfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+const std::vector<std::map<std::string, std::string>> Autoconfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestPerfCountersTest,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_GPU),
+                                ::testing::ValuesIn(configs)),
+                         OVInferRequestPerfCountersTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestPerfCountersTest,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_MULTI),
+                                ::testing::ValuesIn(Multiconfigs)),
+                         OVInferRequestPerfCountersTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestPerfCountersTest,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(Autoconfigs)),
+                         OVInferRequestPerfCountersTest::getTestCaseName);
+}  // namespace
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/wait.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/wait.cpp
new file mode 100644
index 00000000000000..4d37ffc6b8f3ac
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_infer_request/wait.cpp
@@ -0,0 +1,44 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "behavior/ov_infer_request/wait.hpp"
+
+using namespace ov::test::behavior;
+
+namespace {
+
+const std::vector<std::map<std::string, std::string>> configs = {
+        {},
+        {{InferenceEngine::PluginConfigParams::KEY_GPU_THROUGHPUT_STREAMS, InferenceEngine::PluginConfigParams::GPU_THROUGHPUT_AUTO}},
+};
+
+const std::vector<std::map<std::string, std::string>> Multiconfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+const std::vector<std::map<std::string, std::string>> Autoconfigs = {
+        {{ MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , CommonTestUtils::DEVICE_GPU}}
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestWaitTests,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_GPU),
+                                ::testing::ValuesIn(configs)),
+                            OVInferRequestWaitTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestWaitTests,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_MULTI),
+                                ::testing::ValuesIn(Multiconfigs)),
+                            OVInferRequestWaitTests::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestWaitTests,
+                        ::testing::Combine(
+                                ::testing::Values(CommonTestUtils::DEVICE_AUTO),
+                                ::testing::ValuesIn(Autoconfigs)),
+                            OVInferRequestWaitTests::getTestCaseName);
+
+}  // namespace
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
index 6d13029f2a248b..460ca5fed23a9a 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
@@ -17,8 +17,7 @@ const std::vector<ngraph::element::Type> netPrecisions = {
 };
 
 const std::vector<ngraph::pass::low_precision::LayerTransformation::Params> trasformationParamValues = {
-    LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams(),
-    // LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams().setUpdatePrecisions(false),
+    LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams()
 };
 
 const std::vector<LayerTestsDefinitions::ConvolutionTransformationParam> params = {
@@ -61,7 +60,30 @@ const std::vector<LayerTestsDefinitions::ConvolutionTransformationParam> params
         false,
         "Convolution",
         "U8"
-    }
+    },
+    {
+        { 256ul, ngraph::Shape { 1 }, { 0.f }, { 255.f }, { -18.7f }, { 18.8f } },
+        true,
+        {
+            255ul, ngraph::Shape { 6, 1, 1, 1 }, { -0.6f }, { 0.6f },
+            { -1.52806e-39f, -0.2, -0.3, -0.3, -0.2, -0.1 }, { 1.52806e-39f, 0.2, 0.3, 0.3, 0.2, 0.1 }
+        },
+        false,
+        "Convolution",
+        "U8"
+    },
+    {
+        { 256ul, ngraph::Shape { 1 }, { 0.f }, { 255.f }, { -18.7f }, { 18.8f } },
+        true,
+        {
+            255ul, ngraph::Shape { 6, 1, 1, 1 }, { -0.6f }, { 0.6f },
+            { -1.52806e-39f, -1.52806e-39f, -1.52806e-39f, -1.52806e-39f, -1.52806e-39f, -1.52806e-39f },
+            { 1.52806e-39f, 1.52806e-39f, 1.52806e-39f, 1.52806e-39f, 1.52806e-39f, 1.52806e-39f }
+        },
+        false,
+        "Convolution",
+        "U8"
+    },
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_LPT, ConvolutionTransformation,
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp
index 5d07fdf8d3473a..f3d90f0ea72f6b 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/fake_quantize_transformation.cpp
@@ -48,11 +48,11 @@ const std::vector<FakeQuantizeTransformationParam> fakeQuantizeOnDataValues = {
     },
     {
         { 16ul, {}, { 0.f }, { 1.5f }, { 0.f }, { 1.5f } },
-        "Pooling", "FP32"
+        "Pooling", "U8"
     },
     {
         { 16ul, {}, { -8.f }, { 7.f }, { -0.8f }, { 0.7f } },
-        "Pooling", "FP32"
+        "Pooling", "I8"
     },
     // nGraph: I8->FP32 Convert is not supported
     // { 256ul, {}, { -1.28f} , { 1.27f }, { -1.28f} , { 1.27f } },
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/eltwise.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/eltwise.cpp
index df69830326207b..5c29a24c20bb0c 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/eltwise.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/eltwise.cpp
@@ -11,6 +11,7 @@ using namespace ov::test::subgraph;
 namespace {
 std::vector<std::vector<ov::Shape>>  inShapes = {
         {{2}},
+        {{}, {34100}},
         {{2, 200}},
         {{10, 200}},
         {{1, 10, 100}},
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
index f7b4784aec8d87..9fb2778a7b7deb 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
@@ -88,7 +88,7 @@ std::vector<std::string> disabledTestPatterns() {
             R"(.*CanSetOutBlobWithDifferentPrecision/netPRC=(I4|U4).*)",
             R"(.*CanSetOutBlobWithDifferentPrecision/netPRC=BIN.*)",
             // TODO: Issue: 67486
-            R"(.*(EltwiseLayerTest|SoftMaxLayerTest).*)",
+            R"(.*(SoftMaxLayerTest).*)",
             // TODO: Issue: 68712
             R"(.*.MatMul.*CompareWithRefs.*IS0=\(1.5\)_IS1=\(1.5\).*transpose_a=0.*transpose_b=1.*CONSTANT.*FP16.*UNSPECIFIED.*UNSPECIFIED.*ANY.*)",
             // TODO: Issue 66685
diff --git a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/plugin/configuration_tests.cpp b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/plugin/configuration_tests.cpp
index b593c411af29ce..5f244c685e1f52 100644
--- a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/plugin/configuration_tests.cpp
+++ b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/plugin/configuration_tests.cpp
@@ -282,7 +282,7 @@ const std::vector<std::pair<std::string, InferenceEngine::Parameter>>& getDefaul
         {InferenceEngine::MYRIAD_NUMBER_OF_SHAVES, {InferenceEngine::MYRIAD_NUMBER_OF_SHAVES_AUTO}},
         {InferenceEngine::MYRIAD_THROUGHPUT_STREAMS, {InferenceEngine::MYRIAD_THROUGHPUT_STREAMS_AUTO}},
         {InferenceEngine::MYRIAD_NUMBER_OF_CMX_SLICES, {InferenceEngine::MYRIAD_NUMBER_OF_CMX_SLICES_AUTO}},
-        {InferenceEngine::MYRIAD_IR_WITH_SCALES_DIRECTORY, {std::string()}},
+        {InferenceEngine::MYRIAD_SCALES_PATTERN, {std::string()}},
         {InferenceEngine::MYRIAD_TENSOR_STRIDES, {std::map<std::string, std::vector<int>>()}},
         {InferenceEngine::MYRIAD_IGNORE_UNKNOWN_LAYERS, {false}},
         {InferenceEngine::MYRIAD_FORCE_PURE_TENSOR_ITERATOR, {false}},
@@ -433,7 +433,7 @@ const std::vector<std::tuple<std::string, std::string, InferenceEngine::Paramete
         std::make_tuple(InferenceEngine::MYRIAD_PACK_DATA_IN_CMX, InferenceEngine::PluginConfigParams::NO,
             InferenceEngine::Parameter{false}),
 
-        std::make_tuple(InferenceEngine::MYRIAD_IR_WITH_SCALES_DIRECTORY, "/.", InferenceEngine::Parameter{"/."}),
+        std::make_tuple(InferenceEngine::MYRIAD_SCALES_PATTERN, "", InferenceEngine::Parameter{""}),
 
         std::make_tuple(InferenceEngine::MYRIAD_TENSOR_STRIDES, "tensor[1,2,3,4]",
             InferenceEngine::Parameter{std::map<std::string, std::vector<int>>{{"tensor", {4, 3, 2, 1}}}}),
@@ -614,7 +614,7 @@ const std::vector<std::string>& getPrivateOptions() {
         InferenceEngine::MYRIAD_WATCHDOG,
         InferenceEngine::MYRIAD_PERF_REPORT_MODE,
         InferenceEngine::MYRIAD_PACK_DATA_IN_CMX,
-        InferenceEngine::MYRIAD_IR_WITH_SCALES_DIRECTORY,
+        InferenceEngine::MYRIAD_SCALES_PATTERN,
         InferenceEngine::MYRIAD_TENSOR_STRIDES,
         InferenceEngine::MYRIAD_IGNORE_UNKNOWN_LAYERS,
         InferenceEngine::MYRIAD_FORCE_PURE_TENSOR_ITERATOR,
diff --git a/inference-engine/tests/functional/plugin/shared/CMakeLists.txt b/inference-engine/tests/functional/plugin/shared/CMakeLists.txt
index ef26009a2b256c..a1c7d795127784 100644
--- a/inference-engine/tests/functional/plugin/shared/CMakeLists.txt
+++ b/inference-engine/tests/functional/plugin/shared/CMakeLists.txt
@@ -16,6 +16,11 @@ if(ENABLE_MULTI)
     list(APPEND DEPENDENCIES MultiDevicePlugin)
 endif()
 
+# remove once CVS-69781 is fixed
+if(NGRAPH_IR_FRONTEND_ENABLE)
+    list(APPEND DEPENDENCIES ir_ngraph_frontend)
+endif()
+
 if (NGRAPH_ONNX_FRONTEND_ENABLE)
     list(APPEND DEPENDENCIES test_model_zoo)
     list(APPEND DEFINES TEST_MODELS="${TEST_MODEL_ZOO}/func_tests/models/")
@@ -46,6 +51,7 @@ addIeTarget(
                 lptNgraphFunctions
                 sharedTestClasses
             PRIVATE
+                inference_engine_legacy # CVS-55376
                 openvino::util
                 inference_engine_transformations
         DEPENDENCIES
diff --git a/inference-engine/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp b/inference-engine/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp
index 6fcb0efc552ba5..7c4e595b9450b9 100644
--- a/inference-engine/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/base/behavior_test_utils.hpp
@@ -104,9 +104,11 @@ class InferRequestTests : public testing::WithParamInterface<InferRequestParams>
 inline InferenceEngine::Core createIECoreWithTemplate() {
     PluginCache::get().reset();
     InferenceEngine::Core ie;
+#ifndef OPENVINO_STATIC_LIBRARY
     std::string pluginName = "templatePlugin";
     pluginName += IE_BUILD_POSTFIX;
-    ie.RegisterPlugin(pluginName, "TEMPLATE");
+    ie.RegisterPlugin(pluginName, CommonTestUtils::DEVICE_TEMPLATE);
+#endif // !OPENVINO_STATIC_LIBRARY
     return ie;
 }
 
diff --git a/inference-engine/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp b/inference-engine/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp
index 8cce834dc2b676..e5d693e13d52ed 100644
--- a/inference-engine/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp
@@ -69,9 +69,11 @@ class OVInferRequestTests : public testing::WithParamInterface<InferRequestParam
 inline ov::runtime::Core createCoreWithTemplate() {
     ov::test::utils::PluginCache::get().reset();
     ov::runtime::Core core;
+#ifndef OPENVINO_STATIC_LIBRARY
     std::string pluginName = "templatePlugin";
     pluginName += IE_BUILD_POSTFIX;
     core.register_plugin(pluginName, CommonTestUtils::DEVICE_TEMPLATE);
+#endif // !OPENVINO_STATIC_LIBRARY
     return core;
 }
 
diff --git a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp
index 0de6b629da6142..1b1704b472c0ca 100644
--- a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp
@@ -10,13 +10,11 @@
 #include "common_test_utils/file_utils.hpp"
 #include "common_test_utils/unicode_utils.hpp"
 
-#ifdef OPENVINO_OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
+#ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
 #    include <iostream>
 #    define GTEST_COUT std::cerr << "[          ] [ INFO ] "
 #    include <codecvt>
 #    include <functional_test_utils/skip_tests_config.hpp>
-
-
 #endif
 
 namespace ov {
@@ -114,6 +112,9 @@ TEST_P(OVClassBasicTestP, registerExistingPluginThrows) {
     ASSERT_THROW(ie.register_plugin(pluginName, deviceName), ov::Exception);
 }
 
+// TODO: CVS-68982
+#ifndef OPENVINO_STATIC_LIBRARY
+
 TEST_P(OVClassBasicTestP, registerNewPluginNoThrows) {
     ov::runtime::Core ie = createCoreWithTemplate();
     ASSERT_NO_THROW(ie.register_plugin(pluginName, "NEW_DEVICE_NAME"));
@@ -150,7 +151,7 @@ TEST(OVClassBasicTest, smoke_createMockEngineConfigThrows) {
 
 #endif
 
-#ifdef OPENVINO_OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
+#ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
 
 TEST_P(OVClassBasicTestP, smoke_registerPluginsXMLUnicodePath) {
     std::string pluginXML{"mock_engine_valid.xml"};
@@ -196,7 +197,8 @@ TEST_P(OVClassBasicTestP, smoke_registerPluginsXMLUnicodePath) {
     CommonTestUtils::removeFile(pluginXML);
 }
 
-#endif  // OPENVINO_OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
+#endif  // OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
+#endif // !OPENVINO_STATIC_LIBRARY
 
 //
 // GetVersions()
diff --git a/inference-engine/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp b/inference-engine/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp
index e95d4572dbc4aa..7213021011c6a5 100644
--- a/inference-engine/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/behavior/plugin/core_integration.hpp
@@ -106,6 +106,9 @@ TEST(IEClassBasicTest, smoke_createDefault) {
     ASSERT_NO_THROW(InferenceEngine::Core  ie);
 }
 
+// TODO: CVS-68982
+#ifndef OPENVINO_STATIC_LIBRARY
+
 TEST_P(IEClassBasicTestP, registerExistingPluginThrows) {
     InferenceEngine::Core  ie = BehaviorTestsUtils::createIECoreWithTemplate();
     ASSERT_THROW(ie.RegisterPlugin(pluginName, deviceName), InferenceEngine::Exception);
@@ -193,6 +196,7 @@ TEST_P(IEClassBasicTestP, smoke_registerPluginsXMLUnicodePath) {
 }
 
 #endif  // OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
+#endif // !OPENVINO_STATIC_LIBRARY
 
 //
 // GetVersions()
diff --git a/inference-engine/tests/functional/plugin/shared/include/onnx/quantized_models_tests.hpp b/inference-engine/tests/functional/plugin/shared/include/onnx/quantized_models_tests.hpp
index c0ddbde30a9880..33631d1a28d96c 100644
--- a/inference-engine/tests/functional/plugin/shared/include/onnx/quantized_models_tests.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/onnx/quantized_models_tests.hpp
@@ -16,7 +16,8 @@ class QuantizedModelsTests : public testing::WithParamInterface<std::string>,
 
 protected:
     void SetUp() override;
-    void runModel(const char* model, const std::unordered_map<std::string, ngraph::element::Type_t>& expected_layer_types);
+    using LayerInputTypes = std::unordered_map<std::string, std::vector<ngraph::element::Type_t>>;
+    void runModel(const char* model, const LayerInputTypes& expected_layer_input_types, float thr);
 };
 
 } // namespace ONNXTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/experimental_detectron_roifeatureextractor.hpp b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/experimental_detectron_roifeatureextractor.hpp
new file mode 100644
index 00000000000000..4a51c18d9585f2
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/experimental_detectron_roifeatureextractor.hpp
@@ -0,0 +1,19 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <shared_test_classes/single_layer/experimental_detectron_roifeatureextractor.hpp>
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+TEST_P(ExperimentalDetectronROIFeatureExtractorLayerTest, ExperimentalDetectronROIFeatureExtractorTests) {
+    run();
+}
+
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/gather_nd.hpp b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/gather_nd.hpp
index 4ef0b9c7dc18ff..5865e3f0c68758 100644
--- a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/gather_nd.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/gather_nd.hpp
@@ -12,4 +12,8 @@ TEST_P(GatherNDLayerTest, CompareWithRefs) {
     Run();
 }
 
+TEST_P(GatherND8LayerTest, CompareWithRefs) {
+    Run();
+}
+
 }  // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/slice.hpp b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/slice.hpp
new file mode 100644
index 00000000000000..cf432f4ada6498
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/slice.hpp
@@ -0,0 +1,13 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/single_layer/slice.hpp"
+
+namespace LayerTestsDefinitions {
+TEST_P(SliceLayerTest, CompareWithRefs) {
+    Run();
+}
+}  // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/shared/include/subgraph_tests/basic_lstm.hpp b/inference-engine/tests/functional/plugin/shared/include/subgraph_tests/basic_lstm.hpp
index 7ae1d21a886e4f..a31e73f37b6942 100644
--- a/inference-engine/tests/functional/plugin/shared/include/subgraph_tests/basic_lstm.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/subgraph_tests/basic_lstm.hpp
@@ -42,8 +42,7 @@ TEST_P(Basic_LSTM_S, CompareWithRefImpl_LowLatencyTransformation) {
     manager.register_pass<ngraph::pass::LowLatency2>(); // LowLatency enables UnrollTI
     manager.run_passes(function);
     LoadNetwork();
-    IE_SUPPRESS_DEPRECATED_START
-    auto states = executableNetwork.QueryState();
+    auto states = inferRequest.QueryState();
     for (auto& state : states) {
         auto name = state.GetName();
         if (name.find("cell_state_1") != std::string::npos) {
@@ -58,7 +57,6 @@ TEST_P(Basic_LSTM_S, CompareWithRefImpl_LowLatencyTransformation) {
             GTEST_FAIL() << "unknown memory state";
         }
     }
-    IE_SUPPRESS_DEPRECATED_END
     // Run and compare
     Infer();
     const auto& actualOutputs = GetOutputs();
diff --git a/inference-engine/tests/functional/plugin/shared/models/convolution_fq.prototxt b/inference-engine/tests/functional/plugin/shared/models/convolution_fq.prototxt
new file mode 100644
index 00000000000000..37b8297c6020a3
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/shared/models/convolution_fq.prototxt
@@ -0,0 +1,199 @@
+ir_version: 6
+producer_name: "ngraph"
+graph {
+  node {
+    output: "895"
+    name: "Constant_21"
+    op_type: "Constant"
+    attribute {
+      name: "value"
+      t {
+        dims: 1
+        data_type: 1
+        raw_data: "\000\000\000\000"
+      }
+      type: TENSOR
+    }
+  }
+  node {
+    output: "896"
+    name: "Constant_22"
+    op_type: "Constant"
+    attribute {
+      name: "value"
+      t {
+        dims: 1
+        data_type: 1
+        raw_data: "@M\371?"
+      }
+      type: TENSOR
+    }
+  }
+  node {
+    input: "894"
+    input: "895"
+    input: "896"
+    input: "895"
+    input: "896"
+    output: "897"
+    name: "FakeQuantize_23"
+    op_type: "FakeQuantize"
+    attribute {
+      name: "levels"
+      i: 256
+      type: INT
+    }
+    domain: "org.openvinotoolkit"
+  }
+  node {
+    output: "898"
+    name: "Constant_24"
+    op_type: "Constant"
+    attribute {
+      name: "value"
+      t {
+        dims: 64
+        dims: 64
+        dims: 3
+        dims: 3
+        data_type: 1
+        raw_data: "\017\304/\251\354=1\251\353r\325\250M\202\016\250ry@\250&\006\234\'\004\260\212&\261k\207\247\000?\226\'\251w\204\250\020\336C\250`b(\250\n\3272\250p\345\325\247\230 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diff --git a/inference-engine/tests/functional/plugin/shared/models/convolution_qdq.prototxt b/inference-engine/tests/functional/plugin/shared/models/convolution_qdq.prototxt
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index 00000000000000..914c874db64a60
--- /dev/null
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U\274\"\031;\275\211; \274h[F\273z\2767\274;W4<\244\265\360\274\225M\314\274\376\266\322<\361\355\235\274\313\261\262\274eI\214<\361\322\251<\205\376\275\273{\004T\273\343\371V<\242*x;\314Tf=cj\031\274\025\226\003\275\274\'\000=-\017l=\342\305X\273\r@Y\275\336\214\344=\204\025\034\275F\227\363\275It\255=*=\325\274\300f\303\275\257\016\211=\330w\200\274\271 \206\275\215\255\t=\244\036p<\026\237\000\275T\tA=\020R\r\275\272@\n\275x\307\000\000S\305\000\200Q\232\000\000\374\322\000\200F#\000\200i\314\000\200\rS\000\000\2163\000\000\245\275\000\200X\0219=G\274\250\273\374\252\352:\377Q\340\274\017\201\316<\032\220i=o\221\337<\370\0055\275\023\274L<\237G\000\200TU\000\000L\213\000\200]\004\000\2000\312\000\200gQ\000\200]\310\000\000\326Q\000\20086\000\000\376\305\266<\214!\255</0\004=\242\231\346=>#\220\275\216\311\200\275Y\362\317<\014\227\243<\251\265\273\274\253y\376<\225\271P<\276\270\273\274\202\3704\274F\330\232\274\246\221\020\275\333Q;\274\332Q\206\274\036\216\235\274\313\303\247\274\2163\217\274\021q\347;|\321_\275:\216{\272\0239y=B\240_\274ni\004;`\221\320<\226\001;;3\373?<\350\300h<\332\0315\274wf\326\273\366\2202=\016\222J\274\035\270=;\026A\201=O<E>3\304C\275\371\313\017\276\236\203\302>\276\017[\274n\262\234\276\256m~>\242\327b\275\246\301C\276"
+      }
+      type: TENSOR
+    }
+  }
+  node {
+    output: "904"
+    name: "Constant_30"
+    op_type: "Constant"
+    attribute {
+      name: "value"
+      t {
+        data_type: 1
+        raw_data: "\303\314\360;"
+      }
+      type: TENSOR
+    }
+  }
+  node {
+    output: "905"
+    name: "Constant_31"
+    op_type: "Constant"
+    attribute {
+      name: "value"
+      t {
+        data_type: 3
+        raw_data: "\000"
+      }
+      type: TENSOR
+    }
+  }
+  node {
+    input: "903"
+    input: "904"
+    input: "905"
+    output: "906"
+    name: "QuantizeLinear_32"
+    op_type: "QuantizeLinear"
+  }
+  node {
+    input: "906"
+    input: "904"
+    input: "905"
+    output: "907"
+    name: "DequantizeLinear_33"
+    op_type: "DequantizeLinear"
+  }
+  node {
+    input: "902"
+    input: "907"
+    output: "908"
+    name: "Conv_34"
+    op_type: "Conv"
+    attribute {
+      name: "dilations"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "group"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 1
+      ints: 1
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 1
+      ints: 1
+      type: INTS
+    }
+  }
+  name: "graph"
+  input {
+    name: "898"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 56
+          }
+          dim {
+            dim_value: 56
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "908"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 64
+          }
+          dim {
+            dim_value: 56
+          }
+          dim {
+            dim_value: 56
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 10
+}
diff --git a/inference-engine/tests/functional/plugin/shared/models/custom_abs_op.prototxt b/inference-engine/tests/functional/plugin/shared/models/custom_abs_op.prototxt
new file mode 100644
index 00000000000000..4b42d612bee234
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/shared/models/custom_abs_op.prototxt
@@ -0,0 +1,42 @@
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "A"
+    output: "Y"
+    name: "customrelu"
+    op_type: "CustomAbs"
+    domain: "custom_domain"
+  }
+  name: "test_graph"
+  input {
+    name: "A"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 10
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "Y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 10
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+  domain: "custom_domain"
+}
diff --git a/inference-engine/tests/functional/plugin/shared/models/custom_template_op.prototxt b/inference-engine/tests/functional/plugin/shared/models/custom_template_op.prototxt
new file mode 100644
index 00000000000000..38e0fec48b6dd1
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/shared/models/custom_template_op.prototxt
@@ -0,0 +1,65 @@
+ir_version: 7
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "A"
+    output: "Y"
+    name: "operation"
+    op_type: "Template"
+    domain: "custom_domain"
+    attribute {
+        name: "add"
+        type: INT
+        i: 11
+    }
+  }
+  name: "test_graph"
+  input {
+    name: "A"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "Y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+  domain: "com.example"
+}
diff --git a/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp b/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp
index 8b3e37ee501942..8a6918a51d1d84 100644
--- a/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp
@@ -109,6 +109,9 @@ std::vector<nGraphFunctionWithName> LoadNetworkCacheTestBase::getStandardFunctio
     res.push_back(nGraphFunctionWithName {
         inputShapeWrapper(ngraph::builder::subgraph::makeReadConcatSplitAssign, {1, 1, 2, 4}),
         "ReadConcatSplitAssign"});
+    res.push_back(nGraphFunctionWithName{
+        inputShapeWrapper(ngraph::builder::subgraph::makeMatMulBias, {1, 3, 24, 24}),
+        "MatMulBias" });
 
     return res;
 }
diff --git a/inference-engine/tests/functional/plugin/shared/src/behavior/infer_request/memory_states.cpp b/inference-engine/tests/functional/plugin/shared/src/behavior/infer_request/memory_states.cpp
index 107a68d1b74856..83328034f7c1fa 100644
--- a/inference-engine/tests/functional/plugin/shared/src/behavior/infer_request/memory_states.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/behavior/infer_request/memory_states.cpp
@@ -31,98 +31,6 @@ InferenceEngine::ExecutableNetwork InferRequestVariableStateTest::PrepareNetwork
     return ie->LoadNetwork(net, deviceName);
 }
 
-TEST_P(InferRequestVariableStateTest, smoke_VariableState_QueryState) {
-    IE_SUPPRESS_DEPRECATED_START
-    auto executableNet = PrepareNetwork();
-
-    auto states = executableNet.QueryState();
-    ASSERT_TRUE(states.size() == 2) << "Incorrect number of VariableStates";
-
-    for (auto &&state : states) {
-        auto name = state.GetName();
-        ASSERT_TRUE(std::find(statesToQuery.begin(), statesToQuery.end(), name) != statesToQuery.end())
-                                    << "State " << name << "expected to be in memory states but it is not!";
-    }
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_P(InferRequestVariableStateTest, smoke_VariableState_SetState) {
-    IE_SUPPRESS_DEPRECATED_START
-    auto executableNet = PrepareNetwork();
-    const float new_state_val = 13.0f;
-    for (auto &&state : executableNet.QueryState()) {
-        state.Reset();
-        auto state_val = state.GetState();
-        auto element_count = state_val->size();
-
-        float *new_state_data = new float[element_count];
-        for (int i = 0; i < element_count; i++) {
-            new_state_data[i] = new_state_val;
-        }
-        auto stateBlob = make_blob_with_precision(state_val->getTensorDesc());
-        stateBlob->allocate();
-        std::memcpy(stateBlob->buffer(), new_state_data, element_count * sizeof(float));
-        delete[]new_state_data;
-        state.SetState(stateBlob);
-    }
-
-    for (auto &&state : executableNet.QueryState()) {
-        auto lastState = state.GetState();
-        auto last_state_size = lastState->size();
-        auto last_state_data = lastState->cbuffer().as<float *>();
-        ASSERT_TRUE(last_state_size != 0) << "State size should not be 0";
-
-        for (int i = 0; i < last_state_size; i++) {
-            EXPECT_NEAR(new_state_val, last_state_data[i], 1e-5);
-        }
-    }
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_P(InferRequestVariableStateTest, smoke_VariableState_Reset) {
-    IE_SUPPRESS_DEPRECATED_START
-    auto executableNet = PrepareNetwork();
-    const float new_state_val = 13.0f;
-    for (auto &&state : executableNet.QueryState()) {
-        state.Reset();
-        auto state_val = state.GetState();
-        auto element_count = state_val->size();
-
-        float *new_state_data = new float[element_count];
-        for (int i = 0; i < element_count; i++) {
-            new_state_data[i] = new_state_val;
-        }
-        auto stateBlob = make_blob_with_precision(state_val->getTensorDesc());
-        stateBlob->allocate();
-        std::memcpy(stateBlob->buffer(), new_state_data, element_count * sizeof(float));
-        delete[]new_state_data;
-
-        state.SetState(stateBlob);
-    }
-
-    executableNet.QueryState().front().Reset();
-
-    auto states = executableNet.QueryState();
-    for (int i = 0; i < states.size(); ++i) {
-        auto lastState = states[i].GetState();
-        auto last_state_size = lastState->size();
-        auto last_state_data = lastState->cbuffer().as<float *>();
-
-        ASSERT_TRUE(last_state_size != 0) << "State size should not be 0";
-
-        if (i == 0) {
-            for (int j = 0; j < last_state_size; ++j) {
-                EXPECT_NEAR(0, last_state_data[j], 1e-5);
-            }
-        } else {
-            for (int j = 0; j < last_state_size; ++j) {
-                EXPECT_NEAR(new_state_val, last_state_data[j], 1e-5);
-            }
-        }
-    }
-    IE_SUPPRESS_DEPRECATED_END
-}
-
 TEST_P(InferRequestVariableStateTest, inferreq_smoke_VariableState_QueryState) {
     auto executableNet = PrepareNetwork();
     auto inferReq = executableNet.CreateInferRequest();
diff --git a/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp b/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp
index 1f3f255ec6be54..b078389aa8ac06 100644
--- a/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp
@@ -18,6 +18,8 @@ std::string OVInferRequestIOTensorTest::getTestCaseName(const testing::TestParam
 }
 
 void OVInferRequestIOTensorTest::SetUp() {
+    // Skip test according to plugin specific disabledTestPatterns() (if any)
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
     OVInferRequestTests::SetUp();
     req = execNet.create_infer_request();
     input = execNet.input();
diff --git a/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/wait.cpp b/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/wait.cpp
index 7764b0fbf76a21..8d73e9f749473d 100644
--- a/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/wait.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/behavior/ov_infer_request/wait.cpp
@@ -14,6 +14,8 @@ std::string OVInferRequestWaitTests::getTestCaseName(const testing::TestParamInf
 }
 
 void OVInferRequestWaitTests::SetUp() {
+    // Skip test according to plugin specific disabledTestPatterns() (if any)
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
     OVInferRequestTests::SetUp();
     req = execNet.create_infer_request();
     input = execNet.input();
diff --git a/inference-engine/tests/functional/plugin/shared/src/blob_tests/detect_network_batch_test.cpp b/inference-engine/tests/functional/plugin/shared/src/blob_tests/detect_network_batch_test.cpp
index f598a02792254e..097d1e6fb53b24 100644
--- a/inference-engine/tests/functional/plugin/shared/src/blob_tests/detect_network_batch_test.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/blob_tests/detect_network_batch_test.cpp
@@ -57,6 +57,7 @@ void DetectNetworkBatch::LoadNetwork() {
     functionRefs = ngraph::clone_function(*cnnNetwork.getFunction());
     ConfigureNetwork();
     executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+    inferRequest = executableNetwork.CreateInferRequest();
 }
 
 TEST_P(DetectNetworkBatch, InferWithOneInput) {
diff --git a/inference-engine/tests/functional/plugin/shared/src/blob_tests/dynamic_batch.cpp b/inference-engine/tests/functional/plugin/shared/src/blob_tests/dynamic_batch.cpp
index 4eee1d3b6b20fb..17ff2e2506098c 100644
--- a/inference-engine/tests/functional/plugin/shared/src/blob_tests/dynamic_batch.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/blob_tests/dynamic_batch.cpp
@@ -54,10 +54,10 @@ namespace ConfigurationTestsDefinitions {
         ConfigureNetwork();
         cnnNetwork.setBatchSize(max_batch_size);
         executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+        inferRequest = executableNetwork.CreateInferRequest();
     }
 
     void DynamicBatchTest::Infer() {
-        inferRequest = executableNetwork.CreateInferRequest();
         inputs.clear();
 
         for (int i = 0; i < batch_sizes.size(); i++) {
diff --git a/inference-engine/tests/functional/plugin/shared/src/onnx/quantized_models_tests.cpp b/inference-engine/tests/functional/plugin/shared/src/onnx/quantized_models_tests.cpp
index c33709ecce1cbe..4285b952f8540b 100644
--- a/inference-engine/tests/functional/plugin/shared/src/onnx/quantized_models_tests.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/onnx/quantized_models_tests.cpp
@@ -22,7 +22,8 @@ static std::string getModelFullPath(const char* path) {
     return FileUtils::makePath<char>(TEST_MODELS, path);
 }
 
-void QuantizedModelsTests::runModel(const char* model, const std::unordered_map<std::string, ngraph::element::Type_t>& expected_layer_types) {
+void QuantizedModelsTests::runModel(const char* model, const LayerInputTypes& expected_layer_input_types, float thr) {
+    threshold = thr;
     auto ie = getCore();
     auto network = ie->ReadNetwork(getModelFullPath(model));
     function = network.getFunction();
@@ -31,19 +32,34 @@ void QuantizedModelsTests::runModel(const char* model, const std::unordered_map<
     int ops_found = 0;
     for (const auto& node : runtime_function->get_ordered_ops()) {
         const auto& name = node->get_friendly_name();
-        if (expected_layer_types.count(name)) {
+        if (expected_layer_input_types.count(name)) {
             ops_found++;
-            ASSERT_EQ(expected_layer_types.at(name), node->get_element_type());
+            const auto& expected_input_types = expected_layer_input_types.at(name);
+            auto inputs = node->input_values();
+            ASSERT_EQ(inputs.size(), expected_input_types.size());
+            for (size_t i = 0; i < inputs.size(); i++)
+                ASSERT_EQ(expected_input_types[i], inputs[i].get_element_type());
         }
     }
     ASSERT_GT(ops_found, 0);
 }
 
 TEST_P(QuantizedModelsTests, MaxPoolQDQ) {
-    runModel("max_pool_qdq.onnx", {{"890_original", ngraph::element::u8}});
+    runModel("max_pool_qdq.onnx", {{"890_original", {ngraph::element::u8}}}, 1e-5);
 }
 
 TEST_P(QuantizedModelsTests, MaxPoolFQ) {
-    runModel("max_pool_fq.onnx", {{"887_original", ngraph::element::u8}});
+    runModel("max_pool_fq.onnx", {{"887_original", {ngraph::element::u8}}}, 1e-5);
 }
+
+TEST_P(QuantizedModelsTests, ConvolutionQDQ) {
+    // activations have type uint8 and weights int8
+    runModel("convolution_qdq.onnx", {{"908_original", {ngraph::element::u8, ngraph::element::i8}}}, 1.5e-2);
+}
+
+TEST_P(QuantizedModelsTests, ConvolutionFQ) {
+    // activations have type uint8 and weights int8
+    runModel("convolution_fq.onnx", {{"902_original", {ngraph::element::u8, ngraph::element::i8}}}, 1.5e-2);
+}
+
 } // namespace ONNXTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp
index de8870c21257c7..df0412f3a5d7ae 100644
--- a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/base/ov_subgraph.hpp
@@ -31,8 +31,8 @@ class SubgraphBaseTest : public CommonTestUtils::TestsCommon {
     }
 
 protected:
-    void compare(const std::vector<ov::runtime::Tensor> &expected,
-                 const std::vector<ov::runtime::Tensor> &actual);
+    virtual void compare(const std::vector<ov::runtime::Tensor> &expected,
+                         const std::vector<ov::runtime::Tensor> &actual);
 
     virtual void configure_model();
     virtual void compile_model();
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/experimental_detectron_roifeatureextractor.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/experimental_detectron_roifeatureextractor.hpp
new file mode 100644
index 00000000000000..f068e038f8fb95
--- /dev/null
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/experimental_detectron_roifeatureextractor.hpp
@@ -0,0 +1,36 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "common_test_utils/common_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+using Attrs = ov::op::v6::ExperimentalDetectronROIFeatureExtractor::Attributes;
+using ExperimentalROI = ov::op::v6::ExperimentalDetectronROIFeatureExtractor;
+
+typedef std::tuple<
+        std::vector<InputShape>,                // Input shapes
+        int64_t,                                // Output size
+        int64_t,                                // Sampling ratio
+        std::vector<int64_t>,                   // Pyramid scales
+        bool,                                   // Aligned
+        ElementType,                            // Network precision
+        std::string                             // Device name>;
+> ExperimentalDetectronROIFeatureExtractorTestParams;
+
+class ExperimentalDetectronROIFeatureExtractorLayerTest : public testing::WithParamInterface<ExperimentalDetectronROIFeatureExtractorTestParams>,
+                                                          virtual public SubgraphBaseTest {
+protected:
+    void SetUp() override;
+
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ExperimentalDetectronROIFeatureExtractorTestParams>& obj);
+};
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/gather_nd.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/gather_nd.hpp
index 73d15a9ffb0a3c..53b2742d4ecd1e 100644
--- a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/gather_nd.hpp
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/gather_nd.hpp
@@ -36,4 +36,13 @@ class GatherNDLayerTest : public testing::WithParamInterface<GatherNDParams>,
     void SetUp() override;
 };
 
+class GatherND8LayerTest : public testing::WithParamInterface<GatherNDParams>,
+                           virtual public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<GatherNDParams> &obj);
+
+protected:
+    void SetUp() override;
+};
+
 }  // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/slice.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/slice.hpp
new file mode 100644
index 00000000000000..946cc4e0ad7346
--- /dev/null
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/slice.hpp
@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <string>
+#include <tuple>
+#include <vector>
+
+#include "shared_test_classes/base/layer_test_utils.hpp"
+
+namespace LayerTestsDefinitions {
+
+struct SliceSpecificParams {
+    InferenceEngine::SizeVector inputShape;
+    std::vector<int64_t> start;
+    std::vector<int64_t> stop;
+    std::vector<int64_t> step;
+    std::vector<int64_t> axes;
+};
+
+using SliceParams = std::tuple<
+        SliceSpecificParams,
+        InferenceEngine::Precision,        // Net precision
+        InferenceEngine::Precision,        // Input precision
+        InferenceEngine::Precision,        // Output precision
+        InferenceEngine::Layout,           // Input layout
+        InferenceEngine::Layout,           // Output layout
+        std::string,                       // Device name
+        std::map<std::string, std::string> // Additional network configuration
+>;
+
+class SliceLayerTest : public testing::WithParamInterface<SliceParams>,
+                              virtual public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<SliceParams> &obj);
+
+protected:
+    void SetUp() override;
+};
+}  // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp b/inference-engine/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
index 95fc622a18f4b1..7a0300971211fc 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/base/layer_test_utils.cpp
@@ -344,6 +344,7 @@ void LayerTestsCommon::LoadNetwork() {
     CoreConfiguration(this);
     ConfigureNetwork();
     executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+    inferRequest = executableNetwork.CreateInferRequest();
 }
 
 void LayerTestsCommon::GenerateInputs() {
@@ -361,8 +362,6 @@ void LayerTestsCommon::GenerateInputs() {
 }
 
 void LayerTestsCommon::Infer() {
-    inferRequest = executableNetwork.CreateInferRequest();
-
     const auto& inputsInfo = executableNetwork.GetInputsInfo();
     const auto& functionParams = function->get_parameters();
     for (int i = 0; i < functionParams.size(); ++i) {
diff --git a/inference-engine/tests/functional/shared_test_classes/src/base/ov_subgraph.cpp b/inference-engine/tests/functional/shared_test_classes/src/base/ov_subgraph.cpp
index 699d35f66bb8c8..6ae46c547936d1 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/base/ov_subgraph.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/base/ov_subgraph.cpp
@@ -2,14 +2,15 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <fstream>
 #include <signal.h>
+#include <fstream>
 #include <transformations/utils/utils.hpp>
 
 #ifdef _WIN32
 #include <process.h>
 #endif
 
+#include "openvino/core/preprocess/pre_post_process.hpp"
 #include "openvino/pass/serialize.hpp"
 
 #include "graph_comparator.hpp"
@@ -27,16 +28,16 @@ namespace test {
 
 void SubgraphBaseTest::run() {
     auto crashHandler = [](int errCode) {
-        auto &s = LayerTestsUtils::Summary::getInstance();
+        auto& s = LayerTestsUtils::Summary::getInstance();
         s.saveReport();
         std::cerr << "Unexpected application crash with code: " << errCode << std::endl;
         std::abort();
     };
     signal(SIGSEGV, crashHandler);
 
-    LayerTestsUtils::PassRate::Statuses status =
-            FuncTestUtils::SkipTestsConfig::currentTestIsDisabled() ?
-            LayerTestsUtils::PassRate::Statuses::SKIPPED : LayerTestsUtils::PassRate::Statuses::CRASHED;
+    LayerTestsUtils::PassRate::Statuses status = FuncTestUtils::SkipTestsConfig::currentTestIsDisabled()
+                                                     ? LayerTestsUtils::PassRate::Statuses::SKIPPED
+                                                     : LayerTestsUtils::PassRate::Statuses::CRASHED;
     summary.setDeviceName(targetDevice);
     summary.updateOPsStats(function, status);
     SKIP_IF_CURRENT_TEST_IS_DISABLED();
@@ -54,12 +55,13 @@ void SubgraphBaseTest::run() {
                 generate_inputs(targetStaticShapeVec);
                 infer();
                 validate();
-            } catch (const std::exception &ex) {
-                throw std::runtime_error("Incorrect target static shape: " + CommonTestUtils::vec2str(targetStaticShapeVec) + " " + ex.what());
+            } catch (const std::exception& ex) {
+                throw std::runtime_error("Incorrect target static shape: " +
+                                         CommonTestUtils::vec2str(targetStaticShapeVec) + " " + ex.what());
             }
         }
         status = LayerTestsUtils::PassRate::Statuses::PASSED;
-    } catch (const std::exception &ex) {
+    } catch (const std::exception& ex) {
         status = LayerTestsUtils::PassRate::Statuses::FAILED;
         errorMessage = ex.what();
     } catch (...) {
@@ -89,10 +91,13 @@ void SubgraphBaseTest::serialize() {
 
     bool success;
     std::string message;
-    std::tie(success, message) =
-            compare_functions(result, function, false, false, false,
-                              true,     // precision
-                              true);    // attributes
+    std::tie(success, message) = compare_functions(result,
+                                                   function,
+                                                   false,
+                                                   false,
+                                                   false,
+                                                   true,   // precision
+                                                   true);  // attributes
 
     EXPECT_TRUE(success) << message;
 
@@ -115,8 +120,8 @@ void SubgraphBaseTest::query_model() {
     ASSERT_EQ(expected, actual);
 }
 
-void SubgraphBaseTest::compare(const std::vector<ov::runtime::Tensor> &expected,
-                               const std::vector<ov::runtime::Tensor> &actual) {
+void SubgraphBaseTest::compare(const std::vector<ov::runtime::Tensor>& expected,
+                               const std::vector<ov::runtime::Tensor>& actual) {
     ASSERT_EQ(expected.size(), actual.size());
     for (size_t i = 0; i < expected.size(); i++) {
         ov::test::utils::compare(expected[i], actual[i], abs_threshold, rel_threshold);
@@ -125,11 +130,13 @@ void SubgraphBaseTest::compare(const std::vector<ov::runtime::Tensor> &expected,
 
 void SubgraphBaseTest::configure_model() {
     // configure input precision
+    ov::preprocess::PrePostProcessor p(function);
     {
-        auto params = function->get_parameters();
-        for (auto& param : params) {
+        auto& params = function->get_parameters();
+        for (size_t i = 0; i < params.size(); i++) {
             if (inType != ov::element::Type_t::undefined) {
-                param->get_output_tensor(0).set_element_type(inType);
+                p.input(ov::preprocess::InputInfo(i)
+                        .tensor(ov::preprocess::InputTensorInfo().set_element_type(inType)));
             }
         }
     }
@@ -137,12 +144,14 @@ void SubgraphBaseTest::configure_model() {
     // configure output precision
     {
         auto results = function->get_results();
-        for (auto& result : results) {
+        for (size_t i = 0; i < results.size(); i++) {
             if (outType != ov::element::Type_t::undefined) {
-                result->get_output_tensor(0).set_element_type(outType);
+                p.output(ov::preprocess::OutputInfo(i)
+                             .tensor(ov::preprocess::OutputTensorInfo().set_element_type(outType)));
             }
         }
     }
+    function = p.build();
 }
 
 void SubgraphBaseTest::compile_model() {
@@ -160,7 +169,8 @@ void SubgraphBaseTest::generate_inputs(const std::vector<ov::Shape>& targetInput
         const auto& funcInput = funcInputs[i];
         ov::runtime::Tensor tensor;
         if (funcInput.get_element_type().is_real()) {
-            tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], 10, 0, 1000);
+            tensor = ov::test::utils::create_and_fill_tensor(
+                funcInput.get_element_type(), targetInputStaticShapes[i], 10, 0, 1000);
         } else {
             tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
         }
@@ -197,8 +207,8 @@ void SubgraphBaseTest::validate() {
         return;
     }
 
-    ASSERT_EQ(actualOutputs.size(), expectedOutputs.size()) << "nGraph interpreter has "
-        << expectedOutputs.size() << " outputs, while IE " << actualOutputs.size();
+    ASSERT_EQ(actualOutputs.size(), expectedOutputs.size())
+        << "nGraph interpreter has " << expectedOutputs.size() << " outputs, while IE " << actualOutputs.size();
 
     compare(expectedOutputs, actualOutputs);
 }
@@ -213,11 +223,12 @@ void SubgraphBaseTest::init_input_shapes(const std::vector<InputShape>& shapes)
             dynShape = shape.second.front();
         }
         inputDynamicShapes.push_back(dynShape);
-        ASSERT_EQ(shape.second.size(), targetStaticShapeSize) << "Target static count shapes should be the same for all inputs";
+        ASSERT_EQ(shape.second.size(), targetStaticShapeSize)
+            << "Target static count shapes should be the same for all inputs";
         for (size_t i = 0; i < shape.second.size(); ++i) {
             targetStaticShapes[i].push_back(shape.second.at(i));
         }
     }
 }
 }  // namespace test
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/activation.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/activation.cpp
index 6ac2acea0316d7..ccc6d54df761d7 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/activation.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/activation.cpp
@@ -137,17 +137,7 @@ InferenceEngine::Blob::Ptr ActivationLayerTest::GenerateInput(const InferenceEng
         data_range = 15;
         data_start_from = 0;
     }
-    if (activationType == ngraph::helpers::ActivationTypes::Exp && targetDevice == CommonTestUtils::DEVICE_GNA) {
-        const double max_result_on_GNA = 15.9;
-        const double exp_inverse = std::round(std::log(max_result_on_GNA));
-        if (inPrcSigned) {
-            data_range = exp_inverse * 2.0;
-            data_start_from = -exp_inverse;
-        } else {
-            data_range = exp_inverse;
-            data_start_from = 0;
-        }
-    }
+
     return FuncTestUtils::createAndFillBlob(info.getTensorDesc(), data_range,
                                             data_start_from,
                                             resolution);
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/eltwise.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/eltwise.cpp
index 140f5dfb11ad38..c84364cc8d52a8 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/eltwise.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/eltwise.cpp
@@ -40,6 +40,9 @@ std::string EltwiseLayerTest::getTestCaseName(const testing::TestParamInfo<Eltwi
     results << "InType=" << inType << "_";
     results << "OutType=" << outType << "_";
     results << "trgDev=" << targetName;
+    for (auto const& configItem : additional_config) {
+        results << "_configItem=" << configItem.first << "_" << configItem.second;
+    }
     return results.str();
 }
 
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_roifeatureextractor.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_roifeatureextractor.cpp
new file mode 100644
index 00000000000000..29b533ebc42cc3
--- /dev/null
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_roifeatureextractor.cpp
@@ -0,0 +1,77 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "shared_test_classes/single_layer/experimental_detectron_roifeatureextractor.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+std::string ExperimentalDetectronROIFeatureExtractorLayerTest::getTestCaseName(
+        const testing::TestParamInfo<ExperimentalDetectronROIFeatureExtractorTestParams>& obj) {
+    std::vector<InputShape> inputShapes;
+    int64_t outputSize, samplingRatio;
+    std::vector<int64_t> pyramidScales;
+    bool aligned;
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(inputShapes, outputSize, samplingRatio, pyramidScales, aligned, netPrecision, targetName) = obj.param;
+
+    std::ostringstream result;
+    if (inputShapes.front().first.size() != 0) {
+        result << "IS=(";
+        for (const auto &shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+        }
+        result.seekp(-1, result.cur);
+        result << ")_";
+    }
+    result << "TS=";
+    for (const auto& shape : inputShapes) {
+        for (const auto& item : shape.second) {
+            result << CommonTestUtils::vec2str(item) << "_";
+        }
+    }
+    result << "outputSize=" << outputSize << "_";
+    result << "samplingRatio=" << samplingRatio << "_";
+    result << "pyramidScales=" << CommonTestUtils::vec2str(pyramidScales) << "_";
+    std::string alig = aligned ? "true" : "false";
+    result << "aligned=" << alig << "_";
+    result << "netPRC=" << netPrecision << "_";
+    result << "trgDev=" << targetName;
+    return result.str();
+}
+
+void ExperimentalDetectronROIFeatureExtractorLayerTest::SetUp() {
+    std::vector<InputShape> inputShapes;
+    int64_t outputSize, samplingRatio;
+    std::vector<int64_t> pyramidScales;
+    bool aligned;
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(inputShapes, outputSize, samplingRatio, pyramidScales, aligned, netPrecision, targetName) = this->GetParam();
+
+    inType = outType = netPrecision;
+    targetDevice = targetName;
+
+    init_input_shapes(inputShapes);
+
+    Attrs attrs;
+    attrs.aligned = aligned;
+    attrs.output_size = outputSize;
+    attrs.sampling_ratio = samplingRatio;
+    attrs.pyramid_scales = pyramidScales;
+
+    auto params = ngraph::builder::makeDynamicParams(netPrecision, {inputDynamicShapes});
+    auto paramsOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    auto experimentalDetectronROIFeatureExtractor = std::make_shared<ExperimentalROI>(paramsOuts, attrs);
+    function = std::make_shared<ov::Function>(ov::OutputVector{experimentalDetectronROIFeatureExtractor->output(0),
+                                                               experimentalDetectronROIFeatureExtractor->output(1)},
+                                              "ExperimentalDetectronROIFeatureExtractor");
+}
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/gather_nd.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/gather_nd.cpp
index 7ceca5670ff1ef..6e8806ec8a1fbc 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/gather_nd.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/gather_nd.cpp
@@ -54,4 +54,30 @@ void GatherNDLayerTest::SetUp() {
     ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(gather)};
     function = std::make_shared<ngraph::Function>(results, params, "gatherND");
 }
+
+std::string GatherND8LayerTest::getTestCaseName(const testing::TestParamInfo<GatherNDParams>& obj) {
+    return GatherNDLayerTest::getTestCaseName(obj);
+}
+
+void GatherND8LayerTest::SetUp() {
+    InferenceEngine::SizeVector dataShape, indicesShape;
+    InferenceEngine::Precision dPrecision, iPrecision;
+    int batchDims;
+    GatherNDParamsSubset gatherArgsSubset;
+    std::tie(gatherArgsSubset, dPrecision, iPrecision, targetDevice, configuration) = this->GetParam();
+    std::tie(dataShape, indicesShape, batchDims) = gatherArgsSubset;
+
+    auto ngDPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(dPrecision);
+    auto ngIPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(iPrecision);
+
+    auto params = ngraph::builder::makeParams(ngDPrc, {dataShape});
+    auto paramOuts = ngraph::helpers::convert2OutputVector(
+                ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    auto dataNode = paramOuts[0];
+    auto gather = std::dynamic_pointer_cast<ngraph::opset8::GatherND>(
+                ngraph::builder::makeGatherND(dataNode, indicesShape, ngIPrc, batchDims));
+    ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(gather)};
+    function = std::make_shared<ngraph::Function>(results, params, "gatherND");
+}
+
 }  // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/memory.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/memory.cpp
index d3da4c2c4cfd4b..bf8e79a8e30416 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/memory.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/memory.cpp
@@ -81,6 +81,7 @@ namespace LayerTestsDefinitions {
                 CoreConfiguration(this);
                 ConfigureNetwork();
                 executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+                inferRequest = executableNetwork.CreateInferRequest();
             }
             GenerateInputs();
             for (int64_t i = 0; i < iteration_count; ++i) {
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/non_max_suppression.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/non_max_suppression.cpp
index 2a8d8f8497ec38..8c477178a257d4 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/non_max_suppression.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/non_max_suppression.cpp
@@ -91,12 +91,12 @@ void NmsLayerTest::CompareBuffer(const std::vector<std::pair<ngraph::element::Ty
                     case ngraph::element::Type_t::f32:
                         LayerTestsUtils::LayerTestsCommon::Compare(
                                 reinterpret_cast<const float *>(expectedBuffer),
-                                reinterpret_cast<const float *>(actualBuffer), size, 0);
+                                reinterpret_cast<const float *>(actualBuffer), size, threshold);
                         break;
                     case ngraph::element::Type_t::f64:
                         LayerTestsUtils::LayerTestsCommon::Compare(
                                 reinterpret_cast<const double *>(expectedBuffer),
-                                reinterpret_cast<const float *>(actualBuffer), size, 0);
+                                reinterpret_cast<const float *>(actualBuffer), size, threshold);
                         break;
                     default:
                         break;
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/slice.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/slice.cpp
new file mode 100644
index 00000000000000..60130e82480cfd
--- /dev/null
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/slice.cpp
@@ -0,0 +1,61 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph_functions/builders.hpp"
+#include "ngraph/ngraph.hpp"
+
+#include "shared_test_classes/single_layer/slice.hpp"
+
+using namespace ngraph;
+
+namespace LayerTestsDefinitions {
+
+std::string SliceLayerTest::getTestCaseName(const testing::TestParamInfo<SliceParams> &obj) {
+    SliceSpecificParams params;
+    InferenceEngine::Precision netPrc;
+    InferenceEngine::Precision inPrc, outPrc;
+    InferenceEngine::Layout inLayout, outLayout;
+    std::string targetName;
+    std::map<std::string, std::string> additionalConfig;
+    std::tie(params, netPrc, inPrc, outPrc, inLayout, outLayout, targetName, additionalConfig) = obj.param;
+    std::ostringstream result;
+    result << "inShape=" << CommonTestUtils::vec2str(params.inputShape) << "_";
+    result << "netPRC=" << netPrc.name() << "_";
+    result << "start=" << CommonTestUtils::vec2str(params.start) << "_";
+    result << "stop=" << CommonTestUtils::vec2str(params.stop) << "_";
+    result << "step=" << CommonTestUtils::vec2str(params.step) << "_";
+    result << "axes=" << CommonTestUtils::vec2str(params.axes) << "_";
+    result << "trgDev=" << targetName;
+    return result.str();
+}
+
+void SliceLayerTest::SetUp() {
+    SliceSpecificParams sliceParams;
+    InferenceEngine::Precision netPrecision;
+    std::map<std::string, std::string> additionalConfig;
+    std::tie(sliceParams, netPrecision, inPrc, outPrc, inLayout, outLayout, targetDevice, additionalConfig) = this->GetParam();
+    configuration.insert(additionalConfig.begin(), additionalConfig.end());
+
+    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+
+    element::Type_t et = element::i32;
+
+    const auto data = std::make_shared<opset8::Parameter>(ngPrc, Shape(sliceParams.inputShape));
+    const auto start = std::make_shared<opset8::Constant>(et, Shape{sliceParams.start.size()}, sliceParams.start);
+    const auto stop = std::make_shared<opset8::Constant>(et, Shape{sliceParams.stop.size()}, sliceParams.stop);
+    const auto step = std::make_shared<opset8::Constant>(et, Shape{sliceParams.step.size()}, sliceParams.step);
+
+    Output<Node> slice;
+    if (sliceParams.axes.empty()) {
+        slice = std::make_shared<opset8::Slice>(data, start, stop, step);
+    } else {
+        const auto axes = std::make_shared<opset8::Constant>(et, Shape{sliceParams.axes.size()}, sliceParams.axes);
+        slice = std::make_shared<opset8::Slice>(data, start, stop, step, axes);
+    }
+
+    ResultVector results{std::make_shared<opset8::Result>(slice)};
+    function = std::make_shared<Function>(results, ov::ParameterVector{data}, "Slice");
+}
+
+}  // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/concat_quantization_during_memory_requantization.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/concat_quantization_during_memory_requantization.cpp
index 729dfdb4e508ef..c17b5267471e18 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/concat_quantization_during_memory_requantization.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/concat_quantization_during_memory_requantization.cpp
@@ -10,13 +10,17 @@ namespace SubgraphTestsDefinitions {
         std::string targetName;
         size_t inputSize;
         size_t hiddenSize;
-        std::tie(netPrecision, targetName, inputSize, hiddenSize, std::ignore) = obj.param;
+        std::map<std::string, std::string> config;
+        std::tie(netPrecision, targetName, inputSize, hiddenSize, config) = obj.param;
         std::ostringstream results;
 
         results << "netPRC=" << netPrecision.name() << "_";
         results << "IS=" << inputSize << "_";
         results << "HS=" << hiddenSize << "_";
         results << "targetDevice=" << targetName;
+        for (auto const& configItem : config) {
+           results << "_configItem=" << configItem.second;
+        }
         return results.str();
     }
 
@@ -99,8 +103,8 @@ namespace SubgraphTestsDefinitions {
         SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
         LoadNetwork();
-        IE_SUPPRESS_DEPRECATED_START
-        auto states = executableNetwork.QueryState();
+
+        auto states = inferRequest.QueryState();
         for (auto& state : states) {
             auto name = state.GetName();
             if (name == "memory_1") {
@@ -115,7 +119,6 @@ namespace SubgraphTestsDefinitions {
                 GTEST_FAIL() << "unknown memory state";
             }
         }
-        IE_SUPPRESS_DEPRECATED_END
         GenerateInputs();
         Infer();
         switchToNgraphFriendlyModel();
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/delayed_copy_layer.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/delayed_copy_layer.cpp
index f8b094bc278c0c..885d24b6f2ba0e 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/delayed_copy_layer.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/delayed_copy_layer.cpp
@@ -6,8 +6,7 @@
 
 namespace SubgraphTestsDefinitions {
     void DelayedCopyTestBase::InitMemory() {
-        IE_SUPPRESS_DEPRECATED_START
-        auto states = executableNetwork.QueryState();
+        auto states = inferRequest.QueryState();
         for (auto& state : states) {
             auto name = state.GetName();
             if (name.find("id") != std::string::npos) {
@@ -18,7 +17,6 @@ namespace SubgraphTestsDefinitions {
                 GTEST_FAIL() << "unknown memory state";
             }
         }
-        IE_SUPPRESS_DEPRECATED_END
     }
 
     void DelayedCopyTestBase::Run() {
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/first_connect_input_concat.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/first_connect_input_concat.cpp
index 9a92a5936dba70..f2999ab75294a7 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/first_connect_input_concat.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/first_connect_input_concat.cpp
@@ -18,7 +18,9 @@ std::string ConcatFirstInputTest::getTestCaseName(const testing::TestParamInfo<c
     result << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_";
     result << "netPRC=" << netPrecision.name() << "_";
     result << "targetDevice=" << targetDevice;
-
+    for (auto const& configItem : additional_config) {
+        result << "_configItem=" << configItem.first << "_" << configItem.second;
+    }
     return result.str();
 }
 
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_LSTMCell.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_LSTMCell.cpp
index 2baa728a18913d..755478c3d7ccbe 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_LSTMCell.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_LSTMCell.cpp
@@ -276,8 +276,7 @@ namespace SubgraphTestsDefinitions {
     }
 
     void MemoryLSTMCellTest::InitMemory() {
-        IE_SUPPRESS_DEPRECATED_START
-        auto states = executableNetwork.QueryState();
+        auto states = inferRequest.QueryState();
         for (auto& state : states) {
             auto name = state.GetName();
             if (name.find("cell_state_1") != std::string::npos) {
@@ -292,7 +291,6 @@ namespace SubgraphTestsDefinitions {
                 GTEST_FAIL() << "unknown memory state";
             }
         }
-        IE_SUPPRESS_DEPRECATED_END
     }
 
     void MemoryLSTMCellTest::ApplyLowLatency() {
@@ -330,6 +328,7 @@ namespace SubgraphTestsDefinitions {
 
             ConfigureNetwork();
             executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+            inferRequest = executableNetwork.CreateInferRequest();
         } else if (transformation == ngraph::helpers::MemoryTransformation::LOW_LATENCY_V2_REGULAR_API) {
             cnnNetwork = InferenceEngine::CNNNetwork{function};
             InferenceEngine::lowLatency2(cnnNetwork);
@@ -339,6 +338,7 @@ namespace SubgraphTestsDefinitions {
 
             ConfigureNetwork();
             executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+            inferRequest = executableNetwork.CreateInferRequest();
         }
     }
 }  // namespace SubgraphTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_eltwise_reshape_concat.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_eltwise_reshape_concat.cpp
index 3c4d39558ac917..56e82d8dbb511d 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_eltwise_reshape_concat.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/memory_eltwise_reshape_concat.cpp
@@ -122,7 +122,7 @@ void MemoryEltwiseReshapeConcatTest::Run() {
                                                   InferenceEngine::Layout::NC);
 
     IE_SUPPRESS_DEPRECATED_START
-    auto states = executableNetwork.QueryState();
+    auto states = inferRequest.QueryState();
     auto state_values_blob = FuncTestUtils::createAndFillBlobWithFloatArray(state_description,
                                                                             memory_init.data(), memory_init.size());
     states[0].SetState(state_values_blob);
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_LSTMCell.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_LSTMCell.cpp
index a3a131eb5b2345..29ea5c09798736 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_LSTMCell.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_LSTMCell.cpp
@@ -397,8 +397,7 @@ void MultipleLSTMCellTest::InitMemory() {
     InferenceEngine::TensorDesc state_description(InferenceEngine::Precision::FP32,
                                                   InferenceEngine::SizeVector({1, hiddenSize}),
                                                   InferenceEngine::Layout::NC);
-    IE_SUPPRESS_DEPRECATED_START
-    auto states = executableNetwork.QueryState();
+    auto states = inferRequest.QueryState();
     for (auto& state : states) {
         auto name = state.GetName();
         if (name.find("cell_state_1") != std::string::npos) {
@@ -421,7 +420,6 @@ void MultipleLSTMCellTest::InitMemory() {
             GTEST_FAIL() << "unknown memory state";
         }
     }
-    IE_SUPPRESS_DEPRECATED_END
 }
 
 void MultipleLSTMCellTest::ApplyLowLatency() {
@@ -459,6 +457,7 @@ void MultipleLSTMCellTest::ApplyLowLatency() {
 
         ConfigureNetwork();
         executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+        inferRequest = executableNetwork.CreateInferRequest();
     } else if (transformation == ngraph::helpers::MemoryTransformation::LOW_LATENCY_V2_REGULAR_API) {
         cnnNetwork = InferenceEngine::CNNNetwork{function};
         InferenceEngine::lowLatency2(cnnNetwork);
@@ -468,6 +467,7 @@ void MultipleLSTMCellTest::ApplyLowLatency() {
 
         ConfigureNetwork();
         executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice, configuration);
+        inferRequest = executableNetwork.CreateInferRequest();
     }
 }
 
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_concat.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_concat.cpp
index 60a16462320449..1aae54a8343b05 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_concat.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/multiple_concat.cpp
@@ -20,6 +20,9 @@ std::string MultipleConcatTest::getTestCaseName(const testing::TestParamInfo<mul
     result << "IS=" << inputSize << "_";
     result << "CS=" << constantSize << "_";
     result << "targetDevice=" << targetDevice;
+    for (auto const& configItem : config) {
+        result << "_configItem=" << configItem.second;
+    }
     return result.str();
 }
 
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/negative_memory_layer_offset.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/negative_memory_layer_offset.cpp
index 9375b7b6baf31f..c09567dff9d206 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/subgraph/negative_memory_layer_offset.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/negative_memory_layer_offset.cpp
@@ -72,8 +72,7 @@ namespace SubgraphTestsDefinitions {
         SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
         LoadNetwork();
-        IE_SUPPRESS_DEPRECATED_START
-        auto states = executableNetwork.QueryState();
+        auto states = inferRequest.QueryState();
         for (auto& state : states) {
             auto name = state.GetName();
             if (name == "memory") {
@@ -84,7 +83,6 @@ namespace SubgraphTestsDefinitions {
                 GTEST_FAIL() << "unknown memory state";
             }
         }
-        IE_SUPPRESS_DEPRECATED_END
         GenerateInputs();
         Infer();
         switchToNgraphFriendlyModel();
diff --git a/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_iexecutable_network_internal.hpp b/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_iexecutable_network_internal.hpp
index 805df1d61dcf5b..6e3ea6248252c2 100644
--- a/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_iexecutable_network_internal.hpp
+++ b/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/interface/mock_iexecutable_network_internal.hpp
@@ -24,7 +24,6 @@ class MockIExecutableNetworkInternal : public IExecutableNetworkInternal {
     MOCK_METHOD0(CreateInferRequest, IInferRequestInternal::Ptr(void));
     MOCK_METHOD1(Export, void(const std::string &));
     void Export(std::ostream &) override {};
-    MOCK_METHOD0(QueryState, std::vector<IVariableStateInternal::Ptr>(void));
     MOCK_METHOD0(GetExecGraphInfo, std::shared_ptr<ngraph::Function>(void));
 
     MOCK_METHOD1(SetConfig, void(const std::map<std::string, Parameter> &config));
diff --git a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/convolution_function.hpp b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/convolution_function.hpp
index 325b981ec16e2e..1a93cc8a2b0220 100644
--- a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/convolution_function.hpp
+++ b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/convolution_function.hpp
@@ -22,9 +22,15 @@ class ConvolutionFunction {
         const ngraph::element::Type netPrecision,
         const ngraph::element::Type inputPrecision,
         const ngraph::PartialShape& inputShape,
-        const ngraph::builder::subgraph::DequantizationOperations& dequantizationBefore,
+        const ngraph::builder::subgraph::DequantizationOperations& dequantizationOnActivations,
         std::shared_ptr<ngraph::opset1::Constant> weights,
-        const ngraph::builder::subgraph::FakeQuantizeOnWeights fakeQuantizeOnWeights);
+        const ngraph::builder::subgraph::FakeQuantizeOnWeights fqOnWeights,
+        const ngraph::builder::subgraph::DequantizationOperations& dequantizationOnWeights = DequantizationOperations(),
+        const bool fqOnWeightsTransposeOnData = false,
+        const bool fqOnWeightsTransposeOnInputLow = false,
+        const bool fqOnWeightsTransposeOnInputHigh = false,
+        const bool fqOnWeightsTransposeOnOutputLow = false,
+        const bool fqOnWeightsTransposeOnOutputHigh = false);
 
     static std::shared_ptr<ngraph::Function> getOriginalWithIncorrectWeights(
         const ngraph::Shape& inputShape,
diff --git a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/convolution_function.cpp b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/convolution_function.cpp
index 1d2dc22bb860df..e5606d9647b8c8 100644
--- a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/convolution_function.cpp
+++ b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/convolution_function.cpp
@@ -25,11 +25,17 @@ std::shared_ptr<ngraph::Function> ConvolutionFunction::getOriginal(
     const ngraph::element::Type netPrecision,
     const ngraph::element::Type inputPrecision,
     const ngraph::PartialShape& inputShape,
-    const ngraph::builder::subgraph::DequantizationOperations& dequantizationBefore,
+    const ngraph::builder::subgraph::DequantizationOperations& dequantizationOnActivations,
     std::shared_ptr<ngraph::opset1::Constant> weights,
-    const ngraph::builder::subgraph::FakeQuantizeOnWeights fakeQuantizeOnWeights) {
+    const ngraph::builder::subgraph::FakeQuantizeOnWeights fqOnWeights,
+    const ngraph::builder::subgraph::DequantizationOperations& dequantizationOnWeights,
+    const bool transposeOnData,
+    const bool transposeOnInputLow,
+    const bool transposeOnInputHigh,
+    const bool transposeOnOutputLow,
+    const bool transposeOnOutputHigh) {
     const auto input = std::make_shared<ngraph::opset1::Parameter>(inputPrecision, inputShape);
-    auto dequantizationStructure = dequantizationBefore;
+    auto dequantizationStructure = dequantizationOnActivations;
     dequantizationStructure.multiply.outPrecision = netPrecision;
     const auto dequantization = makeDequantization(input, dequantizationStructure);
 
@@ -48,20 +54,45 @@ std::shared_ptr<ngraph::Function> ConvolutionFunction::getOriginal(
             weights, op::Constant::create(ngraph::element::i64, Shape{ targetShape.size() }, targetShape)));
     }
 
-    const auto convertOnWeights = std::make_shared<opset1::Convert>(weights, netPrecision);
-    OutputVector convertedOutput(1);
-    convertOnWeights->constant_fold(convertedOutput, convertOnWeights->input_values());
-    const auto convertedWeights = convertedOutput[0].get_node_shared_ptr();
+    std::shared_ptr<Node> convertedWeights;
+    if (dequantizationOnWeights.empty()) {
+        const auto convertOnWeights = std::make_shared<opset1::Convert>(weights, netPrecision);
+        OutputVector convertedOutput(1);
+        convertOnWeights->constant_fold(convertedOutput, convertOnWeights->input_values());
+        convertedWeights = convertedOutput[0].get_node_shared_ptr();
+    } else {
+        convertedWeights = weights;
+    }
 
-    const auto onWeights = fakeQuantizeOnWeights.empty() ? convertedWeights :
-        ngraph::builder::makeFakeQuantize(
-            convertedWeights, netPrecision,
-            fakeQuantizeOnWeights.quantizationLevel,
-            fakeQuantizeOnWeights.constantShape,
-            fakeQuantizeOnWeights.inputLowValues,
-            fakeQuantizeOnWeights.inputHighValues,
-            fakeQuantizeOnWeights.outputLowValues,
-            fakeQuantizeOnWeights.outputHighValues);
+    const std::shared_ptr<ngraph::Node> constant = ngraph::opset1::Constant::create(ngraph::element::u64, ngraph::Shape{4}, {0, 1, 2, 3});
+    std::shared_ptr<Node> onWeights;
+    if (fqOnWeights.empty()) {
+        onWeights = dequantizationOnWeights.empty() ? convertedWeights : makeDequantization(convertedWeights, dequantizationOnWeights);
+    } else {
+        onWeights = std::make_shared<opset1::FakeQuantize>(
+            transposeOnData ? std::make_shared<opset1::Transpose>(convertedWeights, constant) : convertedWeights,
+            transposeOnInputLow ?
+                std::make_shared<opset1::Transpose>(
+                    makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.inputLowValues, fqOnWeights.inputLowValues.empty()),
+                    constant->clone_with_new_inputs({})) :
+                makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.inputLowValues, fqOnWeights.inputLowValues.empty()),
+            transposeOnInputHigh ?
+                std::make_shared<opset1::Transpose>(
+                    makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.inputHighValues, fqOnWeights.inputHighValues.empty()),
+                    constant->clone_with_new_inputs({})) :
+                makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.inputHighValues, fqOnWeights.inputHighValues.empty()),
+            transposeOnOutputLow ?
+                std::make_shared<opset1::Transpose>(
+                    makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.outputLowValues, fqOnWeights.outputLowValues.empty()),
+                    constant->clone_with_new_inputs({})) :
+                makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.outputLowValues, fqOnWeights.outputLowValues.empty()),
+            transposeOnOutputHigh ?
+                std::make_shared<opset1::Transpose>(
+                    makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.outputHighValues, fqOnWeights.outputHighValues.empty()),
+                    constant->clone_with_new_inputs({})) :
+                makeConstant(netPrecision, fqOnWeights.constantShape, fqOnWeights.outputHighValues, fqOnWeights.outputHighValues.empty()),
+            fqOnWeights.quantizationLevel);
+    }
 
     auto convolutionOriginal = ngraph::opset1::Convolution(
         ngraph::op::TemporaryReplaceOutputType(dequantization, netPrecision).get(),
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/preprocess/preprocess_builders.hpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/preprocess/preprocess_builders.hpp
index 381317bcb2fca1..ed9fdc34c9ebc8 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/preprocess/preprocess_builders.hpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/preprocess/preprocess_builders.hpp
@@ -81,90 +81,90 @@ inline std::shared_ptr<Function> create_preprocess_2inputs_trivial() {
 inline std::shared_ptr<Function> mean_only() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, Shape{1, 3, 24, 24});
-    function = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().mean(1.1f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo().preprocess(PreProcessSteps().mean(1.1f))).build();
     return function;
 }
 
 inline std::shared_ptr<Function> scale_only() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, Shape{1, 3, 24, 24});
-    function = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().scale(2.1f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo().preprocess(PreProcessSteps().scale(2.1f))).build();
     return function;
 }
 
 inline std::shared_ptr<Function> mean_scale() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, Shape{1, 3, 24, 24});
-    function = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().mean(1.1f).scale(2.1f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo().preprocess(PreProcessSteps().mean(1.1f).scale(2.1f))).build();
     return function;
 }
 
 inline std::shared_ptr<Function> scale_mean() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, Shape{1, 3, 24, 24});
-    function = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().scale(2.1f).mean(1.1f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo().preprocess(PreProcessSteps().scale(2.1f).mean(1.1f))).build();
     return function;
 }
 
 inline std::shared_ptr<Function> mean_vector() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, Shape{1, 3, 24, 24});
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_layout("NCHW"))
-                                                .preprocess(PreProcessSteps().mean({2.2f, 3.3f, 4.4f}))).build(function);
+                                                .preprocess(PreProcessSteps().mean({2.2f, 3.3f, 4.4f}))).build();
     return function;
 }
 
 inline std::shared_ptr<Function> scale_vector() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, Shape{1, 3, 24, 24});
-    function = PrePostProcessor().input(InputInfo()
+    function = PrePostProcessor(function).input(InputInfo()
                                                 .tensor(InputTensorInfo().set_layout("NCHW"))
-                                                .preprocess(PreProcessSteps().scale({2.2f, 3.3f, 4.4f}))).build(function);
+                                                .preprocess(PreProcessSteps().scale({2.2f, 3.3f, 4.4f}))).build();
     return function;
 }
 
 inline std::shared_ptr<Function> convert_element_type_and_mean() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::u8, Shape{1, 3, 24, 24});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .preprocess(PreProcessSteps()
                                                .convert_element_type(element::f32)
                                                .mean(0.2f)
                                                .convert_element_type(element::u8)))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> tensor_element_type_and_mean() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::u8, Shape{1, 3, 12, 12});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_element_type(element::f32))
                            .preprocess(PreProcessSteps().mean(0.1f).convert_element_type(element::u8)))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> custom_preprocessing() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::i32, Shape{3, 4, 10, 20});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo().preprocess(PreProcessSteps().custom([](const Output<Node>& node) {
                 auto abs = std::make_shared<op::v0::Abs>(node);
                 abs->set_friendly_name(node.get_node_shared_ptr()->get_friendly_name() + "/abs");
                 return abs;
             })))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> lvalues_multiple_ops() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::u8, Shape{1, 3, 3, 3});
-    auto p = PrePostProcessor();
+    auto p = PrePostProcessor(function);
     auto p1 = std::move(p);
     p = std::move(p1);
     auto inputInfo = InputInfo();
@@ -195,21 +195,21 @@ inline std::shared_ptr<Function> lvalues_multiple_ops() {
         inputInfo.preprocess(std::move(same));
     }
     p.input(std::move(inputInfo));
-    function = p.build(function);
+    function = p.build();
     return function;
 }
 
 inline std::shared_ptr<Function> two_inputs_basic() {
     using namespace ov::preprocess;
     auto function = create_preprocess_2inputs(element::f32, Shape{1, 3, 1, 1});
-    function = PrePostProcessor().input(InputInfo(1).preprocess(PreProcessSteps().mean(1.f).scale(2.0f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo(1).preprocess(PreProcessSteps().mean(1.f).scale(2.0f))).build();
     return function;
 }
 
 inline std::shared_ptr<Function> two_inputs_trivial() {
     using namespace ov::preprocess;
     auto function = create_preprocess_2inputs_trivial();
-    function = PrePostProcessor().input(InputInfo(1).preprocess(PreProcessSteps().mean(1.f).scale(2.0f))).build(function);
+    function = PrePostProcessor(function).input(InputInfo(1).preprocess(PreProcessSteps().mean(1.f).scale(2.0f))).build();
     return function;
 }
 
@@ -217,9 +217,9 @@ inline std::shared_ptr<Function> reuse_network_layout() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{4, 3, 2, 1});
     function->get_parameters().front()->set_layout("NC??");
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo().preprocess(PreProcessSteps().mean({1.1f, 2.2f, 3.3f}).scale({2.f, 3.f, 4.f})))
-            .build(function);
+            .build();
     return function;
 }
 
@@ -227,66 +227,66 @@ inline std::shared_ptr<Function> tensor_layout() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{4, 3, 2, 1});
     function->get_parameters().front()->set_layout("NC??");
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_layout("NC??"))
                            .preprocess(PreProcessSteps().mean({1.1f, 2.2f, 3.3f}).scale({2.f, 3.f, 4.f})))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> resize_linear() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 3, 10, 10});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_spatial_static_shape(20, 20))
                            .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                            .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> resize_nearest() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 3, 10, 10});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_spatial_static_shape(20, 20))
                            .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_NEAREST))
                            .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> resize_linear_nhwc() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 10, 10, 3});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_spatial_static_shape(20, 20))
                            .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                            .network(InputNetworkInfo().set_layout("NHWC")))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> resize_cubic() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 3, 20, 20});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_spatial_static_shape(10, 10))
                            .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_CUBIC))
                            .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> resize_and_convert_layout() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 30, 20, 3});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo()
                                            .set_layout("NHWC")
@@ -295,25 +295,25 @@ inline std::shared_ptr<Function> resize_and_convert_layout() {
                                                .convert_layout()
                                                .resize(ResizeAlgorithm::RESIZE_LINEAR))
                            .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> convert_layout_by_dims() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 30, 20, 3});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .preprocess(PreProcessSteps()
                                                .convert_layout({0, 3, 1, 2})))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> resize_and_convert_layout_i8() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::i8, PartialShape{1, 30, 20, 3});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo()
                                            .set_layout("NHWC")
@@ -322,36 +322,36 @@ inline std::shared_ptr<Function> resize_and_convert_layout_i8() {
                                                .convert_layout()
                                                .resize(ResizeAlgorithm::RESIZE_LINEAR))
                            .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> cvt_color_nv12_to_rgb_single_plane() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 20, 20, 3});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_SINGLE_PLANE))
                            .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB)))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> cvt_color_nv12_to_bgr_two_planes() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 20, 20, 3});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES))
                            .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR)))
-            .build(function);
+            .build();
     return function;
 }
 
 inline std::shared_ptr<Function> cvt_color_nv12_cvt_layout_resize() {
     using namespace ov::preprocess;
     auto function = create_preprocess_1input(element::f32, PartialShape{1, 3, 10, 10});
-    function = PrePostProcessor()
+    function = PrePostProcessor(function)
             .input(InputInfo()
                            .tensor(InputTensorInfo()
                                 .set_color_format(ColorFormat::NV12_TWO_PLANES)
@@ -363,7 +363,7 @@ inline std::shared_ptr<Function> cvt_color_nv12_cvt_layout_resize() {
                                 .convert_element_type(element::f32)
                                 .resize(ResizeAlgorithm::RESIZE_LINEAR))
                            .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(function);
+            .build();
     return function;
 }
 
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp
index d8bb4a027165d2..a518b080af3f0e 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/subgraph_builders.hpp
@@ -550,6 +550,23 @@ inline std::shared_ptr<ngraph::Function> makeReadConcatSplitAssign(std::vector<s
     fn_ptr->set_friendly_name("ReadConcatSplitAssign");
     return fn_ptr;
 }
+
+inline std::shared_ptr<ngraph::Function> makeMatMulBias(std::vector<size_t> inputShape = { 1, 3, 24, 24 },
+                                                        ngraph::element::Type type = ngraph::element::Type_t::f32) {
+    auto parameter = ngraph::builder::makeParams(type, { inputShape });
+    parameter[0]->set_friendly_name("parameter");
+    auto weights = ngraph::opset1::Constant::create(type, ngraph::Shape{ 24, 24 }, { 1 });
+    auto biases = ngraph::opset1::Constant::create(type, ngraph::Shape{ 1, 24 }, { 1 });
+    auto matmul = std::make_shared<opset1::MatMul>(parameter[0], weights);
+    matmul->set_friendly_name("matmul");
+    auto add = std::make_shared<opset1::Add>(matmul, biases);
+    add->set_friendly_name("add");
+    auto result = std::make_shared<ngraph::opset1::Result>(add);
+    result->set_friendly_name("result");
+    std::shared_ptr<ngraph::Function> fn_ptr = std::make_shared<ngraph::Function>(ngraph::ResultVector{ result }, ngraph::ParameterVector{ parameter });
+    fn_ptr->set_friendly_name("MatMulBias");
+    return fn_ptr;
+}
 }  // namespace subgraph
 }  // namespace builder
 }  // namespace ngraph
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/batch_to_space.cpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/batch_to_space.cpp
index 59f23e654b9dcf..a60a708afa5293 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/batch_to_space.cpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/batch_to_space.cpp
@@ -12,7 +12,7 @@ std::shared_ptr<ngraph::Node> makeBatchToSpace(const ngraph::Output<Node> &in,
                                                const std::vector<int64_t> &blockShape,
                                                const std::vector<int64_t> &cropsBegin,
                                                const std::vector<int64_t> &cropsEnd) {
-    ngraph::Shape constShape = {in.get_shape().size()};
+    ngraph::Shape constShape = {in.get_partial_shape().size()};
     auto blockShapeNode = std::make_shared<ngraph::opset1::Constant>(ngraph::element::i64, constShape,
                                                                      blockShape.data());
     auto cropsBeginNode = std::make_shared<ngraph::opset1::Constant>(ngraph::element::i64, constShape,
diff --git a/inference-engine/tests/unit/cpu/CMakeLists.txt b/inference-engine/tests/unit/cpu/CMakeLists.txt
index a66c27aa5c2a29..d44d258421261a 100644
--- a/inference-engine/tests/unit/cpu/CMakeLists.txt
+++ b/inference-engine/tests/unit/cpu/CMakeLists.txt
@@ -8,8 +8,8 @@ addIeTargetTest(
         NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
         INCLUDES
-            ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin
-            ${IE_MAIN_SOURCE_DIR}/src/mkldnn_plugin/nodes
+            $<TARGET_PROPERTY:MKLDNNPlugin,SOURCE_DIR>
+            $<TARGET_PROPERTY:MKLDNNPlugin,SOURCE_DIR>/nodes
             $<TARGET_PROPERTY:openvino::conditional_compilation,INTERFACE_INCLUDE_DIRECTORIES>
         OBJECT_FILES
             $<TARGET_OBJECTS:MKLDNNPlugin_obj>
diff --git a/inference-engine/tests/unit/gna/CMakeLists.txt b/inference-engine/tests/unit/gna/CMakeLists.txt
index 76a2a1ac94fb76..cff4aa3d20e995 100644
--- a/inference-engine/tests/unit/gna/CMakeLists.txt
+++ b/inference-engine/tests/unit/gna/CMakeLists.txt
@@ -4,15 +4,22 @@
 
 set(TARGET_NAME gnaUnitTests)
 
+# TODO: fix CVS-71010 and remove BUILD_SHARED_LIBS
+if(NOT BUILD_SHARED_LIBS)
+    set(exclude_path EXCLUDED_SOURCE_PATHS "${CMAKE_CURRENT_SOURCE_DIR}/(gna_api_stub|gna_wait_test).cpp")
+endif()
+
 addIeTargetTest(
         NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
+        ${exclude_path}
         LINK_LIBRARIES
             PRIVATE
                 ngraphFunctions
             gmock
             commonTestUtils_s
             GNAPlugin_test_static
+            engines_test_util
         ADD_CPPLINT
         LABELS
             GNA
diff --git a/inference-engine/tests/unit/gna/ngraph/op_eval/softsign.cpp b/inference-engine/tests/unit/gna/ngraph/op_eval/softsign.cpp
new file mode 100644
index 00000000000000..4df575cbb3414a
--- /dev/null
+++ b/inference-engine/tests/unit/gna/ngraph/op_eval/softsign.cpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ops/softsign.hpp"
+
+#include <string>
+#include <vector>
+
+#include "execute_tools.hpp"
+#include "gtest/gtest.h"
+#include "ngraph/runtime/host_tensor.hpp"
+#include "ngraph/validation_util.hpp"
+#include "ngraph/opsets/opset8.hpp"
+
+using namespace GNAPluginNS;
+
+TEST(op_eval, softsign) {
+    auto p = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, ngraph::Shape{4});
+    auto softsign = std::make_shared<SoftSign>(p);
+    auto fun = std::make_shared<ngraph::Function>(ngraph::OutputVector{softsign}, ngraph::ParameterVector{p});
+
+    float inputs[] = {-1.0, 0.0, 1.0, 20.0};
+    std::vector<float> expected_result{0.5, 1.0, 0.5, 0.047619};
+
+    ov::runtime::TensorVector result(1);
+    ov::runtime::Tensor input{ov::element::f32, ov::Shape{4}, inputs};
+
+    ASSERT_TRUE(fun->evaluate(result, ov::runtime::TensorVector{input}));
+
+    EXPECT_EQ(result.size(), 1);
+    EXPECT_EQ(result[0].get_element_type(), ngraph::element::f32);
+    EXPECT_EQ(result[0].get_shape(), ngraph::Shape{4});
+    EXPECT_EQ(result[0].get_size(), 4);
+
+    const float * result_data = result[0].data<float>();
+    for (size_t i = 0; i < result[0].get_size(); ++i)
+        EXPECT_NEAR(result_data[i], expected_result[i], 0.000001);
+}
+
diff --git a/inference-engine/tests/unit/gna/ngraph/transformations/gna_substitute_softsign.cpp b/inference-engine/tests/unit/gna/ngraph/transformations/gna_substitute_softsign.cpp
new file mode 100644
index 00000000000000..8df3db75000113
--- /dev/null
+++ b/inference-engine/tests/unit/gna/ngraph/transformations/gna_substitute_softsign.cpp
@@ -0,0 +1,167 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "transformations/substitute_softsign.hpp"
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <transformations/init_node_info.hpp>
+#include <ops/softsign.hpp>
+
+namespace testing {
+
+namespace {
+
+std::shared_ptr<ngraph::Function> createSoftSignFunction() {
+    auto input_params = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32,
+                                                                    ngraph::Shape{ 1, 1, 1, 64 });
+
+    auto softsign = std::make_shared<GNAPluginNS::SoftSign>(input_params);
+
+    ngraph::ResultVector results{ std::make_shared<ngraph::op::Result>(softsign) };
+
+    return std::make_shared<ngraph::Function>(ngraph::ResultVector{results},
+                                              ngraph::ParameterVector{input_params});
+}
+
+} // namespace
+
+TEST(TransformationTests, SubstituteSoftSignMulPower) {
+    std::shared_ptr<ngraph::Function> func(nullptr), reference_func(nullptr);
+
+    {
+        auto input_params = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32,
+                                                                        ngraph::Shape{ 1, 1, 1, 64 });
+
+        auto abs = std::make_shared<ngraph::op::Abs>(input_params);
+
+        auto const_1 = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1});
+        auto const_neg_1 = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{}, {-1});
+
+        auto add = std::make_shared<ngraph::opset8::Add>(abs, const_1);
+        auto power = std::make_shared<ngraph::opset8::Power>(add, const_neg_1);
+
+        auto mul = std::make_shared<ngraph::opset8::Multiply>(power, input_params);
+        ngraph::ResultVector results{ std::make_shared<ngraph::op::Result>(mul) };
+
+        func = std::make_shared<ngraph::Function>(ngraph::ResultVector{results},
+                                                  ngraph::ParameterVector{input_params});
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<GNAPluginNS::SubstituteSoftsign>();
+        m.run_passes(func);
+        ASSERT_NO_THROW(check_rt_info(func));
+    }
+
+    reference_func = createSoftSignFunction();
+
+    const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::ATTRIBUTES);
+    const FunctionsComparator::Result result = func_comparator(func, reference_func);
+    ASSERT_TRUE(result.valid);
+}
+
+TEST(TransformationTests, SubstituteSoftSignDivide) {
+    std::shared_ptr<ngraph::Function> func(nullptr), reference_func(nullptr);
+
+    {
+        auto input_params = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32,
+                                                                        ngraph::Shape{ 1, 1, 1, 64 });
+
+        auto abs = std::make_shared<ngraph::opset8::Abs>(input_params);
+
+        auto const_1 = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1});
+        auto add = std::make_shared<ngraph::opset8::Add>(abs, const_1);
+
+        auto divide = std::make_shared<ngraph::opset8::Divide>(input_params, add);
+        ngraph::ResultVector results{ std::make_shared<ngraph::opset8::Result>(divide) };
+
+        func = std::make_shared<ngraph::Function>(ngraph::ResultVector{results},
+                                                  ngraph::ParameterVector{input_params});
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<GNAPluginNS::SubstituteSoftsign>();
+        m.run_passes(func);
+        ASSERT_NO_THROW(check_rt_info(func));
+    }
+
+    reference_func = createSoftSignFunction();
+
+    const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::ATTRIBUTES);
+    const FunctionsComparator::Result result = func_comparator(func, reference_func);
+    ASSERT_TRUE(result.valid);
+}
+
+TEST(TransformationTests, SubstituteSoftSignMulPowerInvalidAddConst) {
+    std::shared_ptr<ngraph::Function> func(nullptr), reference_func(nullptr);
+
+    {
+        auto input_params = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32,
+                                                                        ngraph::Shape{ 1, 1, 1, 64 });
+
+        auto abs = std::make_shared<ngraph::op::Abs>(input_params);
+
+        auto const_1 = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1.1});
+        auto const_neg_1 = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{}, {-1});
+
+        auto add = std::make_shared<ngraph::opset8::Add>(abs, const_1);
+        auto power = std::make_shared<ngraph::opset8::Power>(add, const_neg_1);
+
+        auto mul = std::make_shared<ngraph::opset8::Multiply>(power, input_params);
+        ngraph::ResultVector results{ std::make_shared<ngraph::op::Result>(mul) };
+
+        func = std::make_shared<ngraph::Function>(ngraph::ResultVector{results},
+                                                  ngraph::ParameterVector{input_params});
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<GNAPluginNS::SubstituteSoftsign>();
+        m.run_passes(func);
+        ASSERT_NO_THROW(check_rt_info(func));
+    }
+
+    reference_func = ngraph::clone_function(*func);
+
+    const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::ATTRIBUTES);
+    const FunctionsComparator::Result result = func_comparator(func, reference_func);
+    ASSERT_TRUE(result.valid);
+}
+
+TEST(TransformationTests, SubstituteSoftSignMulPowerInvalidPowerConst) {
+    std::shared_ptr<ngraph::Function> func(nullptr), reference_func(nullptr);
+
+    {
+        auto input_params = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32,
+                                                                        ngraph::Shape{ 1, 1, 1, 64 });
+
+        auto abs = std::make_shared<ngraph::op::Abs>(input_params);
+
+        auto const_1 = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{}, {1});
+        auto const_neg_1 = ngraph::opset8::Constant::create(ngraph::element::f32, ngraph::Shape{}, {-1.1});
+
+        auto add = std::make_shared<ngraph::opset8::Add>(abs, const_1);
+        auto power = std::make_shared<ngraph::opset8::Power>(add, const_neg_1);
+
+        auto mul = std::make_shared<ngraph::opset8::Multiply>(power, input_params);
+        ngraph::ResultVector results{ std::make_shared<ngraph::op::Result>(mul) };
+
+        func = std::make_shared<ngraph::Function>(ngraph::ResultVector{results},
+                                                  ngraph::ParameterVector{input_params});
+        ngraph::pass::Manager m;
+        m.register_pass<ngraph::pass::InitNodeInfo>();
+        m.register_pass<GNAPluginNS::SubstituteSoftsign>();
+        m.run_passes(func);
+        ASSERT_NO_THROW(check_rt_info(func));
+    }
+
+    reference_func = ngraph::clone_function(*func);
+
+    const FunctionsComparator func_comparator = FunctionsComparator::with_default().enable(FunctionsComparator::ATTRIBUTES);
+    const FunctionsComparator::Result result = func_comparator(func, reference_func);
+    ASSERT_TRUE(result.valid);
+}
+
+} // namespace testing
diff --git a/inference-engine/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp b/inference-engine/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp
index c4f0395023e360..8d7d39b92a3d4f 100644
--- a/inference-engine/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp
+++ b/inference-engine/tests/unit/inference_engine/cpp_interfaces/ie_memory_state_internal_test.cpp
@@ -43,162 +43,6 @@ class InferRequestVariableStateTests : public ::testing::Test {
     }
 };
 
-TEST_F(InferRequestVariableStateTests, ExecutableNetworkCanConvertOneVariableStateFromCppToAPI) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn(1);
-    toReturn[0] = mockVariableStateInternal;
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-
-    auto state = net->QueryState();
-    ASSERT_EQ(state.size(), 1);
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, ExecutableNetworkCanConvertZeroVariableStateFromCppToAPI) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).WillOnce(Return(toReturn));
-
-    auto state = net->QueryState();
-    ASSERT_EQ(state.size(), 0);
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, ExecutableNetworkCanConvert2VariableStatesFromCPPtoAPI) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-
-    auto state = net->QueryState();
-    ASSERT_EQ(state.size(), 2);
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, VariableStatePropagatesReset) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), Reset()).Times(1);
-
-    auto state = net->QueryState();
-    state.front()->Reset();
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, VariableStatePropagatesExceptionsFromReset) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), Reset()).WillOnce(Throw(std::logic_error("some error")));
-
-    auto state = net->QueryState();
-    EXPECT_ANY_THROW(state.front()->Reset());
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, VariableStatePropagatesGetName) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), GetName()).WillOnce(Return("someName"));
-
-    auto state = net->QueryState();
-    EXPECT_STREQ(state.front()->GetName().c_str(), "someName");
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, VariableStatePropagatesGetNameWithZeroLen) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), GetName()).WillOnce(Return("someName"));
-
-    auto pState = net->QueryState().front();
-    EXPECT_NO_THROW(pState->GetName());
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-
-TEST_F(InferRequestVariableStateTests, VariableStatePropagatesGetNameWithLenOfOne) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), GetName()).WillOnce(Return("someName"));
-
-    auto pState = net->QueryState().front();
-    std::string name;
-    EXPECT_NO_THROW(name = pState->GetName());
-    EXPECT_EQ(name, "someName");
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, VariableStatePropagatesGetNameWithLenOfTwo) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).Times(1).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), GetName()).WillOnce(Return("someName"));
-
-    auto pState = net->QueryState().front();
-    std::string name;
-    EXPECT_NO_THROW(name = pState->GetName());
-    EXPECT_EQ(name, "someName");
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, VariableStateCanPropagateSetState) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    Blob::Ptr saver;
-    toReturn.push_back(mockVariableStateInternal);
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), SetState(_)).WillOnce(SaveArg<0>(&saver));
-
-    float data[] = {123, 124, 125};
-    auto stateBlob = make_shared_blob<float>({ Precision::FP32, {3}, C }, data, sizeof(data) / sizeof(*data));
-
-    EXPECT_NO_THROW(net->QueryState().front()->SetState(stateBlob));
-    ASSERT_FLOAT_EQ(saver->buffer().as<float*>()[0], 123);
-    ASSERT_FLOAT_EQ(saver->buffer().as<float*>()[1], 124);
-    ASSERT_FLOAT_EQ(saver->buffer().as<float*>()[2], 125);
-    IE_SUPPRESS_DEPRECATED_END
-}
-
-TEST_F(InferRequestVariableStateTests, VariableStateCanPropagateGetLastState) {
-    IE_SUPPRESS_DEPRECATED_START
-    std::vector<IVariableStateInternal::Ptr> toReturn;
-    toReturn.push_back(mockVariableStateInternal);
-
-    float data[] = {123, 124, 125};
-    auto stateBlob = make_shared_blob<float>({ Precision::FP32, {3}, C }, data, sizeof(data) / sizeof(*data));
-
-    EXPECT_CALL(*mockExeNetworkInternal.get(), QueryState()).WillRepeatedly(Return(toReturn));
-    EXPECT_CALL(*mockVariableStateInternal.get(), GetState()).WillOnce(Return(stateBlob));
-
-    auto saver = net->QueryState().front()->GetState();
-    ASSERT_NE(saver, nullptr);
-    ASSERT_FLOAT_EQ(saver->cbuffer().as<const float*>()[0], 123);
-    ASSERT_FLOAT_EQ(saver->cbuffer().as<const float*>()[1], 124);
-    ASSERT_FLOAT_EQ(saver->cbuffer().as<const float*>()[2], 125);
-    IE_SUPPRESS_DEPRECATED_END
-}
 class VariableStateInternalMockImpl : public IVariableStateInternal {
  public:
     VariableStateInternalMockImpl(const char* name) : IVariableStateInternal(name) {}
diff --git a/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp b/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp
index 7c9a41fb5d2f99..8b5021093943e7 100644
--- a/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp
+++ b/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp
@@ -88,26 +88,6 @@ TEST_F(ExecutableNetworkTests, GetInputsInfo) {
     ASSERT_EQ(info, InferenceEngine::ConstInputsDataMap{});
 }
 
-IE_SUPPRESS_DEPRECATED_START
-
-TEST_F(ExecutableNetworkTests, QueryStateThrowsIfReturnErr) {
-    EXPECT_CALL(*mockIExeNet.get(), QueryState())
-            .Times(1)
-            .WillOnce(Throw(InferenceEngine::GeneralError{""}));
-    EXPECT_THROW(exeNetwork->QueryState(), InferenceEngine::Exception);
-}
-
-TEST_F(ExecutableNetworkTests, QueryState) {
-    auto mockIMemState_p = std::make_shared<MockIVariableStateInternal>();
-    EXPECT_CALL(*mockIExeNet.get(), QueryState())
-            .Times(1)
-            .WillOnce(Return(std::vector<std::shared_ptr<InferenceEngine::IVariableStateInternal>>(1, mockIMemState_p)));
-    std::vector<InferenceEngine::IVariableStateInternal::Ptr> MemState_v;
-    EXPECT_NO_THROW(MemState_v = exeNetwork->QueryState());
-    EXPECT_EQ(MemState_v.size(), 1);
-}
-
-IE_SUPPRESS_DEPRECATED_END
 
 class ExecutableNetworkWithIInferReqTests : public ExecutableNetworkTests {
 protected:
@@ -135,6 +115,29 @@ TEST_F(ExecutableNetworkWithIInferReqTests, CreateInferRequestThrowsIfReturnNotO
     ASSERT_THROW(exeNetwork->CreateInferRequest(), InferenceEngine::Exception);
 }
 
+TEST_F(ExecutableNetworkWithIInferReqTests, QueryStateThrowsIfReturnErr) {
+    EXPECT_CALL(*mockIExeNet.get(), CreateInferRequest()).WillOnce(Return(mockIInferReq_p));
+    IInferRequestInternal::Ptr actualInferReq;
+    ASSERT_NO_THROW(actualInferReq = exeNetwork->CreateInferRequest());
+    EXPECT_CALL(*mockIInferReq_p.get(), QueryState())
+            .Times(1)
+            .WillOnce(Throw(InferenceEngine::GeneralError{""}));
+    EXPECT_THROW(actualInferReq->QueryState(), InferenceEngine::Exception);
+}
+
+TEST_F(ExecutableNetworkWithIInferReqTests, QueryState) {
+    EXPECT_CALL(*mockIExeNet.get(), CreateInferRequest()).WillOnce(Return(mockIInferReq_p));
+    IInferRequestInternal::Ptr actualInferReq;
+    ASSERT_NO_THROW(actualInferReq = exeNetwork->CreateInferRequest());
+    auto mockIMemState_p = std::make_shared<MockIVariableStateInternal>();
+    EXPECT_CALL(*mockIInferReq_p.get(), QueryState())
+            .Times(1)
+            .WillOnce(Return(std::vector<std::shared_ptr<InferenceEngine::IVariableStateInternal>>(1, mockIMemState_p)));
+    std::vector<InferenceEngine::IVariableStateInternal::Ptr> MemState_v;
+    EXPECT_NO_THROW(MemState_v = actualInferReq->QueryState());
+    EXPECT_EQ(MemState_v.size(), 1);
+}
+
 IE_SUPPRESS_DEPRECATED_START
 
 class ExecutableNetworkBaseTests : public ::testing::Test {
diff --git a/inference-engine/tests/unit/vpu/base/graph_transformer_tests.cpp b/inference-engine/tests/unit/vpu/base/graph_transformer_tests.cpp
index 15cfffd0343a34..8f89c28a8e50f3 100644
--- a/inference-engine/tests/unit/vpu/base/graph_transformer_tests.cpp
+++ b/inference-engine/tests/unit/vpu/base/graph_transformer_tests.cpp
@@ -26,7 +26,7 @@
 #include <vpu/configuration/options/number_of_shaves.hpp>
 #include <vpu/configuration/options/number_of_cmx_slices.hpp>
 #include <vpu/configuration/options/throughput_streams.hpp>
-#include <vpu/configuration/options/ir_with_scales_directory.hpp>
+#include <vpu/configuration/options/vpu_scales_option.hpp>
 #include <vpu/configuration/options/tensor_strides.hpp>
 #include <vpu/configuration/options/ignore_unknown_layers.hpp>
 #include <vpu/configuration/options/force_pure_tensor_iterator.hpp>
@@ -414,7 +414,7 @@ PluginConfiguration createConfiguration() {
     configuration.registerOption<NumberOfSHAVEsOption>();
     configuration.registerOption<NumberOfCMXSlicesOption>();
     configuration.registerOption<ThroughputStreamsOption>();
-    configuration.registerOption<IRWithScalesDirectoryOption>();
+    configuration.registerOption<VPUScalesOption>();
     configuration.registerOption<TensorStridesOption>();
     configuration.registerOption<IgnoreUnknownLayersOption>();
     configuration.registerOption<ForcePureTensorIteratorOption>();
diff --git a/inference-engine/tests/unit/vpu/middleend_tests/passes_tests/vpu_scale_tests.cpp b/inference-engine/tests/unit/vpu/middleend_tests/passes_tests/vpu_scale_tests.cpp
new file mode 100644
index 00000000000000..5d3e42e5286086
--- /dev/null
+++ b/inference-engine/tests/unit/vpu/middleend_tests/passes_tests/vpu_scale_tests.cpp
@@ -0,0 +1,162 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "graph_transformer_tests.hpp"
+#include <vpu/stages/mx_stage.hpp>
+#include <vpu/middleend/hw/utility.hpp>
+#include "ngraph_functions/subgraph_builders.hpp"
+#include "vpu/private_plugin_config.hpp"
+#include "common_test_utils/common_utils.hpp"
+
+#if defined(__GNUC__) && (__GNUC__ <= 4) && (__GNUC_MINOR__ < 9) && !defined(__clang__) && !defined(IE_GCC_4_8)
+#define IE_GCC_4_8
+#endif
+
+using namespace vpu;
+IE_SUPPRESS_DEPRECATED_START
+namespace LayerTestsDefinitions {
+typedef std::tuple<
+    std::string
+> VpuScaleParams;
+
+class VpuScaleTest : public testing::WithParamInterface<VpuScaleParams>,
+                     public GraphTransformerTest {
+protected:
+    void SetUp() override;
+    void Compile() {
+        m_pipeline.run(m_testModel);
+    }
+
+protected:
+    std::string configValue = {};
+    Model m_testModel;
+
+private:
+    void InitModel() {
+        int kernelx = 16;
+        int kernely = 1;
+        int kernelStrideX = 1;
+        int kernelStrideY = 1;
+        int dilationX = 1;
+        int dilationY = 1;
+        int padx_begin = 7;
+        int pady_begin = 0;
+        int padx_end = 8;
+        int pady_end = 0;
+        m_testModel = CreateModel();
+        int inputX = 32;
+        int inputY = 32;
+
+        auto input = m_testModel->addInputData(
+            "Input",
+            DataDesc(DataType::FP16, DimsOrder::NCHW, {inputX, inputY, 8, 1}));
+        m_testModel->attrs().set<int>("numInputs", 1);
+
+        Data output;
+
+        output = m_testModel->addOutputData(
+            "Output",
+            DataDesc(
+                DataType::FP16, DimsOrder::NCHW,
+                {(inputX + padx_begin + padx_end - kernelx) / kernelStrideX + 1,
+                 (inputY + pady_begin + pady_end - kernely) / kernelStrideY + 1,
+                 8, 1}));
+
+        auto conv = std::make_shared<ie::ConvolutionLayer>(ie::LayerParams{"conv1", "Convolution", ie::Precision::FP16});
+        conv->_kernel_x = kernelx;
+        conv->_kernel_y = kernely;
+        conv->_stride_x = kernelStrideX;
+        conv->_stride_y = kernelStrideY;
+        conv->_dilation_x = dilationX;
+        conv->_dilation_x = dilationY;
+
+        conv->_padding.insert(0, padx_begin);
+        conv->_padding.insert(1, pady_begin);
+        conv->_pads_end.insert(0, padx_end);
+        conv->_pads_end.insert(1, pady_end);
+        conv->_auto_pad = "same_upper";
+
+        conv->_weights = ie::make_shared_blob<short>({ ie::Precision::FP16, {static_cast<size_t>(kernelx * kernely * 8 * 8)}, ie::Layout::C });
+        conv->_weights->allocate();
+
+        frontEnd->parseConvolution(m_testModel, conv, {input}, {output});
+        Data output2;
+
+        output2 = m_testModel->addOutputData(
+            "Output",
+            DataDesc(
+                DataType::FP16, DimsOrder::NCHW,
+                {(inputX + padx_begin + padx_end - kernelx) / kernelStrideX + 1,
+                 (inputY + pady_begin + pady_end - kernely) / kernelStrideY + 1,
+                 8, 1}));
+        auto conv2 = std::make_shared<ie::ConvolutionLayer>(ie::LayerParams{"conv2", "Convolution", ie::Precision::FP16});
+        conv2->_kernel_x = kernelx;
+        conv2->_kernel_y = kernely;
+        conv2->_stride_x = kernelStrideX;
+        conv2->_stride_y = kernelStrideY;
+        conv2->_dilation_x = dilationX;
+        conv2->_dilation_x = dilationY;
+
+        conv2->_padding.insert(0, padx_begin);
+        conv2->_padding.insert(1, pady_begin);
+        conv2->_pads_end.insert(0, padx_end);
+        conv2->_pads_end.insert(1, pady_end);
+        conv2->_auto_pad = "same_upper";
+
+        conv2->_weights = ie::make_shared_blob<short>({ ie::Precision::FP16, {static_cast<size_t>(kernelx * kernely * 8 * 8)}, ie::Layout::C });
+        conv2->_weights->allocate();
+
+        frontEnd->parseConvolution(m_testModel, conv2, {output}, {output2});
+    }
+
+    void InitPipeline() {
+        m_pipeline = PassSet();
+        m_pipeline.addPass(passManager->analyzeWeightableLayers());
+    }
+    PassSet m_pipeline;
+};
+
+void VpuScaleTest::SetUp() {
+    ASSERT_NO_FATAL_FAILURE(GraphTransformerTest::SetUp());
+    config.set(InferenceEngine::MYRIAD_SCALES_PATTERN, configValue);
+    ASSERT_NO_FATAL_FAILURE(InitCompileEnv());
+    ASSERT_NO_FATAL_FAILURE(InitPipeline());
+    ASSERT_NO_FATAL_FAILURE(InitModel());
+}
+
+TEST_F(VpuScaleTest, IsScaleWorkCorrectly) {
+    #ifdef IE_GCC_4_8
+        GTEST_SKIP();
+    #endif
+    configValue = "conv1:0.2; conv2:1.4";
+    SetUp();
+    ASSERT_NO_THROW(Compile());
+    for (const auto& stage : m_testModel->getStages()) {
+        auto scale = stage->attrs().getOrDefault<float>("scaleFactor");
+        if (stage->name() == "conv1") {
+            ASSERT_FLOAT_EQ(scale, 0.2);
+            continue;
+        }
+        if (stage->name() == "conv2") {
+            ASSERT_FLOAT_EQ(scale, 1.4);
+        }
+    }
+}
+
+TEST_F(VpuScaleTest, IsRegexInScaleWorksCorrectly) {
+    #ifdef IE_GCC_4_8
+        GTEST_SKIP();
+    #endif
+    configValue = "conv1:0.2";
+    SetUp();
+    ASSERT_NO_THROW(Compile());
+    for (const auto& stage : m_testModel->getStages()) {
+        auto scale = stage->attrs().getOrDefault<float>("scaleFactor");
+        if (stage->name() == "conv1") {
+            ASSERT_FLOAT_EQ(scale, 0.2);
+            continue;
+        }
+    }
+}
+} // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt b/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt
index e48b259815533c..b66da71d4bdab8 100644
--- a/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt
@@ -18,6 +18,7 @@ target_include_directories(${TARGET_NAME} PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/pl
 
 target_link_libraries(${TARGET_NAME} PUBLIC
         funcTestUtils
+        inference_engine_legacy
         ieTestHelpers
         )
 
diff --git a/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt b/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt
index a2956a887dd220..72c1b13436bdf6 100644
--- a/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt
@@ -57,7 +57,7 @@ function(enable_vpu TARGET_NAME FLAG_NAME PLUGIN_NAME)
         target_include_directories(${TARGET_NAME} PRIVATE
                 $<TARGET_PROPERTY:vpu_graph_transformer,INTERFACE_INCLUDE_DIRECTORIES>
                 ${CMAKE_CURRENT_SOURCE_DIR}/myriad_tests
-                ${IE_MAIN_SOURCE_DIR}/src/inference_engine/src
+                $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src
                 ${XLINK_INCLUDE}
                 ${XLINK_PLATFORM_INCLUDE})
         target_link_libraries(${TARGET_NAME} PRIVATE mvnc)
diff --git a/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt b/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt
index fcda30314227c1..d8a66f328305a9 100644
--- a/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt
@@ -20,8 +20,7 @@ if(ENABLE_HETERO)
 endif()
 
 if(ENABLE_MKL_DNN)
-    list(APPEND DEPENDENCIES
-        MKLDNNPlugin)
+    list(APPEND DEPENDENCIES MKLDNNPlugin)
 endif()
 
 add_executable(${TARGET_NAME} ${TEST_SRC} ${TEST_INCLUDE})
diff --git a/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt b/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt
index 50f53518083b50..c6fa691c53d3ae 100644
--- a/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt
@@ -75,3 +75,7 @@ addIeTargetTest(
         VPU
         MYRIAD
     )
+# because vpu_graph_transformer is compiled with LTO
+if(CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 9.0)
+    set_target_properties(VPUCommonTests PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
+endif()
diff --git a/inference-engine/tests_deprecated/functional/vpu/graph_transformer/gt_functional_tests.cpp b/inference-engine/tests_deprecated/functional/vpu/graph_transformer/gt_functional_tests.cpp
index b6b82f72eb25fa..84029dcd48868e 100644
--- a/inference-engine/tests_deprecated/functional/vpu/graph_transformer/gt_functional_tests.cpp
+++ b/inference-engine/tests_deprecated/functional/vpu/graph_transformer/gt_functional_tests.cpp
@@ -26,7 +26,7 @@
 #include <vpu/configuration/options/number_of_shaves.hpp>
 #include <vpu/configuration/options/number_of_cmx_slices.hpp>
 #include <vpu/configuration/options/throughput_streams.hpp>
-#include <vpu/configuration/options/ir_with_scales_directory.hpp>
+#include <vpu/configuration/options/vpu_scales_option.hpp>
 #include <vpu/configuration/options/tensor_strides.hpp>
 #include <vpu/configuration/options/ignore_unknown_layers.hpp>
 #include <vpu/configuration/options/force_pure_tensor_iterator.hpp>
@@ -108,7 +108,7 @@ void graphTransformerFunctionalTests::PrepareGraphCompilation() {
     _configuration.registerOption<NumberOfSHAVEsOption>();
     _configuration.registerOption<NumberOfCMXSlicesOption>();
     _configuration.registerOption<ThroughputStreamsOption>();
-    _configuration.registerOption<IRWithScalesDirectoryOption>();
+    _configuration.registerOption<VPUScalesOption>();
     _configuration.registerOption<TensorStridesOption>();
     _configuration.registerOption<IgnoreUnknownLayersOption>();
     _configuration.registerOption<ForcePureTensorIteratorOption>();
diff --git a/inference-engine/tests_deprecated/unit/CMakeLists.txt b/inference-engine/tests_deprecated/unit/CMakeLists.txt
index 5f109be495a110..415563ed2d8568 100644
--- a/inference-engine/tests_deprecated/unit/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/unit/CMakeLists.txt
@@ -18,7 +18,8 @@ file(GLOB
         inference_engine_tests/*.cpp
         )
 
-if (ENABLE_GNA)
+# TODO: fix CVS-71010 and remove BUILD_SHARED_LIBS
+if (ENABLE_GNA AND BUILD_SHARED_LIBS)
     file(GLOB
             GNA_TESTS
             engines/gna/*cpp
@@ -82,7 +83,7 @@ if (ENABLE_MYRIAD)
             )
     include_directories(
             engines/vpu/myriad_tests/helpers
-            ${IE_MAIN_SOURCE_DIR}/src/vpu/myriad_plugin
+            $<TARGET_PROPERTY:myriadPlugin,SOURCE_DIR>
             ${XLINK_INCLUDE}
             ${XLINK_PLATFORM_INCLUDE})
 
@@ -142,4 +143,11 @@ install(TARGETS ${TARGET_NAME}
         COMPONENT tests
         EXCLUDE_FROM_ALL)
 
+if(TARGET libGNAStubs)
+    install(TARGETS libGNAStubs
+            RUNTIME DESTINATION tests
+            COMPONENT tests
+            EXCLUDE_FROM_ALL)
+endif()
+
 add_dependencies(${TARGET_NAME} mock_engine)
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp b/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp
index 3570511851511d..8166d0be1e5802 100644
--- a/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp
+++ b/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp
@@ -41,22 +41,6 @@ static std::string getTestName(testing::TestParamInfo<FP32TestParams> obj) {
     return  "channels_" + std::to_string(obj.param.nChannels) + "_" + (obj.param.eltwise_type == FP32TestParams::eSumm ? "summ" : "mull");
 }
 
-
-TEST_P(GNAFP32ParametricTest, SplitFollowedByEltwiseMulOnAllignedCPU) {
-    auto c = GetParam().nChannels;
-    auto isMull = GetParam().eltwise_type == FP32TestParams::eMul;
-    std::vector<float> input_data1(c, 3.0);
-    std::vector<float> input_data2(c, 2.0);
-    std::vector<float> input_data;
-    input_data.insert(input_data.end(), input_data1.begin(), input_data1.end());
-    input_data.insert(input_data.end(), input_data2.begin(), input_data2.end());
-
-    std::vector<float> expected_result(c, isMull ? 6.0 : 5.0);
-    assert_that().onInferModel(EltwiseAfterSplitModel(c, isMull))
-        .inNotCompactMode().gna().propagate_forward().onCPU()
-        .called_with().input("input_1", input_data).equals_to(expected_result);
-}
-
 FP32TestParams gna_fp32_test_params[] = {
     {7, FP32TestParams::eMul},
     {7, FP32TestParams::eSumm},
@@ -106,49 +90,6 @@ TEST_F(FP32NonQuantizedTest, SliceFollowedBy2FCsAnd2EltwisesOnCPU) {
         .called_with_input_and_expected_output(input_data, expected_result);
 }
 
-TEST_F(FP32NonQuantizedTest, SplitAfterFCFollowedByFCAndEltwiseOnCPU) {
-    std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-                                     1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
-    std::vector<float> expected_result = {232.0, 232.0, 232.0, 232.0, 232.0,
-                                          232.0, 232.0, 232.0, 232.0, 232.0};
-    assert_that().onInferModel(FCBeforeSplitModel())
-        .inNotCompactMode().gna().propagate_forward().onCPU()
-        .called_with_input_and_expected_output(input_data, expected_result);
-}
-
-
-TEST_F(FP32NonQuantizedTest, ConcatPropagateForwardWithSuccessOnCPU) {
-    std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-                                     1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
-    std::vector<float> expected_result = {121.0, 121.0, 121.0, 121.0, 121.0,
-                                          121.0, 121.0, 121.0, 121.0, 121.0,
-                                          121.0, 121.0, 121.0, 121.0, 121.0,
-                                          121.0, 121.0, 121.0, 121.0, 121.0};
-
-    assert_that().onInferModel(concatModel())
-        .inNotCompactMode().gna().propagate_forward().onCPU()
-        .called_with_input_and_expected_output(input_data, expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, DoubleConcatPropagateForwardWithSuccessOnCPU) {
-    std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-                                     1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-                                     1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-                                     1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
-    std::vector<float> expected_result = {141.0, 141.0, 141.0, 141.0, 141.0,
-                                          141.0, 141.0, 141.0, 141.0, 141.0,
-                                          141.0, 141.0, 141.0, 141.0, 141.0,
-                                          141.0, 141.0, 141.0, 141.0, 141.0,
-                                          141.0, 141.0, 141.0, 141.0, 141.0,
-                                          141.0, 141.0, 141.0, 141.0, 141.0,
-                                          141.0, 141.0, 141.0, 141.0, 141.0,
-                                          141.0, 141.0, 141.0, 141.0, 141.0};
-
-    assert_that().onInferModel(doubleConcatModel())
-        .inNotCompactMode().gna().propagate_forward().onCPU()
-        .called_with_input_and_expected_output(input_data, expected_result);
-}
-
 TEST_F(FP32NonQuantizedTest, multiple_inputs_correct_results) {
     std::vector<float> input_data  = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
     std::vector<float> input2_data = {2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0};
@@ -301,19 +242,6 @@ TEST_F(FP32NonQuantizedTest, ScaleShiftWithBroadcastSupported) {
         .called_with_input_and_expected_output(input_data, expected_result);
 }
 
-TEST_F(FP32NonQuantizedTest, ConcatWithConstInputPropagatedForward) {
-    std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
-
-    std::vector<float> expected_result = {121.0, 121.0, 121.0, 121.0, 121.0,
-                                          121.0, 121.0, 121.0, 121.0, 121.0,
-                                          121.0, 121.0, 121.0, 121.0, 121.0,
-                                          121.0, 121.0, 121.0, 121.0, 121.0};
-
-    assert_that().onInferModel(concatModelWithConstLayer())
-        .inNotCompactMode().gna().propagate_forward().onCPU()
-        .called_with_input(input_data).equals_to(expected_result);
-}
-
 TEST_F(FP32NonQuantizedTest, InputSplitConcatPropagateForward) {
     std::vector<float> input_data(64, 1.0f);
     std::vector<float> expected_result(10, 64.f);
@@ -411,33 +339,6 @@ TEST_F(FP32NonQuantizedTest, TI2PropagateForward) {
             .called_with_input(input_data).equals_to(expected_result1).equals_to(expected_result2);
 }
 
-TEST_F(FP32NonQuantizedTest, EltwiseWithConstInputPropagatedForward) {
-    std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
-    std::vector<float> expected_result = {2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0};
-
-    assert_that().onInferModel(eltwiseSumModelWithConstLayer())
-        .inNotCompactMode().gna().propagate_forward().onCPU()
-        .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, EltwiseWithConstInputReorderPropagatedForward) {
-    std::vector<float> input_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
-    std::vector<float> expected_result = {2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0};
-
-    assert_that().onInferModel(eltwiseSumModelWithConstLayer2())
-        .inNotCompactMode().gna().propagate_forward().onCPU()
-        .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, EltwiseMulWithConstInputReorderPropagatedForward) {
-    std::vector<float> input_data = {3.0, 1.0, 3.0, 1.0, 3.0, 1.0, 3.0, 1.0, 3.0, 1.0};
-    std::vector<float> expected_result = {6.0, 2.0, 6.0, 2.0, 6.0, 2.0, 6.0, 2.0, 6.0, 2.0};
-
-    assert_that().onInferModel(eltwiseMulModelWithConstLayer())
-        .inNotCompactMode().withWeigthsPattern({2}).gna().propagate_forward().onCPU()
-        .called_with_input(input_data).equals_to(expected_result);
-}
-
 TEST_F(FP32NonQuantizedTest, PowerWithScaleFactorPropagateForward) {
     std::vector<float> input_data(10, 2.f);
     std::vector<float> expected_result(12, 21.f);
@@ -723,4 +624,3 @@ TEST_F(FP32NonQuantizedTest, ReshapeConvolutionLessThan48Filters) {
             .called_with_input(input_data)
             .equals_to(expected_result);
 }
-
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/gna_matcher.cpp b/inference-engine/tests_deprecated/unit/engines/gna/gna_matcher.cpp
index c0a5719783dda6..15cf4951c586a0 100644
--- a/inference-engine/tests_deprecated/unit/engines/gna/gna_matcher.cpp
+++ b/inference-engine/tests_deprecated/unit/engines/gna/gna_matcher.cpp
@@ -798,30 +798,6 @@ void GNAQueryStateMatcher :: match() {
 
     EXPECT_CALL(mockApi, Gna2InstrumentationConfigAssignToRequestConfig(_,_)).Times(AtLeast(1)).WillRepeatedly(Return(Gna2StatusSuccess));
 #endif
-    IE_SUPPRESS_DEPRECATED_START
-    try {
-        loadNetwork();
-        if (GnaPluginTestEnvironment::kAnyNotNull == _env.numberOfStates) {
-            auto states = executer->QueryState();
-            ASSERT_NE(states.size(), 0);
-            // usually states are callable
-            for (auto & state : states) {
-                state->Reset();
-            }
-        } else if (_env.numberOfStates >= 0) {
-            ASSERT_EQ(executer->QueryState().size(), _env.numberOfStates);
-        } else {
-            FAIL() << "number of memory states expectation not set";
-        }
-
-    }
-    catch(std::exception &ex) {
-        FAIL() << ex.what();
-    }
-    catch(...) {
-        FAIL() << "unknown exception thrown";
-    }
-    IE_SUPPRESS_DEPRECATED_END
 }
 
 
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/gna_query_state_tests.cpp b/inference-engine/tests_deprecated/unit/engines/gna/gna_query_state_tests.cpp
deleted file mode 100644
index 7ead4aa815336d..00000000000000
--- a/inference-engine/tests_deprecated/unit/engines/gna/gna_query_state_tests.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <vector>
-#include <gtest/gtest.h>
-#include "gna_matcher.hpp"
-
-class QueryStateTest : public GNATest<> {
- protected:
-    void SetUp() override  {
-    }
-};
-using namespace GNATestIRs;
-
-// Recursive Algorithm
-// Precision Threshold
-
-TEST_F(QueryStateTest, returnEmptyCollectionOfStatesIfNoMemoryInIR) {
-    assert_that().afterLoadingModel(TanhActivationModel()).withGNAConfig(GNA_CONFIG_KEY(SCALE_FACTOR), 1.0f).queryState().isEmpty();
-}
-
-TEST_F(QueryStateTest, returnNonEmptyCollectionOfStatesForMemoryIR) {
-    assert_that().afterLoadingModel(affineToMemoryModel()).withGNAConfig(GNA_CONFIG_KEY(SCALE_FACTOR), 1.0f).queryState().isNotEmpty();
-}
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp b/inference-engine/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp
index 36c201d60f9eae..b8f32671fe943d 100644
--- a/inference-engine/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp
+++ b/inference-engine/tests_deprecated/unit/engines/gna/i16_quantisation_test.cpp
@@ -218,12 +218,6 @@ TEST_F(I16QuantisationTest, canDetectLeakyRelu) {
         .gna().propagate_forward().called_with().pwl_inserted_into_nnet();
 }
 
-TEST_F(I16QuantisationTest, canDetectSoftWSignSubgraph) {
-    assert_that().onInferModel(TFSoftsignUnfoldedModel())
-        .inNotCompactMode().withGNAConfig(GNA_CONFIG_KEY(SCALE_FACTOR), 1.0f)
-        .gna().propagate_forward().called_with().pwls_inserted_into_nnet({kActSigmoid});
-}
-
 TEST_F(I16QuantisationTest, MaxPool_followedAfterActivation) {
     assert_that().onInferModel(maxpoolAfterRelu())
         .inNotCompactMode().withGNAConfig(GNA_CONFIG_KEY(SCALE_FACTOR), 1.0f)
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/layers/activation_test.cpp b/inference-engine/tests_deprecated/unit/engines/gna/layers/activation_test.cpp
deleted file mode 100644
index 0d768f823a1c1c..00000000000000
--- a/inference-engine/tests_deprecated/unit/engines/gna/layers/activation_test.cpp
+++ /dev/null
@@ -1,97 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <vector>
-#include <gtest/gtest.h>
-#include <single_layer_common.hpp>
-#include <ngraph/op/parameter.hpp>
-#include <ngraph/ops.hpp>
-#include <ie_precision.hpp>
-#include <legacy/ngraph_ops/power.hpp>
-#include <debug.h>
-#include "../gna_matcher.hpp"
-
-typedef struct {
-    std::string activationType;
-    size_t input_shape;
-    std::pair<float, float> range;
-} ActivationCaseParam;
-
-using ActivationCaseParam2 = std::tuple<InferenceEngine::Precision, ActivationCaseParam>;
-
-class GNAActivationTest : public GNATest<>,
-                          public testing::WithParamInterface<ActivationCaseParam2> {
- public:
-
-    static std::string getTestName(const testing::TestParamInfo<ActivationCaseParam2>& params) {
-        std::string test_name = std::string(std::get<0>(params.param).name()) + "_";
-        test_name += std::get<1>(params.param).activationType + "_";
-        test_name += std::to_string(std::get<1>(params.param).input_shape) + "_";
-        test_name += std::to_string(std::get<1>(params.param).range.first) + "_";
-        test_name += std::to_string(std::get<1>(params.param).range.second);
-        return test_name;
-    }
-
-    std::shared_ptr<ngraph::Function> buildNgraphFunction(const ActivationCaseParam& param) {
-
-        auto shape = ngraph::Shape{1, param.input_shape};
-        auto inputN = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, shape);
-
-        auto absN = std::make_shared<ngraph::op::v0::Abs>(inputN);
-
-        auto powerN = std::make_shared<ngraph::op::PowerIE>(absN, -1, 1, 1.0);
-
-        auto eltwiseN = std::make_shared<ngraph::op::v1::Multiply>(powerN, inputN);
-
-        auto function = std::make_shared<ngraph::Function>(ngraph::NodeVector{eltwiseN}, ngraph::ParameterVector{inputN});
-        return function;
-    }
-};
-
-TEST_P(GNAActivationTest, ActivationTest) {
-    const auto precision = std::get<0>(GetParam());
-    const auto param = std::get<1>(GetParam());
-
-    if (precision == InferenceEngine::Precision::FP32) {
-        auto input_data = generate_random_1d<float>(param.input_shape, param.range.first, param.range.second);
-        std::vector<float> expected_result(param.input_shape);
-
-
-        for (std::size_t i = 0; i < expected_result.size(); i++) {
-            auto & x = input_data[i];
-            if (param.activationType == "softsign") {
-                expected_result[i] =  x  / (1 + fabs(x));
-            } else {
-                FAIL() << "Unsupported activation type: " << param.activationType;
-            }
-        }
-
-        assert_that().onInferNgraphModel(buildNgraphFunction(param))
-            .inNotCompactMode()
-            .gna()
-            .propagate_forward()
-            .onCPU()
-            .called_with_input(input_data)
-            .equals_to(expected_result);
-    } else {
-        assert_that().onInferNgraphModel(buildNgraphFunction(param))
-            .inNotCompactMode()
-            .gna()
-            .withGNAConfig(GNA_CONFIG_KEY(SCALE_FACTOR), 1.0f)
-            .withGNAConfig(GNA_CONFIG_KEY(PRECISION), precision.name())
-            .propagate_forward()
-            .called_with().pwls_inserted_into_nnet({kActSigmoid});
-    }
-}
-
-static const ActivationCaseParam gna_activation_test_params[] = {
-    {"softsign", 200, {-10, 10}},
-};
-
-INSTANTIATE_TEST_SUITE_P(
-    GNALayerTests, GNAActivationTest,
-    ::testing::Combine(
-        ::testing::Values(InferenceEngine::Precision::FP32, InferenceEngine::Precision::I16, InferenceEngine::Precision::I8),
-        ::testing::ValuesIn(gna_activation_test_params)),
-         GNAActivationTest::getTestName);
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/test_irs.cpp b/inference-engine/tests_deprecated/unit/engines/gna/test_irs.cpp
index bd41ec0118b4c6..8d20b04311f794 100644
--- a/inference-engine/tests_deprecated/unit/engines/gna/test_irs.cpp
+++ b/inference-engine/tests_deprecated/unit/engines/gna/test_irs.cpp
@@ -2164,146 +2164,6 @@ std::string TFLeakyReluModel() {
     )V0G0N";
 }
 
-std::string TFSoftsignUnfoldedModel() {
-    return R"V0G0N(
-<?xml version="1.0" ?>
-<net name="LSTM_statics_1000_1frame_test_unpack" version="7">
-<layers>
-    <layer id="0" name="input_batch" type="Input">
-        <output>
-            <port id="0" precision="FP32">
-                <dim>1</dim>
-                <dim>64</dim>
-            </port>
-        </output>
-    </layer>
-	<layer id="42" name="FC_layer0/packed/ExpandDims_/Dims/Output_0/Data__const" type="Const">
-		<output>
-			<port id="1" precision="I32">
-				<dim>1</dim>
-			</port>
-		</output>
-		<blobs>
-			<custom offset="0" precision="I32" size="4"/>
-		</blobs>
-	</layer>
-    <layer id="43" name="FC_layer0/packed/ExpandDims_131" type="Unsqueeze">
-            <input>
-                <port id="0">
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-                <port id="1">
-                    <dim>1</dim>
-                </port>
-            </input>
-            <output>
-                <port id="2" precision="FP32">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </output>
-        </layer>
-        <layer id="44" name="abs_FC_layer0/Softsign" type="Abs">
-            <input>
-                <port id="0">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </input>
-            <output>
-                <port id="1" precision="FP32">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </output>
-        </layer>
-        <layer id="45" name="FC_layer0/Softsign_plus_1/fused_power" type="Power">
-            <data power="-1.0" scale="1" shift="1.0"/>
-            <input>
-                <port id="0">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </input>
-            <output>
-                <port id="1" precision="FP32">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </output>
-        </layer>
-        <layer id="46" name="div_FC_layer0/Softsign/mul_" type="Eltwise">
-            <data operation="mul"/>
-            <input>
-                <port id="0">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-                <port id="1">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </input>
-            <output>
-                <port id="2" precision="FP32">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </output>
-        </layer>
-        <layer id="47" name="unstack_6/Squeeze_/value/Output_0/Data__const" type="Const">
-            <output>
-                <port id="1" precision="I32">
-                    <dim>1</dim>
-                </port>
-            </output>
-            <blobs>
-                <custom offset="4" precision="I32" size="4"/>
-            </blobs>
-        </layer>
-        <layer id="48" name="unstack_6/Squeeze_" type="Squeeze">
-            <input>
-                <port id="0">
-                    <dim>1</dim>
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-                <port id="1">
-                    <dim>1</dim>
-                </port>
-            </input>
-            <output>
-                <port id="2" precision="FP32">
-                    <dim>1</dim>
-                    <dim>64</dim>
-                </port>
-            </output>
-        </layer>
-    </layers>
-    <edges>
-	<edge from-layer="0" from-port="0" to-layer="43" to-port="0"/>
-	<edge from-layer="42" from-port="1" to-layer="43" to-port="1"/>
-	<edge from-layer="43" from-port="2" to-layer="44" to-port="0"/>
-	<edge from-layer="44" from-port="1" to-layer="45" to-port="0"/>
-	<edge from-layer="45" from-port="1" to-layer="46" to-port="0"/>
-	<edge from-layer="43" from-port="2" to-layer="46" to-port="1"/>
-	<edge from-layer="46" from-port="2" to-layer="48" to-port="0"/>
-    <edge from-layer="47" from-port="1" to-layer="48" to-port="1"/>
-    </edges>
-</net>
-
-  )V0G0N";
-}
-
 std::string maxpoolAfterRelu() {
     return R"V0G0N(
 <?xml version="1.0" ?>
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/test_irs.hpp b/inference-engine/tests_deprecated/unit/engines/gna/test_irs.hpp
index 929cc644fc4198..745c4dd426200b 100644
--- a/inference-engine/tests_deprecated/unit/engines/gna/test_irs.hpp
+++ b/inference-engine/tests_deprecated/unit/engines/gna/test_irs.hpp
@@ -39,7 +39,6 @@ std::string ClampActivationModel();
 std::string IdentityActivationModel();
 std::string maxpoolAfterRelu();
 std::string TFLeakyReluModel();
-std::string TFSoftsignUnfoldedModel();
 std::string cropWithoutOffsetModel();
 std::string cropWithAlignedOffsetModel();
 std::string cropWithOffsetModel();
diff --git a/inference-engine/tests_deprecated/unit/engines/vpu/get_vpu_scale_from_ir_tests.cpp b/inference-engine/tests_deprecated/unit/engines/vpu/get_vpu_scale_from_ir_tests.cpp
deleted file mode 100644
index 66f8493b563f47..00000000000000
--- a/inference-engine/tests_deprecated/unit/engines/vpu/get_vpu_scale_from_ir_tests.cpp
+++ /dev/null
@@ -1,128 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "graph_transformer_tests.hpp"
-#include "tests_vpu_common.hpp"
-
-#include <cpp/ie_executable_network.hpp>
-
-#include <ngraph/type/element_type.hpp>
-#include <ngraph/op/parameter.hpp>
-#include <ngraph/ops.hpp>
-#include <legacy/ngraph_ops/fully_connected.hpp>
-#include <ngraph/op/constant.hpp>
-#include <ngraph/opsets/opset1.hpp>
-#include <limits>
-
-using namespace vpu;
-using namespace InferenceEngine;
-
-using VPU_AddVpuScaleTest = GraphTransformerTest;
-
-TEST_F(VPU_AddVpuScaleTest, CanAddVpuScaleToNetwork) {
-    InitCompileEnv();
-
-    auto& env = CompileEnv::get();
-    auto config = createConfiguration();
-    config.set(InferenceEngine::MYRIAD_IR_WITH_SCALES_DIRECTORY, "/");
-    env.updateConfig(config);
-
-    std::shared_ptr<ngraph::Function> function;
-
-    {
-        auto input = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f16, ngraph::Shape{4, 2, 2});
-        input->set_friendly_name("input");
-        auto weights = ngraph::opset1::Constant::create(ngraph::element::f16, ngraph::Shape{2, 2}, {1});
-        auto bias = ngraph::opset1::Constant::create(ngraph::element::f16, ngraph::Shape{2}, {1});
-        auto fc = std::make_shared<ngraph::op::FullyConnected>(input, weights, bias, ngraph::Shape{4, 2, 2});
-        fc->set_friendly_name("FullyConnected");
-        auto result = std::make_shared<ngraph::op::Result>(fc);
-        ngraph::ResultVector results { result };
-        ngraph::ParameterVector params {input };
-        function = std::make_shared<ngraph::Function>(results, params);
-    }
-
-    auto network = InferenceEngine::CNNNetwork(function);
-    auto model = frontEnd->buildInitialModel(network);
-
-    const auto getFullyConnectedStage = [model]() -> Stage {
-        const auto isFullyConnected = [](const Stage& stage) {
-            const auto& layer = stage->origLayer();
-            return layer && layer->type == "FullyConnected";
-        };
-        const auto stages = model->getStages();
-        const auto stageIt = std::find_if(begin(stages), end(stages), isFullyConnected);
-        return *stageIt;
-    };
-
-    const auto fcStage = getFullyConnectedStage();
-    EXPECT_EQ(fcStage->origLayer()->params.find("vpu_scale"), fcStage->origLayer()->params.end());
-
-    auto middleEnd = passManager->buildMiddleEnd();
-    middleEnd->run(model);
-
-    const auto fcStageAfterMiddleEnd = getFullyConnectedStage();
-    EXPECT_NE(fcStageAfterMiddleEnd->origLayer()->params.find("vpu_scale"), fcStageAfterMiddleEnd->origLayer()->params.end());
-}
-
-TEST_F(VPU_AddVpuScaleTest, VpuScaleFromIrChangesWeights) {
-    InitCompileEnv();
-    const auto& env = CompileEnv::get();
-    auto config = createConfiguration();
-    config.set(InferenceEngine::MYRIAD_IR_WITH_SCALES_DIRECTORY, "/");
-    env.updateConfig(config);
-
-    std::shared_ptr<ngraph::Function> function;
-    {
-        ngraph::element::Type elementType = ngraph::element::Type_t::f16;
-        ngraph::Shape shape { 1, 1, 4, 5 };
-        auto input = std::make_shared<ngraph::op::Parameter>(elementType, shape);
-        input->set_friendly_name("input");
-
-        auto weights = std::make_shared<ngraph::op::Constant>(
-                elementType, ngraph::Shape{1, 1, 1, 1}, std::vector<float>(1, 1.0f));
-        auto conv = std::make_shared<ngraph::op::v1::Convolution>(
-                input, weights, ngraph::Strides {1, 1},
-                ngraph::CoordinateDiff{0, 0}, ngraph::CoordinateDiff{0, 0}, ngraph::Strides{1, 1});
-        conv->set_friendly_name("Convolution");
-        auto result = std::make_shared<ngraph::op::Result>(conv);
-
-        ngraph::ResultVector results { result };
-        ngraph::ParameterVector params { input };
-        function = std::make_shared<ngraph::Function>(results, params);
-    }
-
-    auto network = InferenceEngine::CNNNetwork(function);
-    auto model = frontEnd->buildInitialModel(network);
-
-    auto middleEnd = passManager->buildMiddleEnd();
-    auto checkWeightWasChanged = [this, &network](const float scale) {
-        auto model = frontEnd->buildInitialModel(network);
-        for (const auto& stage : model->getStages()) {
-            if (stage->name() == "Convolution") {
-                stage->origLayer()->params["vpu_scale"] = toString(scale);
-            }
-        }
-
-        auto middleEnd = passManager->buildMiddleEnd();
-        middleEnd->run(model);
-        for (const auto& stage : model->getStages()) {
-            if (stage->name() == "Convolution") {
-                auto content = stage->input(1)->content()->get<ie_fp16>();
-                if (scale < 0) {
-                    EXPECT_EQ(scale, PrecisionUtils::f16tof32(content[0]));
-                } else {
-                    EXPECT_EQ(scale, fabs(PrecisionUtils::f16tof32(content[0])));
-                }
-            }
-        }
-    };
-
-    const auto maxVal = std::numeric_limits<float>::infinity();
-
-    checkWeightWasChanged(32);
-    checkWeightWasChanged(64);
-    checkWeightWasChanged(maxVal);
-
-}
diff --git a/inference-engine/tests_deprecated/unit/engines/vpu/graph_transformer_tests.cpp b/inference-engine/tests_deprecated/unit/engines/vpu/graph_transformer_tests.cpp
index a75d0ba76217a7..4e46ccbb1b5b8d 100644
--- a/inference-engine/tests_deprecated/unit/engines/vpu/graph_transformer_tests.cpp
+++ b/inference-engine/tests_deprecated/unit/engines/vpu/graph_transformer_tests.cpp
@@ -28,7 +28,7 @@
 #include <vpu/configuration/options/number_of_shaves.hpp>
 #include <vpu/configuration/options/number_of_cmx_slices.hpp>
 #include <vpu/configuration/options/throughput_streams.hpp>
-#include <vpu/configuration/options/ir_with_scales_directory.hpp>
+#include <vpu/configuration/options/vpu_scales_option.hpp>
 #include <vpu/configuration/options/tensor_strides.hpp>
 #include <vpu/configuration/options/ignore_unknown_layers.hpp>
 #include <vpu/configuration/options/force_pure_tensor_iterator.hpp>
@@ -226,7 +226,7 @@ PluginConfiguration createConfiguration() {
     configuration.registerOption<NumberOfSHAVEsOption>();
     configuration.registerOption<NumberOfCMXSlicesOption>();
     configuration.registerOption<ThroughputStreamsOption>();
-    configuration.registerOption<IRWithScalesDirectoryOption>();
+    configuration.registerOption<VPUScalesOption>();
     configuration.registerOption<TensorStridesOption>();
     configuration.registerOption<IgnoreUnknownLayersOption>();
     configuration.registerOption<ForcePureTensorIteratorOption>();
diff --git a/inference-engine/thirdparty/CMakeLists.txt b/inference-engine/thirdparty/CMakeLists.txt
index 4aea987153459d..10d9e495254c97 100644
--- a/inference-engine/thirdparty/CMakeLists.txt
+++ b/inference-engine/thirdparty/CMakeLists.txt
@@ -24,9 +24,12 @@ if (ENABLE_CLDNN)
         set(CLDNN__INCLUDE_TESTS OFF CACHE BOOL "" FORCE)
     endif()
     add_subdirectory(clDNN)
+    ov_install_static_lib(clDNN_lib gpu)
 endif()
 
 function(ie_add_mkldnn)
+    set(DNNL_ENABLE_JIT_PROFILING ${BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
+    set(DNNL_ENABLE_ITT_TASKS ${BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
     set(DNNL_ENABLE_CONCURRENT_EXEC ON CACHE BOOL "" FORCE)
     set(DNNL_ENABLE_PRIMITIVE_CACHE OFF CACHE BOOL "" FORCE) ## TODO: try it later
     set(DNNL_ENABLE_MAX_CPU_ISA OFF CACHE BOOL "" FORCE)     ## TODO: try it later
@@ -53,6 +56,7 @@ function(ie_add_mkldnn)
 
     add_subdirectory(mkl-dnn EXCLUDE_FROM_ALL)
     add_library(mkldnn ALIAS dnnl)
+    ov_install_static_lib(dnnl cpu)
 endfunction()
 
 if(ENABLE_MKL_DNN)
diff --git a/inference-engine/thirdparty/clDNN/CMakeLists.txt b/inference-engine/thirdparty/clDNN/CMakeLists.txt
index 37b824c0698d95..18270b771a5fcb 100644
--- a/inference-engine/thirdparty/clDNN/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/CMakeLists.txt
@@ -12,14 +12,6 @@ project("${CLDNN__PROJ_NAME}")
 # ====================================== HELPER CONSTANT VARIABLES =====================================
 # ======================================================================================================
 # ======================================================================================================
-if (ENABLE_ONEDNN_FOR_GPU)
-    ExternalProject_Get_property(onednn_gpu_build SOURCE_DIR)
-    ExternalProject_Get_property(onednn_gpu_build BINARY_DIR)
-
-    set(ONEDNN_TARGET_NAME onednn_gpu)
-    set(ONEDNN_INCLUDE_DIRS "${BINARY_DIR}/include/" "${SOURCE_DIR}/include/")
-    set(ONEDNN_LIBRARY "${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/${CMAKE_STATIC_LIBRARY_PREFIX}onednn_gpu${CMAKE_STATIC_LIBRARY_SUFFIX}")
-endif()
 
 if(ENABLE_GPU_DEBUG_CAPS)
   add_definitions(-DGPU_DEBUG_CONFIG=1)
diff --git a/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device.hpp b/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device.hpp
index 7f5e1f13d7d1e7..d552b74ce61dd3 100644
--- a/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device.hpp
+++ b/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device.hpp
@@ -18,6 +18,8 @@ struct device {
     virtual device_info get_info() const = 0;
     virtual memory_capabilities get_mem_caps() const = 0;
 
+    virtual bool is_same(const device::ptr other) = 0;
+
     virtual ~device() = default;
 };
 
diff --git a/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device_query.hpp b/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device_query.hpp
index 4ff170b5ae242c..fc3df799a3cf92 100644
--- a/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device_query.hpp
+++ b/inference-engine/thirdparty/clDNN/api/cldnn/runtime/device_query.hpp
@@ -20,6 +20,7 @@ struct device_query {
                           runtime_types runtime_type,
                           void* user_context = nullptr,
                           void* user_device = nullptr,
+                          int ctx_device_id = 0,
                           int target_tile_id = -1);
 
     std::map<std::string, device::ptr> get_available_devices() const {
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt b/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt
index 6c47bcec69d0a5..eeebbe2d9c7b02 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt
@@ -168,5 +168,6 @@ add_custom_command(
     TARGET "${CLDNN_BUILD__PROJ}" POST_BUILD
     COMMAND "${CMAKE_COMMAND}" -E copy_if_different ${__CLDNN_Directory__core}/cache/cache.json ${CLDNN_CACHE_PATH}/cache.json)
 
+ov_install_static_lib("${CLDNN_BUILD__PROJ}" gpu)
 
 # ======================================================================================================
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.cpp
index 2bed9a76962a74..bed25eb288a72b 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/border/border_kernel_ref.cpp
@@ -23,12 +23,16 @@ ParamsKey BorderKernelRef::GetSupportedKey() const {
     k.EnableInputLayout(DataLayout::byxf);
     k.EnableInputLayout(DataLayout::bfzyx);
     k.EnableInputLayout(DataLayout::bfwzyx);
+    k.EnableInputLayout(DataLayout::b_fs_yx_fsv16);
+    k.EnableInputLayout(DataLayout::b_fs_zyx_fsv16);
 
     k.EnableOutputLayout(DataLayout::bfyx);
     k.EnableOutputLayout(DataLayout::yxfb);
     k.EnableOutputLayout(DataLayout::byxf);
     k.EnableOutputLayout(DataLayout::bfzyx);
     k.EnableOutputLayout(DataLayout::bfwzyx);
+    k.EnableOutputLayout(DataLayout::b_fs_yx_fsv16);
+    k.EnableOutputLayout(DataLayout::b_fs_zyx_fsv16);
 
     k.EnableTensorOffset();
     k.EnableTensorPitches();
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/border_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/border_gpu_ref.cl
index 38234b49b5fbd4..3bc1f018f1fb41 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/border_gpu_ref.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/border_gpu_ref.cl
@@ -5,10 +5,35 @@
 #include "include/batch_headers/data_types.cl"
 #include "include/batch_headers/fetch_data.cl"
 
+inline uint FUNC(get_input_index)(uint b, uint f, uint w, uint z, uint y, uint x)
+{
+#if INPUT0_DIMS < 5
+    return INPUT0_GET_INDEX(b, f, y, x);
+#elif INPUT0_DIMS == 5
+    return INPUT0_GET_INDEX(b, f, z, y, x);
+#elif INPUT0_DIMS == 6
+    return INPUT0_GET_INDEX(b, f, w, z, y, x);
+#else
+#error [clDNN border_gpu_ref.cl]: input format - not supported
+#endif
+}
+
+inline uint FUNC(get_output_index)(uint b, uint f, uint w, uint z, uint y, uint x)
+{
+#if OUTPUT_DIMS < 5
+    return OUTPUT_GET_INDEX(b, f, y, x);
+#elif OUTPUT_DIMS == 5
+    return OUTPUT_GET_INDEX(b, f, z, y, x);
+#elif OUTPUT_DIMS == 6
+    return OUTPUT_GET_INDEX(b, f, w, z, y, x);
+#else
+#error [clDNN border_gpu_ref.cl]: output format - not supported
+#endif
+}
 
 KERNEL(border_gpu_ref)(
-    const __global UNIT_TYPE* input,
-    __global UNIT_TYPE* output)
+    const __global INPUT0_TYPE* input,
+    __global OUTPUT_TYPE* output)
 {
     // [CONSTEXPR]
     // Border sizes (left-top set and right-bottom set):
@@ -72,7 +97,7 @@ KERNEL(border_gpu_ref)(
     const uint out_w  = out_yw / OUTPUT_SIZE_Y;
 
 #ifdef BORDER_TYPE_CONSTANT
-    UNIT_TYPE in_val = TO_UNIT_TYPE(BORDER_VALUE);
+    INPUT0_TYPE in_val = TO_INPUT0_TYPE(BORDER_VALUE);
 
     if (out_x >= blt_sx & out_x < in_lx &
         out_y >= blt_sy & out_y < in_ly &
@@ -88,7 +113,7 @@ KERNEL(border_gpu_ref)(
         const uint in_f = out_f - blt_sf;
         const uint in_b = out_b - blt_sb;
 
-        const uint in_pos = GET_DATA_INDEX_6D(INPUT0, in_b, in_f, in_w, in_z, in_y, in_x);
+        const uint in_pos = FUNC_CALL(get_input_index)(in_b, in_f, in_w, in_z, in_y, in_x);
         in_val = input[in_pos];
     }
 #elif defined BORDER_TYPE_EDGE
@@ -99,8 +124,8 @@ KERNEL(border_gpu_ref)(
     const uint in_f = (out_f >= blt_sf & out_f < in_lf) ? out_f - blt_sf : (out_f < blt_sf ? 0 : in_sf - 1);
     const uint in_b = (out_b >= blt_sb & out_b < in_lb) ? out_b - blt_sb : (out_b < blt_sb ? 0 : in_sb - 1);
 
-    const uint in_pos = GET_DATA_INDEX_6D(INPUT0, in_b, in_f, in_w, in_z, in_y, in_x);
-    UNIT_TYPE in_val = input[in_pos];
+    const uint in_pos = FUNC_CALL(get_input_index)(in_b, in_f, in_w, in_z, in_y, in_x);
+    INPUT0_TYPE in_val = input[in_pos];
 #elif defined BORDER_TYPE_MIRROR
     const uint in_x = (out_x >= blt_sx & out_x < in_lx) ? out_x - blt_sx : (out_x < blt_sx ? blt_sx - 1 - out_x : in_sx + in_lx - 1 - out_x);
     const uint in_y = (out_y >= blt_sy & out_y < in_ly) ? out_y - blt_sy : (out_y < blt_sy ? blt_sy - 1 - out_y : in_sy + in_ly - 1 - out_y);
@@ -109,8 +134,8 @@ KERNEL(border_gpu_ref)(
     const uint in_f = (out_f >= blt_sf & out_f < in_lf) ? out_f - blt_sf : (out_f < blt_sf ? blt_sf - 1 - out_f : in_sf + in_lf - 1 - out_f);
     const uint in_b = (out_b >= blt_sb & out_b < in_lb) ? out_b - blt_sb : (out_b < blt_sb ? blt_sb - 1 - out_b : in_sb + in_lb - 1 - out_b);
 
-    const uint in_pos = GET_DATA_INDEX_6D(INPUT0, in_b, in_f, in_w, in_z, in_y, in_x);
-    UNIT_TYPE in_val = input[in_pos];
+    const uint in_pos = FUNC_CALL(get_input_index)(in_b, in_f, in_w, in_z, in_y, in_x);
+    INPUT0_TYPE in_val = input[in_pos];
 #elif defined BORDER_TYPE_MIRROR_101
     const uint in_x = (out_x >= blt_sx & out_x < in_lx) ? out_x - blt_sx : (out_x < blt_sx ? blt_sx - out_x : in_sx + in_lx - 2 - out_x);
     const uint in_y = (out_y >= blt_sy & out_y < in_ly) ? out_y - blt_sy : (out_y < blt_sy ? blt_sy - out_y : in_sy + in_ly - 2 - out_y);
@@ -119,12 +144,12 @@ KERNEL(border_gpu_ref)(
     const uint in_f = (out_f >= blt_sf & out_f < in_lf) ? out_f - blt_sf : (out_f < blt_sf ? blt_sf - out_f : in_sf + in_lf - 2 - out_f);
     const uint in_b = (out_b >= blt_sb & out_b < in_lb) ? out_b - blt_sb : (out_b < blt_sb ? blt_sb - out_b : in_sb + in_lb - 2 - out_b);
 
-    const uint in_pos = GET_DATA_INDEX_6D(INPUT0, in_b, in_f, in_w, in_z, in_y, in_x);
-    UNIT_TYPE in_val = input[in_pos];
+    const uint in_pos = FUNC_CALL(get_input_index)(in_b, in_f, in_w, in_z, in_y, in_x);
+    INPUT0_TYPE in_val = input[in_pos];
 #else
     #error Unsupported border type.
 #endif
 
-    const uint out_pos = GET_DATA_INDEX_6D(OUTPUT, out_b, out_f, out_w, out_z, out_y, out_x);
+    const uint out_pos = FUNC_CALL(get_output_index)(out_b, out_f, out_w, out_z, out_y, out_x);
     output[out_pos] = in_val;
 }
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl
index 3b55a1a401c72a..ca8342d122507a 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/pooling_gpu_b_fs_zyx_fsv16_imad.cl
@@ -126,26 +126,45 @@ KERNEL(pooling_gpu_b_fs_zyx_fsv16)(
         ACTIVATION_VEC16 pool_result;
 #if defined AVG_POOLING
 #if INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE != 0
-        __attribute__((opencl_unroll_hint(INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE)))
-        for (uint i = 0; i < INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE; i++) {
+        if (last_in_f_group) {
+            __attribute__((opencl_unroll_hint(INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE)))
+            for (uint i = 0; i < INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE; i++) {
+#if ENABLE_ROUND
+#if defined(DYNAMIC_KERNEL_DIVIDER) || defined(DYNAMIC_WITH_PADDING_KERNEL_DIVIDER)
+                pool_result[i] = convert_int(round(((float)result[i] / max(num_elements, (uint)1))));
 #else
-        __attribute__((opencl_unroll_hint(FEATURE_SLICE_SIZE)))
-        for (uint i = 0; i < FEATURE_SLICE_SIZE; i++) {
+                pool_result[i] = convert_int(round((float)result[i] / (int)(POOL_SIZE_X * INPUT0_SIZE_Z * INPUT0_SIZE_Y)));
+#endif
+#else // ENABLE_ROUND
+#if defined(DYNAMIC_KERNEL_DIVIDER) || defined(DYNAMIC_WITH_PADDING_KERNEL_DIVIDER)
+                pool_result[i] = (float)result[i] / max(num_elements, (uint)1);
+#else
+                pool_result[i] = (float)result[i] / (int)(POOL_SIZE_X * INPUT0_SIZE_Z * INPUT0_SIZE_Y);
 #endif
+#endif  // ENABLE_ROUND
+            }
+        } else {
+#endif
+            __attribute__((opencl_unroll_hint(FEATURE_SLICE_SIZE)))
+            for (uint i = 0; i < FEATURE_SLICE_SIZE; i++) {
+// INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE != 0
 #if ENABLE_ROUND
 #if defined(DYNAMIC_KERNEL_DIVIDER) || defined(DYNAMIC_WITH_PADDING_KERNEL_DIVIDER)
-            pool_result[i] = convert_int(round(((float)result[i] / max(num_elements, (uint)1))));
+                pool_result[i] = convert_int(round(((float)result[i] / max(num_elements, (uint)1))));
 #else
-            pool_result[i] = convert_int(round((float)result[i] / (int)(POOL_SIZE_X * INPUT0_SIZE_Z * INPUT0_SIZE_Y)));
+                pool_result[i] = convert_int(round((float)result[i] / (int)(POOL_SIZE_X * INPUT0_SIZE_Z * INPUT0_SIZE_Y)));
 #endif
 #else // ENABLE_ROUND
 #if defined(DYNAMIC_KERNEL_DIVIDER) || defined(DYNAMIC_WITH_PADDING_KERNEL_DIVIDER)
-            pool_result[i] = (float)result[i] / max(num_elements, (uint)1);
+                pool_result[i] = (float)result[i] / max(num_elements, (uint)1);
 #else
-            pool_result[i] = (float)result[i] / (int)(POOL_SIZE_X * INPUT0_SIZE_Z * INPUT0_SIZE_Y);
+                pool_result[i] = (float)result[i] / (int)(POOL_SIZE_X * INPUT0_SIZE_Z * INPUT0_SIZE_Y);
 #endif
 #endif  // ENABLE_ROUND
+            }
+#if INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE != 0
         }
+#endif // INPUT0_FEATURE_NUM % FEATURE_SLICE_SIZE != 0
 #else   // AVG_POOLING
         pool_result = TO_ACTIVATION_VEC16(result);
 #endif  // AVG_POOLING
diff --git a/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt b/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt
index 0367cf831ddedb..d4d6b319dcddfb 100644
--- a/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt
@@ -67,10 +67,7 @@ target_link_libraries("${CLDNN_BUILD__PROJ}" PRIVATE
   )
 
 if(ENABLE_ONEDNN_FOR_GPU)
-  add_dependencies("${CLDNN_BUILD__PROJ}" onednn_gpu_build)
-  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC ${ONEDNN_LIBRARY})
-  target_include_directories("${CLDNN_BUILD__PROJ}" PUBLIC ${ONEDNN_INCLUDE_DIRS})
-  target_compile_definitions("${CLDNN_BUILD__PROJ}" PUBLIC ENABLE_ONEDNN_FOR_GPU)
+  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC onednn_gpu)
 endif()
 
 if(WIN32)
@@ -80,4 +77,7 @@ elseif((NOT ANDROID) AND (UNIX))
 endif()
 
 target_link_libraries("${CLDNN_BUILD__PROJ}" PRIVATE inference_engine)
+
+ov_install_static_lib("${CLDNN_BUILD__PROJ}" gpu)
+
 # ======================================================================================================
diff --git a/inference-engine/thirdparty/clDNN/runtime/device_query.cpp b/inference-engine/thirdparty/clDNN/runtime/device_query.cpp
index 6285b36f4cb7b9..07e5cbe1f043c0 100644
--- a/inference-engine/thirdparty/clDNN/runtime/device_query.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/device_query.cpp
@@ -10,14 +10,19 @@
 
 namespace cldnn {
 
-device_query::device_query(engine_types engine_type, runtime_types runtime_type, void* user_context, void* user_device, int target_tile_id) {
+device_query::device_query(engine_types engine_type,
+                           runtime_types runtime_type,
+                           void* user_context,
+                           void* user_device,
+                           int ctx_device_id,
+                           int target_tile_id) {
     switch (engine_type) {
     case engine_types::ocl: {
         if (runtime_type != runtime_types::ocl)
             throw std::runtime_error("Unsupported runtime type for ocl engine");
 
         ocl::ocl_device_detector ocl_detector;
-        _available_devices = ocl_detector.get_available_devices(user_context, user_device, target_tile_id);
+        _available_devices = ocl_detector.get_available_devices(user_context, user_device, ctx_device_id, target_tile_id);
         break;
     }
     default: throw std::runtime_error("Unsupported engine type in device_query");
diff --git a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.cpp b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.cpp
index e864877cc3e69c..4ba20d57dfa2df 100644
--- a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.cpp
@@ -300,5 +300,13 @@ ocl_device::ocl_device(const cl::Device dev, const cl::Context& ctx, const cl_pl
 , _info(init_device_info(dev))
 , _mem_caps(init_memory_caps(dev, _info)) { }
 
+bool ocl_device::is_same(const device::ptr other) {
+    auto casted = downcast<ocl_device>(other.get());
+    if (!casted)
+        return false;
+
+    return _context == casted->get_context() && _device == casted->get_device() && _platform == casted->get_platform();
+}
+
 }  // namespace ocl
 }  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.hpp b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.hpp
index ecb3498e4886b1..e68a6078490f53 100644
--- a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.hpp
+++ b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device.hpp
@@ -28,6 +28,8 @@ struct ocl_device : public device {
     const cl::Context& get_context() const { return _context; }
     cl_platform_id get_platform() const { return _platform; }
 
+    bool is_same(const device::ptr other) override;
+
     ~ocl_device() = default;
 
 private:
diff --git a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.cpp b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.cpp
index c22b831da3be13..5334b1e64335a0 100644
--- a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.cpp
@@ -91,11 +91,14 @@ static std::vector<cl::Device> getSubDevices(cl::Device& rootDevice) {
     return subDevices;
 }
 
-std::map<std::string, device::ptr> ocl_device_detector::get_available_devices(void* user_context, void* user_device, int target_tile_id) const {
+std::map<std::string, device::ptr> ocl_device_detector::get_available_devices(void* user_context,
+                                                                              void* user_device,
+                                                                              int ctx_device_id,
+                                                                              int target_tile_id) const {
     bool host_out_of_order = true;  // Change to false, if debug requires in-order queue.
     std::vector<device::ptr> dev_orig, dev_sorted;
     if (user_context != nullptr) {
-        dev_orig = create_device_list_from_user_context(host_out_of_order, user_context);
+        dev_orig = create_device_list_from_user_context(host_out_of_order, user_context, ctx_device_id);
     } else if (user_device != nullptr) {
         dev_orig = create_device_list_from_user_device(host_out_of_order, user_device);
     } else {
@@ -171,13 +174,14 @@ std::vector<device::ptr> ocl_device_detector::create_device_list(bool out_out_or
     return ret;
 }
 
-std::vector<device::ptr>  ocl_device_detector::create_device_list_from_user_context(bool out_out_order, void* user_context) const {
+std::vector<device::ptr>  ocl_device_detector::create_device_list_from_user_context(bool out_out_order, void* user_context, int ctx_device_id) const {
     cl::Context ctx = cl::Context(static_cast<cl_context>(user_context), true);
     auto all_devices = ctx.getInfo<CL_CONTEXT_DEVICES>();
 
     std::vector<device::ptr> ret;
-    for (auto& device : all_devices) {
-        if (!does_device_match_config(out_out_order, device))
+    for (size_t i = 0; i < all_devices.size(); i++) {
+        auto& device = all_devices[i];
+        if (!does_device_match_config(out_out_order, device) || i != ctx_device_id)
             continue;
         ret.emplace_back(std::make_shared<ocl_device>(device, ctx, device.getInfo<CL_DEVICE_PLATFORM>()));
     }
diff --git a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.hpp b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.hpp
index 9ee7aeea11deda..81cf6d8836c60c 100644
--- a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.hpp
+++ b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_device_detector.hpp
@@ -19,10 +19,11 @@ class ocl_device_detector {
 public:
     ocl_device_detector() = default;
 
-    std::map<std::string, device::ptr> get_available_devices(void* user_context, void* user_device, int target_tile_id = -1) const;
+    std::map<std::string, device::ptr> get_available_devices(void *user_context, void *user_device, int ctx_device_id = 0, int target_tile_id = -1) const;
+
 private:
     std::vector<device::ptr> create_device_list(bool out_out_order) const;
-    std::vector<device::ptr> create_device_list_from_user_context(bool out_out_order, void* user_context) const;
+    std::vector<device::ptr> create_device_list_from_user_context(bool out_out_order, void* user_context, int ctx_device_id = 0) const;
     std::vector<device::ptr> create_device_list_from_user_device(bool out_out_order, void* user_device) const;
 };
 
diff --git a/inference-engine/thirdparty/clDNN/src/CMakeLists.txt b/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
index c0b86111581102..df2cde95958f03 100644
--- a/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
@@ -128,9 +128,7 @@ target_link_libraries("${CLDNN_BUILD__PROJ}" PRIVATE
 )
 
 if(ENABLE_ONEDNN_FOR_GPU)
-  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC ${ONEDNN_LIBRARY})
-  target_include_directories("${CLDNN_BUILD__PROJ}" PUBLIC ${ONEDNN_INCLUDE_DIRS})
-  target_compile_definitions("${CLDNN_BUILD__PROJ}" PUBLIC ENABLE_ONEDNN_FOR_GPU)
+  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC onednn_gpu)
 endif()
 
 if(COMMAND add_cpplint_target)
diff --git a/inference-engine/thirdparty/clDNN/src/border.cpp b/inference-engine/thirdparty/clDNN/src/border.cpp
index 886275d6719562..478acf99fb8b81 100644
--- a/inference-engine/thirdparty/clDNN/src/border.cpp
+++ b/inference-engine/thirdparty/clDNN/src/border.cpp
@@ -87,23 +87,12 @@ std::string border_inst::to_string(border_node const& node) {
 border_inst::typed_primitive_inst(network& network, border_node const& node) : parent(network, node) {
     auto input_layout = node.input().get_output_layout();
 
-    const auto input_format = input_layout.format;
     const auto& input_sizes = input_layout.size;
 
     auto lt_sizes = argument.left_top_sizes.sub(tensor(0));
     auto rb_sizes = argument.right_bottom_sizes.sub(tensor(0));
     auto b_type = argument.type;
 
-    CLDNN_ERROR_NOT_PROPER_FORMAT(node.id(),
-                                  "Input format",
-                                  input_format.value,
-                                  "supported border primitive input formats",
-                                  format::bfyx,
-                                  format::yxfb,
-                                  format::byxf,
-                                  format::bfzyx,
-                                  format::bfwzyx);
-
     tensor null_tensor = tensor(0);
 
     // Check if sizes of border are in proper range.
diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/add_onednn_optimization_attributes.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/add_onednn_optimization_attributes.cpp
new file mode 100644
index 00000000000000..bb7b396467ce75
--- /dev/null
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/add_onednn_optimization_attributes.cpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+#include "pass_manager.h"
+#include "program_node.h"
+
+using namespace cldnn;
+
+void add_onednn_optimization_attributes::run(program& p) {
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    for (auto& node : p.get_processing_order()) {
+        if (node->get_preferred_impl_type() == impl_types::onednn) {
+            node->init_onednn_primitive_attributes();
+        }
+    }
+#endif // ENABLE_ONEDNN_FOR_GPU
+}
diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/basic_memory_dependencies.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/basic_memory_dependencies.cpp
index 13b95c7478ad8f..306c16b58e8b21 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/basic_memory_dependencies.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/basic_memory_dependencies.cpp
@@ -36,7 +36,7 @@ void basic_memory_dependencies::run(program& p) {
             add_memory_dependency(it, node);
         }
 
-        if (node->is_type<convolution>()) {
+        if (node->is_type<convolution>() && node->get_preferred_impl_type() == impl_types::onednn) {
             auto& conv = node->as<convolution>();
             bool can_reuse_eltwise_mem = false;
             size_t eltw_dep = 0;
@@ -59,6 +59,7 @@ void basic_memory_dependencies::run(program& p) {
                     }
                 }
             }
+
             if (can_reuse_eltwise_mem) {
                 auto& eltw_node = conv.get_dependency(eltw_dep);
                 eltw_node.can_share_buffer(false);
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/border.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/border.cpp
index 83ef5ef102e9f0..c95189ea099f9c 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/border.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/border.cpp
@@ -72,22 +72,36 @@ attach_border_impl::attach_border_impl() {
         std::make_tuple(data_types::f16, format::yxfb),
         std::make_tuple(data_types::i8, format::yxfb),
         std::make_tuple(data_types::u8, format::yxfb),
+
         std::make_tuple(data_types::f32, format::bfyx),
         std::make_tuple(data_types::f16, format::bfyx),
         std::make_tuple(data_types::i8, format::bfyx),
         std::make_tuple(data_types::u8, format::bfyx),
+
         std::make_tuple(data_types::f32, format::byxf),
         std::make_tuple(data_types::f16, format::byxf),
         std::make_tuple(data_types::i8, format::byxf),
         std::make_tuple(data_types::u8, format::byxf),
+
         std::make_tuple(data_types::f32, format::bfzyx),
         std::make_tuple(data_types::f16, format::bfzyx),
         std::make_tuple(data_types::i8, format::bfzyx),
         std::make_tuple(data_types::u8, format::bfzyx),
+
         std::make_tuple(data_types::f32, format::bfwzyx),
         std::make_tuple(data_types::f16, format::bfwzyx),
         std::make_tuple(data_types::i8, format::bfwzyx),
         std::make_tuple(data_types::u8, format::bfwzyx),
+
+        std::make_tuple(data_types::f32, format::b_fs_yx_fsv16),
+        std::make_tuple(data_types::f16, format::b_fs_yx_fsv16),
+        std::make_tuple(data_types::i8, format::b_fs_yx_fsv16),
+        std::make_tuple(data_types::u8, format::b_fs_yx_fsv16),
+
+        std::make_tuple(data_types::f32, format::b_fs_zyx_fsv16),
+        std::make_tuple(data_types::f16, format::b_fs_zyx_fsv16),
+        std::make_tuple(data_types::i8, format::b_fs_zyx_fsv16),
+        std::make_tuple(data_types::u8, format::b_fs_zyx_fsv16),
     });
 }
 
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/reduce.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/reduce.cpp
index ef412f5dc345b3..e63cf6dd315e70 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/reduce.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/reduce.cpp
@@ -101,6 +101,11 @@ attach_reduce_impl::attach_reduce_impl() {
         std::make_tuple(data_types::i32, format::b_fs_yx_fsv16),
         std::make_tuple(data_types::i8, format::b_fs_yx_fsv16),
         std::make_tuple(data_types::u8, format::b_fs_yx_fsv16),
+        std::make_tuple(data_types::f32, format::b_fs_yx_fsv32),
+        std::make_tuple(data_types::f16, format::b_fs_yx_fsv32),
+        std::make_tuple(data_types::i32, format::b_fs_yx_fsv32),
+        std::make_tuple(data_types::i8, format::b_fs_yx_fsv32),
+        std::make_tuple(data_types::u8, format::b_fs_yx_fsv32),
     });
 }
 
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/concatenation_onednn.cpp b/inference-engine/thirdparty/clDNN/src/impls/onednn/concatenation_onednn.cpp
index 8051ff4fcbf105..2367674d7624d1 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/concatenation_onednn.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/concatenation_onednn.cpp
@@ -73,7 +73,7 @@ struct concatenation_onednn : typed_primitive_onednn_impl<concatenation, void, d
 public:
     static primitive_impl* create(const concatenation_node& arg) {
         auto desc = get_concatenation_descriptor(arg);
-        auto attr = get_primitive_attributes(arg);
+        auto attr = arg.get_onednn_primitive_attributes();
 
         std::shared_ptr<void> dummy = nullptr;
 
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/convolution_onednn.cpp b/inference-engine/thirdparty/clDNN/src/impls/onednn/convolution_onednn.cpp
index b186d770314af7..214f62ba090bd1 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/convolution_onednn.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/convolution_onednn.cpp
@@ -47,6 +47,7 @@ struct convolution_onednn : typed_primitive_onednn_impl<convolution, dnnl::convo
 
     std::unordered_map<int, dnnl::memory> get_arguments(convolution_inst& instance) const override {
         std::unordered_map<int, dnnl::memory> args = parent::get_arguments(instance);
+        auto attrs = instance.get_node().get_onednn_primitive_attributes();
 
         {
             auto weights = instance.weights_memory(0);
@@ -58,13 +59,13 @@ struct convolution_onednn : typed_primitive_onednn_impl<convolution, dnnl::convo
             args.insert({DNNL_ARG_BIAS, bias->get_onednn_memory(_pd.weights_desc(1))});
         }
 
-        if (has_zero_points(DNNL_ARG_SRC, _attrs)) {
+        if (has_zero_points(DNNL_ARG_SRC, attrs)) {
             auto a_zp = instance.activations_zero_points_memory(0);
             dnnl::memory::desc desc = onednn::layout_to_memory_desc(a_zp->get_layout(), dnnl::memory::format_tag::a, true);
             args.insert({DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_SRC, a_zp->get_onednn_memory(desc)});
         }
 
-        if (has_zero_points(DNNL_ARG_WEIGHTS, _attrs)) {
+        if (has_zero_points(DNNL_ARG_WEIGHTS, attrs)) {
             auto w_zp = instance.weights_zero_points_memory(0);
             dnnl::memory::desc desc = onednn::layout_to_memory_desc(w_zp->get_layout(), dnnl::memory::format_tag::a, true);
             args.insert({DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_WEIGHTS, w_zp->get_onednn_memory(desc)});
@@ -74,7 +75,7 @@ struct convolution_onednn : typed_primitive_onednn_impl<convolution, dnnl::convo
     }
 
     static std::shared_ptr<dnnl::primitive_attr> get_primitive_attributes(const typed_program_node<convolution>& arg) {
-        auto attrs = parent::get_primitive_attributes(arg);
+        auto attrs = arg.get_onednn_primitive_attributes();
 
         if (arg.activations_zero_points_term()) {
             auto& a_zp = arg.activations_zero_points();
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/deconvolution_onednn.cpp b/inference-engine/thirdparty/clDNN/src/impls/onednn/deconvolution_onednn.cpp
index f6400f80eddf28..00c98348eadd14 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/deconvolution_onednn.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/deconvolution_onednn.cpp
@@ -62,7 +62,7 @@ struct deconvolution_onednn : typed_primitive_onednn_impl<deconvolution, dnnl::d
     }
 
     static std::shared_ptr<dnnl::primitive_attr> get_primitive_attributes(const typed_program_node<deconvolution>& arg) {
-        auto attrs = parent::get_primitive_attributes(arg);
+        auto attrs = arg.get_onednn_primitive_attributes();
 
         return attrs;
     }
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp b/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp
index f150760558c667..2796070a51b93a 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/fully_connected_onednn.cpp
@@ -142,7 +142,7 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected, dnn
             }
         }
 
-        auto attr = get_primitive_attributes(arg);
+        auto attr = arg.get_onednn_primitive_attributes();
         dnnl::primitive_desc prim_desc{&desc->data, attr.get(), engine.get_onednn_engine(), nullptr};
 
         return new fully_connected_onednn(arg, desc, attr, prim_desc, get_weights_reorder(arg, prim_desc));
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/pooling_onednn.cpp b/inference-engine/thirdparty/clDNN/src/impls/onednn/pooling_onednn.cpp
index a23af05094dee3..361d1bfd3719b6 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/pooling_onednn.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/pooling_onednn.cpp
@@ -71,7 +71,7 @@ struct pooling_onednn : typed_primitive_onednn_impl<pooling, dnnl::pooling_forwa
     static primitive_impl* create(const pooling_node& arg) {
         auto& engine = arg.get_program().get_engine();
         auto desc = get_pooling_descriptor(arg);
-        auto attr = get_primitive_attributes(arg);
+        auto attr = arg.get_onednn_primitive_attributes();
         dnnl::primitive_desc prim_desc{&desc->data, attr.get(), engine.get_onednn_engine(), nullptr};
 
         return new pooling_onednn(arg, desc, attr, prim_desc);
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/primitive_onednn_base.h b/inference-engine/thirdparty/clDNN/src/impls/onednn/primitive_onednn_base.h
index 10748a4c68ba4d..4f0bcc3a874199 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/primitive_onednn_base.h
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/primitive_onednn_base.h
@@ -26,36 +26,6 @@
 namespace cldnn {
 namespace onednn {
 
-enum class onednn_post_op_type : uint32_t {
-    eltwise_act,
-    eltwise_clip,
-    eltwise_linear,
-    eltwise_round,
-    binary_mul,
-    binary_add,
-    binary_max,
-    binary_min,
-    scale,
-    sum,
-    optimized,
-    optimized_eltwise,
-    optimized_sum
-};
-
-struct onednn_post_op_desc {
-    onednn_post_op_type op_type;
-    size_t mem_offset;
-    size_t mem_dep;
-};
-
-// This map contains information about onednn post-ops types, memory buffer offsets and dependencies
-//     key is cldnn::primitive_id,
-//     value is an instance of struct onednn_post_op_desc containing info about post-ops related to the node defined by key:
-//         op_type - onednn_post_op_type (enum),
-//         mem_offset - index of memory buffer for current post-operation,
-//         mem_dep - memory dependency for working with fused node
-static std::unordered_map<cldnn::primitive_id, std::vector<onednn_post_op_desc>> onednn_fusing_map;
-
 template <class PType, class DescType, class PrimDescType = dnnl::primitive_desc, class PrimType = dnnl::primitive>
 struct typed_primitive_onednn_impl : public typed_primitive_impl<PType> {
     const typed_program_node<PType>& _outer;
@@ -82,7 +52,7 @@ struct typed_primitive_onednn_impl : public typed_primitive_impl<PType> {
 protected:
     virtual bool optimized_out(typed_primitive_inst<PType>&) const { return false; }
 
-    static bool has_out_scales(const std::shared_ptr<dnnl::primitive_attr>& attr) {
+    static bool has_output_scales(const std::shared_ptr<dnnl::primitive_attr>& attr) {
         int mask;
         std::vector<float> scales;
         attr->get_output_scales(mask, scales);
@@ -98,408 +68,22 @@ struct typed_primitive_onednn_impl : public typed_primitive_impl<PType> {
         return !zp.empty() && (reinterpret_cast<const int32_t&>(zp[0]) == drsv);
     }
 
-    static dnnl::post_ops try_optimize_post_ops(const typed_program_node<PType>& arg, dnnl::post_ops& p_ops,
-                                                const std::shared_ptr<dnnl::primitive_attr>& attr,
-                                                bool& optimization_is_completed) {
-        // Get current node id for creating of optimization map
-        auto node_id = arg.id();
-
-        // Create new dnnl::post_ops object which will be filled inside the optimization process
-        dnnl::post_ops optimized_p_ops;
-
-        // Add new post-op into optimized_p_ops structure
-        auto add_post_op = [&](onednn_post_op_type type, const dnnl::post_ops& cur_p_ops, dnnl::post_ops& new_p_ops, int idx) {
-            switch (type) {
-                case onednn_post_op_type::eltwise_act:
-                case onednn_post_op_type::eltwise_clip:
-                case onednn_post_op_type::eltwise_linear:
-                case onednn_post_op_type::eltwise_round:
-                {
-                    dnnl::algorithm alg;
-                    float scale, alpha, beta;
-                    cur_p_ops.get_params_eltwise(idx, scale, alg, alpha, beta);
-                    new_p_ops.append_eltwise(scale, alg, alpha, beta);
-                    break;
-                }
-
-                case onednn_post_op_type::binary_add:
-                case onednn_post_op_type::binary_mul:
-                case onednn_post_op_type::binary_max:
-                case onednn_post_op_type::binary_min:
-                {
-                    dnnl::algorithm alg;
-                    dnnl::memory::desc desc;
-                    cur_p_ops.get_params_binary(idx, alg, desc);
-                    new_p_ops.append_binary(alg, desc);
-                    break;
-                }
-
-                case onednn_post_op_type::scale:
-                {
-                    break;
-                }
-
-                case onednn_post_op_type::sum:
-                {
-                    float scale;
-                    dnnl::memory::data_type data_type;
-                    cur_p_ops.get_params_sum(idx, scale, data_type);
-                    new_p_ops.append_sum(scale, data_type);
-                    break;
-                }
-
-                case onednn_post_op_type::optimized:
-                case onednn_post_op_type::optimized_sum:
-                case onednn_post_op_type::optimized_eltwise:
-                {
-                    // Current operation already has been optimized => don't need extra actions
-                    break;
-                }
-
-                default:
-                    throw std::runtime_error("Unsupported onednn post-operation type");
-            }
-        };
-
-        // Check that post-op type is any optimized
-        auto type_is_any_optimized = [](onednn_post_op_type type) -> bool {
-            return type == onednn_post_op_type::optimized || type == onednn_post_op_type::optimized_sum ||
-                   type == onednn_post_op_type::optimized_eltwise;
-        };
-
-        // Check that post-op type is eltwise
-        auto type_is_eltwise = [](onednn_post_op_type type) -> bool {
-            return type == onednn_post_op_type::eltwise_round || type == onednn_post_op_type::eltwise_linear ||
-                   type == onednn_post_op_type::eltwise_clip  || type == onednn_post_op_type::eltwise_act;
-        };
-
-        // Check that post-op type is binary_add or binary_mul
-        auto type_is_binary_add_or_mul = [](onednn_post_op_type type) -> bool {
-            return type == onednn_post_op_type::binary_add || type == onednn_post_op_type::binary_mul;
-        };
-
-        // Simple post-op type checks
-        auto type_is_optimized         = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::optimized; };
-        auto type_is_eltwise_linear    = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::eltwise_linear; };
-        auto type_is_optimized_eltwise = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::optimized_eltwise; };
-        auto type_is_binary_add        = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::binary_add; };
-        auto type_is_binary_mul        = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::binary_mul; };
-        auto type_is_sum               = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::sum; };
-        auto type_is_optimized_sum     = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::optimized_sum; };
-        auto type_is_scale             = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::scale; };
-
-        auto& cur_post_ops = onednn_fusing_map[node_id];
-
-        size_t cur_post_op_idx = 1;
-        size_t prev_post_op_idx = 0;
-        bool optimization_done = false;
-
-        // Check and update post-op map if we already optimized something
-        for (size_t post_op_idx = 0; post_op_idx < cur_post_ops.size(); post_op_idx++) {
-            if (type_is_optimized_sum(cur_post_ops[post_op_idx].op_type))
-                cur_post_ops[post_op_idx].op_type = onednn_post_op_type::sum;
-            else if (type_is_optimized_eltwise(cur_post_ops[post_op_idx].op_type))
-                cur_post_ops[post_op_idx].op_type = onednn_post_op_type::eltwise_linear;
-            else if (type_is_optimized(cur_post_ops[post_op_idx].op_type))
-                cur_post_ops.erase(cur_post_ops.begin() + post_op_idx);
-        }
-
-        // Get post-ops size for current node
-        auto post_ops_size = cur_post_ops.size();
-
-        // Try to combine pairs of arithmetic post-ops (adds and muls) into one operation inside this cycle
-        while (!optimization_done) {
-            auto cur_type = cur_post_ops[cur_post_op_idx].op_type;
-            auto prev_type = cur_post_ops[prev_post_op_idx].op_type;
-
-            // Ignore optimized operations for "previous" operation in our operation pair
-            while (type_is_any_optimized(prev_type) && cur_post_op_idx < post_ops_size - 1) {
-                prev_post_op_idx++;
-                cur_post_op_idx++;
-                prev_type = cur_post_ops[prev_post_op_idx].op_type;
-                cur_type = cur_post_ops[cur_post_op_idx].op_type;
-            }
-
-            // Ignore optimized operations for "current" operation in our operation pair
-            while (type_is_any_optimized(cur_type) && cur_post_op_idx < post_ops_size - 1) {
-                cur_post_op_idx++;
-                cur_type = cur_post_ops[cur_post_op_idx].op_type;
-            }
-
-            auto cur_idx = static_cast<int>(has_out_scales(attr) ? (cur_post_op_idx >= 1 ? cur_post_op_idx - 1 : 0) : cur_post_op_idx);
-            auto prev_idx = static_cast<int>(has_out_scales(attr) ? (prev_post_op_idx >= 1 ? prev_post_op_idx - 1 : 0) : prev_post_op_idx);
-
-            // If this is the last pair and it's optimized - add the last post-op and go out from the cycle
-            if (cur_post_op_idx == post_ops_size - 1 && (type_is_any_optimized(cur_type) || type_is_any_optimized(prev_type))) {
-                if (!type_is_any_optimized(prev_type)) {
-                    add_post_op(prev_type, p_ops, optimized_p_ops, prev_idx);
-                }
-                if (!type_is_any_optimized(cur_type)) {
-                    add_post_op(cur_type, p_ops, optimized_p_ops, cur_idx);
-                }
-                break;
-            }
-
-            // Post-ops combinations which can be simplified
-            auto eltw_and_eltw  = type_is_eltwise(cur_type) && type_is_eltwise(prev_type);
-            auto bin_and_eltw   = type_is_binary_add_or_mul(cur_type) && type_is_eltwise_linear(prev_type);
-            auto eltw_and_bin   = type_is_eltwise_linear(cur_type) && type_is_binary_add_or_mul(prev_type);
-            auto sum_and_eltw   = type_is_sum(cur_type) && type_is_eltwise(prev_type);
-            auto eltw_and_scale = type_is_eltwise_linear(cur_type) && type_is_scale(prev_type);
-
-            auto can_try_optimize = eltw_and_eltw ||
-                                    bin_and_eltw ||
-                                    eltw_and_bin ||
-                                    sum_and_eltw ||
-                                    eltw_and_scale;
-
-            bool cur_ops_pair_is_optimized = false;
-
-            if (can_try_optimize) {
-                if (eltw_and_eltw) {
-                    dnnl::algorithm cur_alg, prev_alg;
-                    float cur_scale, prev_scale, cur_alpha, prev_alpha, cur_beta, prev_beta;
-
-                    p_ops.get_params_eltwise(prev_idx, prev_scale, prev_alg, prev_alpha, prev_beta);
-                    p_ops.get_params_eltwise(cur_idx, cur_scale, cur_alg, cur_alpha, cur_beta);
-
-                    auto eltw_linear_and_eltw_linear = type_is_eltwise_linear(cur_type) && type_is_eltwise_linear(prev_type);
-                    auto eltw_linear_and_eltw_non_linear = type_is_eltwise_linear(cur_type) && !type_is_eltwise_linear(prev_type) && cur_beta == 0;
-
-                    // eltwise_linear + eltwise_linear combination can be optimized always
-                    if (eltw_linear_and_eltw_linear) {
-                        dnnl::post_ops eltw_p_op;
-                        float optimized_alpha = cur_alpha * prev_alpha * prev_scale;
-                        float optimized_beta = cur_alpha * prev_beta * prev_scale + cur_beta;
-                        float optimized_scale = cur_scale;
-                        eltw_p_op.append_eltwise(optimized_scale, cur_alg, optimized_alpha, optimized_beta);
-
-                        // Combine 2 eltwises into one
-                        add_post_op(cur_type, eltw_p_op, optimized_p_ops, 0);
-                    } else if (eltw_linear_and_eltw_non_linear) {
-                        dnnl::post_ops eltw_p_op;
-                        eltw_p_op.append_eltwise(cur_scale * prev_scale * cur_alpha, prev_alg, prev_alpha, prev_beta);
-
-                        // Combine 2 eltwises into one
-                        add_post_op(prev_type, eltw_p_op, optimized_p_ops, 0);
-                    }
-
-                    if (eltw_linear_and_eltw_linear || eltw_linear_and_eltw_non_linear) {
-                        // Marked current and previous eltwise operations as 'optimized' (they will be ignored on the next iteration of cycle)
-                        cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized;
-                        cur_post_ops[prev_post_op_idx].op_type = onednn_post_op_type::optimized_eltwise;
-
-                        // Set the flag if extra optimizations checking is needed
-                        if (cur_post_op_idx < post_ops_size - 1) {
-                            if (type_is_eltwise_linear(cur_post_ops[cur_post_op_idx + 1].op_type) ||
-                                type_is_binary_add_or_mul(cur_post_ops[cur_post_op_idx + 1].op_type) ||
-                                type_is_optimized_eltwise(cur_post_ops[cur_post_op_idx + 1].op_type)) {
-                                optimization_is_completed = true;
-                            }
-                        }
-
-                        cur_ops_pair_is_optimized = true;
-                    }
-                } else if (bin_and_eltw) {
-                    dnnl::algorithm alg;
-                    dnnl::memory::desc desc;
-                    float scale, alpha, beta;
-
-                    cldnn::program_node& cur_node = arg.get_dependency(cur_post_ops[cur_post_op_idx].mem_dep);
-
-                    p_ops.get_params_binary(cur_idx, alg, desc);
-                    p_ops.get_params_eltwise(prev_idx, scale, alg, alpha, beta);
-
-                    // Eltwise operations can use runtime non-constant data buffers, so check that memory buffers consist of constant data only
-                    auto bin_ops_can_be_optimized = cur_node.is_type<data>() && cur_node.is_constant() &&
-                                                    cur_node.get_users().size() == 1 && desc.data_type() == dnnl_f32;
-
-                    auto bin_add_and_eltw = alpha == 1.0f && scale == 1.0f && type_is_binary_add(cur_type) && bin_ops_can_be_optimized;
-                    auto bin_mul_and_eltw = beta == 0.f && type_is_binary_mul(cur_type) && bin_ops_can_be_optimized;
-
-                    if (bin_add_and_eltw || bin_mul_and_eltw) {
-                        memory::ptr cur_bin_mem_ptr = cur_node.as<data>().get_attached_memory_ptr();
-                        if (cur_bin_mem_ptr == nullptr)
-                            throw std::runtime_error("OneDNN post-ops optimization error: nonexistent node for bin + eltw");
-                        auto& stream = cur_bin_mem_ptr->get_engine()->get_program_stream();
-                        mem_lock<float, mem_lock_type::write> bin_and_eltw_lock(cur_bin_mem_ptr, stream);
-
-                        size_t cur_bin_mem_size = cur_node.get_output_layout().count();
-
-                        // Update all binary coefficients
-                        if (bin_add_and_eltw) {
-                            for (size_t data_idx = 0; data_idx < cur_bin_mem_size; data_idx++) {
-                                bin_and_eltw_lock[data_idx] += beta;
-                            }
-                        } else {
-                            for (size_t data_idx = 0; data_idx < cur_bin_mem_size; data_idx++) {
-                                bin_and_eltw_lock[data_idx] *= alpha * scale;
-                            }
-                        }
-
-                        // Marked previous eltwise operation as 'optimized' (it will be ignored on the next iteration of cycle)
-                        cur_post_ops[prev_post_op_idx].op_type = onednn_post_op_type::optimized;
-
-                        cur_ops_pair_is_optimized = true;
-                    }
-                } else if (eltw_and_bin) {
-                    dnnl::algorithm alg;
-                    dnnl::memory::desc desc;
-                    float scale, alpha, beta;
-
-                    cldnn::program_node& prev_node = arg.get_dependency(cur_post_ops[prev_post_op_idx].mem_dep);
-
-                    p_ops.get_params_eltwise(cur_idx, scale, alg, alpha, beta);
-                    p_ops.get_params_binary(prev_idx, alg, desc);
-
-                    // Eltwise operations can use runtime non-constant data buffers, so check that memory buffers consist of constant data only
-                    auto bin_ops_can_be_optimized = prev_node.is_type<data>() && prev_node.is_constant() &&
-                                                    prev_node.get_users().size() == 1 && desc.data_type() == dnnl_f32;
-
-                    auto eltw_and_bin_add = alpha == 1.0f && scale == 1.0f && type_is_binary_add(prev_type) && bin_ops_can_be_optimized;
-                    auto eltw_and_bin_mul = beta == 0.f && type_is_binary_mul(prev_type) && bin_ops_can_be_optimized;
-
-                    if (eltw_and_bin_add || eltw_and_bin_mul) {
-                        memory::ptr prev_bin_mem_ptr = prev_node.as<data>().get_attached_memory_ptr();
-                        if (prev_bin_mem_ptr == nullptr)
-                            throw std::runtime_error("OneDNN post-ops optimization error: nonexistent node for eltw + bin");
-                        auto& stream = prev_bin_mem_ptr->get_engine()->get_program_stream();
-                        mem_lock<float, mem_lock_type::write> eltw_and_bin_lock(prev_bin_mem_ptr, stream);
-
-                        size_t prev_bin_mem_size = prev_node.get_output_layout().count();
-
-                        // Update all binary coefficients
-                        if (eltw_and_bin_add) {
-                            for (size_t data_idx = 0; data_idx < prev_bin_mem_size; data_idx++) {
-                                eltw_and_bin_lock[data_idx] += beta;
-                            }
-                        } else {
-                            for (size_t data_idx = 0; data_idx < prev_bin_mem_size; data_idx++) {
-                                eltw_and_bin_lock[data_idx] *= alpha * scale;
-                            }
-                        }
-
-                        // Marked current eltwise operation as 'optimized' (it will be ignored on the next iteration of cycle)
-                        cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized;
-
-                        cur_ops_pair_is_optimized = true;
-                    }
-                } else if (sum_and_eltw) {
-                    dnnl::algorithm alg;
-                    float sum_scale, eltw_scale, alpha, beta;
-                    dnnl::memory::data_type data_type;
-
-                    dnnl::algorithm next_alg;
-                    float next_scale, next_alpha, next_beta;
-                    size_t next_idx = cur_idx + 1;
-                    size_t next_post_op_idx = cur_post_op_idx + 1;
-
-                    bool can_optimize_eltw_and_sum = false;
-
-                    if (cur_post_op_idx < post_ops_size - 1) {
-                        auto next_type = cur_post_ops[next_post_op_idx].op_type;
-                        if (type_is_eltwise_linear(next_type)) {
-                            p_ops.get_params_eltwise(next_idx, next_scale, next_alg, next_alpha, next_beta);
-
-                            if (next_beta == 0)
-                                can_optimize_eltw_and_sum = true;
-                        }
-                    }
-
-                    // Try to optimize eltwise (any) + sum + eltwise_linear (with beta = 0) chain of operations
-                    if (can_optimize_eltw_and_sum) {
-                        p_ops.get_params_sum(cur_idx, sum_scale, data_type);
-                        p_ops.get_params_eltwise(prev_idx, eltw_scale, alg, alpha, beta);
-
-                        dnnl::post_ops eltw_p_op_prev, sum_p_op;
-
-                        eltw_p_op_prev.append_eltwise(eltw_scale * next_alpha * next_scale, alg, alpha, beta);
-                        sum_p_op.append_sum(sum_scale * next_alpha, data_type);
-
-                        add_post_op(prev_type, eltw_p_op_prev, optimized_p_ops, 0);
-                        add_post_op(cur_type, sum_p_op, optimized_p_ops, 0);
-
-                        // Marked current, previous and next operations as 'optimized' (they will be ignored on the next iteration of cycle)
-                        cur_post_ops[prev_post_op_idx].op_type = onednn_post_op_type::optimized_eltwise;
-                        cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized_sum;
-                        cur_post_ops[next_post_op_idx].op_type = onednn_post_op_type::optimized;
-
-                        // Set the flag if extra optimizations checking is needed
-                        if (next_post_op_idx < post_ops_size - 1) {
-                            if (type_is_eltwise_linear(cur_post_ops[next_post_op_idx + 1].op_type) ||
-                                type_is_optimized_eltwise(cur_post_ops[next_post_op_idx + 1].op_type)) {
-                                optimization_is_completed = true;
-                            }
-                        }
-
-                        cur_ops_pair_is_optimized = true;
-                    }
-                } else if (eltw_and_scale) {
-                    dnnl::algorithm alg;
-                    float eltw_scale, alpha, beta;
-
-                    cldnn::program_node& prev_node = arg.get_dependency(cur_post_ops[prev_post_op_idx].mem_dep);
-
-                    p_ops.get_params_eltwise(cur_idx, eltw_scale, alg, alpha, beta);
-
-                    // Eltwise can be inserted into the output_scale if cur_beta is equal to 0.f
-                    if (beta == 0.f && prev_node.get_output_layout().data_type == data_types::f32) {
-                        memory::ptr prev_scale_mem_ptr = prev_node.as<data>().get_attached_memory_ptr();
-                        if (prev_scale_mem_ptr == nullptr)
-                            throw std::runtime_error("OneDNN post-ops optimization error: nonexistent node for eltw + scale");
-                        auto& stream = prev_scale_mem_ptr->get_engine()->get_program_stream();
-                        mem_lock<float, mem_lock_type::write> eltw_and_scale_lock(prev_scale_mem_ptr, stream);
-
-                        size_t prev_scale_mem_size = prev_node.get_output_layout().count();
-
-                        // Update all scale coefficients
-                        for (size_t data_idx = 0; data_idx < prev_scale_mem_size; data_idx++) {
-                            eltw_and_scale_lock[data_idx] *= alpha * eltw_scale;
-                        }
-
-                        // Marked current eltwise operation as 'optimized' (it will be ignored on the next iteration of cycle)
-                        cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized;
-
-                        cur_ops_pair_is_optimized = true;
-                    }
-                }
-            }
-
-            // If no optimizations have been applied then copy post-op info into the new optimized_p_ops structure
-            if (!(has_out_scales(attr) && prev_post_op_idx == 0) && !cur_ops_pair_is_optimized) {
-                add_post_op(prev_type, p_ops, optimized_p_ops, prev_idx);
-            }
-
-            if (cur_post_op_idx == post_ops_size - 1 && !cur_ops_pair_is_optimized) {
-                add_post_op(cur_type, p_ops, optimized_p_ops, cur_idx);
-                optimization_done = true;
-            } else if (cur_post_ops[cur_post_op_idx].op_type != onednn_post_op_type::optimized) {
-                cur_post_op_idx++;
-                prev_post_op_idx++;
-            }
-        }
-
-        optimization_is_completed = !optimization_is_completed;
-
-        return optimized_p_ops;
-    }
-
     void configure_post_ops_arguments(typed_primitive_inst<PType>& instance, std::unordered_map<int, dnnl::memory>& args) const {
-        // Get current node id for creating of optimization map
-        auto node_id = instance.id();
+        auto& node = instance.get_node();
         auto& engine = instance.get_network().get_engine();
         auto dnnl_engine = engine.get_onednn_engine();
 
         // Get current post-ops info
-        dnnl::post_ops post_ops = _attrs->get_post_ops();
+        auto onednn_attrs = node.get_onednn_primitive_attributes();
+        dnnl::post_ops post_ops = onednn_attrs->get_post_ops();
 
         // Create onednn memory buffers for post-ops
-        auto& cur_post_ops = onednn_fusing_map[node_id];
+        auto& cur_post_ops = node.get_fused_primitives_onednn();
         auto post_ops_size = cur_post_ops.size();
         for (size_t post_op_idx = 0, num_of_optimized_post_ops = 0; post_op_idx < post_ops_size; post_op_idx++) {
             auto post_op_type = cur_post_ops[post_op_idx].op_type;
             auto memory_offset = cur_post_ops[post_op_idx].mem_offset;
-            auto onednn_post_op_idx = has_out_scales(_attrs) && post_op_idx > 0 ? post_op_idx - 1 : post_op_idx;
+            auto onednn_post_op_idx = has_output_scales(onednn_attrs) && post_op_idx > 0 ? post_op_idx - 1 : post_op_idx;
             onednn_post_op_idx -= num_of_optimized_post_ops;
 
             switch (post_op_type) {
@@ -576,296 +160,6 @@ struct typed_primitive_onednn_impl : public typed_primitive_impl<PType> {
 
     void init_kernels() override { }
 
-    static std::shared_ptr<dnnl::primitive_attr> get_primitive_attributes(const typed_program_node<PType>& arg) {
-        const std::vector<fused_primitive_desc>& cldnn_post_ops = arg.get_fused_primitives();
-        auto attrs = std::make_shared<dnnl::primitive_attr>();
-        dnnl::post_ops post_ops;
-        size_t memory_offset = 0;
-
-        // Create onednn post-ops list related to the current node
-        std::vector<onednn_post_op_desc> fused_ops;
-
-        // Added this for debug purposes only
-        size_t empty_mem = 0xff;
-
-        // Add information about post-operation into the list, update indices
-        auto update_onednn_post_op_list = [&](onednn_post_op_type type, size_t m_dep) {
-            onednn_post_op_desc cur_op_desc = { type, memory_offset, m_dep };
-            fused_ops.push_back(cur_op_desc);
-
-            auto has_memory_buffers = type == onednn_post_op_type::binary_add ||
-                                      type == onednn_post_op_type::binary_mul ||
-                                      type == onednn_post_op_type::binary_max ||
-                                      type == onednn_post_op_type::binary_min ||
-                                      type == onednn_post_op_type::scale ||
-                                      type == onednn_post_op_type::sum;
-            if (has_memory_buffers)
-                memory_offset++;
-        };
-
-        for (size_t idx = 0; idx < cldnn_post_ops.size(); idx++) {
-            auto node = cldnn_post_ops[idx].node;
-
-            if (node->is_type<activation>()) {
-                auto fused_desc = node->as<activation>().get_primitive();
-                dnnl::algorithm alg = onednn::convert_activation_func(fused_desc->activation_function);
-                post_ops.append_eltwise(1.0f, alg, fused_desc->additional_params.a, fused_desc->additional_params.b);
-                update_onednn_post_op_list(onednn_post_op_type::eltwise_act, empty_mem);
-            } else if (node->is_type<eltwise>()) {
-                auto& e_node = node->as<eltwise>();
-                auto dep_idx = cldnn_post_ops[idx].dep_start_idx;
-                auto in = arg.get_dependency(dep_idx).get_output_layout();
-
-                if (e_node.get_primitive()->mode == eltwise_mode::sum) {
-                    if (e_node.get_primitive()->needs_onednn_sum_post_op(in)) {
-                        post_ops.append_sum(1.0f, onednn::convert_data_type(in.data_type));
-                        update_onednn_post_op_list(onednn_post_op_type::sum, dep_idx);
-                    } else {
-                        dnnl::memory::desc in_desc = onednn::layout_to_memory_desc(in, dnnl::memory::format_tag::ab, true);
-                        post_ops.append_binary(dnnl::algorithm::binary_add, in_desc);
-                        update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx);
-                    }
-                } else {
-                    if (in.size.spatial[0] > 1 || in.size.spatial[1] > 1 || in.size.batch[0] > 1)
-                        throw std::runtime_error("Unsupported eltwise mode for fused onednn op");
-                    if (idx == 0 && !has_out_scales(attrs)) {
-                        int mask = in.count() > 1 ? 2 : 0;
-                        attrs->set_output_scales(mask, {DNNL_RUNTIME_F32_VAL});
-                        update_onednn_post_op_list(onednn_post_op_type::scale, dep_idx);
-                    } else {
-                        dnnl::memory::desc in_desc = onednn::layout_to_memory_desc(in, dnnl::memory::format_tag::ab, true);
-                        post_ops.append_binary(dnnl::algorithm::binary_mul, in_desc);
-                        update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx);
-                    }
-                }
-            } else if (node->is_type<quantize>()) {
-                auto& q_node = node->as<quantize>();
-                auto dep_idx = cldnn_post_ops[idx].dep_start_idx;
-
-                if (q_node.get_per_tensor_output_range() && q_node.get_output_lo_val() < q_node.get_output_hi_val()) {
-                    // 1. pre-scale & pre-shift
-                    {
-                        if (q_node.get_per_tensor_input_scale() && q_node.get_per_tensor_input_shift()) {
-                            post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), q_node.get_input_shift_val());
-                            update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                        } else {
-                            if (q_node.get_per_tensor_input_scale()) {
-                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), 0.0f);
-                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                            } else {
-                                auto in_scale = arg.get_dependency(dep_idx++).get_output_layout();
-                                if (idx == 0 && !has_out_scales(attrs) && in_scale.data_type == data_types::f32 &&
-                                    arg.type() == convolution::type_id() &&
-                                    !data_type_traits::is_floating_point(arg.get_dependency(0).get_output_layout().data_type)) {
-                                    int mask = in_scale.count() > 1 ? 2 : 0;
-                                    attrs->set_output_scales(mask, {DNNL_RUNTIME_F32_VAL});
-                                    update_onednn_post_op_list(onednn_post_op_type::scale, dep_idx - 1);
-                                } else {
-                                    dnnl::memory::desc in_scale_desc = onednn::layout_to_memory_desc(in_scale, dnnl::memory::format_tag::ab, true);
-                                    post_ops.append_binary(dnnl::algorithm::binary_mul, in_scale_desc);
-                                    update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
-                                }
-                            }
-
-                            if (q_node.get_need_pre_shift()) {
-                                if (q_node.get_per_tensor_input_shift()) {
-                                    post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_input_shift_val());
-                                    update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                                } else {
-                                    auto in_shift = arg.get_dependency(dep_idx++).get_output_layout();
-                                    dnnl::memory::desc in_shift_desc = onednn::layout_to_memory_desc(in_shift, dnnl::memory::format_tag::ab, true);
-                                    post_ops.append_binary(dnnl::algorithm::binary_add, in_shift_desc);
-                                    update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
-                                }
-                            }
-                        }
-                    }
-
-                    // 2. round
-                    auto out_dt = cldnn_post_ops[idx].output_layout.data_type;
-                    bool output_type_is_int8 = out_dt == data_types::u8 || out_dt == data_types::i8;
-                    if (!output_type_is_int8) {
-                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_round, 0.0f, 0.0f);
-                        update_onednn_post_op_list(onednn_post_op_type::eltwise_round, empty_mem);
-                    }
-
-                    // 3. post-scale & post-shift
-                    if (q_node.get_need_post_scale() && q_node.get_need_post_shift() &&
-                        q_node.get_per_tensor_output_scale() && q_node.get_per_tensor_output_shift()) {
-                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), q_node.get_output_shift_val());
-                        update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                    } else {
-                        if (q_node.get_need_post_scale()) {
-                            if (q_node.get_per_tensor_output_scale()) {
-                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), 0.0f);
-                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                            } else {
-                                auto out_scale = arg.get_dependency(dep_idx++).get_output_layout();
-                                dnnl::memory::desc out_scale_desc = onednn::layout_to_memory_desc(out_scale, dnnl::memory::format_tag::ab, true);
-                                post_ops.append_binary(dnnl::algorithm::binary_mul, out_scale_desc);
-                                update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
-                            }
-                        }
-
-                        if (q_node.get_need_post_shift()) {
-                            if (q_node.get_per_tensor_output_shift()) {
-                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_output_shift_val());
-                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                            } else {
-                                auto out_shift = arg.get_dependency(dep_idx++).get_output_layout();
-                                dnnl::memory::desc out_shift_desc = onednn::layout_to_memory_desc(out_shift, dnnl::memory::format_tag::ab, true);
-                                post_ops.append_binary(dnnl::algorithm::binary_add, out_shift_desc);
-                                update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
-                            }
-                        }
-                    }
-
-                    // 4. clamp
-                    if (q_node.get_need_clamp()) {
-                        float out_lo = q_node.get_need_min_clamp() ? q_node.get_output_lo_val() : data_type_traits::min<float>(out_dt);
-                        float out_hi = q_node.get_need_max_clamp() ? q_node.get_output_hi_val() : data_type_traits::max<float>(out_dt);
-                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_clip, out_lo, out_hi);
-                        update_onednn_post_op_list(onednn_post_op_type::eltwise_clip, empty_mem);
-                    }
-                } else {
-                    // 1. clamp
-                    if (q_node.get_need_clamp()) {
-                        auto in_lo = arg.get_dependency(dep_idx++).get_output_layout();
-                        auto in_hi = arg.get_dependency(dep_idx++).get_output_layout();
-                        dnnl::algorithm clamp_max = dnnl::algorithm::binary_max;
-                        dnnl::algorithm clamp_min = dnnl::algorithm::binary_min;
-                        dnnl::memory::desc in_lo_desc = onednn::layout_to_memory_desc(in_lo, dnnl::memory::format_tag::ab, true);
-                        dnnl::memory::desc in_hi_desc = onednn::layout_to_memory_desc(in_hi, dnnl::memory::format_tag::ab, true);
-
-                        post_ops.append_binary(clamp_max, in_lo_desc);
-                        update_onednn_post_op_list(onednn_post_op_type::binary_max, dep_idx - 2);
-                        post_ops.append_binary(clamp_min, in_hi_desc);
-                        update_onednn_post_op_list(onednn_post_op_type::binary_min, dep_idx - 1);
-                    }
-
-                    // 2. pre-scale & pre-shift
-                    {
-                        if (q_node.get_per_tensor_input_scale() && q_node.get_per_tensor_input_shift()) {
-                            post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), q_node.get_input_shift_val());
-                            update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                        } else {
-                            if (q_node.get_per_tensor_input_scale()) {
-                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), 0.0f);
-                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                            } else {
-                                auto in_scale = arg.get_dependency(dep_idx++).get_output_layout();
-                                if (idx == 0 && !q_node.get_need_clamp() && !has_out_scales(attrs) && in_scale.data_type == data_types::f32 &&
-                                    arg.type() == convolution::type_id() &&
-                                    !data_type_traits::is_floating_point(arg.get_dependency(0).get_output_layout().data_type)) {
-                                    int mask = in_scale.count() > 1 ? 2 : 0;
-                                    attrs->set_output_scales(mask, {DNNL_RUNTIME_F32_VAL});
-                                    update_onednn_post_op_list(onednn_post_op_type::scale, dep_idx - 1);
-                                } else {
-                                    dnnl::memory::desc in_scale_desc = onednn::layout_to_memory_desc(in_scale, dnnl::memory::format_tag::ab, true);
-                                    post_ops.append_binary(dnnl::algorithm::binary_mul, in_scale_desc);
-                                    update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
-                                }
-                            }
-
-                            if (q_node.get_need_pre_shift()) {
-                                if (q_node.get_per_tensor_input_shift()) {
-                                    post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_input_shift_val());
-                                    update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                                } else {
-                                    auto in_shift = arg.get_dependency(dep_idx++).get_output_layout();
-                                    dnnl::memory::desc in_shift_desc = onednn::layout_to_memory_desc(in_shift, dnnl::memory::format_tag::ab, true);
-                                    post_ops.append_binary(dnnl::algorithm::binary_add, in_shift_desc);
-                                    update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
-                                }
-                            }
-                        }
-                    }
-
-                    // 3. round
-                    {
-                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_round, 0.0f, 0.0f);
-                        update_onednn_post_op_list(onednn_post_op_type::eltwise_round, empty_mem);
-                    }
-
-                    // 4. post-scale & post-shift
-                    if (q_node.get_need_post_scale() && q_node.get_need_post_shift() &&
-                        q_node.get_per_tensor_output_scale() && q_node.get_per_tensor_output_shift()) {
-                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), q_node.get_output_shift_val());
-                        update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                    } else {
-                        if (q_node.get_need_post_scale()) {
-                            if (q_node.get_per_tensor_output_scale()) {
-                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), 0.0f);
-                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                            } else {
-                                auto out_scale = arg.get_dependency(dep_idx++).get_output_layout();
-                                dnnl::memory::desc out_scale_desc = onednn::layout_to_memory_desc(out_scale, dnnl::memory::format_tag::ab, true);
-                                post_ops.append_binary(dnnl::algorithm::binary_mul, out_scale_desc);
-                                update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
-                            }
-                        }
-
-                        if (q_node.get_need_post_shift()) {
-                            if (q_node.get_per_tensor_output_shift()) {
-                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_output_shift_val());
-                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
-                            } else {
-                                auto out_shift = arg.get_dependency(dep_idx++).get_output_layout();
-                                dnnl::memory::desc out_shift_desc = onednn::layout_to_memory_desc(out_shift, dnnl::memory::format_tag::ab, true);
-                                post_ops.append_binary(dnnl::algorithm::binary_add, out_shift_desc);
-                                update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
-                            }
-                        }
-                    }
-                }
-            } else if (node->is_type<reorder>()) {
-                continue;
-            } else {
-                throw std::runtime_error("Unsupported fused op of " + node->get_primitive()->type_string() + " type for oneDNN primitive");
-            }
-        }
-
-        if (cldnn_post_ops.size() && arg.get_fused_activations_funcs().size())
-            throw std::runtime_error("Unsupported mix of fused ops and activations");
-
-        for (size_t i = 0; i < arg.get_fused_activations_funcs().size(); i++) {
-            auto activation_type = arg.get_fused_activations_funcs()[i];
-            auto params = arg.get_fused_activations_params()[i];
-            dnnl::algorithm alg = onednn::convert_activation_func(activation_type);
-            post_ops.append_eltwise(1.0f, alg, params.a, params.b);
-            update_onednn_post_op_list(onednn_post_op_type::eltwise_act, empty_mem);
-        }
-
-        // Update total onednn post-ops info
-        auto it = onednn_fusing_map.find(arg.id());
-        if (it != onednn_fusing_map.end()) {
-            it->second = std::move(fused_ops);
-        } else {
-            onednn_fusing_map.emplace(arg.id(), std::move(fused_ops));
-        }
-
-        // Trying to optimize more than 1 post-ops
-        auto post_ops_size = onednn_fusing_map[arg.id()].size();
-
-        if (post_ops_size > 1) {
-            dnnl::post_ops optimized_post_ops = post_ops;
-            bool optimization_is_finished = false;
-
-            // Trying to combine multiplications and additions which are placed one after another.
-            // We do it in the cycle because "eltw + eltw" cases can be simplified again in some cases.
-            do {
-                optimized_post_ops = try_optimize_post_ops(arg, optimized_post_ops, attrs, optimization_is_finished);
-            } while (!optimization_is_finished);
-
-            attrs->set_post_ops(optimized_post_ops);
-        } else {
-            // Set post-ops without any optimizations
-            attrs->set_post_ops(post_ops);
-        }
-
-        return attrs;
-    }
-
     event::ptr aggregate_events(const std::vector<event::ptr>& events, stream& stream, bool group = false, bool is_output = false) const {
         if (events.size() == 1 && !is_output)
             return events[0];
diff --git a/inference-engine/thirdparty/clDNN/src/impls/onednn/reorder_onednn.cpp b/inference-engine/thirdparty/clDNN/src/impls/onednn/reorder_onednn.cpp
index 31a09be251c0f3..86f9efecb38686 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/onednn/reorder_onednn.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/onednn/reorder_onednn.cpp
@@ -55,13 +55,13 @@ struct reorder_onednn : typed_primitive_onednn_impl<reorder, void, dnnl::reorder
             input_md,
             engine.get_onednn_engine(),
             output_md,
-            *get_primitive_attributes(arg));
+            *(arg.get_onednn_primitive_attributes()));
     }
 
 public:
     static primitive_impl* create(const reorder_node& arg) {
         auto desc = get_reorder_descriptor(arg);
-        auto attr = get_primitive_attributes(arg);
+        auto attr = arg.get_onednn_primitive_attributes();
 
         std::shared_ptr<void> dummy = nullptr;
 
diff --git a/inference-engine/thirdparty/clDNN/src/include/pass_manager.h b/inference-engine/thirdparty/clDNN/src/include/pass_manager.h
index 670dac7adc6092..f825bd060efe2c 100644
--- a/inference-engine/thirdparty/clDNN/src/include/pass_manager.h
+++ b/inference-engine/thirdparty/clDNN/src/include/pass_manager.h
@@ -393,4 +393,10 @@ class update_loop_primitive_map : public base_pass {
     void run(program& p) override;
 };
 
+class add_onednn_optimization_attributes : public base_pass {
+public:
+    add_onednn_optimization_attributes() : base_pass("add_onednn_optimization_attributes") {}
+    void run(program& p) override;
+};
+
 }  // namespace cldnn
diff --git a/inference-engine/thirdparty/clDNN/src/include/program_node.h b/inference-engine/thirdparty/clDNN/src/include/program_node.h
index 9b11846ee6ea77..3804b285065955 100644
--- a/inference-engine/thirdparty/clDNN/src/include/program_node.h
+++ b/inference-engine/thirdparty/clDNN/src/include/program_node.h
@@ -33,6 +33,29 @@ struct typed_program_node;
 class json_composite;
 class xml_composite;
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+enum class onednn_post_op_type : uint32_t {
+    eltwise_act,
+    eltwise_clip,
+    eltwise_linear,
+    eltwise_round,
+    binary_mul,
+    binary_add,
+    binary_max,
+    binary_min,
+    scale,
+    sum,
+    optimized,
+    optimized_eltwise,
+    optimized_sum
+};
+
+struct fused_primitive_desc_onednn {
+    onednn_post_op_type op_type; // onednn post-operation type
+    size_t mem_offset;           // index of a memory buffer for current post-operation
+    size_t mem_dep;              // memory dependency for working with fused node
+};
+#endif // ENABLE_ONEDNN_FOR_GPU
 
 struct fused_primitive_desc {
     std::shared_ptr<program_node> node;
@@ -57,7 +80,7 @@ struct fused_primitive_desc {
     to API level where all primitives store only ids of related ones.
 */
 struct program_node {
-    friend struct program;                     // to be removed when possible
+    friend struct program;                          // to be removed when possible
     friend class compile_graph;                     // to be removed when possible
     friend class graph_initializations;             // to be removed when possible
     friend class pre_replace_deconv;                // to be removed when possible
@@ -293,6 +316,16 @@ struct program_node {
     const std::vector<fused_primitive_desc>& get_fused_primitives() const { return fused_prims; }
     std::vector<fused_primitive_desc>& get_fused_primitives() { return fused_prims; }
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    const std::shared_ptr<dnnl::primitive_attr>& get_onednn_primitive_attributes() const { return onednn_attrs; }
+    std::shared_ptr<dnnl::primitive_attr>& get_onednn_primitive_attributes() { return onednn_attrs; }
+
+    const std::vector<fused_primitive_desc_onednn>& get_fused_primitives_onednn() const { return fused_prims_onednn; }
+    std::vector<fused_primitive_desc_onednn>& get_fused_primitives_onednn() { return fused_prims_onednn; }
+
+    void init_onednn_primitive_attributes();
+#endif // ENABLE_ONEDNN_FOR_GPU
+
     size_t get_fused_inputs_count() const {
         size_t count = 0;
         for (auto& fp : get_fused_primitives()) {
@@ -360,7 +393,26 @@ struct program_node {
 
     std::vector<fused_activation_params> fused_activations;
     std::vector<fused_primitive_desc> fused_prims;
+
     void invalidate_users() const;
+
+private:
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    std::vector<fused_primitive_desc_onednn> fused_prims_onednn;
+    std::shared_ptr<dnnl::primitive_attr> onednn_attrs;
+
+    void add_onednn_fused_primitives(std::vector<fused_primitive_desc_onednn> descs) {
+        fused_prims_onednn.erase(fused_prims_onednn.begin(), fused_prims_onednn.end());
+        fused_prims_onednn.insert(fused_prims_onednn.end(), descs.begin(), descs.end());
+    }
+
+    void add_onednn_attrs(std::shared_ptr<dnnl::primitive_attr> attrs) {
+        onednn_attrs = attrs;
+    }
+
+    bool has_out_scales(const std::shared_ptr<dnnl::primitive_attr>& attr);
+    dnnl::post_ops try_optimize_post_ops(dnnl::post_ops& p_ops, const std::shared_ptr<dnnl::primitive_attr>& attr, bool& optimization_is_completed);
+#endif // ENABLE_ONEDNN_FOR_GPU
 };
 
 /*
diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
index 5b9f0fb311f275..c2b8aed63f49e3 100644
--- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
+++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
@@ -859,17 +859,12 @@ layout layout_optimizer::get_expected_layout(layout const& current_layout,
                 if (input_layout.size.batch[0] % 16 == 0) {
                     expected_format = cldnn::format::bs_fs_yx_bsv32_fsv32;
                 } else {
-                    if (data_type_traits::is_floating_point(output_layout.data_type))
-                        expected_format = cldnn::format::b_fs_yx_fsv16;
-                    else
-                        expected_format = cldnn::format::b_fs_yx_fsv32;
+                    expected_format = cldnn::format::b_fs_yx_fsv32;
                 }
             } else if ((_optimization_attributes.b_fs_yx_fsv16_network &&
                        convolution_b_fs_yx_fsv16_opt(input_layout, output_layout, weights_layout, prim)) && is_2d) {
-                if (is_dw)
-                    expected_format = cldnn::format::b_fs_yx_fsv32;
-                else
-                    expected_format = cldnn::format::b_fs_yx_fsv16;
+                // TODO: optimize clDNN kernels for good support of b_fs_yx_fsv32 format
+                expected_format = cldnn::format::b_fs_yx_fsv32;
             } else {
                 expected_format = imad_case(node);
             }
diff --git a/inference-engine/thirdparty/clDNN/src/network.cpp b/inference-engine/thirdparty/clDNN/src/network.cpp
index 1293b628d30895..d38ad87060f461 100644
--- a/inference-engine/thirdparty/clDNN/src/network.cpp
+++ b/inference-engine/thirdparty/clDNN/src/network.cpp
@@ -497,6 +497,9 @@ void network::allocate_primitives() {
                     auto eltw_in_layout = eltw_in.get_output_layout();
                     auto out_layout = node->get_output_layout();
 
+                    if (!fused_op.node->as<eltwise>().get_primitive()->needs_onednn_sum_post_op(eltw_in_layout))
+                        continue;
+
                     if (eltw_in_layout.size == out_layout.size &&
                         eltw_in_layout.format == out_layout.format &&
                         eltw_in_layout.data_padding == out_layout.data_padding &&
diff --git a/inference-engine/thirdparty/clDNN/src/primitive_inst.cpp b/inference-engine/thirdparty/clDNN/src/primitive_inst.cpp
index c99329cb6e3251..7aa46da302df86 100644
--- a/inference-engine/thirdparty/clDNN/src/primitive_inst.cpp
+++ b/inference-engine/thirdparty/clDNN/src/primitive_inst.cpp
@@ -269,7 +269,7 @@ memory::ptr primitive_inst::allocate_output() {
     };
 
     bool usm_device_allocatable = true;
-    const auto& total_device_input_mem_size = std::accumulate(node_deps.begin(), node_deps.end(), 0, device_mem_acc);
+    const auto& total_device_input_mem_size = std::accumulate(node_deps.begin(), node_deps.end(), (uint64_t)0, device_mem_acc);
     if (total_device_input_mem_size > engine.get_device_info().max_global_mem_size)
         usm_device_allocatable = false;
 
diff --git a/inference-engine/thirdparty/clDNN/src/program.cpp b/inference-engine/thirdparty/clDNN/src/program.cpp
index 13accf3b8a5999..74f07313392fe6 100644
--- a/inference-engine/thirdparty/clDNN/src/program.cpp
+++ b/inference-engine/thirdparty/clDNN/src/program.cpp
@@ -47,6 +47,7 @@
 #include "lstm_gemm_inst.h"
 #include "mutable_data_inst.h"
 #include "pooling_inst.h"
+#include "border_inst.h"
 #include "primitive_inst.h"
 #include "prior_box_inst.h"
 #include "proposal_inst.h"
@@ -534,6 +535,9 @@ void program::pre_optimize_graph(bool is_internal) {
 
     // check if there exists some layout incompatibilities and add an reorder node if required
     apply_opt_pass<add_required_reorders>();
+
+    // add optimization attributes for onednn primitives
+    apply_opt_pass<add_onednn_optimization_attributes>();
 }
 
 void program::post_optimize_graph(bool is_internal) {
@@ -1292,6 +1296,7 @@ void program::set_layout_optimizer_attributes(layout_optimizer& lo) {
             prim.type() != cldnn::input_layout::type_id() &&
             prim.type() != cldnn::softmax::type_id() &&
             prim.type() != cldnn::prior_box::type_id() &&
+            prim.type() != cldnn::border::type_id() &&
             prim.type() != cldnn::resample::type_id() &&
             prim.type() != cldnn::crop::type_id() &&
             prim.type() != cldnn::scale::type_id() &&
diff --git a/inference-engine/thirdparty/clDNN/src/program_node.cpp b/inference-engine/thirdparty/clDNN/src/program_node.cpp
index a37bd13b5ffafc..b2b432e8cfd2f7 100644
--- a/inference-engine/thirdparty/clDNN/src/program_node.cpp
+++ b/inference-engine/thirdparty/clDNN/src/program_node.cpp
@@ -5,6 +5,14 @@
 #include "program_node.h"
 #include "cldnn/graph/program.hpp"
 #include "primitive_inst.h"
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#include "convolution_inst.h"
+#include "quantize_inst.h"
+#include "reorder_inst.h"
+#include <impls/onednn/utils.hpp>
+#endif // ENABLE_ONEDNN_FOR_GPU
+
 #include "to_string_utils.h"
 #include "json_object.h"
 #include <vector>
@@ -280,3 +288,699 @@ bool program_node::need_lockable_memory() const {
 
     return need_lockable_mem;
 }
+
+    /* ----------------------------------------- */
+    /* Onednn fused operations integration logic */
+    /* ----------------------------------------- */
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+
+bool program_node::has_out_scales(const std::shared_ptr<dnnl::primitive_attr>& attr) {
+    int mask;
+    std::vector<float> scales;
+    attr->get_output_scales(mask, scales);
+    const auto drfv = reinterpret_cast<const int32_t&>(DNNL_RUNTIME_F32_VAL);
+    return !scales.empty() && (reinterpret_cast<const int32_t&>(scales[0]) == drfv);
+}
+
+dnnl::post_ops program_node::try_optimize_post_ops(dnnl::post_ops& p_ops, const std::shared_ptr<dnnl::primitive_attr>& attr,
+                                                   bool& optimization_is_completed) {
+    // Create new dnnl::post_ops object which will be filled inside the optimization process
+    dnnl::post_ops optimized_p_ops;
+
+    // Add new post-op into optimized_p_ops structure
+    auto add_post_op = [&](onednn_post_op_type type, const dnnl::post_ops& cur_p_ops, dnnl::post_ops& new_p_ops, int idx) {
+        switch (type) {
+            case onednn_post_op_type::eltwise_act:
+            case onednn_post_op_type::eltwise_clip:
+            case onednn_post_op_type::eltwise_linear:
+            case onednn_post_op_type::eltwise_round:
+            {
+                dnnl::algorithm alg;
+                float scale, alpha, beta;
+                cur_p_ops.get_params_eltwise(idx, scale, alg, alpha, beta);
+                new_p_ops.append_eltwise(scale, alg, alpha, beta);
+                break;
+            }
+
+            case onednn_post_op_type::binary_add:
+            case onednn_post_op_type::binary_mul:
+            case onednn_post_op_type::binary_max:
+            case onednn_post_op_type::binary_min:
+            {
+                dnnl::algorithm alg;
+                dnnl::memory::desc desc;
+                cur_p_ops.get_params_binary(idx, alg, desc);
+                new_p_ops.append_binary(alg, desc);
+                break;
+            }
+
+            case onednn_post_op_type::scale:
+            {
+                break;
+            }
+
+            case onednn_post_op_type::sum:
+            {
+                float scale;
+                dnnl::memory::data_type data_type;
+                cur_p_ops.get_params_sum(idx, scale, data_type);
+                new_p_ops.append_sum(scale, data_type);
+                break;
+            }
+
+            case onednn_post_op_type::optimized:
+            case onednn_post_op_type::optimized_sum:
+            case onednn_post_op_type::optimized_eltwise:
+            {
+                // Current operation already has been optimized => don't need extra actions
+                break;
+            }
+
+            default:
+                throw std::runtime_error("Unsupported onednn post-operation type");
+        }
+    };
+
+    // Check that post-op type is any optimized
+    auto type_is_any_optimized = [](onednn_post_op_type type) -> bool {
+        return type == onednn_post_op_type::optimized || type == onednn_post_op_type::optimized_sum ||
+               type == onednn_post_op_type::optimized_eltwise;
+    };
+
+    // Check that post-op type is eltwise
+    auto type_is_eltwise = [](onednn_post_op_type type) -> bool {
+        return type == onednn_post_op_type::eltwise_round || type == onednn_post_op_type::eltwise_linear ||
+               type == onednn_post_op_type::eltwise_clip  || type == onednn_post_op_type::eltwise_act;
+    };
+
+    // Check that post-op type is binary_add or binary_mul
+    auto type_is_binary_add_or_mul = [](onednn_post_op_type type) -> bool {
+        return type == onednn_post_op_type::binary_add || type == onednn_post_op_type::binary_mul;
+    };
+
+    // Simple post-op type checks
+    auto type_is_optimized         = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::optimized; };
+    auto type_is_eltwise_linear    = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::eltwise_linear; };
+    auto type_is_optimized_eltwise = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::optimized_eltwise; };
+    auto type_is_binary_add        = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::binary_add; };
+    auto type_is_binary_mul        = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::binary_mul; };
+    auto type_is_sum               = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::sum; };
+    auto type_is_optimized_sum     = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::optimized_sum; };
+    auto type_is_scale             = [](onednn_post_op_type type) -> bool { return type == onednn_post_op_type::scale; };
+
+    auto& cur_post_ops = get_fused_primitives_onednn();
+
+    size_t cur_post_op_idx = 1;
+    size_t prev_post_op_idx = 0;
+    bool optimization_done = false;
+
+    // Check and update post-op map if we already optimized something
+    for (size_t post_op_idx = 0; post_op_idx < cur_post_ops.size(); post_op_idx++) {
+        if (type_is_optimized_sum(cur_post_ops[post_op_idx].op_type))
+            cur_post_ops[post_op_idx].op_type = onednn_post_op_type::sum;
+        else if (type_is_optimized_eltwise(cur_post_ops[post_op_idx].op_type))
+            cur_post_ops[post_op_idx].op_type = onednn_post_op_type::eltwise_linear;
+        else if (type_is_optimized(cur_post_ops[post_op_idx].op_type))
+            cur_post_ops.erase(cur_post_ops.begin() + post_op_idx);
+    }
+
+    // Get post-ops size for current node
+    auto post_ops_size = cur_post_ops.size();
+
+    // Try to combine pairs of arithmetic post-ops (adds and muls) into one operation inside this cycle
+    while (!optimization_done) {
+        auto cur_type = cur_post_ops[cur_post_op_idx].op_type;
+        auto prev_type = cur_post_ops[prev_post_op_idx].op_type;
+
+        // Ignore optimized operations for "previous" operation in our operation pair
+        while (type_is_any_optimized(prev_type) && cur_post_op_idx < post_ops_size - 1) {
+            prev_post_op_idx++;
+            cur_post_op_idx++;
+            prev_type = cur_post_ops[prev_post_op_idx].op_type;
+            cur_type = cur_post_ops[cur_post_op_idx].op_type;
+        }
+
+        // Ignore optimized operations for "current" operation in our operation pair
+        while (type_is_any_optimized(cur_type) && cur_post_op_idx < post_ops_size - 1) {
+            cur_post_op_idx++;
+            cur_type = cur_post_ops[cur_post_op_idx].op_type;
+        }
+
+        auto cur_idx = static_cast<int>(has_out_scales(attr) ? (cur_post_op_idx >= 1 ? cur_post_op_idx - 1 : 0) : cur_post_op_idx);
+        auto prev_idx = static_cast<int>(has_out_scales(attr) ? (prev_post_op_idx >= 1 ? prev_post_op_idx - 1 : 0) : prev_post_op_idx);
+
+        // If this is the last pair and it's optimized - add the last post-op and go out from the cycle
+        if (cur_post_op_idx == post_ops_size - 1 && (type_is_any_optimized(cur_type) || type_is_any_optimized(prev_type))) {
+            if (!type_is_any_optimized(prev_type)) {
+                add_post_op(prev_type, p_ops, optimized_p_ops, prev_idx);
+            }
+            if (!type_is_any_optimized(cur_type)) {
+                add_post_op(cur_type, p_ops, optimized_p_ops, cur_idx);
+            }
+            break;
+        }
+
+        // Post-ops combinations which can be simplified
+        auto eltw_and_eltw  = type_is_eltwise(cur_type) && type_is_eltwise(prev_type);
+        auto bin_and_eltw   = type_is_binary_add_or_mul(cur_type) && type_is_eltwise_linear(prev_type);
+        auto eltw_and_bin   = type_is_eltwise_linear(cur_type) && type_is_binary_add_or_mul(prev_type);
+        auto sum_and_eltw   = type_is_sum(cur_type) && type_is_eltwise(prev_type);
+        auto eltw_and_scale = type_is_eltwise_linear(cur_type) && type_is_scale(prev_type);
+
+        auto can_try_optimize = eltw_and_eltw ||
+                                bin_and_eltw ||
+                                eltw_and_bin ||
+                                sum_and_eltw ||
+                                eltw_and_scale;
+
+        bool cur_ops_pair_is_optimized = false;
+
+        if (can_try_optimize) {
+            if (eltw_and_eltw) {
+                dnnl::algorithm cur_alg, prev_alg;
+                float cur_scale, prev_scale, cur_alpha, prev_alpha, cur_beta, prev_beta;
+
+                p_ops.get_params_eltwise(prev_idx, prev_scale, prev_alg, prev_alpha, prev_beta);
+                p_ops.get_params_eltwise(cur_idx, cur_scale, cur_alg, cur_alpha, cur_beta);
+
+                auto eltw_linear_and_eltw_linear = type_is_eltwise_linear(cur_type) && type_is_eltwise_linear(prev_type);
+                auto eltw_linear_and_eltw_non_linear = type_is_eltwise_linear(cur_type) && !type_is_eltwise_linear(prev_type) && cur_beta == 0;
+
+                // eltwise_linear + eltwise_linear combination can be optimized always
+                if (eltw_linear_and_eltw_linear) {
+                    dnnl::post_ops eltw_p_op;
+                    float optimized_alpha = cur_alpha * prev_alpha * prev_scale;
+                    float optimized_beta = cur_alpha * prev_beta * prev_scale + cur_beta;
+                    float optimized_scale = cur_scale;
+                    eltw_p_op.append_eltwise(optimized_scale, cur_alg, optimized_alpha, optimized_beta);
+
+                    // Combine 2 eltwises into one
+                    add_post_op(cur_type, eltw_p_op, optimized_p_ops, 0);
+                } else if (eltw_linear_and_eltw_non_linear) {
+                    dnnl::post_ops eltw_p_op;
+                    eltw_p_op.append_eltwise(cur_scale * prev_scale * cur_alpha, prev_alg, prev_alpha, prev_beta);
+
+                    // Combine 2 eltwises into one
+                    add_post_op(prev_type, eltw_p_op, optimized_p_ops, 0);
+                }
+
+                if (eltw_linear_and_eltw_linear || eltw_linear_and_eltw_non_linear) {
+                    // Marked current and previous eltwise operations as 'optimized' (they will be ignored on the next iteration of cycle)
+                    cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized;
+                    cur_post_ops[prev_post_op_idx].op_type = onednn_post_op_type::optimized_eltwise;
+
+                    // Set the flag if extra optimizations checking is needed
+                    if (cur_post_op_idx < post_ops_size - 1) {
+                        if (type_is_eltwise_linear(cur_post_ops[cur_post_op_idx + 1].op_type) ||
+                            type_is_binary_add_or_mul(cur_post_ops[cur_post_op_idx + 1].op_type) ||
+                            type_is_optimized_eltwise(cur_post_ops[cur_post_op_idx + 1].op_type)) {
+                            optimization_is_completed = true;
+                        }
+                    }
+
+                    cur_ops_pair_is_optimized = true;
+                }
+            } else if (bin_and_eltw) {
+                dnnl::algorithm alg;
+                dnnl::memory::desc desc;
+                float scale, alpha, beta;
+
+                cldnn::program_node& cur_node = get_dependency(cur_post_ops[cur_post_op_idx].mem_dep);
+
+                p_ops.get_params_binary(cur_idx, alg, desc);
+                p_ops.get_params_eltwise(prev_idx, scale, alg, alpha, beta);
+
+                // Eltwise operations can use runtime non-constant data buffers, so check that memory buffers consist of constant data only
+                auto bin_ops_can_be_optimized = cur_node.is_type<data>() && cur_node.is_constant() &&
+                                                cur_node.get_users().size() == 1 && desc.data_type() == dnnl_f32;
+
+                auto bin_add_and_eltw = alpha == 1.0f && scale == 1.0f && type_is_binary_add(cur_type) && bin_ops_can_be_optimized;
+                auto bin_mul_and_eltw = beta == 0.f && type_is_binary_mul(cur_type) && bin_ops_can_be_optimized;
+
+                if (bin_add_and_eltw || bin_mul_and_eltw) {
+                    memory::ptr cur_bin_mem_ptr = cur_node.as<data>().get_attached_memory_ptr();
+                    if (cur_bin_mem_ptr == nullptr)
+                        throw std::runtime_error("OneDNN post-ops optimization error: nonexistent node for bin + eltw");
+                    auto& stream = cur_bin_mem_ptr->get_engine()->get_program_stream();
+                    mem_lock<float, mem_lock_type::read_write> bin_and_eltw_lock(cur_bin_mem_ptr, stream);
+
+                    size_t cur_bin_mem_size = cur_node.get_output_layout().count();
+
+                    // Update all binary coefficients
+                    if (bin_add_and_eltw) {
+                        for (size_t data_idx = 0; data_idx < cur_bin_mem_size; data_idx++) {
+                            bin_and_eltw_lock[data_idx] += beta;
+                        }
+                    } else {
+                        for (size_t data_idx = 0; data_idx < cur_bin_mem_size; data_idx++) {
+                            bin_and_eltw_lock[data_idx] *= alpha * scale;
+                        }
+                    }
+
+                    // Marked previous eltwise operation as 'optimized' (it will be ignored on the next iteration of cycle)
+                    cur_post_ops[prev_post_op_idx].op_type = onednn_post_op_type::optimized;
+
+                    cur_ops_pair_is_optimized = true;
+                }
+            } else if (eltw_and_bin) {
+                dnnl::algorithm alg;
+                dnnl::memory::desc desc;
+                float scale, alpha, beta;
+
+                cldnn::program_node& prev_node = get_dependency(cur_post_ops[prev_post_op_idx].mem_dep);
+
+                p_ops.get_params_eltwise(cur_idx, scale, alg, alpha, beta);
+                p_ops.get_params_binary(prev_idx, alg, desc);
+
+                // Eltwise operations can use runtime non-constant data buffers, so check that memory buffers consist of constant data only
+                auto bin_ops_can_be_optimized = prev_node.is_type<data>() && prev_node.is_constant() &&
+                                                prev_node.get_users().size() == 1 && desc.data_type() == dnnl_f32;
+
+                auto eltw_and_bin_add = alpha == 1.0f && scale == 1.0f && type_is_binary_add(prev_type) && bin_ops_can_be_optimized;
+                auto eltw_and_bin_mul = beta == 0.f && type_is_binary_mul(prev_type) && bin_ops_can_be_optimized;
+
+                if (eltw_and_bin_add || eltw_and_bin_mul) {
+                    memory::ptr prev_bin_mem_ptr = prev_node.as<data>().get_attached_memory_ptr();
+                    if (prev_bin_mem_ptr == nullptr)
+                        throw std::runtime_error("OneDNN post-ops optimization error: nonexistent node for eltw + bin");
+                    auto& stream = prev_bin_mem_ptr->get_engine()->get_program_stream();
+                    mem_lock<float, mem_lock_type::read_write> eltw_and_bin_lock(prev_bin_mem_ptr, stream);
+
+                    size_t prev_bin_mem_size = prev_node.get_output_layout().count();
+
+                    // Update all binary coefficients
+                    if (eltw_and_bin_add) {
+                        for (size_t data_idx = 0; data_idx < prev_bin_mem_size; data_idx++) {
+                            eltw_and_bin_lock[data_idx] += beta;
+                        }
+                    } else {
+                        for (size_t data_idx = 0; data_idx < prev_bin_mem_size; data_idx++) {
+                            eltw_and_bin_lock[data_idx] *= alpha * scale;
+                        }
+                    }
+
+                    // Marked current eltwise operation as 'optimized' (it will be ignored on the next iteration of cycle)
+                    cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized;
+
+                    cur_ops_pair_is_optimized = true;
+                }
+            } else if (sum_and_eltw) {
+                dnnl::algorithm alg;
+                float sum_scale, eltw_scale, alpha, beta;
+                dnnl::memory::data_type data_type;
+
+                dnnl::algorithm next_alg;
+                float next_scale, next_alpha, next_beta;
+                size_t next_idx = cur_idx + 1;
+                size_t next_post_op_idx = cur_post_op_idx + 1;
+
+                bool can_optimize_eltw_and_sum = false;
+
+                if (cur_post_op_idx < post_ops_size - 1) {
+                    auto next_type = cur_post_ops[next_post_op_idx].op_type;
+                    if (type_is_eltwise_linear(next_type)) {
+                        p_ops.get_params_eltwise(next_idx, next_scale, next_alg, next_alpha, next_beta);
+
+                        if (next_beta == 0)
+                            can_optimize_eltw_and_sum = true;
+                    }
+                }
+
+                // Try to optimize eltwise (any) + sum + eltwise_linear (with beta = 0) chain of operations
+                if (can_optimize_eltw_and_sum) {
+                    p_ops.get_params_sum(cur_idx, sum_scale, data_type);
+                    p_ops.get_params_eltwise(prev_idx, eltw_scale, alg, alpha, beta);
+
+                    dnnl::post_ops eltw_p_op_prev, sum_p_op;
+
+                    eltw_p_op_prev.append_eltwise(eltw_scale * next_alpha * next_scale, alg, alpha, beta);
+                    sum_p_op.append_sum(sum_scale * next_alpha, data_type);
+
+                    add_post_op(prev_type, eltw_p_op_prev, optimized_p_ops, 0);
+                    add_post_op(cur_type, sum_p_op, optimized_p_ops, 0);
+
+                    // Marked current, previous and next operations as 'optimized' (they will be ignored on the next iteration of cycle)
+                    cur_post_ops[prev_post_op_idx].op_type = onednn_post_op_type::optimized_eltwise;
+                    cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized_sum;
+                    cur_post_ops[next_post_op_idx].op_type = onednn_post_op_type::optimized;
+
+                    // Set the flag if extra optimizations checking is needed
+                    if (next_post_op_idx < post_ops_size - 1) {
+                        if (type_is_eltwise_linear(cur_post_ops[next_post_op_idx + 1].op_type) ||
+                            type_is_optimized_eltwise(cur_post_ops[next_post_op_idx + 1].op_type)) {
+                            optimization_is_completed = true;
+                        }
+                    }
+
+                    cur_ops_pair_is_optimized = true;
+                }
+            } else if (eltw_and_scale) {
+                dnnl::algorithm alg;
+                float eltw_scale, alpha, beta;
+
+                cldnn::program_node& prev_node = get_dependency(cur_post_ops[prev_post_op_idx].mem_dep);
+
+                p_ops.get_params_eltwise(cur_idx, eltw_scale, alg, alpha, beta);
+
+                // Eltwise can be inserted into the output_scale if cur_beta is equal to 0.f
+                if (beta == 0.f && prev_node.get_output_layout().data_type == data_types::f32) {
+                    memory::ptr prev_scale_mem_ptr = prev_node.as<data>().get_attached_memory_ptr();
+                    if (prev_scale_mem_ptr == nullptr)
+                        throw std::runtime_error("OneDNN post-ops optimization error: nonexistent node for eltw + scale");
+                    auto& stream = prev_scale_mem_ptr->get_engine()->get_program_stream();
+                    mem_lock<float, mem_lock_type::read_write> eltw_and_scale_lock(prev_scale_mem_ptr, stream);
+
+                    size_t prev_scale_mem_size = prev_node.get_output_layout().count();
+
+                    // Update all scale coefficients
+                    for (size_t data_idx = 0; data_idx < prev_scale_mem_size; data_idx++) {
+                        eltw_and_scale_lock[data_idx] *= alpha * eltw_scale;
+                    }
+
+                    // Marked current eltwise operation as 'optimized' (it will be ignored on the next iteration of cycle)
+                    cur_post_ops[cur_post_op_idx].op_type = onednn_post_op_type::optimized;
+
+                    cur_ops_pair_is_optimized = true;
+                }
+            }
+        }
+
+        // If no optimizations have been applied then copy post-op info into the new optimized_p_ops structure
+        if (!(has_out_scales(attr) && prev_post_op_idx == 0) && !cur_ops_pair_is_optimized) {
+            add_post_op(prev_type, p_ops, optimized_p_ops, prev_idx);
+        }
+
+        if (cur_post_op_idx == post_ops_size - 1 && !cur_ops_pair_is_optimized) {
+            add_post_op(cur_type, p_ops, optimized_p_ops, cur_idx);
+            optimization_done = true;
+        } else if (cur_post_ops[cur_post_op_idx].op_type != onednn_post_op_type::optimized) {
+            cur_post_op_idx++;
+            prev_post_op_idx++;
+        }
+    }
+
+    optimization_is_completed = !optimization_is_completed;
+
+    add_onednn_fused_primitives(cur_post_ops);
+
+    return optimized_p_ops;
+}
+
+
+void program_node::init_onednn_primitive_attributes() {
+    const std::vector<fused_primitive_desc>& cldnn_post_ops = get_fused_primitives();
+    auto attrs = std::make_shared<dnnl::primitive_attr>();
+    dnnl::post_ops post_ops;
+    size_t memory_offset = 0;
+
+    // Create onednn post-ops list related to the current node
+    std::vector<fused_primitive_desc_onednn> fused_ops;
+
+    // Added this for debug purposes only
+    size_t empty_mem = 0xff;
+
+    // Add information about post-operation into the list, update indices
+    auto update_onednn_post_op_list = [&](onednn_post_op_type type, size_t m_dep) {
+        fused_primitive_desc_onednn cur_op_desc = { type, memory_offset, m_dep };
+        fused_ops.push_back(cur_op_desc);
+
+        auto has_memory_buffers = type == onednn_post_op_type::binary_add ||
+                                  type == onednn_post_op_type::binary_mul ||
+                                  type == onednn_post_op_type::binary_max ||
+                                  type == onednn_post_op_type::binary_min ||
+                                  type == onednn_post_op_type::scale ||
+                                  type == onednn_post_op_type::sum;
+        if (has_memory_buffers)
+            memory_offset++;
+    };
+
+    for (size_t idx = 0; idx < cldnn_post_ops.size(); idx++) {
+        auto node = cldnn_post_ops[idx].node;
+
+        if (node->is_type<activation>()) {
+            auto fused_desc = node->as<activation>().get_primitive();
+            dnnl::algorithm alg = onednn::convert_activation_func(fused_desc->activation_function);
+            post_ops.append_eltwise(1.0f, alg, fused_desc->additional_params.a, fused_desc->additional_params.b);
+            update_onednn_post_op_list(onednn_post_op_type::eltwise_act, empty_mem);
+        } else if (node->is_type<eltwise>()) {
+            auto& e_node = node->as<eltwise>();
+            auto dep_idx = cldnn_post_ops[idx].dep_start_idx;
+            auto in = get_dependency(dep_idx).get_output_layout();
+
+            if (e_node.get_primitive()->mode == eltwise_mode::sum) {
+                if (e_node.get_primitive()->needs_onednn_sum_post_op(in)) {
+                    post_ops.append_sum(1.0f, onednn::convert_data_type(in.data_type));
+                    update_onednn_post_op_list(onednn_post_op_type::sum, dep_idx);
+                } else {
+                    dnnl::memory::desc in_desc = onednn::layout_to_memory_desc(in, dnnl::memory::format_tag::ab, true);
+                    post_ops.append_binary(dnnl::algorithm::binary_add, in_desc);
+                    update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx);
+                }
+            } else {
+                if (in.size.spatial[0] > 1 || in.size.spatial[1] > 1 || in.size.batch[0] > 1)
+                    throw std::runtime_error("Unsupported eltwise mode for fused onednn op");
+                if (idx == 0 && !has_out_scales(attrs)) {
+                    int mask = in.count() > 1 ? 2 : 0;
+                    attrs->set_output_scales(mask, {DNNL_RUNTIME_F32_VAL});
+                    update_onednn_post_op_list(onednn_post_op_type::scale, dep_idx);
+                } else {
+                    dnnl::memory::desc in_desc = onednn::layout_to_memory_desc(in, dnnl::memory::format_tag::ab, true);
+                    post_ops.append_binary(dnnl::algorithm::binary_mul, in_desc);
+                    update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx);
+                }
+            }
+        } else if (node->is_type<quantize>()) {
+            auto& q_node = node->as<quantize>();
+            auto dep_idx = cldnn_post_ops[idx].dep_start_idx;
+
+            // ********************************* Common case with output range usage ********************************* //
+            if (q_node.get_per_tensor_output_range() && q_node.get_output_lo_val() < q_node.get_output_hi_val()) {
+                // 1. pre-scale & pre-shift
+                {
+                    if (q_node.get_per_tensor_input_scale() && q_node.get_per_tensor_input_shift()) {
+                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), q_node.get_input_shift_val());
+                        update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                    } else {
+                        if (q_node.get_per_tensor_input_scale()) {
+                            post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), 0.0f);
+                            update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                        } else {
+                            auto in_scale = get_dependency(dep_idx++).get_output_layout();
+                            if (idx == 0 && !has_out_scales(attrs) && in_scale.data_type == data_types::f32 &&
+                                is_type<convolution>() &&
+                                !data_type_traits::is_floating_point(get_dependency(0).get_output_layout().data_type)) {
+                                int mask = in_scale.count() > 1 ? 2 : 0;
+                                attrs->set_output_scales(mask, {DNNL_RUNTIME_F32_VAL});
+                                update_onednn_post_op_list(onednn_post_op_type::scale, dep_idx - 1);
+                            } else {
+                                dnnl::memory::desc in_scale_desc = onednn::layout_to_memory_desc(in_scale, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_mul, in_scale_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
+                            }
+                        }
+
+                        if (q_node.get_need_pre_shift()) {
+                            if (q_node.get_per_tensor_input_shift()) {
+                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_input_shift_val());
+                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                            } else {
+                                auto in_shift = get_dependency(dep_idx++).get_output_layout();
+                                dnnl::memory::desc in_shift_desc = onednn::layout_to_memory_desc(in_shift, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_add, in_shift_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
+                            }
+                        }
+                    }
+                }
+
+                // 2. round
+                auto out_dt = cldnn_post_ops[idx].output_layout.data_type;
+                {
+                    bool output_type_is_int8 = out_dt == data_types::u8 || out_dt == data_types::i8;
+                    if (!output_type_is_int8) {
+                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_round, 0.0f, 0.0f);
+                        update_onednn_post_op_list(onednn_post_op_type::eltwise_round, empty_mem);
+                    }
+                }
+
+                // 3. post-scale & post-shift
+                {
+                    if (q_node.get_need_post_scale() && q_node.get_need_post_shift() &&
+                        q_node.get_per_tensor_output_scale() && q_node.get_per_tensor_output_shift()) {
+                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), q_node.get_output_shift_val());
+                        update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                    } else {
+                        if (q_node.get_need_post_scale()) {
+                            if (q_node.get_per_tensor_output_scale()) {
+                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), 0.0f);
+                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                            } else {
+                                auto out_scale = get_dependency(dep_idx++).get_output_layout();
+                                dnnl::memory::desc out_scale_desc = onednn::layout_to_memory_desc(out_scale, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_mul, out_scale_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
+                            }
+                        }
+
+                        if (q_node.get_need_post_shift()) {
+                            if (q_node.get_per_tensor_output_shift()) {
+                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_output_shift_val());
+                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                            } else {
+                                auto out_shift = get_dependency(dep_idx++).get_output_layout();
+                                dnnl::memory::desc out_shift_desc = onednn::layout_to_memory_desc(out_shift, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_add, out_shift_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
+                            }
+                        }
+                    }
+                }
+
+                // 4. clamp
+                {
+                    if (q_node.get_need_clamp()) {
+                        float out_lo = q_node.get_need_min_clamp() ? q_node.get_output_lo_val() : data_type_traits::min<float>(out_dt);
+                        float out_hi = q_node.get_need_max_clamp() ? q_node.get_output_hi_val() : data_type_traits::max<float>(out_dt);
+                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_clip, out_lo, out_hi);
+                        update_onednn_post_op_list(onednn_post_op_type::eltwise_clip, empty_mem);
+                    }
+                }
+            // ********************************* Rare case with input range usage ********************************* //
+            } else {
+                // 1. clamp
+                {
+                    if (q_node.get_need_clamp()) {
+                        auto in_lo = get_dependency(dep_idx++).get_output_layout();
+                        auto in_hi = get_dependency(dep_idx++).get_output_layout();
+                        dnnl::algorithm clamp_max = dnnl::algorithm::binary_max;
+                        dnnl::algorithm clamp_min = dnnl::algorithm::binary_min;
+                        dnnl::memory::desc in_lo_desc = onednn::layout_to_memory_desc(in_lo, dnnl::memory::format_tag::ab, true);
+                        dnnl::memory::desc in_hi_desc = onednn::layout_to_memory_desc(in_hi, dnnl::memory::format_tag::ab, true);
+
+                        post_ops.append_binary(clamp_max, in_lo_desc);
+                        update_onednn_post_op_list(onednn_post_op_type::binary_max, dep_idx - 2);
+                        post_ops.append_binary(clamp_min, in_hi_desc);
+                        update_onednn_post_op_list(onednn_post_op_type::binary_min, dep_idx - 1);
+                    }
+                }
+
+                // 2. pre-scale & pre-shift
+                {
+                    if (q_node.get_per_tensor_input_scale() && q_node.get_per_tensor_input_shift()) {
+                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), q_node.get_input_shift_val());
+                        update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                    } else {
+                        if (q_node.get_per_tensor_input_scale()) {
+                            post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_input_scale_val(), 0.0f);
+                            update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                        } else {
+                            auto in_scale = get_dependency(dep_idx++).get_output_layout();
+                            if (idx == 0 && !q_node.get_need_clamp() && !has_out_scales(attrs) && in_scale.data_type == data_types::f32 &&
+                                is_type<convolution>() &&
+                                !data_type_traits::is_floating_point(get_dependency(0).get_output_layout().data_type)) {
+                                int mask = in_scale.count() > 1 ? 2 : 0;
+                                attrs->set_output_scales(mask, {DNNL_RUNTIME_F32_VAL});
+                                update_onednn_post_op_list(onednn_post_op_type::scale, dep_idx - 1);
+                            } else {
+                                dnnl::memory::desc in_scale_desc = onednn::layout_to_memory_desc(in_scale, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_mul, in_scale_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
+                            }
+                        }
+
+                        if (q_node.get_need_pre_shift()) {
+                            if (q_node.get_per_tensor_input_shift()) {
+                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_input_shift_val());
+                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                            } else {
+                                auto in_shift = get_dependency(dep_idx++).get_output_layout();
+                                dnnl::memory::desc in_shift_desc = onednn::layout_to_memory_desc(in_shift, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_add, in_shift_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
+                            }
+                        }
+                    }
+                }
+
+                // 3. round
+                {
+                    post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_round, 0.0f, 0.0f);
+                    update_onednn_post_op_list(onednn_post_op_type::eltwise_round, empty_mem);
+                }
+
+                // 4. post-scale & post-shift
+                {
+                    if (q_node.get_need_post_scale() && q_node.get_need_post_shift() &&
+                        q_node.get_per_tensor_output_scale() && q_node.get_per_tensor_output_shift()) {
+                        post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), q_node.get_output_shift_val());
+                        update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                    } else {
+                        if (q_node.get_need_post_scale()) {
+                            if (q_node.get_per_tensor_output_scale()) {
+                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, q_node.get_output_scale_val(), 0.0f);
+                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                            } else {
+                                auto out_scale = get_dependency(dep_idx++).get_output_layout();
+                                dnnl::memory::desc out_scale_desc = onednn::layout_to_memory_desc(out_scale, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_mul, out_scale_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1);
+                            }
+                        }
+
+                        if (q_node.get_need_post_shift()) {
+                            if (q_node.get_per_tensor_output_shift()) {
+                                post_ops.append_eltwise(1.0f, dnnl::algorithm::eltwise_linear, 1.0f, q_node.get_output_shift_val());
+                                update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
+                            } else {
+                                auto out_shift = get_dependency(dep_idx++).get_output_layout();
+                                dnnl::memory::desc out_shift_desc = onednn::layout_to_memory_desc(out_shift, dnnl::memory::format_tag::ab, true);
+                                post_ops.append_binary(dnnl::algorithm::binary_add, out_shift_desc);
+                                update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1);
+                            }
+                        }
+                    }
+                }
+            }
+        } else if (node->is_type<reorder>()) {
+            continue;
+        } else {
+            throw std::runtime_error("Unsupported fused op of " + node->get_primitive()->type_string() + " type for oneDNN primitive");
+        }
+    }
+
+    if (cldnn_post_ops.size() && get_fused_activations_funcs().size())
+        throw std::runtime_error("Unsupported mix of fused ops and activations");
+
+    for (size_t i = 0; i < get_fused_activations_funcs().size(); i++) {
+        auto activation_type = get_fused_activations_funcs()[i];
+        auto params = get_fused_activations_params()[i];
+        dnnl::algorithm alg = onednn::convert_activation_func(activation_type);
+        post_ops.append_eltwise(1.0f, alg, params.a, params.b);
+        update_onednn_post_op_list(onednn_post_op_type::eltwise_act, empty_mem);
+    }
+
+    // Trying to optimize more than 1 post-ops
+    if (fused_ops.size() > 1) {
+        dnnl::post_ops optimized_post_ops = post_ops;
+        bool optimization_is_finished = false;
+
+        add_onednn_fused_primitives(fused_ops);
+
+        // Trying to combine multiplications and additions which are placed one after another.
+        // We do it in the cycle because some optimization cases can be simplified again from time to time
+        do {
+            optimized_post_ops = try_optimize_post_ops(optimized_post_ops, attrs, optimization_is_finished);
+        } while (!optimization_is_finished);
+
+        attrs->set_post_ops(optimized_post_ops);
+    } else {
+        // Set post-ops without any optimizations
+        add_onednn_fused_primitives(fused_ops);
+        attrs->set_post_ops(post_ops);
+    }
+
+    add_onednn_attrs(attrs);
+}
+
+#endif // ENABLE_ONEDNN_FOR_GPU
diff --git a/inference-engine/thirdparty/clDNN/tests/CMakeLists.txt b/inference-engine/thirdparty/clDNN/tests/CMakeLists.txt
index 02189c28ffc01c..ca83c08ffa955d 100644
--- a/inference-engine/thirdparty/clDNN/tests/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/tests/CMakeLists.txt
@@ -99,6 +99,11 @@ if(COMMAND set_ie_threading_interface_for)
   set_ie_threading_interface_for("${CLDNN_BUILD__PROJ}")
 endif()
 
+# Workaround to avoid warnings during LTO build
+if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+    set_target_properties("${CLDNN_BUILD__PROJ}" PROPERTIES LINK_FLAGS_RELEASE "-Wno-error=maybe-uninitialized -Wno-maybe-uninitialized")
+endif()
+
 set_property(TARGET "${CLDNN_BUILD__PROJ}" PROPERTY PROJECT_LABEL "${CLDNN_BUILD__PROJ_LABEL}")
 
 # Set library dependencies
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/border_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/border_gpu_test.cpp
index df8a8d47c203a3..d2b3daf18685a7 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/border_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/border_gpu_test.cpp
@@ -106,6 +106,84 @@ TEST(border_gpu, basic_yxfb_0x0x1x2_0x0x3x4_border_constant) {
     }
 }
 
+TEST(border_gpu, basic_fsv16_0x0x1x2_0x0x3x4_border_constant) {
+    //  Input (XY) : 4x3
+    //  Output (XY): 10x7
+
+    constexpr auto in_size_b = 1;
+    constexpr auto in_size_f = 1;
+    constexpr auto in_size_y = 3;
+    constexpr auto in_size_x = 4;
+
+    constexpr auto blt_size_b = 0;
+    constexpr auto blt_size_f = 0;
+    constexpr auto blt_size_y = 1;
+    constexpr auto blt_size_x = 2;
+
+    constexpr auto brb_size_b = 0;
+    constexpr auto brb_size_f = 0;
+    constexpr auto brb_size_y = 3;
+    constexpr auto brb_size_x = 4;
+
+    constexpr auto out_size_b = in_size_b + blt_size_b + brb_size_b;
+    constexpr auto out_size_f = in_size_f + blt_size_f + brb_size_f;
+    constexpr auto out_size_y = in_size_y + blt_size_y + brb_size_y;
+    constexpr auto out_size_x = in_size_x + blt_size_x + brb_size_x;
+
+    auto& engine = get_test_engine();
+    auto input = engine.allocate_memory({data_types::f32, format::yxfb, {in_size_b, in_size_f, in_size_x, in_size_y}});
+
+    topology topology;
+    topology.add(
+        input_layout("input", input->get_layout())
+    );
+    topology.add(
+        reorder("border_input", "input", cldnn::format::b_fs_yx_fsv16, cldnn::data_types::f32),
+        border("border", "border_input",
+               {blt_size_b, blt_size_f, blt_size_x, blt_size_y},
+               {brb_size_b, brb_size_f, brb_size_x, brb_size_y},
+               border_type::constant, 0.0f),
+        reorder("output", "border", cldnn::format::yxfb, cldnn::data_types::f32)
+    );
+
+    std::vector<float> input_data = {
+          1, -2,  3,  -4,
+          5,  6,  7,   8,
+        -10, 12, 13, -13,
+    };
+    std::vector<float> out_data = {
+        0, 0,   0,  0,  0,   0, 0, 0, 0, 0,
+        0, 0,   1, -2,  3,  -4, 0, 0, 0, 0,
+        0, 0,   5,  6,  7,   8, 0, 0, 0, 0,
+        0, 0, -10, 12, 13, -13, 0, 0, 0, 0,
+        0, 0,   0,  0,  0,   0, 0, 0, 0, 0,
+        0, 0,   0,  0,  0,   0, 0, 0, 0, 0,
+        0, 0,   0,  0,  0,   0, 0, 0, 0, 0,
+    };
+    set_values(input, input_data);
+
+    cldnn::network network(engine, topology);
+    network.set_input_data("input", input);
+    auto outputs = network.execute();
+
+    auto output = outputs.at("output").get_memory();
+    cldnn::mem_lock<float> output_ptr(output, get_test_stream());
+
+    ASSERT_EQ(out_data.size(), static_cast<std::size_t>(out_size_b * out_size_f * out_size_y * out_size_x));
+
+    for (auto b = 0; b < out_size_b; ++b) {             // B
+        for (auto f = 0; f < out_size_f; ++f) {         // F
+            for (auto y = 0; y < out_size_y; ++y) {     // Y
+                for (auto x = 0; x < out_size_x; ++x) { // X
+                    auto output_off = ((y * out_size_x + x) * out_size_f + f) * out_size_b + b; // YXFB
+
+                    EXPECT_EQ(output_ptr[output_off], out_data[output_off]);
+                }
+            }
+        }
+    }
+}
+
 TEST(border_gpu, basic_bfzyx_0x0x1x01_0x0x0x0x3_border_constant) {
 
     constexpr auto in_size_b = 1;
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
index 70173fc326fde4..c99c788fa76a3d 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
@@ -545,6 +545,7 @@ class ConvEltwTest : public ::BaseFusingTest<conv_eltw_test_params> {
 #define CASE_CONV_U8S8_12 {32, 15, 5, 5}, {32, 30, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
 #define CASE_CONV_U8S8_13 {32, 16, 4, 5}, {32, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
 #define CASE_CONV_U8S8_14 {32, 17, 4, 5}, {32, 17, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 17, data_types::u8, format::bfyx, data_types::i8, format::goiyx, data_types::f32, format::bfyx
+#define CASE_CONV_U8S8_15 {1, 15, 2, 2}, {1, 30, 1, 1}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
 
 #define CASE_CONV_S8S8_1 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
 #define CASE_CONV_S8S8_2 {1, 15, 5, 5}, {1, 30, 3, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::bfyx, data_types::f32, format::bfyx
@@ -9434,6 +9435,47 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_scale_shift_swish_onednn,
                                 bc_test_params{CASE_CONV_S8S8_15, 2, 7},
                         }));
 
+class conv_int8_eltwise_scale_onednn : public ConvFusingTestOneDNN {};
+TEST_P(conv_int8_eltwise_scale_onednn, u8_eltwise_prod_out_reuse) {
+    auto p = GetParam();
+
+    create_topologies(input_layout("input", get_input_layout(p)),
+                 data("weights", get_mem(get_weights_layout(p), -2, 2)),
+                 data("bias", get_mem(get_bias_layout(p))),
+                 data("sum_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+                 data("scale_data", get_mem(get_per_channel_layout(p), 1.0f/p.kernel.count())),
+                 convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation, p.out_shape, data_types::f32, false),
+                 eltwise("sum", {"conv_prim", "sum_data"}, eltwise_mode::sum, data_types::f32),
+                 eltwise("scale", {"sum", "scale_data"}, eltwise_mode::prod, data_types::f32),
+                 crop("crop", "scale", get_output_layout(p).size, {0, 0, 0, 0}),
+                 reorder("reorder_bfyx", "crop", p.default_format, data_types::f32)
+    );
+
+    tolerance = 1.f;
+
+    auto input_prim = get_mem(get_input_layout(p));
+
+    auto forcing_format = p.input_format;
+    implementation_desc conv_impl = { forcing_format, "", impl_types::onednn };
+    bo_fused.set_option(build_option::force_implementations({ {"conv_prim", conv_impl} }));
+
+    network network_not_fused(this->engine, this->topology_non_fused, bo_not_fused);
+    network network_fused(this->engine, this->topology_fused, bo_fused);
+    network_fused.set_input_data("input", input_prim);
+    network_not_fused.set_input_data("input", input_prim);
+
+    // First network.execute() call
+    compare(network_not_fused, network_fused, p);
+    // Second network.execute() call to make sure that scales have not been wrongly overwritten within first iteration
+    // and don't affect final result of second iteration
+    compare(network_not_fused, network_fused, p);
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_int8_eltwise_scale_onednn,
+                         ::testing::ValuesIn(std::vector<bc_test_params>{
+                                bc_test_params{CASE_CONV_U8S8_15, 2, 5},
+                        }));
+
 /* ----------------------------------------------------------------------------------------------------- */
 /* ------------------------------ OneDNN post-ops cases with optimizations ----------------------------- */
 /* ----------------------------------------------------------------------------------------------------- */
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/pooling_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/pooling_gpu_test.cpp
index 8650127f4085ed..ac5629a8f01c3d 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/pooling_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/pooling_gpu_test.cpp
@@ -2719,7 +2719,7 @@ INSTANTIATE_TEST_SUITE_P(
     smoke_low_precision_2d_spatial,
     pooling_random_test,
     testing::Combine(testing::Values(1, 2),
-                     testing::Values(3, 8, 64),
+                     testing::Values(3, 8, 19, 64),
                      testing::Values(std::tuple<size_t, size_t, size_t>(12, 12, 1), std::tuple<size_t, size_t, size_t>(24, 24, 1)),
                      testing::Values(std::tuple<size_t, size_t, size_t>(4, 4, 1), std::tuple<size_t, size_t, size_t>(2, 2, 1)),
                      testing::Values(std::tuple<int, int, int>(2, 2, 1)),
@@ -2736,7 +2736,7 @@ INSTANTIATE_TEST_SUITE_P(
     smoke_low_precision_3d_spatial,
     pooling_random_test,
     testing::Combine(testing::Values(1, 2),
-                     testing::Values(3, 8, 64),
+                     testing::Values(3, 8, 27, 64),
                      testing::Values(std::tuple<size_t, size_t, size_t>(12, 12, 12), std::tuple<size_t, size_t, size_t>(24, 24, 24)),
                      testing::Values(std::tuple<size_t, size_t, size_t>(4, 4, 4), std::tuple<size_t, size_t, size_t>(2, 2, 2)),
                      testing::Values(std::tuple<int, int, int>(2, 2, 2)),
diff --git a/inference-engine/thirdparty/movidius/XLink/CMakeLists.txt b/inference-engine/thirdparty/movidius/XLink/CMakeLists.txt
index bf513666b7d6da..ca5bc6a001562b 100644
--- a/inference-engine/thirdparty/movidius/XLink/CMakeLists.txt
+++ b/inference-engine/thirdparty/movidius/XLink/CMakeLists.txt
@@ -19,11 +19,10 @@ if(NOT WIN32)
             ${LIBUSB_LIBRARY})
 endif()
 
-target_include_directories(${TARGET_NAME}
-        PUBLIC
-        ${XLINK_INCLUDE}
-        PRIVATE
-        ${XLINK_PLATFORM_INCLUDE})
+target_include_directories(${TARGET_NAME} PRIVATE ${XLINK_PLATFORM_INCLUDE})
+foreach(dir IN LISTS XLINK_INCLUDE)
+    target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${dir}>)
+endforeach()
 
 target_compile_definitions(${TARGET_NAME}
         PRIVATE
@@ -42,3 +41,4 @@ endif()
 set_property(TARGET ${TARGET_NAME} PROPERTY C_STANDARD 99)
 
 openvino_developer_export_targets(COMPONENT inference_engine_vpu TARGETS ${TARGET_NAME})
+ov_install_static_lib(${TARGET_NAME} myriad)
diff --git a/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt b/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt
index 94d461ab911799..f62a65ea8390c6 100644
--- a/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt
+++ b/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt
@@ -10,15 +10,14 @@ include(${XLINK_DIR}/XLink.cmake)
 
 file(GLOB_RECURSE MVNC_SOURCES "include/*" "src/*")
 
-set(WATCHDOG_INCLUDE
-        ${CMAKE_CURRENT_SOURCE_DIR}/include/watchdog/)
+set(WATCHDOG_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/include/watchdog/)
 
 add_library(${TARGET_NAME} STATIC ${MVNC_SOURCES})
 
 target_include_directories(${TARGET_NAME}
         PUBLIC
-                "${CMAKE_CURRENT_SOURCE_DIR}/include"
-                ${WATCHDOG_INCLUDE}
+                $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+                $<BUILD_INTERFACE:${WATCHDOG_INCLUDE}>
         PRIVATE
                 ${XLINK_INCLUDE}
                 ${XLINK_PLATFORM_INCLUDE})
@@ -74,6 +73,7 @@ if(NOT WIN32)
 endif()
 
 openvino_developer_export_targets(COMPONENT inference_engine_vpu TARGETS ${TARGET_NAME})
+ov_install_static_lib(${TARGET_NAME} myriad)
 
 if(ENABLE_TESTS AND ENABLE_MYRIAD_MVNC_TESTS)
     add_subdirectory(tests)
diff --git a/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c b/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c
index de5d39bb7c80e5..d91cd98f8a0694 100644
--- a/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c
+++ b/inference-engine/thirdparty/movidius/mvnc/src/mvnc_api.c
@@ -103,7 +103,11 @@ static int global_lock_fd = -1;
 
 #define STRINGIFY(_text) #_text
 #define CASE(entry) case entry: return STRINGIFY(entry);
-
+#define FREE_AND_NULL(fDynMem, fPointer, sDynMem, sPointer) \
+    free(fDynMem); \
+    fPointer = NULL; \
+    free(sDynMem); \
+    sPointer = NULL
 
 // To suppress warning in the macro below
 #if defined __GNUC__ || defined __clang__
@@ -2199,10 +2203,7 @@ ncStatus_t ncGraphDestroy(struct ncGraphHandle_t ** graphHandle)
     CHECK_HANDLE_CORRECT_WINFO(g, MVLOG_ERROR, "Graph handle is corrupt or has been destroyed");
 
     if (g->state == NC_GRAPH_CREATED || g->state == NC_GRAPH_DEALLOCATED) {
-        free(g);
-        gh->private_data = NULL;
-        free(gh);
-        *graphHandle = NULL;
+        FREE_AND_NULL(g, gh->private_data, gh, *graphHandle);
         return NC_OK;
     }
     GLOBAL_LOCK();
@@ -2220,28 +2221,24 @@ ncStatus_t ncGraphDestroy(struct ncGraphHandle_t ** graphHandle)
     cmd.id = g->id;
     CHECK_MUTEX_SUCCESS(pthread_mutex_lock(&d->graph_stream_m));
     rc = trySendCommand(d->graph_monitor_stream_id, &cmd, sizeof(cmd));
-    if(rc != 0){
-        CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->graph_stream_m));
-        return rc;
-    }
-    if (checkGraphMonitorResponse(d->graph_monitor_stream_id)) {
-        CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->graph_stream_m));
-        return NC_ERROR;
+    if (rc != 0){
+        mvLog(MVLOG_WARN, "can't send command\n");
+        rc = NC_ERROR;
+    } else if (checkGraphMonitorResponse(d->graph_monitor_stream_id)) {
+        mvLog(MVLOG_WARN, "myriad NACK\n");
+        rc = NC_ERROR;
     }
     XLinkCloseStream(g->graph_stream_id);
     CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->graph_stream_m));
     CHECK_MUTEX_SUCCESS(pthread_mutex_lock(&d->dev_data_m));
     if (deallocateGraph(gh->private_data)) {
         mvLog(MVLOG_ERROR, "This graph has already been destroyed");
-        CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->dev_data_m));
-        return NC_INVALID_PARAMETERS;
+        rc =  NC_INVALID_PARAMETERS;
     }
     CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->dev_data_m));
-    free(g);
-    gh->private_data = NULL;
-    free(gh);
-    *graphHandle = NULL;
-    return NC_OK;
+    FREE_AND_NULL(g, gh->private_data, gh, *graphHandle);
+
+    return rc;
 }
 
 ncStatus_t ncGraphSetOption(struct ncGraphHandle_t * graphHandle,
@@ -3021,10 +3018,6 @@ ncStatus_t ncFifoAllocate(struct ncFifoHandle_t * fifoHandle,
 
     handle->datasize = handle->host_tensor_desc.totalSize;
 
-    if (d->fifos)
-        handle->next = d->fifos;
-    d->fifos = handle;
-
     bufferAllocateCommand_t cmd;
     cmd.type = GRAPH_BUFFER_ALLOCATE_CMD;
     struct tensorDescriptor_t privateDesc;
@@ -3077,6 +3070,10 @@ ncStatus_t ncFifoAllocate(struct ncFifoHandle_t * fifoHandle,
         mvLog(MVLOG_ERROR, "myriad NACK\n");
         return NC_ERROR;
     }
+    if (d->fifos)
+        handle->next = d->fifos;
+    d->fifos = handle;
+
     CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->graph_stream_m));
 
     handle->state = NC_FIFO_ALLOCATED;
@@ -3094,7 +3091,7 @@ ncStatus_t ncFifoDestroy(struct ncFifoHandle_t ** fifoHandle)
     }
 
     struct _fifoPrivate_t *handle = fh->private_data;
-
+    ncStatus_t rc = NC_OK;
     if (handle->state == NC_FIFO_CREATED || handle->state == NC_FIFO_DEALLOCATED) {
         pthread_mutex_t * fifo_mutex = &fh->private_data->fifo_mutex;
 #if !(defined(_WIN32) || defined(_WIN64))
@@ -3116,18 +3113,14 @@ ncStatus_t ncFifoDestroy(struct ncFifoHandle_t ** fifoHandle)
         CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(fifo_mutex));
         CHECK_MUTEX_SUCCESS(pthread_mutex_destroy(fifo_mutex));
 
-        free(fh->private_data);
-        fh->private_data = NULL;
-
-        free(fh);
-        *fifoHandle = NULL;
+        FREE_AND_NULL(fh->private_data, fh->private_data, fh, *fifoHandle);
 
         return NC_OK;
     }
     if (!findFifo(handle)) {
         mvLog(MVLOG_ERROR,
               "fifo handle seems to be corrupt or has been destroyed");
-        return NC_INVALID_HANDLE;
+        rc = NC_INVALID_HANDLE;
     }
     //clean up fifo
     /*if (fifoReadAccess(handle)) {
@@ -3146,11 +3139,10 @@ ncStatus_t ncFifoDestroy(struct ncFifoHandle_t ** fifoHandle)
         if (XLinkWriteData(handle->streamId, (uint8_t *) & msg, sizeof(msg)) !=
             0) {
             mvLog(MVLOG_ERROR, "Failed to write to fifo before deleting it!");
-            return NC_ERROR;
+            rc = NC_ERROR;
         }
     }
 
-    ncStatus_t rc = NC_OK;
     graphCommonCommand_t cmd;
     cmd.type = GRAPH_BUFFER_DEALLOCATE_CMD;
     cmd.id = handle->id;
@@ -3158,30 +3150,24 @@ ncStatus_t ncFifoDestroy(struct ncFifoHandle_t ** fifoHandle)
     struct _devicePrivate_t *d = handle->dev;
     CHECK_MUTEX_SUCCESS(pthread_mutex_lock(&d->graph_stream_m));
     rc = trySendCommand(d->graph_monitor_stream_id, &cmd, sizeof(cmd));
-    if(rc != 0){
-        CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->graph_stream_m));
+    if (rc != 0) {
         mvLog(MVLOG_WARN, "can't send command\n");
-        return rc;
-    }
-    if (checkGraphMonitorResponse(d->graph_monitor_stream_id)) {
-        CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->graph_stream_m));
+        rc = NC_ERROR;
+    } else if (checkGraphMonitorResponse(d->graph_monitor_stream_id)) {
         mvLog(MVLOG_WARN, "myriad NACK\n");
-        return NC_ERROR;
+        rc = NC_ERROR;
     }
     CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->graph_stream_m));
 
     CHECK_MUTEX_SUCCESS(pthread_mutex_lock(&d->dev_data_m));
     if (deallocateFifo(handle)) {
-        CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->dev_data_m));
-        return NC_INVALID_PARAMETERS;
+        mvLog(MVLOG_WARN, "failed deallocateFifo\n");
+        rc =  NC_INVALID_PARAMETERS;
     }
     CHECK_MUTEX_SUCCESS(pthread_mutex_unlock(&d->dev_data_m));
 
-    free(fh->private_data);
-    fh->private_data = NULL;
-    free(fh);
-    *fifoHandle = NULL;
-    return NC_OK;
+    FREE_AND_NULL(fh->private_data, fh->private_data, fh, *fifoHandle);
+    return rc;
 
 }
 
diff --git a/licensing/onednn_third-party-programs.txt b/licensing/onednn_third-party-programs.txt
index 5bfd9c6864a894..0fed79906332f4 100644
--- a/licensing/onednn_third-party-programs.txt
+++ b/licensing/onednn_third-party-programs.txt
@@ -102,9 +102,9 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 --------------------------------------------------------------------------------
-3. Instrumentation and Tracing Technology API (src/cpu/jit_utils/jitprofiling/)
-Copyright (c) 2011, Intel Corporation
-All rights reserved.
+3. Instrumentation and Tracing Technology API (src/common/ittnotify/)
+Copyright (c) 2011, Intel Corporation. All rights reserved.
+Copyright (c) 2005-2014 Intel Corporation. All rights reserved.
 
 3-Clause BSD License
 
@@ -168,14 +168,111 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
---------------------------------------------------------------------------------
-5. Font Roboto (doc/assets/fonts/Roboto*)
-Copyright 2011 The Roboto Project Authors (https://github.com/google/roboto)
+------------------------------------------------------------------------------
+
+The following individuals and institutions are among the Contributors:
+
+* Aaron C. Meadows <cmake@shadowguarddev.com>
+* Adriaan de Groot <groot@kde.org>
+* Aleksey Avdeev <solo@altlinux.ru>
+* Alexander Neundorf <neundorf@kde.org>
+* Alexander Smorkalov <alexander.smorkalov@itseez.com>
+* Alexey Sokolov <sokolov@google.com>
+* Alex Merry <alex.merry@kde.org>
+* Alex Turbov <i.zaufi@gmail.com>
+* Andreas Pakulat <apaku@gmx.de>
+* Andreas Schneider <asn@cryptomilk.org>
+* André Rigland Brodtkorb <Andre.Brodtkorb@ifi.uio.no>
+* Axel Huebl, Helmholtz-Zentrum Dresden - Rossendorf
+* Benjamin Eikel
+* Bjoern Ricks <bjoern.ricks@gmail.com>
+* Brad Hards <bradh@kde.org>
+* Christopher Harvey
+* Christoph Grüninger <foss@grueninger.de>
+* Clement Creusot <creusot@cs.york.ac.uk>
+* Daniel Blezek <blezek@gmail.com>
+* Daniel Pfeifer <daniel@pfeifer-mail.de>
+* Enrico Scholz <enrico.scholz@informatik.tu-chemnitz.de>
+* Eran Ifrah <eran.ifrah@gmail.com>
+* Esben Mose Hansen, Ange Optimization ApS
+* Geoffrey Viola <geoffrey.viola@asirobots.com>
+* Google Inc
+* Gregor Jasny
+* Helio Chissini de Castro <helio@kde.org>
+* Ilya Lavrenov <ilya.lavrenov@itseez.com>
+* Insight Software Consortium <insightsoftwareconsortium.org>
+* Jan Woetzel
+* Julien Schueller
+* Kelly Thompson <kgt@lanl.gov>
+* Konstantin Podsvirov <konstantin@podsvirov.pro>
+* Laurent Montel <montel@kde.org>
+* Mario Bensi <mbensi@ipsquad.net>
+* Martin Gräßlin <mgraesslin@kde.org>
+* Mathieu Malaterre <mathieu.malaterre@gmail.com>
+* Matthaeus G. Chajdas
+* Matthias Kretz <kretz@kde.org>
+* Matthias Maennich <matthias@maennich.net>
+* Michael Hirsch, Ph.D. <www.scivision.co>
+* Michael Stürmer
+* Miguel A. Figueroa-Villanueva
+* Mike Jackson
+* Mike McQuaid <mike@mikemcquaid.com>
+* Nicolas Bock <nicolasbock@gmail.com>
+* Nicolas Despres <nicolas.despres@gmail.com>
+* Nikita Krupen'ko <krnekit@gmail.com>
+* NVIDIA Corporation <www.nvidia.com>
+* OpenGamma Ltd. <opengamma.com>
+* Patrick Stotko <stotko@cs.uni-bonn.de>
+* Per Øyvind Karlsen <peroyvind@mandriva.org>
+* Peter Collingbourne <peter@pcc.me.uk>
+* Petr Gotthard <gotthard@honeywell.com>
+* Philip Lowman <philip@yhbt.com>
+* Philippe Proulx <pproulx@efficios.com>
+* Raffi Enficiaud, Max Planck Society
+* Raumfeld <raumfeld.com>
+* Roger Leigh <rleigh@codelibre.net>
+* Rolf Eike Beer <eike@sf-mail.de>
+* Roman Donchenko <roman.donchenko@itseez.com>
+* Roman Kharitonov <roman.kharitonov@itseez.com>
+* Ruslan Baratov
+* Sebastian Holtermann <sebholt@xwmw.org>
+* Stephen Kelly <steveire@gmail.com>
+* Sylvain Joubert <joubert.sy@gmail.com>
+* The Qt Company Ltd.
+* Thomas Sondergaard <ts@medical-insight.com>
+* Tobias Hunger <tobias.hunger@qt.io>
+* Todd Gamblin <tgamblin@llnl.gov>
+* Tristan Carel
+* University of Dundee
+* Vadim Zhukov
+* Will Dicharry <wdicharry@stellarscience.com>
+
+See version control history for details of individual contributions.
+
+The above copyright and license notice applies to distributions of
+CMake in source and binary form.  Third-party software packages supplied
+with CMake under compatible licenses provide their own copyright notices
+documented in corresponding subdirectories or source files.
+
+------------------------------------------------------------------------------
+
+CMake was initially developed by Kitware with the following sponsorship:
+
+ * National Library of Medicine at the National Institutes of Health
+   as part of the Insight Segmentation and Registration Toolkit (ITK).
+
+ * US National Labs (Los Alamos, Livermore, Sandia) ASC Parallel
+   Visualization Initiative.
+
+ * National Alliance for Medical Image Computing (NAMIC) is funded by the
+   National Institutes of Health through the NIH Roadmap for Medical Research,
+   Grant U54 EB005149.
+
+ * Kitware, Inc.
 
-MathJax (doc/assets/mathjax)
-Copyright (c) 2009-2018 The MathJax Consortium
-Copyright (c) 2015-2017 Martin Hensel
-Copyright (c) 2007, Apostolos Syropoulos (<asyropoulos@yahoo.com)
+--------------------------------------------------------------------------------
+5. Xbyak_aarch64 (src/cpu/aarch64/xbyak_aarch64/)
+Copyright 2019-2020 FUJITSU LIMITED
 
 Apache License, Version 2.0
 
@@ -405,6 +502,11 @@ Copyright (c) 2018 Intel Corporation
 Intel(R) Graphics Compiler (src/gpu/jit/ngen/npack/neo_structs.hpp)
 Copyright (c) 2019 Intel Corporation
 
+Doxyrest toolkit (doc/doxyrest/*)
+Copyright (c) 2016, Tibbo Technology Inc
+Copyright (c) 2016, Vladimir Gladkov
+Copyright (c) 2016, Doxyrest maintainers
+
 MIT License
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
@@ -425,99 +527,31 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 
---------------------------------------------------------------------------------
-8. Font Awesome (doc/assets/fonts/fontawesome*)
-Copyright Dave Gandy 2016. All rights reserved. 
-
-Font Lato (doc/assets/fonts/lato*)
-Copyright (c) 2010-2014 by tyPoland Lukasz Dziedzic (team@latofonts.com)
-with Reserved Font Name "Lato"
-
-This Font Software is licensed under the SIL Open Font License, Version 1.1.
-This license is copied below, and is also available with a FAQ at:
-http://scripts.sil.org/OFL
-
-SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
-
-PREAMBLE
-The goals of the Open Font License (OFL) are to stimulate worldwide
-development of collaborative font projects, to support the font creation
-efforts of academic and linguistic communities, and to provide a free and
-open framework in which fonts may be shared and improved in partnership
-with others.
-
-The OFL allows the licensed fonts to be used, studied, modified and
-redistributed freely as long as they are not sold by themselves. The
-fonts, including any derivative works, can be bundled, embedded, 
-redistributed and/or sold with any software provided that any reserved
-names are not used by derivative works. The fonts and derivatives,
-however, cannot be released under any other type of license. The
-requirement for fonts to remain under this license does not apply
-to any document created using the fonts or their derivatives.
-
-DEFINITIONS
-"Font Software" refers to the set of files released by the Copyright
-Holder(s) under this license and clearly marked as such. This may
-include source files, build scripts and documentation.
-
-"Reserved Font Name" refers to any names specified as such after the
-copyright statement(s).
-
-"Original Version" refers to the collection of Font Software components as
-distributed by the Copyright Holder(s).
-
-"Modified Version" refers to any derivative made by adding to, deleting,
-or substituting -- in part or in whole -- any of the components of the
-Original Version, by changing formats or by porting the Font Software to a
-new environment.
-
-"Author" refers to any designer, engineer, programmer, technical
-writer or other person who contributed to the Font Software.
-
-PERMISSION & CONDITIONS
-Permission is hereby granted, free of charge, to any person obtaining
-a copy of the Font Software, to use, study, copy, merge, embed, modify,
-redistribute, and sell modified and unmodified copies of the Font
-Software, subject to the following conditions:
-
-1) Neither the Font Software nor any of its individual components,
-in Original or Modified Versions, may be sold by itself.
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-2) Original or Modified Versions of the Font Software may be bundled,
-redistributed and/or sold with any software, provided that each copy
-contains the above copyright notice and this license. These can be
-included either as stand-alone text files, human-readable headers or
-in the appropriate machine-readable metadata fields within text or
-binary files as long as those fields can be easily viewed by the user.
-
-3) No Modified Version of the Font Software may use the Reserved Font
-Name(s) unless explicit written permission is granted by the corresponding
-Copyright Holder. This restriction only applies to the primary font name as
-presented to the users.
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-4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
-Software shall not be used to promote, endorse or advertise any
-Modified Version, except to acknowledge the contribution(s) of the
-Copyright Holder(s) and the Author(s) or with their explicit written
-permission.
-
-5) The Font Software, modified or unmodified, in part or in whole,
-must be distributed entirely under this license, and must not be
-distributed under any other license. The requirement for fonts to
-remain under this license does not apply to any document created
-using the Font Software.
-
-TERMINATION
-This license becomes null and void if any of the above conditions are
-not met.
-
-DISCLAIMER
-THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
-OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
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-INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
-DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
-OTHER DEALINGS IN THE FONT SOFTWARE.
\ No newline at end of file
+8. Sphinx (doc/sphinx/conf/py)
+Copyright (c) 2007-2021 by the Sphinx team (see AUTHORS file).
+All rights reserved.
+
+2-Clause BSD License
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/licensing/third-party-programs.txt b/licensing/third-party-programs.txt
index 4e13fc1289335d..57b0c7fb8c8eec 100644
--- a/licensing/third-party-programs.txt
+++ b/licensing/third-party-programs.txt
@@ -37,6 +37,14 @@ Open Computer Vision Library (OpenCV)
 Roboto Fonts
    Copyright 2011 Google Inc. All Rights Reserved.
 
+PaddlePaddle
+   Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+
+Viewer.js
+Copyright 2014 Box, Inc. All rights reserved.
+
+onnx onnx (http://github.com/onnx/onnx/)
+
    
 http://www.apache.org/licenses/
 
@@ -594,6 +602,336 @@ SOFTWARE.
 
 -------------------------------------------------------------
 
+16. TensorFlow (https://github.com/tensorflow/tensorflow/)
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
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+
+------------------
+Files: third_party/compute_library/...
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+------------------
+Files: ACKNOWLEDGEMENTS
+LICENSE
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+   ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+   WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+   DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+   ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+   (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+   LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+   ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+   (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+------------------
+Files: third_party/hexagon
+
+Copyright (c) 2016-2019, The Linux Foundation. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted (subject to the limitations in the
+disclaimer below) provided that the following conditions are met:
+
+   * Redistributions of source code must retain the above copyright
+     notice, this list of conditions and the following disclaimer.
+
+   * Redistributions in binary form must reproduce the above
+     copyright notice, this list of conditions and the following
+     disclaimer in the documentation and/or other materials provided
+     with the distribution.
+
+   * Neither the name of The Linux Foundation nor the names of its
+     contributors may be used to endorse or promote products derived
+     from this software without specific prior written permission.
+
+NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE
+GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT
+HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
+IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+-------------------------------------------------------------
+
+17. Chosen  (https://github.com/harvesthq/chosen/)
+by Patrick Filler for Harvest
+Copyright (c) 2011-2016 by Harvest
+Available for use under the MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), 
+to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, 
+and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+-------------------------------------------------------------
+
+18. ncc (https://github.com/nithinn/ncc)
+
+Copyright (c) 2018 Nithin Nellikunnu (nithin.nn@gmail.com)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+-------------------------------------------------------------
+
 The following third party programs have their own third party program files. These additional third party program files are as follows:
 
 oneAPI Deep Neural Network Library (oneDNN) Third Party Programs File is available here https://github.com/openvinotoolkit/openvino/blob/master/licensing/onednn_third-party-programs.txt
diff --git a/model-optimizer/CMakeLists.txt b/model-optimizer/CMakeLists.txt
index 0f7f2bffa629b7..f6e44fd82feeb1 100644
--- a/model-optimizer/CMakeLists.txt
+++ b/model-optimizer/CMakeLists.txt
@@ -46,17 +46,17 @@ install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/
         )
 
 install(FILES requirements_dev.txt
-        DESTINATION tools/model_optimizer
+        DESTINATION tests/model_optimizer
         COMPONENT tests
         EXCLUDE_FROM_ALL)
 
 install(DIRECTORY unit_tests
-        DESTINATION tools/model_optimizer
+        DESTINATION tests/model_optimizer
         COMPONENT tests
         EXCLUDE_FROM_ALL)
 
 install(DIRECTORY automation
-        DESTINATION tools/model_optimizer
+        DESTINATION tests/model_optimizer
         COMPONENT tests
         EXCLUDE_FROM_ALL)
 
diff --git a/model-optimizer/automation/package_BOM.txt b/model-optimizer/automation/package_BOM.txt
index dad585c2f43487..99e3db0cee66ee 100644
--- a/model-optimizer/automation/package_BOM.txt
+++ b/model-optimizer/automation/package_BOM.txt
@@ -8,6 +8,7 @@ extensions/analysis/tf_od_api.py
 extensions/analysis/tf_retinanet.py
 extensions/analysis/tf_yolo.py
 extensions/back/__init__.py
+extensions/back/add_outputs_recursive.py
 extensions/back/AvgPool.py
 extensions/back/blob_normalizer.py
 extensions/back/CellNormalizer.py
@@ -22,7 +23,6 @@ extensions/back/CutMemory.py
 extensions/back/EnableConstantStridedSlice.py
 extensions/back/FakeOutputResolver.py
 extensions/back/ForceStrictPrecision.py
-extensions/back/fuse_sub_div_min.py
 extensions/back/FuseTransposesSequence.py
 extensions/back/GatherNormalizer.py
 extensions/back/insert_compatibility_l2normalization.py
@@ -433,6 +433,7 @@ extensions/front/tf/identityN_to_identity.py
 extensions/front/tf/if_ext.py
 extensions/front/tf/InterpolateTransposes.py
 extensions/front/tf/IteratorGetNext_ext.py
+extensions/front/tf/IteratorGetNextCut.py
 extensions/front/tf/log_softmax_ext.py
 extensions/front/tf/LookupTableInsert_ext.py
 extensions/front/tf/LoopCond_ext.py
diff --git a/model-optimizer/extensions/analysis/inputs.py b/model-optimizer/extensions/analysis/inputs.py
index a3fee9ffd6cf27..887dee66a9a41d 100644
--- a/model-optimizer/extensions/analysis/inputs.py
+++ b/model-optimizer/extensions/analysis/inputs.py
@@ -56,22 +56,24 @@ def ignore_mxnet_softmax_inputs(cls, graph: Graph):
     def iterator_get_next_analysis(cls, graph: Graph, inputs_desc: dict):
         message = None
         op_nodes = graph.get_op_nodes(op='IteratorGetNext')
+
+        params = ''
+        for iter_get_next in op_nodes:
+            for port in iter_get_next.out_nodes().keys():
+                inputs_desc['{}:{}'.format(iter_get_next.soft_get('name', iter_get_next.id), port)] = {
+                    'shape': iter_get_next.shapes[port].tolist(),
+                    'value': None,
+                    'data_type': iter_get_next.types[port]
+                }
+                if params != '':
+                    params = params + ','
+                shape = str(iter_get_next.shapes[port].tolist()).replace(',', '')
+                params = params + '{}:{}{}'.format(iter_get_next.soft_get('name', iter_get_next.id), port, shape)
+
         if len(op_nodes):
-            params = ''
-            for iter_get_next in op_nodes:
-                for port in iter_get_next.out_nodes().keys():
-                    inputs_desc['{}:{}'.format(iter_get_next.soft_get('name', iter_get_next.id), port)] = {
-                        'shape': iter_get_next.shapes[port].tolist(),
-                        'value': None,
-                        'data_type': iter_get_next.types[port]
-                    }
-                    if params != '':
-                        params = params + ','
-                    shape = str(iter_get_next.shapes[port].tolist()).replace(',', '')
-                    params = params + '{}:{}{}'.format(iter_get_next.soft_get('name', iter_get_next.id), port, shape)
             message = 'It looks like there is IteratorGetNext as input\n' \
-                      'Run the Model Optimizer with:\n\t\t--input "{}"\n' \
-                      'And replace all negative values with positive values'.format(params)
+                      'Run the Model Optimizer without --input option \n' \
+                      'Otherwise, try to run the Model Optimizer with:\n\t\t--input "{}"\n'.format(params)
         return message
 
     def analyze(self, graph: Graph):
diff --git a/model-optimizer/extensions/back/MatMulNormalizer.py b/model-optimizer/extensions/back/MatMulNormalizer.py
index fb860f86b5678e..96221640025349 100644
--- a/model-optimizer/extensions/back/MatMulNormalizer.py
+++ b/model-optimizer/extensions/back/MatMulNormalizer.py
@@ -73,14 +73,13 @@ def replace_pattern(graph: Graph, match: dict):
 class PullTransposeThroughFQUp(BackReplacementPattern):
     r"""
         BEFORE                                      AFTER
-                                                        T  T T  T  T
-         \ \ | / /                                       \ \ | / /
-        FakeQuantize                                    FakeQuantize
+    Const                                             Const
+        \ \ | / /                                       |
+        FakeQuantize                                    T  T T  T  T
+            |                                            \ \ | / /
+        Transpose                                       FakeQuantize
             |                                                |
-        Transpose                                         next_op
-            |
-         next_op
-
+         next_op                                          next_op
         `T` is Transpose for short
     """
     enabled = True
@@ -94,13 +93,17 @@ def run_after(self):
     def pattern():
         return dict(
             nodes=[
+                ('fq_const_input', dict(kind='op', type='Const')),
+                ('fq_const_input_d', dict()),
                 ('fq', dict(kind='op', type='FakeQuantize')),
-                ('data', dict()),
+                ('fq_d', dict()),
                 ('transpose', dict(kind='op', type='Transpose')),
             ],
             edges=[
-                ('fq', 'data'),
-                ('data', 'transpose'),
+                ('fq_const_input', 'fq_const_input_d'),
+                ('fq_const_input_d', 'fq', {'in': 0}),
+                ('fq', 'fq_d'),
+                ('fq_d', 'transpose'),
             ]
         )
 
diff --git a/model-optimizer/extensions/back/ReverseInputChannels.py b/model-optimizer/extensions/back/ReverseInputChannels.py
index ce0cbb952ae363..8aefd1bc152cc8 100644
--- a/model-optimizer/extensions/back/ReverseInputChannels.py
+++ b/model-optimizer/extensions/back/ReverseInputChannels.py
@@ -50,10 +50,38 @@ class InsertReverseChannels(BackReplacementPattern):
     """
     enabled = False
 
+    @staticmethod
+    def get_fw_index(node: Node, idx: int):
+        if not node.has_valid('rt_info'):
+            return idx
+
+        rt_info = node.rt_info
+        if not rt_info.contains('old_api_map'):
+            return idx
+
+        old_api_map_version = rt_info.get_attribute_version('old_api_map')
+        old_api_map = rt_info.info['old_api_map', old_api_map_version]
+        if 'inverse_order' not in old_api_map.info:
+            return idx
+
+        order = old_api_map.info['inverse_order']
+        node_name = node.soft_get('name', node.id)
+
+        if idx < 0:
+            assert not node.out_port(0).disconnected(), 'Cannot normalize negative axis {} in node {} ' \
+                                                        'as out port is disconnected.'.format(idx, node_name)
+            data_rank = len(list(node.out_port(0).data.get_shape()))
+            idx = data_rank + idx
+
+        assert len(order) > idx >= 0, \
+            'Channel index {} is incompatible with old_api_map in node {}.'.format(idx, node_name)
+        return list(order).index(idx)
+
     def find_and_replace_pattern(self, graph: Graph):
         all_params = [(p.soft_get('name', p.id), p, list(p.out_port(0).data.get_shape()))
                       for p in graph.get_op_nodes(type='Parameter')]
-        suitable_params = [(name, p, shape) for name, p, shape in all_params if len(shape) == 4 and shape[1] == 3]
+        suitable_params = [(name, p, shape) for name, p, shape in all_params if
+                           len(shape) == 4 and shape[self.get_fw_index(p, 1)] == 3]
 
         log.debug('All network inputs: {}'.format({name: shape for name, _, shape in all_params}))
         log.debug('Will reverse input channels for: {}'.format({name: shape for name, _, shape in suitable_params}))
@@ -66,10 +94,14 @@ def find_and_replace_pattern(self, graph: Graph):
                       extra={'is_warning': True})
 
         for name, parameter, _ in suitable_params:
-            reverse_channels = ReverseChannels(graph, {'name': name + '/reverse_input_channels'}).create_node()
-            parameter.out_port(0).get_connection().set_source(reverse_channels.out_port(0),
-                                                              attributes_save_mode='source')
-            parameter.out_port(0).connect(reverse_channels.in_port(0))
+            reverse_index = self.get_fw_index(parameter, 1)
+
+            if parameter.out_port(0).disconnected():
+                continue
+
+            reverse_channels = ReverseChannels(graph, {'name': name + '/reverse_input_channels',
+                                                       'axis': reverse_index}).create_node()
+            parameter.out_port(0).get_connection().insert_node(reverse_channels, attributes_save_mode='source')
 
 
 class ReverseChannelsPropagationDown(BackReplacementPattern):
@@ -95,11 +127,30 @@ class ReverseChannelsPropagationDown(BackReplacementPattern):
         'Shape': lambda node, rc: ReverseChannelsPropagationDown.pass_rc_through_shape(node, rc),
         'ShapeOf': lambda node, rc: ReverseChannelsPropagationDown.pass_rc_through_shape(node, rc),
 
-        'Pad': lambda node, rc: ReverseChannelsPropagationDown.pass_rc_through(node, rc),
+        'Pad': lambda node, rc: ReverseChannelsPropagationDown.pass_rc_through_zero_port_only(node, rc),
+        'Transpose': lambda node, rc: ReverseChannelsPropagationDown.pass_rc_through_transpose(node, rc),
     }
 
     @staticmethod
-    def pass_rc_through(node: Node, reverse_channels: Node):
+    def pass_rc_through_transpose(node: Node, reverse_channels: Node):
+        if node.in_port(1).disconnected() or node.in_port(0).disconnected():
+            return False
+        order = node.in_port(1).data.get_value()
+        reverse_axis = reverse_channels.axis
+        data_rank = len(list(node.in_port(0).data.get_shape()))
+
+        if reverse_axis < 0:
+            reverse_axis = data_rank + reverse_axis
+        assert 0 < reverse_axis < data_rank, "Incorrect ReverseChannels axis in node {}.".format(reverse_channels)
+
+        if order is None:
+            return False
+        new_axis = list(order).index(reverse_axis)
+        reverse_channels.axis = new_axis
+        return ReverseChannelsPropagationDown.pass_rc_through_zero_port_only(node, reverse_channels)
+
+    @staticmethod
+    def pass_rc_through_zero_port_only(node: Node, reverse_channels: Node):
         r"""
         BEFORE                          AFTER
 
@@ -295,11 +346,30 @@ class ReverseChannelsPropagationUp(BackReplacementPattern):
         'Subtract': lambda node, rc: ReverseChannelsPropagationUp.lift_up_through_eltwise(node, rc),
         'Pow': lambda node, rc: ReverseChannelsPropagationUp.lift_up_through_eltwise(node, rc),
         'Convert': lambda node, rc: ReverseChannelsPropagationUp.lift_up_through_eltwise(node, rc),
-        'Pad': lambda node, rc: ReverseChannelsPropagationUp.lift_up_through_pad(node, rc),
+        'Pad': lambda node, rc: ReverseChannelsPropagationUp.lift_up_through_zero_port_only(node, rc),
+        'Transpose': lambda node, rc: ReverseChannelsPropagationUp.lift_up_through_transpose(node, rc),
     }
 
     @staticmethod
-    def lift_up_through_pad(node: Node, reverse_channels: Node):
+    def lift_up_through_transpose(node: Node, reverse_channels: Node):
+        if node.in_port(1).disconnected() or node.in_port(0).disconnected():
+            return False
+        order = node.in_port(1).data.get_value()
+        reverse_axis = reverse_channels.axis
+        data_rank = len(list(node.in_port(0).data.get_shape()))
+
+        if reverse_axis < 0:
+            reverse_axis = data_rank + reverse_axis
+        assert 0 < reverse_axis < data_rank, "Incorrect ReverseChannels axis in node {}.".format(reverse_channels)
+
+        if order is None:
+            return False
+        new_axis = order[reverse_axis]
+        reverse_channels.axis = new_axis
+        return ReverseChannelsPropagationUp.lift_up_through_zero_port_only(node, reverse_channels)
+
+    @staticmethod
+    def lift_up_through_zero_port_only(node: Node, reverse_channels: Node):
         r"""
         BEFORE                       AFTER
 
@@ -307,7 +377,7 @@ def lift_up_through_pad(node: Node, reverse_channels: Node):
                                           \
         previous_op  previous_op       ReverseChannels  previous_op
                  \     /                           \     /
-                   Pad                              Pad
+                   Node                              Node
                     |                                |
               ReverseChannels                      next_op
                     |
@@ -323,7 +393,16 @@ def lift_up_through_pad(node: Node, reverse_channels: Node):
             reverse_channels.out_port(0).disconnect()
             reverse_channels.in_port(0).disconnect()
             src = node_input_port_0.get_connection().get_source()
-            node_input_port_0.get_connection().set_source(reverse_channels.out_port(0))
+
+            if src.node.soft_get('type') == 'Parameter':
+                # For Parameter nodes tensor debug attributes should not move to the last node
+                # of subgraph. It is needed for the proper mapping of input framework name.
+                # For this reason "source" mode is used to keep tensor debug attributes at Parameter node.
+                node_input_port_0.get_connection().set_source(reverse_channels.out_port(0),
+                                                              attributes_save_mode="source")
+            else:
+                node_input_port_0.get_connection().set_source(reverse_channels.out_port(0))
+
             src.connect(reverse_channels.in_port(0))
             for reverse_channels_destination in reverse_channels_out_nodes:
                 node.out_port(0).get_connection().add_destination(reverse_channels_destination)
diff --git a/model-optimizer/extensions/back/add_outputs_recursive.py b/model-optimizer/extensions/back/add_outputs_recursive.py
new file mode 100644
index 00000000000000..f8bff57ec67bde
--- /dev/null
+++ b/model-optimizer/extensions/back/add_outputs_recursive.py
@@ -0,0 +1,318 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np
+
+from extensions.ops.If import If
+from extensions.ops.loop import Loop
+from extensions.ops.tensor_iterator import TensorIterator
+from mo.back.replacement import BackReplacementPattern
+from mo.front.common.partial_infer.utils import int64_array, dynamic_dimension_value
+from mo.front.tf.graph_utils import create_op_node_with_second_input
+from mo.graph.graph import Graph, Node
+from mo.ops.result import Result
+from mo.ops.unsqueeze import Unsqueeze
+
+
+def ti_set_output_port_shape(cycle_node, internal_id, port_num, iterations_count, axis):
+    int_node_name = TensorIterator.find_internal_layer_id(cycle_node.body, internal_id)
+    int_node = Node(cycle_node.body, int_node_name)
+    assert int_node.op == 'Result'
+    out_shape = int_node.in_port(0).data.get_shape().copy()
+    # inside cycle node Unsqueeze was added to have the first dimension for concatenating results along it
+    assert len(out_shape) >= 1
+    if axis is not None:
+        out_shape[axis] = iterations_count
+
+    assert port_num in cycle_node.out_ports()
+    cycle_node.out_port(port_num).data.set_shape(out_shape)
+
+
+def get_iterations_count_from_output_record(output_rec):
+    def check_field(record, field):
+        return field in record and record[field] is not None
+
+    iterations_count = 1
+    # 1. check if we need to concatenate iteration results for given output
+    if not check_field(output_rec, 'axis'):
+        # in this case we do not concatenate outputs, so iterations count is not needed really
+        return None
+
+    # 2. check if given output record contains values for 'end', so iterations count can be calculated from this record
+    if check_field(output_rec, 'end') and output_rec['end'] != -1 and \
+            check_field(output_rec, 'start') and output_rec['start'] != -1:
+        stride = output_rec['stride'] if check_field(output_rec, 'stride') else 1
+        # get iterations count from output record
+        iterations_count = (output_rec['end'] - output_rec['start']) / stride
+        return iterations_count
+
+    return dynamic_dimension_value
+
+
+# shape inference for TensorIterator
+# copy shapes from internal nodes + insert correct iterations count where needed
+def ti_infer(step_node, port_num):
+    out_port_map = step_node.output_port_map
+    port_num = port_num + len(step_node.in_ports())
+    # find out which internal layer maps to port_num
+    found_rec = None
+    for record in out_port_map:
+        if record['external_port_id'] == port_num:
+            found_rec = record
+            break
+    assert found_rec is not None, \
+        "External port {} is not connected with body in node {}".format(port_num,
+                                                                        step_node.soft_get('name', step_node.id))
+
+    port_num = port_num - len(step_node.in_ports())
+
+    # find out iterations count for TensorIterator to set output shape correctly
+
+    iterations_count = get_iterations_count_from_output_record(found_rec)
+    if iterations_count is dynamic_dimension_value:
+        iterations_count = TensorIterator.find_iterations_count_for_output(step_node)
+
+    ti_set_output_port_shape(step_node, found_rec['internal_layer_id'], port_num, iterations_count,
+                             found_rec['axis'])
+
+
+# shape inference for Loop
+# copy shapes from internal nodes + insert correct iterations count where needed
+# iterations count always in the first dimension
+def loop_infer(step_node, port_num):
+    out_port_map = step_node.output_port_map
+    int_layer_id = None
+    iterations_count = Loop.iterations_count(step_node)
+    for record in out_port_map:
+        if record['external_port_id'] == port_num:
+            int_layer_id = record['internal_layer_id']
+
+    ti_set_output_port_shape(step_node, int_layer_id, port_num, iterations_count, 0)
+
+
+def max_internal_layer_id(graph):
+    max_int_layer_id = 0
+    for n in graph.get_op_nodes():
+        if n.has_and_set('internal_layer_id') and n.internal_layer_id > max_int_layer_id:
+            max_int_layer_id = n.internal_layer_id
+    return max_int_layer_id
+
+
+# Add Result (and Unsqueeze is add_unsqueeze=True) to node port port_num in graph cur_graph.
+# New nodes will have internal id equal to cur_max_layer_id + 1 (and cur_max_layer_id + 2 if 2 nodes were added)
+# New nodes will be inserted in tracks on position i.
+def add_output_in_body(node, port_num, cur_graph, cur_max_layer_id, tracks, track_index, add_unsqueeze=True):
+    port = node.out_port(port_num)
+    if add_unsqueeze:
+        unsq_name = port.node.soft_get('name', port.node.id) + "/Unsqueeze"
+        unsq_node = create_op_node_with_second_input(cur_graph, Unsqueeze,
+                                                     int64_array([0]),
+                                                     {'name': unsq_name})
+        port.connect(unsq_node.in_port(0))
+        unsq_node['internal_layer_id'] = cur_max_layer_id + 1
+        cur_max_layer_id += 1
+        tracks.insert(track_index, {'node': unsq_node, 'graph': cur_graph})
+        port = unsq_node.out_port(0)
+
+    out_name = port.node.soft_get('name', port.node.id) + ":" + str(port_num)
+    res_node = Result(cur_graph, {'name': out_name}).create_node()
+    port.connect(res_node.in_port(0))
+    res_node['internal_layer_id'] = cur_max_layer_id + 1
+    cur_max_layer_id += 1
+    tracks.insert(track_index, {'node': res_node, 'graph': cur_graph})
+
+    return res_node
+
+
+class AddOutputRecursive(BackReplacementPattern):
+    """
+    Add output to node inside loops. Path to node set in 'additional_outputs' attribute of graph.
+    Path structure: [node_loop_1, loop_2_in_loop_1,.., if_node, [then_list, else_list]]
+    After if operation should be sub-list with 2 elements then_list and else_list where each is one node or list of
+    nodes in according path
+    For cycles results from all iterations will be concatenated along 0 dimension.
+    """
+    enabled = False
+    run_not_recursively = True
+
+    @staticmethod
+    def add_output_for_path(graphs_nodes_path):
+        # add output to nodes according to path
+        step_node = graphs_nodes_path[-1]['node']
+        cur_graph = graphs_nodes_path[-1]['graph']
+
+        ports_to_add_nodes = []
+        for o_p in step_node.out_ports():
+            ports_to_add_nodes.append(o_p)
+
+        # update internal_layer_id for new Results
+        for i in range(len(graphs_nodes_path)-1, 0, -1):
+            cur_max_layer_id = max_internal_layer_id(cur_graph) + 1
+            cur_loop_node = graphs_nodes_path[i-1]['node']
+            new_out_ports = []
+            if cur_loop_node.op is not 'If':
+                # add Unsqueeze and Result for TensorIterator and Loop and update output_port_map
+                for p_num in ports_to_add_nodes:
+                    res_node = add_output_in_body(step_node, p_num, cur_graph, cur_max_layer_id,
+                                                  graphs_nodes_path, i)
+
+                    # IR reader fix output port map for Loop, but have not change for TensorIterator
+                    new_port_id = len(cur_loop_node.out_ports())
+                    if cur_loop_node.op == 'TensorIterator':
+                        new_port_id = new_port_id + len(cur_loop_node.in_ports())
+                    cur_loop_node.output_port_map.append({'axis': 0, 'stride': 1, 'part_size': 1, 'start': 0,
+                                                          'end': -1, 'external_port_id': new_port_id,
+                                                          'internal_layer_id': res_node['internal_layer_id']})
+                    port_id = new_port_id
+                    if cur_loop_node.op == 'TensorIterator':
+                        port_id = port_id - len(cur_loop_node.in_ports())
+
+                    new_out_ports.append(port_id)
+                    cur_loop_node.add_output_port(port_id)
+            else:
+                # add Result nodes for If and update output_id
+                for p_num in ports_to_add_nodes:
+                    res_node = add_output_in_body(step_node, p_num, cur_graph, cur_max_layer_id, graphs_nodes_path, i,
+                                                  add_unsqueeze=False)
+
+                    if cur_loop_node.then_graph == cur_graph:
+                        new_port_id = len(cur_loop_node.out_ports())
+                        res_node['output_id'] = new_port_id
+                        cur_loop_node.add_output_port(new_port_id)
+                        new_out_ports.append(new_port_id)
+                    else:
+                        res_node['output_id'] = list(cur_loop_node.out_ports().keys())[-1]
+            ports_to_add_nodes = new_out_ports
+            step_node = cur_loop_node
+            cur_graph = graphs_nodes_path[i-1]['graph']
+
+        i = 0
+        for p_num in ports_to_add_nodes:
+            port = step_node.out_port(p_num)
+            out_name = step_node.soft_get('name', step_node.id) + "." + str(p_num)
+            res_node = Result(cur_graph, {'name': out_name}).create_node()
+            port.connect(res_node.in_port(0))
+            # add name of Result to fw_tensor_debug_info to avoid renaming
+            if step_node.out_nodes()[p_num].has_and_set('fw_tensor_debug_info'):
+                step_node.out_nodes()[p_num]['fw_tensor_debug_info'].append(out_name)
+            else:
+                step_node.out_nodes()[p_num]['fw_tensor_debug_info'] = [[out_name, out_name]]
+            if step_node.op == 'TensorIterator':
+                step_node.out_edges()[len(step_node.out_edges())-1]['external_port_id'] = p_num + \
+                                                                                          len(step_node.in_ports())
+            graphs_nodes_path.insert(0, {'node': res_node, 'graph': cur_graph})
+            i += 1
+        return graphs_nodes_path
+
+    @staticmethod
+    def infer_shapes_of_nodes_in_path(graphs_nodes_path):
+        # update shape for new or updated nodes
+        for i in range(len(graphs_nodes_path) - 1, -1, -1):
+            step_node = graphs_nodes_path[i]['node']
+            # update shapes for Loop, TI, If, Unsqueeze
+            # Result to end node in path added to existing port with already calculated shapes
+            for p_num in step_node.out_ports():
+                if not step_node.out_port(p_num).disconnected():
+                    if step_node.op == 'TensorIterator':
+                        ti_infer(step_node, p_num)
+                    elif step_node.op == 'Loop':
+                        loop_infer(step_node, p_num)
+                    elif step_node.op == 'Unsqueeze':
+                        assert step_node.in_port(1).get_source().node.has('value')
+                        axis = step_node.in_port(1).get_source().node.value[0]
+                        out_shape = list(step_node.in_port(0).get_source().data.get_shape())
+                        out_shape.insert(axis, 1)
+                        step_node.out_port(p_num).data.set_shape(out_shape)
+                    elif step_node.op == 'If':
+                        If.update_if_output_ports_shape(step_node)
+
+    @staticmethod
+    def split_path_to_simple_tracks(graph, path):
+        # Split complex path into simple linear tracks.
+        # In path after If node list with 2 sub-lists should be. In this function such path is split into 2 tracks:
+        # one for each sublist with linear structure.
+        # Number of tracks got from path is 2 * number of If operations in path.
+        # Track is looks like list of paths with 2 fields : list of nodes on current path and list of according graphs
+        # Example:
+        # input path : [loop_1, loop_2, if_1, [[loop3_1, node_1], [node_2]]]
+        # output track: [{'nodes': [loop_1, loop_2, if_1, loop3_1, node_1],
+        #                 'graphs':[graph, loop_1.body, loop_2.body, if.then_graph, loop3_1.body]},
+        #                {'nodes': [loop_1, loop_2, if_1, node_2],
+        #                 'graphs':[graph, loop_1.body, loop_2.body, if.else_graph]}]
+
+        # structure to save tracks
+        # list with tracks, each track is list of pairs {'node', 'graph'}
+        paths_nodes_graphs = list()
+        paths_nodes_graphs.append([])
+        # stack for sub-graphs that will be traversed in future
+        future_graphs_stack = [graph]
+        # index for track that we currently fill
+        track_idx = 0
+        # save lists that were started but not finished during processing
+        lists_stack = [{'list': path, 'pos': -1}]
+        while len(lists_stack) != 0:
+            cur_list_pos = lists_stack.pop(-1)
+            # current list to process
+            cur_list = cur_list_pos['list']
+            # index in current list/sub-list
+            list_idx = cur_list_pos['pos'] + 1
+            while list_idx < len(cur_list):
+                el = cur_list[list_idx]
+                if isinstance(el, (list, np.ndarray)):
+                    lists_stack.append({'list': cur_list, 'pos': list_idx})
+                    # if we have previous node non-list then current sublist is for If node
+                    # and new tracks should be added for sub-graphs (the first subgraph will continue current track)
+                    if list_idx != 0 and isinstance(cur_list[list_idx - 1], str):
+                        for i in range(len(el) - 1):
+                            # copy all nodes from existing track to new one
+                            paths_nodes_graphs.append(paths_nodes_graphs[-1][:])
+                    # new sublist started, so reset index
+                    cur_list = el
+                    list_idx = 0
+                else:
+                    assert isinstance(el, str)
+                    cur_graph = future_graphs_stack.pop(-1)
+                    step_node = Node(cur_graph, el)
+                    paths_nodes_graphs[track_idx].append({'node': step_node, 'graph': cur_graph})
+
+                    # if node is not last, check that next node will be on current track or not
+                    if list_idx != len(cur_list) - 1:
+                        # so detect if we are in sublist with branches for If
+                        # then in stack sublist is not the first node of list
+                        # and have previous node with If operation name
+                        if len(lists_stack) != 0 and lists_stack[-1]['pos'] != 0 and \
+                                isinstance(lists_stack[-1]['list'][lists_stack[-1]['pos']-1], str):
+                            # switch to next track
+                            if list_idx != len(cur_list) - 1:
+                                track_idx += 1
+                        else:
+                            assert step_node.has_and_set('sub_graphs'), "Node without sub-graphs is not last in path"
+                            # the first graph should be first in traverse
+                            for sub_graphs_name in reversed(step_node['sub_graphs']):
+                                future_graphs_stack.append(step_node[sub_graphs_name])
+                    list_idx += 1
+
+        return paths_nodes_graphs
+
+    def find_and_replace_pattern(self, graph: Graph):
+
+        if 'additional_outputs' not in graph.graph:
+            return
+
+        path = graph.graph['additional_outputs']
+        paths_nodes_graphs = self.split_path_to_simple_tracks(graph, path)
+
+        paths_nodes_graphs_old = []
+        for i in range(len(paths_nodes_graphs)):
+            paths_nodes_graphs_old.append(paths_nodes_graphs[i][:])
+            paths_nodes_graphs[i] = self.add_output_for_path(paths_nodes_graphs[i])
+
+        for i in range(len(paths_nodes_graphs)):
+            self.infer_shapes_of_nodes_in_path(paths_nodes_graphs[i])
+
+        new_nodes = []
+        for i in range(len(paths_nodes_graphs)):
+            # new Result added to main graph should be on last place
+            if paths_nodes_graphs_old[i][0]['node'] != paths_nodes_graphs[i][0]['node']:
+                new_nodes.append(paths_nodes_graphs[i][0]['node'])
+
+        return new_nodes
diff --git a/model-optimizer/extensions/back/fuse_sub_div_min.py b/model-optimizer/extensions/back/fuse_sub_div_min.py
deleted file mode 100644
index 9c13708150b12e..00000000000000
--- a/model-optimizer/extensions/back/fuse_sub_div_min.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import numpy as np
-
-from extensions.ops.elementwise import Sub, Div, Negative
-from mo.back.replacement import BackReplacementPattern
-from mo.graph.graph import Node, Graph
-
-
-class Negate(BackReplacementPattern):
-    enabled = True
-    force_clean_up = True
-
-    @staticmethod
-    def pattern():
-        return dict(
-            nodes=[
-                ('mul', {'type': 'Multiply'})
-            ],
-            edges=[],
-        )
-
-    @staticmethod
-    def replace_pattern(graph: Graph, match: [str, Node]):
-        mul = match['mul']
-        name = mul.soft_get('name', mul.id)
-
-        mul_POS_port = None
-        if mul.in_port(0).data.get_value() is not None and np.all(mul.in_port(0).data.get_value() == -1):
-            mul_POS_port = mul.in_port(1)
-        if mul.in_port(1).data.get_value() is not None and np.all(mul.in_port(1).data.get_value() == -1):
-            mul_POS_port = mul.in_port(0)
-
-        if mul_POS_port is None:
-            return
-
-        negative = Negative(graph, {'name': name + '/Negate'}).create_node()
-
-        mul.out_port(0).get_connection().set_source(negative.out_port(0))
-        mul_POS_port.get_connection().set_destination(negative.in_port(0))
-
-
-class EltwisesToSubtract(BackReplacementPattern):
-    enabled = True
-    force_clean_up = True
-
-    def run_after(self):
-        return [Negate]
-
-    @staticmethod
-    def pattern():
-        return dict(
-            nodes=[
-                ('neg', {'type': 'Negative'}),
-                ('neg_d', {}),
-                ('add', {'type': 'Add'})
-            ],
-            edges=[
-                ('neg', 'neg_d'),
-                ('neg_d', 'add'),
-            ],
-        )
-
-    @staticmethod
-    def replace_pattern(graph: Graph, match: [str, Node]):
-        neg = match['neg']
-        add = match['add']
-
-        name = add.soft_get('name', add.id)
-
-        minuend_port = add.in_port(0).get_source() \
-            if add.in_port(1).get_source().node.id == neg.id else add.in_port(1).get_source()
-        subtrahned_port = neg.in_port(0).get_source()
-
-        sub = Sub(graph, {'name': name + '/sub'}).create_node()
-
-        add.out_port(0).get_connection().set_source(sub.out_port(0))
-        minuend_port.connect(sub.in_port(0))
-        subtrahned_port.connect(sub.in_port(1))
-
-
-class EltwisesToDiv(BackReplacementPattern):
-    enabled = True
-    force_clean_up = True
-
-    @staticmethod
-    def pattern():
-        return dict(
-            nodes=[
-                ('const', {'type': 'Const'}),
-                ('const_d', {'value': lambda val: val is not None and np.all(val == -1)}),
-                ('inv', {'type': 'Pow'}),
-                ('inv_d', {}),
-                ('mul', {'type': 'Multiply'})
-            ],
-            edges=[
-                ('const', 'const_d'),
-                ('const_d', 'inv', {'in': 1}),
-                ('inv', 'inv_d'),
-                ('inv_d', 'mul'),
-            ],
-        )
-
-    @staticmethod
-    def replace_pattern(graph: Graph, match: [str, Node]):
-        pow = match['inv']
-        mul = match['mul']
-        const = match['const']
-
-        name = mul.soft_get('name', mul.id)
-
-        devidend_port = mul.in_port(0).get_source() if mul.in_port(1).get_source().node.id == pow.id else mul.in_port(
-            1).get_source()
-        divider_port = pow.in_port(0).get_source() if pow.in_port(1).get_source().node.id == const.id else pow.in_port(
-            1).get_source()
-
-        div = Div(graph, {'name': name + '/div'}).create_node()
-
-        mul.out_port(0).get_connection().set_source(div.out_port(0))
-        devidend_port.connect(div.in_port(0))
-        divider_port.connect(div.in_port(1))
diff --git a/model-optimizer/extensions/front/tf/IteratorGetNextCut.py b/model-optimizer/extensions/front/tf/IteratorGetNextCut.py
new file mode 100644
index 00000000000000..3cced57f6587ae
--- /dev/null
+++ b/model-optimizer/extensions/front/tf/IteratorGetNextCut.py
@@ -0,0 +1,45 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from collections import defaultdict
+
+from mo.front.extractor import add_input_ops
+from mo.graph.graph import Graph
+from mo.middle.passes.convert_data_type import SUPPORTED_DATA_TYPES, np_data_type_to_precision
+from mo.utils.error import Error
+from mo.front.common.replacement import FrontReplacementPattern
+
+
+class IteratorGetNextCut(FrontReplacementPattern):
+    """
+    Cuts OneShotIterator -> IteratorGetNext pattern
+    in order to enable Out Of the Box (OOB) usage.
+    Pass is run only if user didn't specify any inputs names and shapes.
+    """
+    enabled = True
+    graph_condition = [lambda graph: graph.graph['cmd_params'].input is None]
+
+    def run_before(self):
+        from extensions.front.output_cut import OutputCut
+        from extensions.front.input_cut import InputCut
+        return [OutputCut, InputCut]
+
+    def run_after(self):
+        return []
+
+    def find_and_replace_pattern(self, graph: Graph):
+        iter_get_next_shapes = defaultdict(list)
+        for iter_get_next in graph.get_op_nodes(op='IteratorGetNext'):
+            iter_get_next_name = iter_get_next.soft_get('name', iter_get_next.id)
+            for port in iter_get_next.out_ports():
+                if not np_data_type_to_precision(iter_get_next.types[port]) in SUPPORTED_DATA_TYPES:
+                    raise Error("In IteratorGetNext node '{}' data type '{}' is not supported".format(
+                        iter_get_next_name, iter_get_next.types[port]))
+
+                iter_get_next_shapes[iter_get_next_name].append(dict(
+                    shape=iter_get_next.shapes[port],
+                    out=port,
+                    data_type=iter_get_next.types[port]
+                ))
+
+        add_input_ops(graph, iter_get_next_shapes, True)
diff --git a/model-optimizer/extensions/front/tf/IteratorGetNext_ext.py b/model-optimizer/extensions/front/tf/IteratorGetNext_ext.py
index e79991647145e3..c9318c976e56d8 100644
--- a/model-optimizer/extensions/front/tf/IteratorGetNext_ext.py
+++ b/model-optimizer/extensions/front/tf/IteratorGetNext_ext.py
@@ -20,5 +20,5 @@ def extract(cls, node):
         result_shapes = []
         for shape_pb in shapes:
             result_shapes.append(tf_tensor_shape(shape_pb))
-        Op.update_node_stat(node, {'shapes': result_shapes, 'types': extracted_types})
+        Op.update_node_stat(node, {'shapes': result_shapes, 'types': extracted_types, 'out_ports_count': 1})
         return cls.enabled
diff --git a/model-optimizer/extensions/front/tf/activation_ext.py b/model-optimizer/extensions/front/tf/activation_ext.py
index 3780d77f7f3392..c2cf3c42ee59c6 100644
--- a/model-optimizer/extensions/front/tf/activation_ext.py
+++ b/model-optimizer/extensions/front/tf/activation_ext.py
@@ -1,8 +1,8 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from extensions.ops.activation_ops import Abs, Elu, Erf, Exp, ReLU, LeakyReLU, LogicalNot, ReLU6, Sigmoid, \
-    Sin, Sinh, Cos, Cosh, Tan, Tanh, Ceiling, Atanh, Acosh, Asinh, Mish, Log
+from extensions.ops.activation_ops import Abs, Acos, Asin, Atan, Elu, Erf, Exp, ReLU, LeakyReLU, LogicalNot, ReLU6, \
+    Sigmoid, Sin, Sinh, Cos, Cosh, Tan, Tanh, Ceiling, Atanh, Acosh, Asinh, Mish, Log
 from mo.front.extractor import FrontExtractorOp
 
 
@@ -218,3 +218,33 @@ class LogExtractor(FrontExtractorOp):
     def extract(cls, node):
         Log.update_node_stat(node)
         return cls.enabled
+
+
+class AsinExtractor(FrontExtractorOp):
+    op = 'Asin'
+    enabled = True
+
+    @classmethod
+    def extract(cls, node):
+        Asin.update_node_stat(node)
+        return cls.enabled
+
+
+class AcosExtractor(FrontExtractorOp):
+    op = 'Acos'
+    enabled = True
+
+    @classmethod
+    def extract(cls, node):
+        Acos.update_node_stat(node)
+        return cls.enabled
+
+
+class AtanExtractor(FrontExtractorOp):
+    op = 'Atan'
+    enabled = True
+
+    @classmethod
+    def extract(cls, node):
+        Atan.update_node_stat(node)
+        return cls.enabled
diff --git a/model-optimizer/extensions/ops/gathernd.py b/model-optimizer/extensions/ops/gathernd.py
index 1175698fa96b89..01e6e2f36c4794 100644
--- a/model-optimizer/extensions/ops/gathernd.py
+++ b/model-optimizer/extensions/ops/gathernd.py
@@ -16,7 +16,7 @@ def __init__(self, graph: Graph, attrs: dict):
         mandatory_props = {
             'type': self.op,
             'op': self.op,
-            'version': 'opset5',
+            'version': 'opset8',
             'infer': self.infer,
             'in_ports_count': 2,
             'out_ports_count': 1,
@@ -56,41 +56,55 @@ def infer(node: Node):
         assert len(indices_shape) > 0, "Indices must not be a scalar"
         assert (batch_dims + indices_shape[-1]) <= len(data_shape), \
             "Length of a tuple with indices must not exceed a rank of data tensor excluding batch dimensions"
+        assert node['version'] in ['opset5', 'opset8'], 'Unsupported version of GatherND operation: {}, operation ' \
+                                                        'name : {}'.format(node['version'], node.soft_get('name'))
 
         # compute output shape
+        batch = []
         if batch_dims > 0:
-            if is_fully_defined(data_shape[:batch_dims]):
-                batch = [np.prod(data_shape[:batch_dims]).tolist()]
-            else:
-                batch = [dynamic_dimension_value]
-        else:
-            batch = []
+            if node['version'] == 'opset5':  # Support old version of gatherND shape inference
+                if is_fully_defined(data_shape[:batch_dims]):
+                    batch = [np.prod(data_shape[:batch_dims]).tolist()]
+                else:
+                    batch = [dynamic_dimension_value]
+            elif node['version'] == 'opset8':
+                for dim in range(batch_dims):
+                    assert compatible_dims(indices_shape[dim], data_shape[dim]),\
+                        "Batch dimensions in data.shape and indices.shape must be compatible"
+                if is_fully_defined(indices_shape[:batch_dims]):
+                    batch = indices_shape[:batch_dims].tolist()
+                elif is_fully_defined(data_shape[:batch_dims]):
+                    batch = data_shape[:batch_dims].tolist()
+                else:
+                    for ind in range(batch_dims):
+                        if indices_shape[ind] != dynamic_dimension_value:
+                            batch.append(indices_shape[ind])
+                        elif data_shape[ind] != dynamic_dimension_value:
+                            batch.append(data_shape[ind])
+                        else:
+                            batch.append(dynamic_dimension_value)
+
         slice_shape = list(data_shape[(batch_dims + indices_shape[-1]):])
-        output_shape = batch + list(indices_shape[batch_dims:-1]) + slice_shape
+
+        output_shape = batch + list(indices_shape)[batch_dims:-1] + slice_shape
         node.out_port(0).data.set_shape(output_shape)
 
-        # compute output value if all input values are defined
-        if is_fully_defined(indices_value) and is_fully_defined(data_value):
-            output_value = np.zeros(output_shape, dtype=data_value.dtype)
-            if batch_dims == 0:
-                output_indices_range = int64_array(indices_shape[:-1])
-                for output_index in np.ndindex(tuple(output_indices_range)):
-                    indices_tuple = indices_value[output_index]
-                    output_value[output_index] = data_value[tuple(indices_tuple.T)]
-            else:
-                batch_dims_range = int64_array(indices_shape[:batch_dims])
-                for batch_indices in np.ndindex(tuple(batch_dims_range)):
-                    # compute batch index in output tensor
-                    batch_ind = 0
-                    num_elements = 1
-                    for ind in reversed(range(len(batch_dims_range))):
-                        batch_ind += batch_indices[ind] * num_elements
-                        num_elements *= batch_dims_range[ind]
-                    output_indices_range = int64_array(indices_shape[batch_dims:-1])
-                    for output_index in np.ndindex(tuple(output_indices_range)):
-                        tmp_ind = batch_indices + output_index
-                        indices_tuple = tuple(indices_value[tmp_ind].T)
-                        full_input_ind = batch_indices + indices_tuple
-                        full_output_ind = tuple(np.array([batch_ind]).T) + output_index
-                        output_value[full_output_ind] = data_value[full_input_ind]
+        # compute output value if all input indices are defined
+        if is_fully_defined(indices_value) and data_value is not None:
+            batch_dims_size = 1
+
+            for i in range(batch_dims):
+                batch_dims_size *= indices_shape[i]
+
+            output_data = []
+
+            reshaped_indices = indices_value.reshape(batch_dims_size, -1, indices_shape[-1])
+
+            reshaped_data = data_value.reshape((batch_dims_size,) + tuple((data_shape[batch_dims:])))
+
+            for batch_dim in range(reshaped_indices.shape[0]):
+                for outer_dim in range(reshaped_indices.shape[1]):
+                    gather_index = tuple(reshaped_indices[batch_dim][outer_dim])
+                    output_data.append(reshaped_data[(batch_dim,) + gather_index])
+            output_value = np.asarray(output_data, dtype=data_value.dtype).reshape(output_shape)
             node.out_port(0).data.set_value(output_value)
diff --git a/model-optimizer/extensions/ops/tensor_iterator.py b/model-optimizer/extensions/ops/tensor_iterator.py
index 0d62d4ad5449c0..acc2c67cccef22 100644
--- a/model-optimizer/extensions/ops/tensor_iterator.py
+++ b/model-optimizer/extensions/ops/tensor_iterator.py
@@ -1,10 +1,10 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-
 from copy import copy, deepcopy
+from math import ceil
 
 from extensions.ops.parameter import Parameter
-from mo.front.common.partial_infer.utils import shape_array
+from mo.front.common.partial_infer.utils import shape_array, is_fully_defined, dynamic_dimension_value
 from mo.graph.graph import Node, dict_includes, Graph
 from mo.ops.const import Const
 from mo.ops.op import Op
@@ -347,6 +347,51 @@ def ti_type_infer(node):
 
         ti_graph.remove_nodes_from([node.id for node in fake_input_const_nodes])
 
+    @staticmethod
+    def find_iterations_count_for_output(ti_node):
+        def check_field(record, field):
+            return field in record and record[field] is not None
+        iterations_count = dynamic_dimension_value
+        # find out iterations count from inputs.
+        # If no input contains 'axis' attribute then no slicing is in TI and it has only one iteration
+        # If several inputs have axis attribute with different iterations count then we use maximum value.
+        for in_rec in ti_node.input_port_map:
+            if not check_field(in_rec, 'axis'):
+                continue
+            assert check_field(in_rec, 'external_port_id'), "external_port_id not set for input of {} node".format(ti_node.id)
+            in_shape = ti_node.in_port(in_rec['external_port_id']).data.get_shape()
+            if check_field(in_rec, 'end') and in_rec['end'] >= 0 and \
+                    check_field(in_rec, 'start') and in_rec['start'] >= 0:
+                in_rec_end = in_rec['end']
+                in_rec_start = in_rec['start']
+            elif check_field(in_rec, 'end') and in_rec['end'] >= 0:
+                in_rec_end = in_rec['end']
+                in_rec_start = in_shape[in_rec['axis']] if not check_field(in_rec, 'start') else \
+                    in_shape[in_rec['axis']] + 1 + in_rec['start']
+            elif check_field(in_rec, 'start') and in_rec['start'] >= 0:
+                in_rec_end = in_shape[in_rec['axis']] if not check_field(in_rec, 'end') else \
+                    in_shape[in_rec['axis']] + 1 + in_rec['end']
+                in_rec_start = in_rec['start']
+            else:
+                in_rec_end = ti_node.in_port(in_rec['external_port_id']).data.get_shape()[in_rec['axis']]
+                in_rec_start = 0
+
+            if check_field(in_rec, 'stride'):
+                in_rec_stride = in_rec['stride']
+            else:
+                in_rec_stride = 1
+
+            # in case of dynamic iterations count don't continue any calculations on this iteration
+            if not is_fully_defined(in_rec_end) or not is_fully_defined(in_rec_start):
+                continue
+
+            if iterations_count is not dynamic_dimension_value and \
+                    ceil((in_rec_end - in_rec_start) / in_rec_stride) != iterations_count:
+                raise Error("TensorIterator node {} have inputs with different iterations count".format(ti_node.id))
+            iterations_count = ceil((in_rec_end - in_rec_start) / in_rec_stride)
+
+        return iterations_count
+
 
 def get_internal_node_by_layer_id(ti, internal_layer_id):
     suitable_nodes = ti.body.get_op_nodes(internal_layer_id=internal_layer_id)
diff --git a/model-optimizer/mo/back/offline_transformations.py b/model-optimizer/mo/back/offline_transformations.py
index c41aa8b95ca9ce..79f9d847323fbe 100644
--- a/model-optimizer/mo/back/offline_transformations.py
+++ b/model-optimizer/mo/back/offline_transformations.py
@@ -33,8 +33,11 @@ def apply_moc_transformations(net: object):
     from openvino.offline_transformations import ApplyMOCTransformations  # pylint: disable=import-error,no-name-in-module
     ApplyMOCTransformations(net, False)
 
+def compress_model(net:object):
+    from openvino.offline_transformations import CompressModelTransformation  # pylint: disable=import-error,no-name-in-module
+    CompressModelTransformation(net)
 
-def apply_offline_transformations(input_model: str, framework: str, transforms: list):
+def apply_offline_transformations(input_model: str, framework: str, transforms: list, compress_fp16=False):
     # This variable is only needed by GenerateMappingFile transformation
     # to produce correct mapping
     extract_names = framework in ['tf', 'mxnet', 'kaldi']
@@ -58,6 +61,10 @@ def read_network(path_to_xml):
 
     apply_user_transformations(net, transforms)
     apply_moc_transformations(net)
+
+    if compress_fp16:
+        compress_model(net)
+
     Serialize(net, str(input_model + ".xml").encode('utf-8'), (input_model + ".bin").encode('utf-8'))
     path_to_mapping = input_model + ".mapping"
     GenerateMappingFile(net, path_to_mapping.encode('utf-8'), extract_names)
@@ -68,6 +75,7 @@ def read_network(path_to_xml):
     parser.add_argument("--input_model")
     parser.add_argument("--framework")
     parser.add_argument("--transform")
+    parser.add_argument("--compress_fp16", action='store_true')
     args = parser.parse_args()
 
-    apply_offline_transformations(args.input_model, args.framework, parse_transform(args.transform))
\ No newline at end of file
+    apply_offline_transformations(args.input_model, args.framework, parse_transform(args.transform), args.compress_fp16)
diff --git a/model-optimizer/mo/main.py b/model-optimizer/mo/main.py
index c939d9523ed919..e8831184125c50 100644
--- a/model-optimizer/mo/main.py
+++ b/model-optimizer/mo/main.py
@@ -205,6 +205,12 @@ def raise_ie_not_found():
     except Exception as e:
         raise_ie_not_found()
 
+    if 'data_type' in argv and argv.data_type in ['FP16', 'half']:
+        argv.data_type = 'FP32'
+        argv.compress_fp16 = True
+    else:
+        argv.compress_fp16 = False
+
     # This is just to check that transform key is valid and transformations are available
     check_available_transforms(parse_transform(argv.transform))
 
@@ -355,10 +361,15 @@ def emit_ir(graph: Graph, argv: argparse.Namespace):
             if not argv.legacy_ir_generation:
                 path_to_offline_transformations = os.path.join(os.path.realpath(os.path.dirname(__file__)), 'back',
                                                                'offline_transformations.py')
-                status = subprocess.run([sys.executable, path_to_offline_transformations,
+                cmd = [sys.executable, path_to_offline_transformations,
                                          "--input_model", orig_model_name,
                                          "--framework", argv.framework,
-                                         "--transform", argv.transform], env=os.environ)
+                                         "--transform", argv.transform]
+                if "compress_fp16" in argv and argv.compress_fp16:
+                    cmd += ["--compress_fp16"]
+                    # restore data_type cmd parameter
+                    argv.data_type = 'FP16'
+                status = subprocess.run(cmd, env=os.environ)
                 return_code = status.returncode
         except Exception as e:
             return_code = "failed"
diff --git a/model-optimizer/mo/moc_frontend/serialize.py b/model-optimizer/mo/moc_frontend/serialize.py
index abe150742e1d62..2de7c800d0225d 100644
--- a/model-optimizer/mo/moc_frontend/serialize.py
+++ b/model-optimizer/mo/moc_frontend/serialize.py
@@ -19,6 +19,10 @@ def moc_emit_ir(ngraph_function: Function, argv: argparse.Namespace):
     apply_user_transformations(network, parse_transform(argv.transform))
     apply_moc_transformations(network)
 
+    if argv.compress_fp16:
+        from mo.back.offline_transformations import compress_model
+        compress_model(network)
+
     orig_model_name = os.path.normpath(os.path.join(output_dir, argv.model_name))
     network.serialize(orig_model_name + ".xml", orig_model_name + ".bin")
 
diff --git a/model-optimizer/mo/pipeline/common.py b/model-optimizer/mo/pipeline/common.py
index 6dfafa840d3ab2..319b99e9d57032 100644
--- a/model-optimizer/mo/pipeline/common.py
+++ b/model-optimizer/mo/pipeline/common.py
@@ -173,18 +173,19 @@ def convert_inputs_of_specific_ops(graph: Graph):
 
 def prepare_emit_ir(graph: Graph, data_type: str, output_dir: str, output_model_name: str,
                     mean_data: [list, None] = None, input_names: list = None, meta_info: dict = None,
-                    use_temporary_path=False):
+                    use_temporary_path=False, used_by_ir_reader=False):
     if input_names is None:
         input_names = []
     if meta_info is None:
         meta_info = {}
     graph.strict_mode = False
 
-    # convert Parameter data types
-    convert_data_type.convert_parameters_data_type(graph, data_type)
-    # convert blobs (usually weights and biases)
-    for sub_graph in [graph] + collect_sub_graphs(graph):
-        convert_data_type.convert_blobs(sub_graph, data_type)
+    if not used_by_ir_reader:
+        # convert Parameter data types
+        convert_data_type.convert_parameters_data_type(graph, data_type)
+        # convert blobs (usually weights and biases)
+        for sub_graph in [graph] + collect_sub_graphs(graph):
+            convert_data_type.convert_blobs(sub_graph, data_type)
 
     # restore data type for specific inputs/outputs of specific ops to the data types required by nGraph
     for_graph_and_each_sub_graph_recursively(graph, convert_inputs_of_specific_ops)
diff --git a/model-optimizer/mo/utils/get_ov_update_message.py b/model-optimizer/mo/utils/get_ov_update_message.py
index 95165f6bafa738..9d8ccdba31c825 100644
--- a/model-optimizer/mo/utils/get_ov_update_message.py
+++ b/model-optimizer/mo/utils/get_ov_update_message.py
@@ -8,9 +8,9 @@
 
 
 def get_ov_update_message():
-    expected_update_date = datetime.date(year=2021, month=10, day=15)
+    expected_update_date = datetime.date(year=2022, month=2, day=15)
     current_date = datetime.date.today()
 
-    link = 'https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html?cid=other&source=prod&campid=ww_2021_bu_IOTG_OpenVINO-2021-4-LTS&content=upg_all&medium=organic'
+    link = 'https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html?cid=other&source=prod&campid=ww_2022_bu_IOTG_OpenVINO-2022-1&content=upg_all&medium=organic'
 
     return msg_fmt.format(link) if current_date >= expected_update_date else None
diff --git a/model-optimizer/mo/utils/ir_reader/extender.py b/model-optimizer/mo/utils/ir_reader/extender.py
index 13bc5dddaed611..afa71ac6ecabb8 100644
--- a/model-optimizer/mo/utils/ir_reader/extender.py
+++ b/model-optimizer/mo/utils/ir_reader/extender.py
@@ -60,5 +60,8 @@ def use_shapes_from_ir(node: Node):
 
         # Set all output shapes the same as restored from IR
         for num in node.out_ports():
-            node.out_port(num).data.set_shape(int64_array(node.ports[i][0]))
+            if i in node.ports:
+                node.out_port(num).data.set_shape(int64_array(node.ports[i][0]))
+            else:
+                assert node.out_port(num).data.get_shape() is not None, "Newly added port does not have set shape"
             i += 1
diff --git a/model-optimizer/mo/utils/ir_reader/restore_graph.py b/model-optimizer/mo/utils/ir_reader/restore_graph.py
index d18aa77089815f..1a4e3cca6df2f1 100644
--- a/model-optimizer/mo/utils/ir_reader/restore_graph.py
+++ b/model-optimizer/mo/utils/ir_reader/restore_graph.py
@@ -88,4 +88,4 @@ def save_restored_graph(graph: Graph, path: str, meta_data, name=None):
     for_graph_and_each_sub_graph_recursively(graph, RemoveConstOps().find_and_replace_pattern)
     for_graph_and_each_sub_graph_recursively(graph, CreateConstNodesReplacement().find_and_replace_pattern)
 
-    prepare_emit_ir(graph, data_type, path, name, meta_info=meta_data)
+    prepare_emit_ir(graph, data_type, path, name, meta_info=meta_data, used_by_ir_reader=True)
diff --git a/model-optimizer/mo/utils/runtime_info.py b/model-optimizer/mo/utils/runtime_info.py
index 1c61eb0e7fbcf2..3941794686ae83 100644
--- a/model-optimizer/mo/utils/runtime_info.py
+++ b/model-optimizer/mo/utils/runtime_info.py
@@ -29,6 +29,17 @@ def __init__(self):
         """
         self.info = defaultdict(dict)
 
+    def contains(self, attribute_name: str):
+        attr_count = [key[0] for key in list(self.info.keys())].count(attribute_name)
+        assert attr_count <= 1, 'Incorrect rt_info attribute, got more than one {}.'.format(attribute_name)
+        return attr_count > 0
+
+    def get_attribute_version(self, attribute_name: str):
+        for name, version in list(self.info.keys()):
+            if name == attribute_name:
+                return version
+        raise Exception("rt_info does not contain attribute with name {}".format(attribute_name))
+
 
 class RTInfoElement:
     """
diff --git a/model-optimizer/unit_tests/extensions/analysis/Iterator_get_next_test.py b/model-optimizer/unit_tests/extensions/analysis/Iterator_get_next_test.py
index 801873bdbb996e..05946988c0b72d 100644
--- a/model-optimizer/unit_tests/extensions/analysis/Iterator_get_next_test.py
+++ b/model-optimizer/unit_tests/extensions/analysis/Iterator_get_next_test.py
@@ -26,8 +26,8 @@ def test_positive(self):
         inputs_desc = {}
         message = InputsAnalysis.iterator_get_next_analysis(graph, inputs_desc)
         ref_message = 'It looks like there is IteratorGetNext as input\n' \
-                      'Run the Model Optimizer with:\n\t\t--input "iter_get_next:0[2 2],iter_get_next:1[1 1]"\n' \
-                      'And replace all negative values with positive values'
+                      'Run the Model Optimizer without --input option \n' \
+                      'Otherwise, try to run the Model Optimizer with:\n\t\t--input "iter_get_next:0[2 2],iter_get_next:1[1 1]"\n'
         self.assertEqual(message, ref_message)
 
     def test_negative(self):
diff --git a/model-optimizer/unit_tests/extensions/back/MatMulNormalizer_test.py b/model-optimizer/unit_tests/extensions/back/MatMulNormalizer_test.py
index 30805aa19aa84b..e6b98ce3b91c44 100644
--- a/model-optimizer/unit_tests/extensions/back/MatMulNormalizer_test.py
+++ b/model-optimizer/unit_tests/extensions/back/MatMulNormalizer_test.py
@@ -15,7 +15,7 @@
 from mo.ops.reshape import Reshape
 from mo.utils.ir_engine.compare_graphs import compare_graphs
 from unit_tests.utils.graph import build_graph, regular_op_with_shaped_data, valued_const_with_data, \
-    result, connect, connect_data
+    shaped_const_with_data, result, connect, connect_data
 from unit_tests.utils.graph import regular_op_with_empty_data as op_with_empty_data
 
 
@@ -101,9 +101,8 @@ def test_reshape_on_the_B_input(self,
 
 
 class FQTransposePullerTest(unittest.TestCase):
-    def nodes(self, input_shape, transpose_shape, fq_shape):
-        return {
-            **regular_op_with_shaped_data('input', input_shape, dict(type='Parameter', op='Parameter')),
+    def nodes(self, input_shape, transpose_shape, fq_shape, is_input_const):
+        nodes = {
             **valued_const_with_data('il', np.array([[[[0]]]])),
             **valued_const_with_data('ih', np.array([[[[255]]]])),
             **valued_const_with_data('ol', np.array([[[[0]]]])),
@@ -116,8 +115,16 @@ def nodes(self, input_shape, transpose_shape, fq_shape):
             **result(),
         }
 
+        if is_input_const:
+            input_node = shaped_const_with_data('input', input_shape)
+        else:
+            input_node = regular_op_with_shaped_data('input', input_shape, dict(type='Parameter', op='Parameter'))
+
+        nodes.update(input_node)
+        return nodes
+
     def test_positive(self):
-        nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 3, 224, 224])
+        nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 3, 224, 224], True)
         edges = [
             *connect('input', '0:FQ'),
             *connect('il', '1:FQ'),
@@ -132,7 +139,7 @@ def test_positive(self):
         PullTransposeThroughFQUp().find_and_replace_pattern(graph)
         graph.clean_up()
 
-        nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 224, 224, 3])
+        nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 224, 224, 3], True)
         edges = [
             *connect('input', '0:transpose'),
             *connect('order:0', '1:transpose'),
@@ -148,8 +155,8 @@ def test_positive(self):
         (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
         self.assertTrue(flag, resp)
 
-    def test_negative(self):
-        nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 3, 224, 224])
+    def test_negative_1(self):
+        nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 3, 224, 224], True)
         edges = [
             *connect('input', '0:FQ'),
             *connect('il', '1:FQ'),
@@ -168,3 +175,21 @@ def test_negative(self):
         (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
         self.assertTrue(flag, resp)
 
+    def test_negative_2(self):
+        nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 3, 224, 224], False)
+        edges = [
+            *connect('input', '0:FQ'),
+            *connect('il', '1:FQ'),
+            *connect('ih', '2:FQ'),
+            *connect('ol', '3:FQ'),
+            *connect('oh', '4:FQ'),
+            *connect('FQ:0', '0:transpose'),
+            *connect('order:0', '1:transpose'),
+            *connect('transpose:0', 'output'),
+        ]
+        graph = build_graph(nodes_attrs=nodes, edges=edges, nodes_with_edges_only=True)
+        graph_ref = graph.copy()
+        PullTransposeThroughFQUp().find_and_replace_pattern(graph)
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
+        self.assertTrue(flag, resp)
diff --git a/model-optimizer/unit_tests/extensions/back/ReverseInputChannels_test.py b/model-optimizer/unit_tests/extensions/back/ReverseInputChannels_test.py
index 634f3ea9aef39c..1b5c8777bc0779 100644
--- a/model-optimizer/unit_tests/extensions/back/ReverseInputChannels_test.py
+++ b/model-optimizer/unit_tests/extensions/back/ReverseInputChannels_test.py
@@ -3,13 +3,16 @@
 
 import unittest
 
-from extensions.back.ReverseInputChannels import ReverseChannelsPropagationUp, ReverseChannelsPropagationDown
+from extensions.back.ReverseInputChannels import ReverseChannelsPropagationUp, ReverseChannelsPropagationDown, \
+    InsertReverseChannels
+from mo.front.common.partial_infer.utils import int64_array, float32_array
 from mo.graph.graph import Node, Graph
+from mo.utils.ir_engine.compare_graphs import compare_graphs
+from mo.utils.runtime_info import OldAPIMap, RTInfo
 from unit_tests.utils.graph import build_graph, result, connect, regular_op_with_shaped_data, valued_const_with_data
-from mo.front.common.partial_infer.utils import int64_array, float32_array
 
 nodes = {
-    **regular_op_with_shaped_data('placeholder1', [1, 3, 10, 10], {'type': 'Parameter'}),
+    **regular_op_with_shaped_data('placeholder1', [1, 3, 10, 10], {'type': 'Parameter', 'rt_info': RTInfo()}),
     **regular_op_with_shaped_data('placeholder2', [1, 1, 1, 1], {'type': 'Parameter'}),
 
     **regular_op_with_shaped_data('mul', [1, 3, 10, 10], {'type': 'Multiply'}),
@@ -35,6 +38,17 @@
     **result('result2'),
 }
 
+nodes3 = {
+    **regular_op_with_shaped_data('placeholder', [1, 3, 10, 10], {'type': 'Parameter'}),
+    **regular_op_with_shaped_data('transpose', [1, 3, 10, 10], {'type': 'Transpose'}),
+    **valued_const_with_data('transpose_order', int64_array([0, 3, 1, 2])),
+    **regular_op_with_shaped_data('reverse_channels_up', [1, 3, 10, 10], {'type': 'ReverseChannels', 'axis': 3}),
+    **regular_op_with_shaped_data('reverse_channels_down', [1, 3, 10, 10], {'type': 'ReverseChannels', 'axis': 1}),
+    **result('result'),
+    **result('result2'),
+}
+
+
 class ReverseInputChannelsTest(unittest.TestCase):
     def check_graph_attrs(self, graph: Graph, parameter_node_names: list):
         for node in graph.get_op_nodes():
@@ -75,12 +89,11 @@ def test_lift_up_through_pad(self):
         node = Node(graph, 'pad')
         reverse_channels = Node(graph, 'reverse_channels')
 
-        keep_moving_up, new_reverses = ReverseChannelsPropagationUp.lift_up_through_pad(node, reverse_channels)
+        keep_moving_up, new_reverses = ReverseChannelsPropagationUp.lift_up_through_zero_port_only(node, reverse_channels)
         self.assertTrue(keep_moving_up is True)
         self.assertTrue(len(new_reverses) == 1)
         self.check_graph_attrs(graph, ['placeholder'])
 
-
     def test_lift_up_through_pad2(self):
         graph = build_graph(nodes2, [*connect('placeholder', '0:mul'), *connect('mul_const', '1:mul'),
                                      *connect('mul', '0:pad'), *connect('pad_const_1', '1:pad'),
@@ -91,12 +104,11 @@ def test_lift_up_through_pad2(self):
         node = Node(graph, 'pad')
         reverse_channels = Node(graph, 'reverse_channels')
 
-        keep_moving_up, new_reverses = ReverseChannelsPropagationUp.lift_up_through_pad(node, reverse_channels)
+        keep_moving_up, new_reverses = ReverseChannelsPropagationUp.lift_up_through_zero_port_only(node, reverse_channels)
         self.assertTrue(keep_moving_up is True)
         self.assertTrue(len(new_reverses) == 1)
         self.check_graph_attrs(graph, ['placeholder'])
 
-
     def test_pass_rc_through(self):
         graph = build_graph(nodes2, [*connect('placeholder', '0:mul'), *connect('mul_const', '1:mul'),
                                      *connect('mul', 'reverse_channels'),  *connect('reverse_channels', '0:pad'),
@@ -107,5 +119,114 @@ def test_pass_rc_through(self):
         node = Node(graph, 'pad')
         reverse_channels = Node(graph, 'reverse_channels')
 
-        ReverseChannelsPropagationDown.pass_rc_through(node, reverse_channels)
+        ReverseChannelsPropagationDown.pass_rc_through_zero_port_only(node, reverse_channels)
+        self.check_graph_attrs(graph, ['placeholder'])
+
+    def test_lift_up_through_transpose(self):
+        graph = build_graph(nodes3, [*connect('placeholder', '0:transpose'), *connect('transpose_order', '1:transpose'),
+                                     *connect('transpose', 'reverse_channels_down'),
+                                     *connect('reverse_channels_down', 'result')])
+        graph_ref = build_graph(nodes3, [*connect('placeholder', 'reverse_channels_down'),
+                                         *connect('transpose_order', '1:transpose'),
+                                         *connect('reverse_channels_down', 'transpose'),
+                                         *connect('transpose', 'result')])
+        self.set_graph_attrs(graph, ['placeholder'])
+
+        node = Node(graph, 'transpose')
+        reverse_channels = Node(graph, 'reverse_channels_down')
+
+        keep_moving_up, new_reverses = ReverseChannelsPropagationUp.lift_up_through_transpose(node, reverse_channels)
+        self.assertTrue(keep_moving_up is True)
+        self.assertTrue(len(new_reverses) == 1)
+        self.check_graph_attrs(graph, ['placeholder'])
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+        reverse_channels = Node(graph, 'reverse_channels_down')
+        self.assertTrue(reverse_channels.axis == 3)
+
+    def test_lift_down_through_transpose(self):
+        graph = build_graph(nodes3, [*connect('placeholder', 'reverse_channels_up'),
+                                     *connect('transpose_order', '1:transpose'),
+                                     *connect('reverse_channels_up', '0:transpose'),
+                                     *connect('transpose', 'result')])
+        graph_ref = build_graph(nodes3, [*connect('placeholder', '0:transpose'),
+                                         *connect('transpose_order', '1:transpose'),
+                                         *connect('transpose', 'reverse_channels_up'),
+                                         *connect('reverse_channels_up', '0:result')])
+        self.set_graph_attrs(graph, ['placeholder'])
+
+        node = Node(graph, 'transpose')
+        reverse_channels = Node(graph, 'reverse_channels_up')
+
+        keep_moving_down = ReverseChannelsPropagationDown.pass_rc_through_transpose(node, reverse_channels)
+
+        self.assertTrue(keep_moving_down is True)
         self.check_graph_attrs(graph, ['placeholder'])
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+        reverse_channels = Node(graph, 'reverse_channels_down')
+        self.assertTrue(reverse_channels.axis == 1)
+
+    def test_lift_up_through_transpose_negative_axis(self):
+        graph = build_graph(nodes3, [*connect('placeholder', '0:transpose'), *connect('transpose_order', '1:transpose'),
+                                     *connect('transpose', 'reverse_channels_down'),
+                                     *connect('reverse_channels_down', 'result')])
+        graph_ref = build_graph(nodes3, [*connect('placeholder', 'reverse_channels_down'),
+                                         *connect('transpose_order', '1:transpose'),
+                                         *connect('reverse_channels_down', 'transpose'),
+                                         *connect('transpose', 'result')])
+        self.set_graph_attrs(graph, ['placeholder'])
+
+        node = Node(graph, 'transpose')
+        reverse_channels = Node(graph, 'reverse_channels_down')
+        reverse_channels.axis = -3
+
+        keep_moving_up, new_reverses = ReverseChannelsPropagationUp.lift_up_through_transpose(node, reverse_channels)
+        self.assertTrue(keep_moving_up is True)
+        self.assertTrue(len(new_reverses) == 1)
+        self.check_graph_attrs(graph, ['placeholder'])
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+        reverse_channels = Node(graph, 'reverse_channels_down')
+        self.assertTrue(reverse_channels.axis == 3)
+
+    def test_lift_down_through_transpose_negative_axis(self):
+        graph = build_graph(nodes3, [*connect('placeholder', 'reverse_channels_up'),
+                                     *connect('transpose_order', '1:transpose'),
+                                     *connect('reverse_channels_up', '0:transpose'),
+                                     *connect('transpose', 'result')])
+        graph_ref = build_graph(nodes3, [*connect('placeholder', '0:transpose'),
+                                         *connect('transpose_order', '1:transpose'),
+                                         *connect('transpose', 'reverse_channels_up'),
+                                         *connect('reverse_channels_up', '0:result')])
+        self.set_graph_attrs(graph, ['placeholder'])
+
+        node = Node(graph, 'transpose')
+        reverse_channels = Node(graph, 'reverse_channels_up')
+        reverse_channels.axis = -1
+
+        keep_moving_down = ReverseChannelsPropagationDown.pass_rc_through_transpose(node, reverse_channels)
+
+        self.assertTrue(keep_moving_down is True)
+        self.check_graph_attrs(graph, ['placeholder'])
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+        reverse_channels = Node(graph, 'reverse_channels_down')
+        self.assertTrue(reverse_channels.axis == 1)
+
+    def test_get_fw_index(self):
+        graph = build_graph(nodes, [*connect('placeholder1', 'result')])
+        node = Node(graph, 'placeholder1')
+        old_api_map = OldAPIMap(version=0)
+        node.rt_info.info[('old_api_map', old_api_map.get_version())] = old_api_map
+        node.rt_info.info[('old_api_map', old_api_map.get_version())].old_api_transpose_parameter([0, 2, 3, 1])
+        self.assertTrue(InsertReverseChannels.get_fw_index(node, 0) == 0)
+        self.assertTrue(InsertReverseChannels.get_fw_index(node, 1) == 3)
+        self.assertTrue(InsertReverseChannels.get_fw_index(node, 2) == 1)
+        self.assertTrue(InsertReverseChannels.get_fw_index(node, 3) == 2)
+        self.assertTrue(InsertReverseChannels.get_fw_index(node, -2) == 1)
+
diff --git a/model-optimizer/unit_tests/extensions/back/add_outputs_recursive_test.py b/model-optimizer/unit_tests/extensions/back/add_outputs_recursive_test.py
new file mode 100644
index 00000000000000..3073ed1e8bc3f4
--- /dev/null
+++ b/model-optimizer/unit_tests/extensions/back/add_outputs_recursive_test.py
@@ -0,0 +1,760 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np
+import unittest
+
+from extensions.back.add_outputs_recursive import AddOutputRecursive
+from extensions.ops.If import If
+from extensions.ops.loop import Loop
+from extensions.ops.tensor_iterator import TensorIterator
+from mo.front.common.partial_infer.elemental import copy_shape_infer
+from mo.front.common.partial_infer.utils import int64_array, dynamic_dimension_value, shape_array
+from mo.graph.graph import Node
+from unit_tests.utils.graph import build_graph, regular_op_with_empty_data, result, connect, shaped_parameter, \
+    valued_const_with_data, shaped_const_with_data, regular_op_with_shaped_data
+
+# test for Loop
+main_graph_nodes = {
+    **shaped_parameter("IN_1", [1, 4, 64, 54]),
+    **shaped_parameter("IN_2", [1, 4, 64, 54]),
+    **valued_const_with_data("M", int64_array([5])),
+    **valued_const_with_data("cond", int64_array([1])),
+    **regular_op_with_empty_data("Loop", {'op': "Loop", 'type': 'Loop', 'sub_graphs': ['body'], "body": None,
+                                          'input_port_map': [{'external_port_id': 1, 'internal_layer_id': 2,
+                                                              'axis': None},
+                                                             {'external_port_id': 2, 'internal_layer_id': 0,
+                                                              'axis': None},
+                                                             {'external_port_id': 3, 'internal_layer_id': 1,
+                                                              'axis': None}],
+                                          'output_port_map': [{'external_port_id': 0, 'internal_layer_id': 4,
+                                                               'axis': None},
+                                                              {'external_port_id': -1, 'internal_layer_id': 5,
+                                                               'axis': None, 'purpose': "execution_condition"}],
+                                          'back_edges': [{'from_layer': 8, 'to_layer': 7},
+                                                         {'from_layer': 10, 'to_layer': 9}],
+                                 'infer': Loop.infer}),
+    **result("OUT_1")
+}
+
+sub_graph_1_nodes = {
+    **shaped_parameter("IN_2", int64_array([1, 4, 64, 54]), {'internal_layer_id': 0}),
+    **valued_const_with_data("M_2", int64_array([10])),
+    **valued_const_with_data("cond_2", int64_array([1])),
+    **regular_op_with_empty_data("Loop_2", {'op': "Loop", 'type': 'Loop', 'sub_graphs': ['body'], "body": None,
+                                            'input_port_map': [{'external_port_id': 1, 'internal_layer_id': 0,
+                                                                'axis': None},
+                                                               {'external_port_id': 2, 'internal_layer_id': 2,
+                                                                'axis': None}],
+                                            'output_port_map': [{'external_port_id': 0, 'internal_layer_id': 7,
+                                                                 'axis': None},
+                                                                {'external_port_id': -1, 'internal_layer_id': 6,
+                                                                 'axis': None,
+                                                                'purpose': "execution_condition"}],
+                                            'back_edges': [{'from_layer': 1, 'to_layer': 0},
+                                                           {'from_layer': 8, 'to_layer': 2}],
+                                            'infer': Loop.infer}),
+    **regular_op_with_empty_data('Loop_2_out', {'op': 'Result', 'type': 'Result', 'infer': lambda x: None,
+                                                'internal_layer_id': 3}),
+    **shaped_parameter("in_1_int", int64_array([1, 4, 64, 54]), {'internal_layer_id': 1}),
+    **regular_op_with_empty_data("in_1_int_out",
+                                 {'op': 'Result', 'type': 'Result', 'infer': lambda x: None, 'internal_layer_id': 4}),
+    **shaped_parameter("cond_1_int", int64_array([1]), {'internal_layer_id': 2}),
+    **regular_op_with_empty_data("cond_1_int_out", {'op': 'Result', 'type': 'Result', 'infer': lambda x: None,
+                                                    'internal_layer_id': 5}),
+}
+
+sub_graph_2_nodes = {
+    **shaped_parameter('cond_2_int', [1, 4, 64, 54], {'internal_layer_id': 0}),
+    **result("cond_2_int_out"),
+    **shaped_parameter('in_2_int', [1, 4, 64, 54], {'internal_layer_id': 1}),
+    **shaped_const_with_data('ones', int64_array([1, 4, 64, 54])),
+    **regular_op_with_shaped_data('OUT_2', int64_array([1, 4, 64, 54]), {'op': "Add", 'infer': copy_shape_infer}),
+    **regular_op_with_empty_data('OUT_2_out',
+                                 {'op': 'Result', 'type': 'Result', 'infer': lambda x: None, 'internal_layer_id': 7}),
+    **regular_op_with_shaped_data('in_2_int_out', int64_array([1, 4, 64, 54]),
+                                  {'op': 'Result', 'type': 'Result', 'infer': lambda x: None, 'internal_layer_id': 6})
+}
+
+
+class AddOutputRecursiveTest(unittest.TestCase):
+
+    def test_add_output_1(self):
+        sub_graph_2 = build_graph(nodes_attrs=sub_graph_2_nodes,
+                                  edges=[*connect('cond_2_int', 'cond_2_int_out'),
+                                         *connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out'),
+                                         *connect('in_2_int', 'in_2_int_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=sub_graph_1_nodes,
+                                  edges=[*connect('M_2', '0:Loop_2'),
+                                         *connect('cond_2', '1:Loop_2'),
+                                         *connect('IN_2', '2:Loop_2'),
+                                         *connect('Loop_2:0', 'Loop_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out'),
+                                         *connect('cond_1_int', 'cond_1_int_out')],
+                                  nodes_with_edges_only=True)
+        loop_node_1 = Node(sub_graph_1, 'Loop_2')
+        loop_node_1.body = sub_graph_2
+
+        main_graph = build_graph(nodes_attrs=main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+        loop_node_output_port_map_len = len(loop_node.output_port_map)
+        loop_node_out_ports_len = len(loop_node.out_ports())
+        loop_2_out_ports_len = len(loop_node_1.out_ports())
+        max_layer_id = 5
+
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+
+        loop_node = Node(main_graph, 'Loop')
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() == int64_array([5, 10, 4, 64, 54])))
+        last_node = Node(sub_graph_1, 'Loop_2')
+        self.assertEqual(len(last_node.out_ports()), loop_2_out_ports_len)
+        unsq_node = last_node.out_port(0).get_destinations()[1].node
+        self.assertEqual(unsq_node.op, 'Unsqueeze')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.op, 'Result')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.internal_layer_id, max_layer_id + 3)
+        self.assertTrue(np.all(unsq_node.out_port(0).data.get_shape() == int64_array([1, 10, 4, 64, 54])))
+
+
+# test for TensorIterator
+ti_main_graph_nodes = {
+    **shaped_parameter("IN_1", [1, 4, 64, 54]),
+    **shaped_parameter("IN_2", [1, 4, 64, 54]),
+    **valued_const_with_data("M", int64_array([5])),
+    **valued_const_with_data("cond", int64_array([1])),
+    **regular_op_with_empty_data("Loop", {'op': "TensorIterator", 'type': 'TensorIterator',
+                                          'sub_graphs': ['body'], "body": None,
+                                          'input_port_map': [{'external_port_id': 1, 'internal_layer_id': 2, 'axis': None},
+                                                             {'external_port_id': 2, 'internal_layer_id': 0, 'axis': None},
+                                                             {'external_port_id': 3, 'internal_layer_id': 1, 'axis': None}],
+                                          'output_port_map': [{'external_port_id': 4, 'internal_layer_id': 4, 'axis': None}],
+                                          'back_edges': [{'from_layer': 8, 'to_layer': 7},
+                                                         {'from_layer': 10, 'to_layer': 9}],
+                                 'infer': TensorIterator.infer}),
+    **result("OUT_1")
+}
+
+ti_sub_graph_1_nodes = {
+    **shaped_parameter("IN_2", int64_array([1, 4, 64, 54]), {'internal_layer_id': 0}),
+    **valued_const_with_data("cond_2", int64_array([1])),
+    **regular_op_with_empty_data("Loop_2", {'op': "TensorIterator", 'type': 'TensorIterator',
+                                            'sub_graphs': ['body'], "body": None,
+                                            'input_port_map': [{'external_port_id': 1, 'internal_layer_id': 0, 'axis': None},
+                                                               {'external_port_id': 0, 'internal_layer_id': 1, 'axis': 0}],
+                                            'output_port_map': [{'external_port_id': 2, 'internal_layer_id': 7,
+                                                                 'axis': None},
+                                                                ],
+                                            'back_edges': [{'from_layer': 1, 'to_layer': 0},
+                                                           {'from_layer': 8, 'to_layer': 2}],
+                                            'infer': TensorIterator.infer}),
+    **regular_op_with_empty_data('Loop_2_out', {'op': 'Result', 'type': 'Result', 'infer': lambda x: None,
+                                                'internal_layer_id': 3}),
+    **shaped_parameter("in_1_int", int64_array([1, 4, 64, 54]), {'internal_layer_id': 1}),
+    **regular_op_with_empty_data("in_1_int_out", {'op': 'Result', 'type': 'Result', 'infer': lambda x: None,
+                                                  'internal_layer_id': 4}),
+    **shaped_parameter("cond_1_int", int64_array([1]), {'internal_layer_id': 2}),
+    **regular_op_with_empty_data("cond_1_int_out", {'op': 'Result', 'type': 'Result', 'infer': lambda x: None,
+                                                    'internal_layer_id': 5}),
+}
+
+ti_sub_graph_2_nodes = {
+    **shaped_parameter('cond_2_int', [1, 4, 64, 54], {'internal_layer_id': 0}),
+    **result("cond_2_int_out"),
+    **shaped_parameter('in_2_int', [1, 4, 64, 54], {'internal_layer_id': 1}),
+    **shaped_const_with_data('ones', int64_array([1, 4, 64, 54])),
+    **regular_op_with_shaped_data('OUT_2', int64_array([1, 4, 64, 54]),
+                                  {'op': "Add", 'infer': copy_shape_infer}),
+    **regular_op_with_empty_data('OUT_2_out', {'op': 'Result', 'type': 'Result', 'infer': lambda x: None,
+                                               'internal_layer_id': 7}),
+    **regular_op_with_empty_data('in_2_int_out', {'op': 'Result', 'type': 'Result', 'infer': lambda x: None,
+                                                  'internal_layer_id': 6})
+}
+
+
+class TI_AddOutputRecursiveTest(unittest.TestCase):
+
+    def test_add_output_1(self):
+        sub_graph_2 = build_graph(nodes_attrs=ti_sub_graph_2_nodes,
+                                  edges=[*connect('cond_2_int', 'cond_2_int_out'),
+                                         *connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out'),
+                                         *connect('in_2_int', 'in_2_int_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=ti_sub_graph_1_nodes,
+                                  edges=[*connect('cond_2', '1:Loop_2'),
+                                         *connect('IN_2', '0:Loop_2'),
+                                         *connect('Loop_2:0', 'Loop_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out'),
+                                         *connect('cond_1_int', 'cond_1_int_out')],
+                                  nodes_with_edges_only=True)
+        loop_node_1 = Node(sub_graph_1, 'Loop_2')
+        loop_node_1.body = sub_graph_2
+        loop_node_1.in_edge(0)['external_port_id'] = 0
+        loop_node_1.in_edge(1)['external_port_id'] = 1
+        loop_node_1.out_edge(0)['external_port_id'] = 2
+
+        main_graph = build_graph(nodes_attrs=ti_main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        loop_node.in_edge(0)['external_port_id'] = 0
+        loop_node.in_edge(1)['external_port_id'] = 1
+        loop_node.in_edge(2)['external_port_id'] = 2
+        loop_node.in_edge(3)['external_port_id'] = 3
+        loop_node.out_edge(0)['external_port_id'] = 4
+        loop_node_output_port_map_len = len(loop_node.output_port_map)
+        loop_node_out_ports_len = len(loop_node.out_ports())
+        loop_2_node_out_ports_len = len(loop_node_1.out_ports())
+        max_layer_id = 5
+
+
+        main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        loop_node = Node(main_graph, 'Loop')
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() == int64_array([1, 1, 4, 64, 54])))
+        last_node = Node(sub_graph_1, 'Loop_2')
+        self.assertEqual(len(last_node.out_ports()), loop_2_node_out_ports_len)
+        unsq_node = last_node.out_port(0).get_destinations()[1].node
+        self.assertEqual(unsq_node.op, 'Unsqueeze')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.op, 'Result')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.internal_layer_id, max_layer_id + 3)
+        self.assertTrue(np.all(unsq_node.out_port(0).data.get_shape() == int64_array([1, 1, 4, 64, 54])))
+
+    def test_add_output_dynamic(self):
+        sub_graph_2 = build_graph(nodes_attrs=ti_sub_graph_2_nodes,
+                                  edges=[*connect('cond_2_int', 'cond_2_int_out'),
+                                         *connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out'),
+                                         *connect('in_2_int', 'in_2_int_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=ti_sub_graph_1_nodes,
+                                  edges=[*connect('cond_2', '1:Loop_2'),
+                                         *connect('IN_2', '0:Loop_2'),
+                                         *connect('Loop_2:0', 'Loop_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out'),
+                                         *connect('cond_1_int', 'cond_1_int_out')],
+                                  nodes_with_edges_only=True)
+        loop_node_1 = Node(sub_graph_1, 'Loop_2')
+        loop_node_1.body = sub_graph_2
+        loop_node_1.in_edge(0)['external_port_id'] = 0
+        loop_node_1.in_edge(1)['external_port_id'] = 1
+        loop_node_1.out_edge(0)['external_port_id'] = 2
+
+        main_graph = build_graph(nodes_attrs=ti_main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        loop_node.in_edge(0)['external_port_id'] = 0
+        loop_node.in_edge(1)['external_port_id'] = 1
+        loop_node.in_edge(2)['external_port_id'] = 2
+        loop_node.in_edge(3)['external_port_id'] = 3
+        loop_node.out_edge(0)['external_port_id'] = 4
+        loop_node_output_port_map_len = len(loop_node.output_port_map)
+        loop_node_out_ports_len = len(loop_node.out_ports())
+        loop_2_node_out_ports_len = len(loop_node_1.out_ports())
+        max_layer_id = 5
+        loop_node.input_port_map[2]['axis'] = 1
+        loop_node.input_port_map[2]['start'] = 0
+        loop_node.input_port_map[2]['end'] = -1
+        loop_node.input_port_map[2]['stride'] = 1
+        in_1_node = Node(main_graph, 'IN_1')
+        in_1_node['shape'] = shape_array([1, dynamic_dimension_value, 64, 54])
+
+        main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        loop_node = Node(main_graph, 'Loop')
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() ==
+                               shape_array([dynamic_dimension_value, 1, 4, 64, 54])))
+        last_node = Node(sub_graph_1, 'Loop_2')
+        self.assertEqual(len(last_node.out_ports()), loop_2_node_out_ports_len)
+        unsq_node = last_node.out_port(0).get_destinations()[1].node
+        self.assertEqual(unsq_node.op, 'Unsqueeze')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.op, 'Result')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.internal_layer_id, max_layer_id + 3)
+        self.assertTrue(np.all(unsq_node.out_port(0).data.get_shape() == int64_array([1, 1, 4, 64, 54])))
+
+    def test_add_output_several_iterations(self):
+        sub_graph_2 = build_graph(nodes_attrs=ti_sub_graph_2_nodes,
+                                  edges=[*connect('cond_2_int', 'cond_2_int_out'),
+                                         *connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out'),
+                                         *connect('in_2_int', 'in_2_int_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=ti_sub_graph_1_nodes,
+                                  edges=[*connect('cond_2', '1:Loop_2'),
+                                         *connect('IN_2', '0:Loop_2'),
+                                         *connect('Loop_2:0', 'Loop_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out'),
+                                         *connect('cond_1_int', 'cond_1_int_out')],
+                                  nodes_with_edges_only=True)
+        loop_node_1 = Node(sub_graph_1, 'Loop_2')
+        loop_node_1.body = sub_graph_2
+        loop_node_1.in_edge(0)['external_port_id'] = 0
+        loop_node_1.in_edge(1)['external_port_id'] = 1
+        loop_node_1.out_edge(0)['external_port_id'] = 2
+
+        main_graph = build_graph(nodes_attrs=ti_main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        loop_node.in_edge(0)['external_port_id'] = 0
+        loop_node.in_edge(1)['external_port_id'] = 1
+        loop_node.in_edge(2)['external_port_id'] = 2
+        loop_node.in_edge(3)['external_port_id'] = 3
+        loop_node.out_edge(0)['external_port_id'] = 4
+        loop_node_output_port_map_len = len(loop_node.output_port_map)
+        loop_node_out_ports_len = len(loop_node.out_ports())
+        loop_2_node_out_ports_len = len(loop_node_1.out_ports())
+        max_layer_id = 5
+        loop_node.input_port_map[2]['axis'] = 1
+        loop_node.input_port_map[2]['start'] = 0
+        loop_node.input_port_map[2]['end'] = -1
+        loop_node.input_port_map[2]['stride'] = 1
+        loop_node.output_port_map[0]['axis'] = 1
+        loop_node.output_port_map[0]['start'] = 0
+        loop_node.output_port_map[0]['end'] = 10
+        loop_node.output_port_map[0]['stride'] = 2
+
+        main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        loop_node = Node(main_graph, 'Loop')
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() == shape_array([4, 1, 4, 64, 54])))
+        self.assertTrue(np.all(loop_node.out_port(0).data.get_shape() == shape_array([1, 5, 64, 54])))
+        last_node = Node(sub_graph_1, 'Loop_2')
+        self.assertEqual(len(last_node.out_ports()), loop_2_node_out_ports_len)
+        unsq_node = last_node.out_port(0).get_destinations()[1].node
+        self.assertEqual(unsq_node.op, 'Unsqueeze')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.op, 'Result')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.internal_layer_id, max_layer_id + 3)
+        self.assertTrue(np.all(unsq_node.out_port(0).data.get_shape() == int64_array([1, 1, 4, 64, 54])))
+
+    def test_add_output_several_iterations_wo_start_end(self):
+        sub_graph_2 = build_graph(nodes_attrs=ti_sub_graph_2_nodes,
+                                  edges=[*connect('cond_2_int', 'cond_2_int_out'),
+                                         *connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out'),
+                                         *connect('in_2_int', 'in_2_int_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=ti_sub_graph_1_nodes,
+                                  edges=[*connect('cond_2', '1:Loop_2'),
+                                         *connect('IN_2', '0:Loop_2'),
+                                         *connect('Loop_2:0', 'Loop_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out'),
+                                         *connect('cond_1_int', 'cond_1_int_out')],
+                                  nodes_with_edges_only=True)
+        loop_node_1 = Node(sub_graph_1, 'Loop_2')
+        loop_node_1.body = sub_graph_2
+        loop_node_1.in_edge(0)['external_port_id'] = 0
+        loop_node_1.in_edge(1)['external_port_id'] = 1
+        loop_node_1.out_edge(0)['external_port_id'] = 2
+
+        main_graph = build_graph(nodes_attrs=ti_main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        loop_node.in_edge(0)['external_port_id'] = 0
+        loop_node.in_edge(1)['external_port_id'] = 1
+        loop_node.in_edge(2)['external_port_id'] = 2
+        loop_node.in_edge(3)['external_port_id'] = 3
+        loop_node.out_edge(0)['external_port_id'] = 4
+        loop_node_output_port_map_len = len(loop_node.output_port_map)
+        loop_node_out_ports_len = len(loop_node.out_ports())
+        loop_2_node_out_ports_len = len(loop_node_1.out_ports())
+        max_layer_id = 5
+        loop_node.input_port_map[2]['axis'] = 1
+        loop_node.input_port_map[2]['stride'] = 1
+
+        main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        loop_node = Node(main_graph, 'Loop')
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() == shape_array([4, 1, 4, 64, 54])))
+        last_node = Node(sub_graph_1, 'Loop_2')
+        self.assertEqual(len(last_node.out_ports()), loop_2_node_out_ports_len)
+        unsq_node = last_node.out_port(0).get_destinations()[1].node
+        self.assertEqual(unsq_node.op, 'Unsqueeze')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.op, 'Result')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.internal_layer_id, max_layer_id + 3)
+        self.assertTrue(np.all(unsq_node.out_port(0).data.get_shape() == int64_array([1, 1, 4, 64, 54])))
+
+    def test_add_output_several_iterations_negative_end(self):
+        sub_graph_2 = build_graph(nodes_attrs=ti_sub_graph_2_nodes,
+                                  edges=[*connect('cond_2_int', 'cond_2_int_out'),
+                                         *connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out'),
+                                         *connect('in_2_int', 'in_2_int_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=ti_sub_graph_1_nodes,
+                                  edges=[*connect('cond_2', '1:Loop_2'),
+                                         *connect('IN_2', '0:Loop_2'),
+                                         *connect('Loop_2:0', 'Loop_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out'),
+                                         *connect('cond_1_int', 'cond_1_int_out')],
+                                  nodes_with_edges_only=True)
+        loop_node_1 = Node(sub_graph_1, 'Loop_2')
+        loop_node_1.body = sub_graph_2
+        loop_node_1.in_edge(0)['external_port_id'] = 0
+        loop_node_1.in_edge(1)['external_port_id'] = 1
+        loop_node_1.out_edge(0)['external_port_id'] = 2
+
+        main_graph = build_graph(nodes_attrs=ti_main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        loop_node.in_edge(0)['external_port_id'] = 0
+        loop_node.in_edge(1)['external_port_id'] = 1
+        loop_node.in_edge(2)['external_port_id'] = 2
+        loop_node.in_edge(3)['external_port_id'] = 3
+        loop_node.out_edge(0)['external_port_id'] = 4
+        loop_node_output_port_map_len = len(loop_node.output_port_map)
+        loop_node_out_ports_len = len(loop_node.out_ports())
+        loop_2_node_out_ports_len = len(loop_node_1.out_ports())
+        max_layer_id = 5
+        loop_node.input_port_map[2]['axis'] = 1
+        loop_node.input_port_map[2]['start'] = 0
+        loop_node.input_port_map[2]['end'] = -3
+        loop_node.input_port_map[2]['stride'] = 1
+        loop_node.output_port_map[0]['axis'] = 1
+        loop_node.output_port_map[0]['start'] = 0
+        loop_node.output_port_map[0]['end'] = -1
+        loop_node.output_port_map[0]['stride'] = 2
+
+        main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        loop_node = Node(main_graph, 'Loop')
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() == shape_array([2, 1, 4, 64, 54])))
+        self.assertTrue(np.all(loop_node.out_port(0).data.get_shape() == shape_array([1, 2, 64, 54])))
+        last_node = Node(sub_graph_1, 'Loop_2')
+        self.assertEqual(len(last_node.out_ports()), loop_2_node_out_ports_len)
+        unsq_node = last_node.out_port(0).get_destinations()[1].node
+        self.assertEqual(unsq_node.op, 'Unsqueeze')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.op, 'Result')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.internal_layer_id, max_layer_id + 3)
+        self.assertTrue(np.all(unsq_node.out_port(0).data.get_shape() == int64_array([1, 1, 4, 64, 54])))
+
+    def test_add_output_several_iterations_negative_stride(self):
+        sub_graph_2 = build_graph(nodes_attrs=ti_sub_graph_2_nodes,
+                                  edges=[*connect('cond_2_int', 'cond_2_int_out'),
+                                         *connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out'),
+                                         *connect('in_2_int', 'in_2_int_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=ti_sub_graph_1_nodes,
+                                  edges=[*connect('cond_2', '1:Loop_2'),
+                                         *connect('IN_2', '0:Loop_2'),
+                                         *connect('Loop_2:0', 'Loop_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out'),
+                                         *connect('cond_1_int', 'cond_1_int_out')],
+                                  nodes_with_edges_only=True)
+        loop_node_1 = Node(sub_graph_1, 'Loop_2')
+        loop_node_1.body = sub_graph_2
+        loop_node_1.in_edge(0)['external_port_id'] = 0
+        loop_node_1.in_edge(1)['external_port_id'] = 1
+        loop_node_1.out_edge(0)['external_port_id'] = 2
+
+        main_graph = build_graph(nodes_attrs=ti_main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        loop_node.in_edge(0)['external_port_id'] = 0
+        loop_node.in_edge(1)['external_port_id'] = 1
+        loop_node.in_edge(2)['external_port_id'] = 2
+        loop_node.in_edge(3)['external_port_id'] = 3
+        loop_node.out_edge(0)['external_port_id'] = 4
+        loop_node_output_port_map_len = len(loop_node.output_port_map)
+        loop_node_out_ports_len = len(loop_node.out_ports())
+        loop_2_node_out_ports_len = len(loop_node_1.out_ports())
+        max_layer_id = 5
+        loop_node.input_port_map[2]['axis'] = 1
+        loop_node.input_port_map[2]['start'] = -1
+        loop_node.input_port_map[2]['end'] = 0
+        loop_node.input_port_map[2]['stride'] = -2
+        loop_node.output_port_map[0]['axis'] = 1
+        loop_node.output_port_map[0]['start'] = 0
+        loop_node.output_port_map[0]['end'] = -1
+        loop_node.output_port_map[0]['stride'] = 2
+
+        main_graph.graph['additional_outputs'] = ['Loop', 'Loop_2']
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        loop_node = Node(main_graph, 'Loop')
+        self.assertEqual(len(loop_node.output_port_map), loop_node_output_port_map_len + 1)
+        self.assertEqual(len(loop_node.out_ports()), loop_node_out_ports_len + 1)
+        self.assertEqual(loop_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(loop_node.out_port(1).data.get_shape() == shape_array([2, 1, 4, 64, 54])))
+        self.assertTrue(np.all(loop_node.out_port(0).data.get_shape() == shape_array([1, 2, 64, 54])))
+        last_node = Node(sub_graph_1, 'Loop_2')
+        self.assertEqual(len(last_node.out_ports()), loop_2_node_out_ports_len)
+        unsq_node = last_node.out_port(0).get_destinations()[1].node
+        self.assertEqual(unsq_node.op, 'Unsqueeze')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.op, 'Result')
+        self.assertEqual(unsq_node.out_port(0).get_destination().node.internal_layer_id, max_layer_id + 3)
+        self.assertTrue(np.all(unsq_node.out_port(0).data.get_shape() == int64_array([1, 1, 4, 64, 54])))
+
+
+
+# test for If
+if_main_graph_nodes = {
+    **shaped_parameter("IN_1", [1, 4, 64, 54]),
+    **shaped_parameter("IN_2", [1, 4, 64, 54]),
+    **valued_const_with_data("cond", int64_array([1])),
+    **regular_op_with_empty_data("If", {'op': "If", 'type': 'If', 'sub_graphs': ['then_graph', 'else_graph'],
+                                        "then_graph": None, 'else_graph': None, 'infer': If.infer}),
+    **result("OUT_1")
+}
+
+if_sub_graph_1_then_nodes = {
+    **shaped_parameter("IN_2", int64_array([1, 4, 64, 54]), {'input_id': 2}),
+    **valued_const_with_data("cond_2", int64_array([1])),
+    **regular_op_with_empty_data("If_2", {'op': "If", 'type': 'If', 'sub_graphs': ['then_graph', 'else_graph'],
+                                          "then_graph": None, 'else_graph': None, 'infer': If.infer}),
+    **regular_op_with_empty_data('If_2_out', {'op': 'Result', 'type': 'Result', 'infer': lambda x: None}),
+    **shaped_parameter("in_1_int", int64_array([1, 4, 64, 54]), {'input_id': 1}),
+    **regular_op_with_empty_data("in_1_int_out", {'op': 'Result', 'type': 'Result', 'output_id': 0})
+}
+
+if_sub_graph_1_else_nodes = {
+    **shaped_parameter("in_1_int", int64_array([1, 4, 64, 54]), {'input_id': 1}),
+    **regular_op_with_empty_data("in_1_int_out", {'op': 'Result', 'type': 'Result', 'output_id': 0})
+}
+
+if_sub_graph_2_then_nodes = {
+    **shaped_parameter('in_2_int', [1, 4, 64, 54], {'input_id': 1}),
+    **shaped_const_with_data('ones', int64_array([1, 4, 64, 54])),
+    **regular_op_with_shaped_data('OUT_2', int64_array([1, 4, 64, 54]), {'op': "Add"}),
+    **regular_op_with_empty_data('OUT_2_out', {'op': 'Result', 'type': 'Result', 'output_id': 0}),
+}
+
+if_sub_graph_2_else_nodes = {
+    **shaped_parameter('in_2_int_else', [1, 4, 64, 54], {'input_id': 1}),
+    **shaped_const_with_data('ones_else', int64_array([1, 4, 64, 54])),
+    **regular_op_with_shaped_data('OUT_2_else', int64_array([1, 4, 64, 54]), {'op': "Sub"}),
+    **regular_op_with_empty_data('OUT_2_out_else', {'op': 'Result', 'type': 'Result', 'output_id': 0}),
+}
+
+
+class IF_AddOutputRecursiveTest(unittest.TestCase):
+
+    def test_add_output_1(self):
+        sub_graph_2 = build_graph(nodes_attrs=if_sub_graph_2_then_nodes,
+                                  edges=[*connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_2_else = build_graph(nodes_attrs=if_sub_graph_2_else_nodes,
+                                       edges=[*connect('in_2_int_else', 'OUT_2_else'),
+                                              *connect('ones_else', 'OUT_2_else'),
+                                              *connect('OUT_2_else', 'OUT_2_out_else')],
+                                       nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=if_sub_graph_1_then_nodes,
+                                  edges=[*connect('cond_2', '0:If_2'),
+                                         *connect('IN_2', '1:If_2'),
+                                         *connect('If_2:0', 'If_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out')],
+                                  nodes_with_edges_only=True)
+        if_node_1 = Node(sub_graph_1, 'If_2')
+        if_node_1.then_graph = sub_graph_2
+        if_node_1.else_graph = sub_graph_2_else
+
+        sub_graph_1_else = build_graph(nodes_attrs=if_sub_graph_1_else_nodes,
+                                       edges=[*connect('in_1_int', 'in_1_int_out')],
+                                       nodes_with_edges_only=True)
+
+        main_graph = build_graph(nodes_attrs=if_main_graph_nodes,
+                                 edges=[*connect('cond', '0:If'),
+                                        *connect('IN_1', '1:If'),
+                                        *connect('IN_2', "2:If"),
+                                        *connect('If:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        if_node = Node(main_graph, 'If')
+        if_node.then_graph = sub_graph_1
+        if_node.else_graph = sub_graph_1_else
+        if_node_out_ports_len = len(if_node.out_ports())
+        if_2_node_out_ports_len = len(if_node_1.out_ports())
+
+        main_graph.graph['additional_outputs'] = ['If', ['If_2', 'in_1_int']]
+
+        AddOutputRecursive().find_and_replace_pattern(main_graph)
+        if_node = Node(main_graph, 'If')
+        self.assertEqual(len(if_node.out_ports()), if_node_out_ports_len + 1)
+        self.assertEqual(if_node.out_port(1).get_destination().node.op, 'Result')
+        self.assertTrue(np.all(if_node.out_port(1).data.get_shape() == int64_array([1, 4, 64, 54])))
+        last_node = Node(sub_graph_1, 'If_2')
+        self.assertEqual(len(last_node.out_ports()), if_2_node_out_ports_len)
+        self.assertEqual(last_node.out_port(0).get_destinations()[1].node.op, 'Result')
+        self.assertTrue(np.all(last_node.out_port(0).data.get_shape() == int64_array([1, 4, 64, 54])))
+
+
+class SplitUserPathTest(unittest.TestCase):
+
+    @staticmethod
+    def create_graph():
+        sub_graph_2 = build_graph(nodes_attrs=if_sub_graph_2_then_nodes,
+                                  edges=[*connect('in_2_int', 'OUT_2'),
+                                         *connect('ones', 'OUT_2'),
+                                         *connect('OUT_2', 'OUT_2_out')],
+                                  nodes_with_edges_only=True)
+
+        sub_graph_2_else = build_graph(nodes_attrs=if_sub_graph_2_else_nodes,
+                                       edges=[*connect('in_2_int_else', 'OUT_2_else'),
+                                              *connect('ones_else', 'OUT_2_else'),
+                                              *connect('OUT_2_else', 'OUT_2_out_else')],
+                                       nodes_with_edges_only=True)
+
+        sub_graph_1 = build_graph(nodes_attrs=if_sub_graph_1_then_nodes,
+                                  edges=[*connect('cond_2', '0:If_2'),
+                                         *connect('IN_2', '1:If_2'),
+                                         *connect('If_2:0', 'If_2_out'),
+                                         *connect('in_1_int', 'in_1_int_out')],
+                                  nodes_with_edges_only=True)
+        if_node_1 = Node(sub_graph_1, 'If_2')
+        if_node_1.then_graph = sub_graph_2
+        if_node_1.else_graph = sub_graph_2_else
+        out_node = Node(sub_graph_1, 'If_2_out')
+        out_node['internal_layer_id'] = 4
+
+        main_graph = build_graph(nodes_attrs=ti_main_graph_nodes,
+                                 edges=[*connect('M', '0:Loop'),
+                                        *connect('cond', '1:Loop'),
+                                        *connect('IN_2', '2:Loop'),
+                                        *connect('IN_1', "3:Loop"),
+                                        *connect('Loop:0', 'OUT_1')],
+                                 nodes_with_edges_only=True)
+        loop_node = Node(main_graph, 'Loop')
+        loop_node.body = sub_graph_1
+        loop_node.in_edge(0)['external_port_id'] = 0
+        loop_node.in_edge(1)['external_port_id'] = 1
+        loop_node.in_edge(2)['external_port_id'] = 2
+        loop_node.in_edge(3)['external_port_id'] = 3
+        loop_node.out_edge(0)['external_port_id'] = 4
+
+        return main_graph
+
+    def test_linear_graph_change(self):
+        graph = self.create_graph()
+        path = ['Loop', 'in_1_int']
+        ref_path = []
+        loop_node = Node(graph, 'Loop')
+        ref_path.append({'node': loop_node, 'graph': graph})
+        ref_path.append({'node': Node(loop_node.body, 'in_1_int'), 'graph': loop_node.body})
+
+        tracks = AddOutputRecursive().split_path_to_simple_tracks(graph, path)
+
+        self.assertTrue(np.all(tracks[0] == ref_path))
+
+    def test_1_if_graph_change(self):
+        graph = self.create_graph()
+        path = ['Loop', 'If_2', ['OUT_2', 'OUT_2_else']]
+        ref_path = [[]]
+        loop_node = Node(graph, 'Loop')
+        ref_path[0].append({'node': loop_node, 'graph': graph})
+        if_node = Node(loop_node.body, 'If_2')
+        ref_path[0].append({'node': if_node, 'graph': loop_node.body})
+        ref_path.append([])
+        ref_path[1] = ref_path[0][:]
+        ref_path[0].append({'node': Node(if_node.then_graph, 'OUT_2'), 'graph': if_node.then_graph})
+        ref_path[1].append({'node': Node(if_node.else_graph, 'OUT_2_else'), 'graph': if_node.else_graph})
+
+        tracks = AddOutputRecursive().split_path_to_simple_tracks(graph, path)
+
+        self.assertTrue(np.all(tracks[0] == ref_path[0]))
+        self.assertTrue(np.all(tracks[1] == ref_path[1]))
+
+    def test_1_if_graph_change_add_output(self):
+        graph = self.create_graph()
+        graph.graph['additional_outputs'] = ['Loop', 'If_2', ['OUT_2', 'OUT_2_else']]
+
+        AddOutputRecursive().find_and_replace_pattern(graph)
+
+        loop_node = Node(graph, 'Loop')
+        if_node = Node(loop_node.body, 'If_2')
+        left_node = Node(if_node.then_graph, 'OUT_2')
+        right_node = Node(if_node.else_graph, 'OUT_2_else')
+        self.assertEqual(len(left_node.out_port(0).get_destinations()), 2)
+        self.assertEqual(left_node.out_port(0).get_destinations()[1].node.op, 'Result')
+
+        self.assertEqual(len(right_node.out_port(0).get_destinations()), 2)
+        self.assertEqual(right_node.out_port(0).get_destinations()[1].node.op, 'Result')
+
+        self.assertTrue(len(if_node.out_ports()), 2)
+        self.assertTrue(if_node.out_port(1).get_destination().node.op, 'Result')
+
+        self.assertTrue(len(loop_node.out_ports()), 2)
+        self.assertTrue(loop_node.out_port(1).get_destination().node.op, 'Result')
diff --git a/model-optimizer/unit_tests/extensions/front/tf/IteratorGetNextCut_test.py b/model-optimizer/unit_tests/extensions/front/tf/IteratorGetNextCut_test.py
new file mode 100644
index 00000000000000..165e5189fc7fc1
--- /dev/null
+++ b/model-optimizer/unit_tests/extensions/front/tf/IteratorGetNextCut_test.py
@@ -0,0 +1,95 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import unittest
+
+import numpy as np
+
+from extensions.front.tf.IteratorGetNextCut import IteratorGetNextCut
+from mo.front.common.partial_infer.utils import shape_array
+from mo.utils.error import Error
+from mo.utils.ir_engine.compare_graphs import compare_graphs
+from unit_tests.utils.graph import build_graph_with_edge_attrs
+
+
+class IteratorGetNextAnalysisTest(unittest.TestCase):
+
+    def test_one_output(self):
+        graph = build_graph_with_edge_attrs(
+            {
+                'iter_get_next': {'kind': 'op', 'op': 'IteratorGetNext', 'shapes': shape_array([[2, 2]]),
+                                  'types': [np.int32]},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+            },
+            [
+                ('iter_get_next', 'sub', {'out': 0, 'in': 0}),
+            ]
+        )
+
+        graph_ref = build_graph_with_edge_attrs(
+            {
+                'parameter_1': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([2, 2]), 'type': np.int32},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+            },
+            [
+                ('parameter_1', 'sub', {'out': 0, 'in': 0}),
+            ]
+        )
+
+        IteratorGetNextCut().find_and_replace_pattern(graph)
+
+        flag, msg = compare_graphs(graph, graph_ref, last_node='sub')
+        self.assertTrue(flag, msg)
+
+    def test_two_outputs(self):
+        graph = build_graph_with_edge_attrs(
+            {
+                'iter_get_next': {'kind': 'op', 'op': 'IteratorGetNext', 'shapes': [shape_array([2, 2]),
+                                                                                    shape_array([1, 1])],
+                                  'types': [np.int32, np.float32]},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+                'add': {'kind': 'op', 'op': 'Add'},
+                'concat': {'kind': 'op', 'op': 'Concat'}
+            },
+            [
+                ('iter_get_next', 'sub', {'out': 0, 'in': 0}),
+                ('iter_get_next', 'add', {'out': 1, 'in': 0}),
+                ('sub', 'concat', {'out': 0, 'in': 0}),
+                ('add', 'concat', {'out': 0, 'in': 1})
+            ]
+        )
+
+        graph_ref = build_graph_with_edge_attrs(
+            {
+                'parameter_1': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([2, 2]), 'data_type': np.int32},
+                'parameter_2': {'kind': 'op', 'op': 'Parameter', 'shape': shape_array([1, 1]), 'data_type': np.float32},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+                'add': {'kind': 'op', 'op': 'Add'},
+                'concat': {'kind': 'op', 'op': 'Concat'}
+            },
+            [
+                ('parameter_1', 'sub', {'out': 0, 'in': 0}),
+                ('parameter_2', 'add', {'out': 0, 'in': 0}),
+                ('sub', 'concat', {'out': 0, 'in': 0}),
+                ('add', 'concat', {'out': 0, 'in': 1})
+            ]
+        )
+
+        IteratorGetNextCut().find_and_replace_pattern(graph)
+
+        flag, msg = compare_graphs(graph, graph_ref, last_node='concat', check_op_attrs=True)
+        self.assertTrue(flag, msg)
+
+    def test_unsupported_data_type(self):
+        graph = build_graph_with_edge_attrs(
+            {
+                'iter_get_next': {'kind': 'op', 'op': 'IteratorGetNext', 'shapes': shape_array([[2, 2]]),
+                                  'types': [None]},
+                'sub': {'kind': 'op', 'op': 'Sub'},
+            },
+            [
+                ('iter_get_next', 'sub', {'out': 0, 'in': 0}),
+            ]
+        )
+
+        self.assertRaises(Error, IteratorGetNextCut().find_and_replace_pattern, graph)
diff --git a/model-optimizer/unit_tests/extensions/ops/gathernd_test.py b/model-optimizer/unit_tests/extensions/ops/gathernd_test.py
index 4ad84bf527150c..8ccaad1a3abfb1 100644
--- a/model-optimizer/unit_tests/extensions/ops/gathernd_test.py
+++ b/model-optimizer/unit_tests/extensions/ops/gathernd_test.py
@@ -14,7 +14,7 @@
                     'data_data': {'shape': None, 'value': None, 'kind': 'data'},
                     'indices': {'kind': 'op'},
                     'indices_data': {'shape': None, 'value': None, 'kind': 'data'},
-                    'gathernd_node': {'op': 'GatherNDUpdate', 'kind': 'op', 'batch_dims': 0},
+                    'gathernd_node': {'op': 'GatherNDUpdate', 'kind': 'op', 'batch_dims': 0, 'version': 'opset8'},
                     'output': {'shape': None, 'value': None, 'kind': 'data'}}
 
 # graph 1
@@ -25,17 +25,21 @@
          ('gathernd_node', 'output', {'out': 0})]
 
 # test data for partial infer: gather elements
-inputs1 = {'data_data': {'shape': int64_array([10, 40]), 'value': None},
+inputs = {'data_data': {'shape': int64_array([10, 40]), 'value': None},
            'indices_data': {'shape': int64_array([3, 2]), 'value': None}}
 
 # test data for partial infer: gather slices
-inputs2 = {'data_data': {'shape': int64_array([10, 40, 30]), 'value': None},
+inputs1 = {'data_data': {'shape': int64_array([10, 40, 30]), 'value': None},
            'indices_data': {'shape': int64_array([3, 2]), 'value': None}}
 
 # test data for partial infer: gather slices and batch_dims=2
-inputs3 = {'data_data': {'shape': int64_array([10, 40, 4, 9]), 'value': None},
+inputs2 = {'data_data': {'shape': int64_array([10, 40, 4, 9]), 'value': None},
            'indices_data': {'shape': int64_array([10, 40, 3, 5, 1]), 'value': None}}
 
+# test data for partial infer: gather slices and batch_dims=3 and indices.shape[-1]=len(data.shape)-batch_dims
+inputs3 = {'data_data': {'shape': int64_array([1, 64, 64, 320]), 'value': None},
+           'indices_data': {'shape': int64_array([1, 64, 64, 1, 1]), 'value': None}}
+
 # test data for constant folding: gather elements, batch_dims = 0
 inputs4 = {'data_data': {'shape': int64_array([2, 2]), 'value': int64_array([[1, 2],
                                                                              [3, 4]])},
@@ -110,6 +114,14 @@
 inputs9 = {'data_data': {'shape': shape_array([dynamic_dimension_value, 40, 4, 9]), 'value': None},
            'indices_data': {'shape': shape_array([dynamic_dimension_value, 40, 3, 5, 1]), 'value': None}}
 
+# test data for partial infer: gather slices and batch_dims=2
+inputs10 = {'data_data': {'shape': shape_array([40, dynamic_dimension_value, 4, 9]), 'value': None},
+           'indices_data': {'shape': shape_array([40, dynamic_dimension_value, 3, 5, 1]), 'value': None}}
+
+# test data for partial infer: gather slices and batch_dims=2
+inputs11 = {'data_data': {'shape': shape_array([dynamic_dimension_value, 40, 4, 9]), 'value': None},
+           'indices_data': {'shape': shape_array([40, dynamic_dimension_value, 3, 5, 1]), 'value': None}}
+
 # invalid test case with incorrect rank for indices
 inputs_inv1 = {'data_data': {'shape': int64_array([10, 40]), 'value': None},
                'indices_data': {'shape': int64_array([5, 3, 4]), 'value': None}}
@@ -123,12 +135,13 @@
                'indices_data': {'shape': int64_array([10, 40, 4]), 'value': None}}
 
 
-class TestGatherNDUpdate(unittest.TestCase):
+class TestGatherND_5(unittest.TestCase):
     def setUp(self):
         nodes_attributes['gathernd_node']['batch_dims'] = 0
+        nodes_attributes['gathernd_node']['version'] = 'opset5'
 
     def test_partial_infer_gather_element(self):
-        graph = build_graph(nodes_attributes, edges, inputs1)
+        graph = build_graph(nodes_attributes, edges, inputs)
         gathernd_node = Node(graph, 'gathernd_node')
         GatherND.infer(gathernd_node)
 
@@ -142,7 +155,7 @@ def test_partial_infer_gather_element(self):
                         'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
 
     def test_partial_infer_gather_slice(self):
-        graph = build_graph(nodes_attributes, edges, inputs2)
+        graph = build_graph(nodes_attributes, edges, inputs1)
         gathernd_node = Node(graph, 'gathernd_node')
         GatherND.infer(gathernd_node)
 
@@ -157,7 +170,7 @@ def test_partial_infer_gather_slice(self):
 
     def test_partial_infer_gather_slice_batch_dims2(self):
         nodes_attributes['gathernd_node']['batch_dims'] = 2
-        graph = build_graph(nodes_attributes, edges, inputs3)
+        graph = build_graph(nodes_attributes, edges, inputs2)
         gathernd_node = Node(graph, 'gathernd_node')
         GatherND.infer(gathernd_node)
 
@@ -170,7 +183,22 @@ def test_partial_infer_gather_slice_batch_dims2(self):
         self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
                         'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
 
-    def test_partial_infer_gather_slice_batch_dims2_dynamic(self):
+    def test_partial_infer_gather_slice_batch_dims3(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 3
+        graph = build_graph(nodes_attributes, edges, inputs3)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = int64_array([4096, 1])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
+    def test_partial_infer_gather_slice_batch_dims2_dynamic1(self):
         nodes_attributes['gathernd_node']['batch_dims'] = 2
         graph = build_graph(nodes_attributes, edges, inputs9)
         gathernd_node = Node(graph, 'gathernd_node')
@@ -185,6 +213,36 @@ def test_partial_infer_gather_slice_batch_dims2_dynamic(self):
         self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
                         'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
 
+    def test_partial_infer_gather_slice_batch_dims2_dynamic2(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        graph = build_graph(nodes_attributes, edges, inputs10)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = shape_array([dynamic_dimension_value, 3, 5, 9])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
+    def test_partial_infer_gather_slice_batch_dims2_dynamic3(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        graph = build_graph(nodes_attributes, edges, inputs11)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = shape_array([dynamic_dimension_value, 3, 5, 9])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
     def test_infer4(self):
         graph = build_graph(nodes_attributes, edges, inputs4)
         gathernd_node = Node(graph, 'gathernd_node')
@@ -205,7 +263,7 @@ def test_infer5(self):
         res_output_value = graph.node['output']['value']
 
         self.assertTrue(np.array_equal(output5, res_output_value),
-                        'values do not match expected: {} and given: {}'.format(output4, res_output_value))
+                        'values do not match expected: {} and given: {}'.format(output5, res_output_value))
 
     def test_infer6(self):
         nodes_attributes['gathernd_node']['batch_dims'] = 1
@@ -217,7 +275,7 @@ def test_infer6(self):
         res_output_value = graph.node['output']['value']
 
         self.assertTrue(np.array_equal(output6, res_output_value),
-                        'values do not match expected: {} and given: {}'.format(output4, res_output_value))
+                        'values do not match expected: {} and given: {}'.format(output6, res_output_value))
 
     def test_infer7(self):
         nodes_attributes['gathernd_node']['batch_dims'] = 2
@@ -228,8 +286,9 @@ def test_infer7(self):
         # get the result
         res_output_value = graph.node['output']['value']
 
-        self.assertTrue(np.array_equal(output7, res_output_value),
-                        'values do not match expected: {} and given: {}'.format(output4, res_output_value))
+        output = output7.reshape([6, 1])
+        self.assertTrue(np.array_equal(output, res_output_value),
+                        'values do not match expected: {} and given: {}'.format(output, res_output_value))
 
     def test_infer8(self):
         nodes_attributes['gathernd_node']['batch_dims'] = 2
@@ -241,7 +300,32 @@ def test_infer8(self):
         res_output_value = graph.node['output']['value']
 
         self.assertTrue(np.array_equal(output8, res_output_value),
-                        'values do not match expected: {} and given: {}'.format(output4, res_output_value))
+                        'values do not match expected: {} and given: {}'.format(output8, res_output_value))
+
+    def test_infer9(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        graph = build_graph(nodes_attributes, edges, inputs8)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # get the result
+        res_output_value = graph.node['output']['value']
+
+        self.assertTrue(np.array_equal(output8, res_output_value),
+                        'values do not match expected: {} and given: {}'.format(output8, res_output_value))
+
+    def test_infer9_opset_5(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        graph = build_graph(nodes_attributes, edges, inputs8)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # get the result
+        res_output_value = graph.node['output']['value']
+
+        output = output8.reshape([6, 3])
+        self.assertTrue(np.array_equal(output, res_output_value),
+                        'values do not match expected: {} and given: {}'.format(output, res_output_value))
 
     def test_infer_invalid1(self):
         graph = build_graph(nodes_attributes, edges, inputs_inv1)
@@ -259,3 +343,114 @@ def test_infer_invalid3(self):
         graph = build_graph(nodes_attributes, edges, inputs_inv3)
         gathernd_node = Node(graph, 'gathernd_node')
         self.assertRaises(AssertionError, GatherND.infer, gathernd_node)
+
+
+    def test_partial_infer_gather_slice_batch_dims2_opset8(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        nodes_attributes['gathernd_node']['version'] = 'opset8'
+        graph = build_graph(nodes_attributes, edges, inputs2)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = int64_array([10, 40, 3, 5, 9])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
+    def test_partial_infer_gather_slice_batch_dims3_opset8(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 3
+        nodes_attributes['gathernd_node']['version'] = 'opset8'
+        graph = build_graph(nodes_attributes, edges, inputs3)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = int64_array([1, 64, 64, 1])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
+    def test_partial_infer_gather_slice_batch_dims2_dynamic1_opset8(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        nodes_attributes['gathernd_node']['version'] = 'opset8'
+        graph = build_graph(nodes_attributes, edges, inputs9)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = shape_array([dynamic_dimension_value, 40, 3, 5, 9])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
+    def test_partial_infer_gather_slice_batch_dims2_dynamic2_opset8(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        nodes_attributes['gathernd_node']['version'] = 'opset8'
+        graph = build_graph(nodes_attributes, edges, inputs10)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = shape_array([40, dynamic_dimension_value, 3, 5, 9])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
+    def test_partial_infer_gather_slice_batch_dims2_dynamic3_opset8(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        nodes_attributes['gathernd_node']['version'] = 'opset8'
+        graph = build_graph(nodes_attributes, edges, inputs11)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # prepare reference results
+        ref_output_shape = shape_array([40, 40, 3, 5, 9])
+
+        # get the result
+        res_output_shape = graph.node['output']['shape']
+
+        self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
+                        'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
+
+    def test_infer7_opset8(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        nodes_attributes['gathernd_node']['version'] = 'opset8'
+        graph = build_graph(nodes_attributes, edges, inputs7)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # get the result
+        res_output_value = graph.node['output']['value']
+
+        output = output7.reshape([2, 3, 1])
+
+        self.assertTrue(np.array_equal(output, res_output_value),
+                        'values do not match expected: {} and given: {}'.format(output, res_output_value))
+
+    def test_infer8_opset8(self):
+        nodes_attributes['gathernd_node']['batch_dims'] = 2
+        nodes_attributes['gathernd_node']['version'] = 'opset8'
+        graph = build_graph(nodes_attributes, edges, inputs8)
+        gathernd_node = Node(graph, 'gathernd_node')
+        GatherND.infer(gathernd_node)
+
+        # get the result
+        res_output_value = graph.node['output']['value']
+
+        output = output8.reshape([2, 3, 3])
+
+        self.assertTrue(np.array_equal(output, res_output_value),
+                        'values do not match expected: {} and given: {}'.format(output, res_output_value))
diff --git a/model-optimizer/unit_tests/mo/bom_test.py b/model-optimizer/unit_tests/mo/bom_test.py
index 454be1912c93d5..11511deaf6c557 100644
--- a/model-optimizer/unit_tests/mo/bom_test.py
+++ b/model-optimizer/unit_tests/mo/bom_test.py
@@ -28,13 +28,15 @@ class TestBOMFile(unittest.TestCase):
     def setUpClass(cls):
         cls.existing_files = []
         cur_path = os.path.join(os.path.realpath(__file__), os.pardir)
-        cls.output_dir = os.path.abspath(os.path.join(cur_path, os.pardir, os.pardir))
-        with open(os.path.join(cls.output_dir, 'automation', 'package_BOM.txt'), 'r') as bom_file:
+        mo_path = os.path.abspath(os.path.join(cur_path, os.pardir, os.pardir))
+        with open(os.path.join(mo_path, 'automation', 'package_BOM.txt'), 'r') as bom_file:
             if platform.system() == 'Windows':
                 cls.existing_files = [name.rstrip().replace('/', '\\') for name in bom_file.readlines()]
             else:
                 cls.existing_files = [name.rstrip() for name in bom_file.readlines()]
 
+        # output_dir is the model_optimizer directory that is located in 'pkg/tools'
+        cls.output_dir = mo_path.replace('tests', 'tools')
         cls.expected_header = [re.compile(pattern) for pattern in [
             r'^# Copyright \([cC]\) [0-9\-]+ Intel Corporation$',
             r'^# SPDX-License-Identifier: Apache-2.0$',
diff --git a/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.cpp b/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.cpp
index 17c647e35b5801..c8af6f81f6ea10 100644
--- a/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.cpp
+++ b/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.cpp
@@ -8,7 +8,7 @@
 #include "ngraph/visibility.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 FeStat FrontEndMockPy::m_stat = {};
 ModelStat InputModelMockPy::m_stat = {};
@@ -33,4 +33,4 @@ extern "C" MOCK_API void* GetFrontEndData()
     res->m_creator = []() { return std::make_shared<FrontEndMockPy>(); };
 
     return res;
-}
\ No newline at end of file
+}
diff --git a/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.hpp b/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.hpp
index 72f533da2de3ca..fd831af203720c 100644
--- a/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.hpp
+++ b/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_ngraph_frontend/mock_mo_frontend.hpp
@@ -18,7 +18,7 @@
 // OK to have 'using' in mock header
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 ////////////////////////////////
 /// \brief This structure holds number static setup values
diff --git a/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp b/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp
index b0ebc36d51747c..3742c9e5d290bd 100644
--- a/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp
+++ b/model-optimizer/unit_tests/mock_mo_frontend/mock_mo_python_api/mock_mo_python_api.cpp
@@ -9,7 +9,7 @@
 
 namespace py = pybind11;
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 static void register_mock_frontend_stat(py::module m)
 {
diff --git a/ngraph/CMakeLists.txt b/ngraph/CMakeLists.txt
index 163c395842735b..9681640b96b5ac 100644
--- a/ngraph/CMakeLists.txt
+++ b/ngraph/CMakeLists.txt
@@ -27,4 +27,5 @@ add_definitions(-DPROJECT_ROOT_DIR="${CMAKE_CURRENT_SOURCE_DIR}")
 add_subdirectory(core)
 
 add_subdirectory(frontend)
+
 add_subdirectory(test)
diff --git a/ngraph/core/CMakeLists.txt b/ngraph/core/CMakeLists.txt
index f93cdfd83bd065..0caa99e50c076a 100644
--- a/ngraph/core/CMakeLists.txt
+++ b/ngraph/core/CMakeLists.txt
@@ -17,7 +17,6 @@ add_subdirectory(reference)
 add_subdirectory(shape_inference)
 
 # WA for Tensor implementation via ie::Blob::Ptr
-set(IE_SRC_ROOT "${IE_MAIN_SOURCE_DIR}/src/inference_engine/src")
 set(IE_SHARED_SRCS "${CMAKE_CURRENT_SOURCE_DIR}/src/ie_legacy/blob_factory.cpp"
                    "${CMAKE_CURRENT_SOURCE_DIR}/src/ie_legacy/ie_blob_common.cpp"
                    "${CMAKE_CURRENT_SOURCE_DIR}/src/ie_legacy/ie_layouts.cpp"
@@ -28,7 +27,7 @@ set(MIXED_SRC ${IE_SHARED_SRCS}
 
 set_property(SOURCE ${MIXED_SRC}
     APPEND PROPERTY INCLUDE_DIRECTORIES
-        ${IE_SRC_ROOT}
+        $<TARGET_PROPERTY:inference_engine,SOURCE_DIR>/src
         $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>
         $<TARGET_PROPERTY:inference_engine_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>)
 
@@ -77,7 +76,7 @@ if(NOT BUILD_SHARED_LIBS)
 endif()
 
 if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-    # ngraph is linked against ngraph_builders, ngraph_reference static libraries
+    # ngraph is linked against ngraph_builders, ngraph_reference, ov_shape_inference static libraries
     # which include ngraph headers with dllimport attribute. Linker complains about it
     # but no way to fix this: linking with no attribute defaults to dllexport and we have
     # multiple defitions for ngraph symbols.
@@ -85,7 +84,13 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
     # The possible way is to use object libraries for ngraph_builders, ngraph_reference
     # but it's not convinient since these libraries are exported from build tree
     # and it's better to use them as static libraries in 3rd party projects
-    set_target_properties(ngraph PROPERTIES LINK_FLAGS "/IGNORE:4217,4286")
+    if(BUILD_SHARED_LIBS)
+        set(link_type PRIVATE)
+    else()
+        set(link_type PUBLIC)
+    endif()
+
+    target_link_options(ngraph ${link_type} "/IGNORE:4217,4286")
 endif()
 
 # some sources are located in ngraph, while headers are in inference_engine_transformations
diff --git a/ngraph/core/builder/CMakeLists.txt b/ngraph/core/builder/CMakeLists.txt
index b5953a6f1709db..b6c2fb0c0cc45d 100644
--- a/ngraph/core/builder/CMakeLists.txt
+++ b/ngraph/core/builder/CMakeLists.txt
@@ -15,7 +15,7 @@ set(BUILDER_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include/)
 source_group("src" FILES ${LIBRARY_SRC})
 source_group("include" FILES ${PUBLIC_HEADERS})
 
-# Create shared library
+# Create static library
 add_library(${TARGET_NAME} STATIC ${LIBRARY_SRC} ${PUBLIC_HEADERS})
 
 if(COMMAND ie_faster_build)
@@ -25,14 +25,21 @@ if(COMMAND ie_faster_build)
     )
 endif()
 
-# Defines macro in C++ to load backend plugin
-target_include_directories(${TARGET_NAME} PUBLIC ${BUILDER_INCLUDE_DIR} ${NGRAPH_INCLUDE_PATH})
+target_include_directories(${TARGET_NAME} PUBLIC
+    $<BUILD_INTERFACE:${BUILDER_INCLUDE_DIR}>
+    $<BUILD_INTERFACE:${NGRAPH_INCLUDE_PATH}>)
+
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME} PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
 # Add an alias so that library can be used inside the build tree, e.g. when testing
 add_library(ngraph::builder ALIAS ${TARGET_NAME})
 
+ov_install_static_lib(ngraph_builders ngraph)
+
 # developer package
 
 openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph::builder)
diff --git a/ngraph/core/include/ngraph/dimension.hpp b/ngraph/core/include/ngraph/dimension.hpp
index 9d748b26b30c94..762d4e430436b5 100644
--- a/ngraph/core/include/ngraph/dimension.hpp
+++ b/ngraph/core/include/ngraph/dimension.hpp
@@ -4,6 +4,7 @@
 
 #pragma once
 
+#include "ngraph/attribute_adapter.hpp"
 #include "ngraph/interval.hpp"
 #include "openvino/core/dimension.hpp"
 
diff --git a/ngraph/core/include/ngraph/op/gather_nd.hpp b/ngraph/core/include/ngraph/op/gather_nd.hpp
index 9689be8b854b0b..4a5bf1e50ff630 100644
--- a/ngraph/core/include/ngraph/op/gather_nd.hpp
+++ b/ngraph/core/include/ngraph/op/gather_nd.hpp
@@ -12,5 +12,8 @@ namespace op {
 namespace v5 {
 using ov::op::v5::GatherND;
 }  // namespace v5
+namespace v8 {
+using ov::op::v8::GatherND;
+}  // namespace v8
 }  // namespace op
 }  // namespace ngraph
diff --git a/ngraph/core/include/ngraph/op/util/gather_nd_base.hpp b/ngraph/core/include/ngraph/op/util/gather_nd_base.hpp
new file mode 100644
index 00000000000000..6585dac61bb401
--- /dev/null
+++ b/ngraph/core/include/ngraph/op/util/gather_nd_base.hpp
@@ -0,0 +1,16 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "ngraph/op/op.hpp"
+#include "openvino/op/util/gather_nd_base.hpp"
+
+namespace ngraph {
+namespace op {
+namespace util {
+using ov::op::util::GatherNDBase;
+}  // namespace util
+}  // namespace op
+}  // namespace ngraph
diff --git a/ngraph/core/include/ngraph/validation_util.hpp b/ngraph/core/include/ngraph/validation_util.hpp
index e52b6096c5d340..37607e91764355 100644
--- a/ngraph/core/include/ngraph/validation_util.hpp
+++ b/ngraph/core/include/ngraph/validation_util.hpp
@@ -216,6 +216,9 @@ NGRAPH_API std::shared_ptr<op::Constant> get_constant_max_of_type(element::Type_
 /// \brief Returns a Constant storing scalar value equal to std::numeric_limits<t>::min()
 NGRAPH_API std::shared_ptr<op::Constant> get_constant_min_of_type(element::Type_t t);
 
+/// \brief Returns a Constant storing scalar value equal to std::numeric_limits<t>::lowest()
+NGRAPH_API std::shared_ptr<op::Constant> get_constant_lowest_of_type(element::Type_t t);
+
 /// \brief Checks if size of HostTensorVector is the same as passed size attribute. Then checks
 /// that all the HostTensorPtrs are not equal to nullptr
 NGRAPH_API bool validate_host_tensor_vector(const HostTensorVector& v, const size_t& size);
diff --git a/ngraph/core/include/openvino/core/descriptor/tensor.hpp b/ngraph/core/include/openvino/core/descriptor/tensor.hpp
index 4418468625c6dc..0f9f7f970c8705 100644
--- a/ngraph/core/include/openvino/core/descriptor/tensor.hpp
+++ b/ngraph/core/include/openvino/core/descriptor/tensor.hpp
@@ -45,8 +45,14 @@ class OPENVINO_API Tensor {
     const std::unordered_set<std::string>& get_names() const;
     void set_names(const std::unordered_set<std::string>& names);
     void add_names(const std::unordered_set<std::string>& names);
+
+    OPENVINO_DEPRECATED("set_tensor_type() is deprecated. To change Tensor type please change the Parameter type")
     void set_tensor_type(const element::Type& element_type, const PartialShape& pshape);
+    OPENVINO_DEPRECATED(
+        "set_element_type() is deprecated. To change Tensor element type please change the Parameter type")
     void set_element_type(const element::Type& elemenet_type);
+    OPENVINO_DEPRECATED(
+        "set_partial_shape() is deprecated. To change Tensor partial shape please change the Parameter partial shape")
     void set_partial_shape(const PartialShape& partial_shape);
 
     /// \brief sets lower bound value description
diff --git a/ngraph/core/include/openvino/core/dimension.hpp b/ngraph/core/include/openvino/core/dimension.hpp
index e54232677540fd..7f013791b7be67 100644
--- a/ngraph/core/include/openvino/core/dimension.hpp
+++ b/ngraph/core/include/openvino/core/dimension.hpp
@@ -8,6 +8,7 @@
 #include <limits>
 #include <stdexcept>
 
+#include "openvino/core/attribute_adapter.hpp"
 #include "openvino/core/core_visibility.hpp"
 #include "openvino/core/interval.hpp"
 
@@ -27,7 +28,7 @@ class OPENVINO_API Dimension {
 
     /// \brief Construct a dynamic dimension with bounded range
     /// \param min_dimension The lower inclusive limit for the dimension
-    /// \param mas_dimension The upper inclusive limit for the dimension
+    /// \param max_dimension The upper inclusive limit for the dimension
     Dimension(value_type min_dimension, value_type max_dimension);
 
     /// \brief Construct a dynamic dimension with range [0, ...]
@@ -169,4 +170,23 @@ class OPENVINO_API Dimension {
 /// Inserts the string `?` if `dimension` is dynamic; else inserts `dimension.get_length()`.
 OPENVINO_API
 std::ostream& operator<<(std::ostream& str, const Dimension& dimension);
+
+template <>
+class OPENVINO_API AttributeAdapter<ov::Dimension> : public ValueAccessor<int64_t> {
+public:
+    AttributeAdapter(ov::Dimension& value) : m_ref(value) {}
+
+    const int64_t& get() override;
+    void set(const int64_t& value) override;
+    operator ov::Dimension&() {
+        return m_ref;
+    }
+
+    OPENVINO_RTTI("AttributeAdapter<ov::Dimension>");
+
+protected:
+    ov::Dimension& m_ref;
+    int64_t m_buffer{0};
+    bool m_buffer_valid{false};
+};
 }  // namespace ov
diff --git a/ngraph/core/include/openvino/core/extension.hpp b/ngraph/core/include/openvino/core/extension.hpp
index 9e997336ac985e..6b8860387706bc 100644
--- a/ngraph/core/include/openvino/core/extension.hpp
+++ b/ngraph/core/include/openvino/core/extension.hpp
@@ -16,8 +16,8 @@
 #    define OPENVINO_EXTENSION_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
 #    define OPENVINO_EXTENSION_API   OPENVINO_CORE_EXPORTS
 #else
-#    define OPENVINO_EXTENSION_C_API OPENVINO_EXTERN_C OPENVINO_API
-#    define OPENVINO_EXTENSION_API   OPENVINO_API
+#    define OPENVINO_EXTENSION_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#    define OPENVINO_EXTENSION_API   OPENVINO_CORE_EXPORTS
 #endif
 
 namespace ov {
@@ -27,7 +27,7 @@ class Extension;
 /**
  * @brief The class provides the base interface for OpenVINO extensions
  */
-class OPENVINO_API Extension : public std::enable_shared_from_this<Extension> {
+class OPENVINO_API Extension {
 public:
     using Ptr = std::shared_ptr<Extension>;
 
diff --git a/ngraph/core/include/openvino/core/function.hpp b/ngraph/core/include/openvino/core/function.hpp
index f8eed3450b1f75..a94cdfd66df5ab 100644
--- a/ngraph/core/include/openvino/core/function.hpp
+++ b/ngraph/core/include/openvino/core/function.hpp
@@ -27,10 +27,15 @@ namespace ov {
 /// A user-defined function.
 class OPENVINO_API Function : public std::enable_shared_from_this<Function> {
 public:
-    static constexpr ov::DiscreteTypeInfo type_info{"Function", 0};
-    const ov::DiscreteTypeInfo& get_type_info() const {
+    static const ::ov::DiscreteTypeInfo& get_type_info_static() {
+        static const ::ov::DiscreteTypeInfo type_info{"Function", 0};
         return type_info;
     }
+    const ::ov::DiscreteTypeInfo& get_type_info() const {
+        return get_type_info_static();
+    }
+    OPENVINO_DEPRECATED("This member was deprecated. Please use ::get_type_info_static() instead.")
+    static const ov::DiscreteTypeInfo type_info;
     Function(const ov::NodeVector& results, const ov::ParameterVector& parameters, const std::string& name = "");
 
     Function(const ov::OutputVector& results, const ov::ParameterVector& parameters, const std::string& name = "");
diff --git a/ngraph/core/include/openvino/core/layout.hpp b/ngraph/core/include/openvino/core/layout.hpp
index 4f7a998c5af220..cf358babd4d751 100644
--- a/ngraph/core/include/openvino/core/layout.hpp
+++ b/ngraph/core/include/openvino/core/layout.hpp
@@ -147,14 +147,11 @@ OPENVINO_API std::int64_t width_idx(const Layout& layout);
 template <>
 class OPENVINO_API AttributeAdapter<Layout> : public ValueAccessor<std::string> {
 public:
+    OPENVINO_RTTI("AttributeAdapter<Layout>");
     explicit AttributeAdapter(Layout& value) : m_ref(value) {}
 
     const std::string& get() override;
     void set(const std::string& value) override;
-    static constexpr DiscreteTypeInfo type_info{"AttributeAdapter<Layout>", 0};
-    const DiscreteTypeInfo& get_type_info() const override {
-        return type_info;
-    }
     explicit operator Layout&() {
         return m_ref;
     }
diff --git a/ngraph/core/include/openvino/core/op_extension.hpp b/ngraph/core/include/openvino/core/op_extension.hpp
index 735d2dba358877..4f9a2cf5cbe736 100644
--- a/ngraph/core/include/openvino/core/op_extension.hpp
+++ b/ngraph/core/include/openvino/core/op_extension.hpp
@@ -14,7 +14,7 @@ namespace ov {
 /**
  * @brief The base interface for OpenVINO operation extensions
  */
-class OPENVINO_EXTENSION_API BaseOpExtension : public Extension {
+class OPENVINO_API BaseOpExtension : public Extension {
 public:
     using Ptr = std::shared_ptr<BaseOpExtension>;
     /**
diff --git a/ngraph/core/include/openvino/core/partial_shape.hpp b/ngraph/core/include/openvino/core/partial_shape.hpp
index 8ad68c66de7746..e2810e6d274e1b 100644
--- a/ngraph/core/include/openvino/core/partial_shape.hpp
+++ b/ngraph/core/include/openvino/core/partial_shape.hpp
@@ -224,6 +224,7 @@ class OPENVINO_API PartialShape {
     ///        element in the shape. Iteration is done in ordinary
     ///        element order.
     iterator begin() noexcept {
+        m_shape_type = ShapeType::SHAPE_IS_UPDATED;
         return m_dimensions.begin();
     }
     /// \brief Returns a read-only (constant) iterator that points to the
@@ -236,6 +237,7 @@ class OPENVINO_API PartialShape {
     ///        element in the shape. Iteration is done in ordinary
     ///        element order.
     iterator end() noexcept {
+        m_shape_type = ShapeType::SHAPE_IS_UPDATED;
         return m_dimensions.end();
     }
     /// \brief Returns a read-only (constant) iterator that points one past
@@ -248,6 +250,7 @@ class OPENVINO_API PartialShape {
     ///        last element in the shape. Iteration is done in reverse
     ///        element order.
     reverse_iterator rbegin() noexcept {
+        m_shape_type = ShapeType::SHAPE_IS_UPDATED;
         return m_dimensions.rbegin();
     }
     /// \brief Returns a read-only (constant) reverse iterator that points
@@ -260,6 +263,7 @@ class OPENVINO_API PartialShape {
     ///        before the first element in the shape. Iteration is done
     ///        in reverse element order.
     reverse_iterator rend() noexcept {
+        m_shape_type = ShapeType::SHAPE_IS_UPDATED;
         return m_dimensions.rend();
     }
     /// \brief Returns a read-only (constant) reverse iterator that points
diff --git a/ngraph/core/include/openvino/core/preprocess/input_info.hpp b/ngraph/core/include/openvino/core/preprocess/input_info.hpp
index 2c3ae8408815c0..61a67afc624341 100644
--- a/ngraph/core/include/openvino/core/preprocess/input_info.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/input_info.hpp
@@ -18,10 +18,6 @@ namespace preprocess {
 ///    - Preprocessing steps applied to user's input (InputInfo::preprocess)
 ///    - Network's input info, which is a final info after preprocessing (InputInfo::network)
 ///
-/// API has Builder-like style to allow chaining calls in client's code, like
-/// \code{.cpp}
-/// auto proc = PrePostProcessor().input(InputInfo().tensor(...).preprocess(...).network(...);
-/// \endcode
 class OPENVINO_API InputInfo final {
     class InputInfoImpl;
     std::unique_ptr<InputInfoImpl> m_impl;
@@ -29,15 +25,21 @@ class OPENVINO_API InputInfo final {
 
 public:
     /// \brief Empty constructor. Should be used only if network will have only one input
+    ///
+    /// \todo Consider remove it (don't allow user to create standalone objects)
     InputInfo();
 
     /// \brief Constructor for particular input index of model
     ///
+    /// \todo Consider remove it (don't allow user to create standalone objects)
+    ///
     /// \param input_index Index to address specified input parameter of model
     explicit InputInfo(size_t input_index);
 
     /// \brief Constructor for particular output of model addressed by it's input name
     ///
+    /// \todo Consider remove it (don't allow user to create standalone objects)
+    ///
     /// \param input_tensor_name Name of input tensor name
     explicit InputInfo(const std::string& input_tensor_name);
 
@@ -50,8 +52,25 @@ class OPENVINO_API InputInfo final {
     /// \brief Default destructor
     ~InputInfo();
 
+    /// \brief Get current input tensor information with ability to change specific data
+    ///
+    /// \return Reference to current input tensor structure
+    InputTensorInfo& tensor();
+
+    /// \brief Get current input preprocess information with ability to add more preprocessing steps
+    ///
+    /// \return Reference to current preprocess steps structure
+    PreProcessSteps& preprocess();
+
+    /// \brief Get current input network/model information with ability to change original network's input data
+    ///
+    /// \return Reference to current network's input information structure
+    InputNetworkInfo& network();
+
     /// \brief Set input tensor information for input - Lvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Input tensor information.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -59,6 +78,8 @@ class OPENVINO_API InputInfo final {
 
     /// \brief Set input tensor information for input - Rvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Input tensor information.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -66,6 +87,8 @@ class OPENVINO_API InputInfo final {
 
     /// \brief Set preprocessing operations for input - Lvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Preprocessing operations.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -73,6 +96,8 @@ class OPENVINO_API InputInfo final {
 
     /// \brief Set preprocessing operations for input - Rvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Preprocessing operations.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -81,6 +106,8 @@ class OPENVINO_API InputInfo final {
 
     /// \brief Set network's tensor information for input - Lvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Input network tensor information.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -88,6 +115,8 @@ class OPENVINO_API InputInfo final {
 
     /// \brief Set input tensor information for input - Rvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Input network tensor information.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
diff --git a/ngraph/core/include/openvino/core/preprocess/input_network_info.hpp b/ngraph/core/include/openvino/core/preprocess/input_network_info.hpp
index b6ef577f40e13b..a61b6a138ac8e6 100644
--- a/ngraph/core/include/openvino/core/preprocess/input_network_info.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/input_network_info.hpp
@@ -21,7 +21,7 @@ namespace preprocess {
 /// \code{.cpp}
 /// <network has input parameter with shape {1, 3, 224, 224}>
 /// auto proc =
-/// PrePostProcessor()
+/// PrePostProcessor(function)
 ///     .input(InputInfo()
 ///            .tensor(<input tensor info>)
 ///            .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
diff --git a/ngraph/core/include/openvino/core/preprocess/input_tensor_info.hpp b/ngraph/core/include/openvino/core/preprocess/input_tensor_info.hpp
index ea73d7fae2db34..40c7e5ae46fc00 100644
--- a/ngraph/core/include/openvino/core/preprocess/input_tensor_info.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/input_tensor_info.hpp
@@ -18,7 +18,7 @@ namespace preprocess {
 ///
 /// \code{.cpp}
 /// auto proc =
-/// PrePostProcessor()
+/// PrePostProcessor(function)
 ///     .input(InputInfo()
 ///            .tensor(InputTensorInfo()
 ///                    .set_element_type(ov::element::u8))
diff --git a/ngraph/core/include/openvino/core/preprocess/output_info.hpp b/ngraph/core/include/openvino/core/preprocess/output_info.hpp
index 879875d16ef808..a0fe39d10a6e5c 100644
--- a/ngraph/core/include/openvino/core/preprocess/output_info.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/output_info.hpp
@@ -17,11 +17,6 @@ namespace preprocess {
 ///    - Network's output info,  (OutputInfo::network)
 ///    - Postprocessing steps applied to user's input (OutputInfo::postprocess)
 ///    - User's desired output parameter information, which is a final one after preprocessing (OutputInfo::tensor)
-///
-/// API has Builder-like style to allow chaining calls in client's code, like
-/// \code{.cpp}
-/// auto proc = PrePostProcessor().output(InputInfo().network(...).preprocess(...).tensor(...);
-/// \endcode
 class OPENVINO_API OutputInfo final {
     class OutputInfoImpl;
     std::unique_ptr<OutputInfoImpl> m_impl;
@@ -29,15 +24,21 @@ class OPENVINO_API OutputInfo final {
 
 public:
     /// \brief Empty constructor. Should be used only if network has exactly one output
+    ///
+    /// \todo Consider making this private to not allow user to create standalone object
     OutputInfo();
 
     /// \brief Constructor for particular output index of model
     ///
+    /// \todo Consider remove it (don't allow user to create standalone objects)
+    ///
     /// \param output_index Index to address specified output parameter of model
     explicit OutputInfo(size_t output_index);
 
     /// \brief Constructor for particular output of model addressed by it's output name
     ///
+    /// \todo Consider remove it (don't allow user to create standalone objects)
+    ///
     /// \param output_tensor_name Name of output tensor name
     explicit OutputInfo(const std::string& output_tensor_name);
 
@@ -50,8 +51,25 @@ class OPENVINO_API OutputInfo final {
     /// \brief Default destructor
     ~OutputInfo();
 
+    /// \brief Get current output network/model information with ability to change original network's output data
+    ///
+    /// \return Reference to current network's output information structure
+    OutputNetworkInfo& network();
+
+    /// \brief Get current output post-process information with ability to add more post-processing steps
+    ///
+    /// \return Reference to current preprocess steps structure
+    PostProcessSteps& postprocess();
+
+    /// \brief Get current output tensor information with ability to change specific data
+    ///
+    /// \return Reference to current output tensor structure
+    OutputTensorInfo& tensor();
+
     /// \brief Set network's tensor information for output - Lvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Output network tensor information.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -59,6 +77,8 @@ class OPENVINO_API OutputInfo final {
 
     /// \brief Set network's tensor information for output - Rvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Output network tensor information.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -66,6 +86,8 @@ class OPENVINO_API OutputInfo final {
 
     /// \brief Set postprocessing operations for output - Lvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Postprocessing operations.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -73,6 +95,8 @@ class OPENVINO_API OutputInfo final {
 
     /// \brief Set postprocessing operations for output - Rvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Postprocessing operations.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -81,6 +105,8 @@ class OPENVINO_API OutputInfo final {
 
     /// \brief Set final output tensor information for output after postprocessing - Lvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Output tensor information.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -88,6 +114,8 @@ class OPENVINO_API OutputInfo final {
 
     /// \brief Set final output tensor information for output after postprocessing - Rvalue version
     ///
+    /// \todo Consider removing it in future
+    ///
     /// \param builder Output tensor information.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
diff --git a/ngraph/core/include/openvino/core/preprocess/output_network_info.hpp b/ngraph/core/include/openvino/core/preprocess/output_network_info.hpp
index b32c2181ae122f..b0a96c8274385f 100644
--- a/ngraph/core/include/openvino/core/preprocess/output_network_info.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/output_network_info.hpp
@@ -14,17 +14,15 @@ namespace preprocess {
 /// info may not be needed. However it can be set to specify additional information about network, like 'layout'.
 ///
 /// Example of usage of network 'layout':
-/// Support network has output parameter with shape {1, 3, 224, 224} and `NHWC` layout. User may need to transpose
+/// Support network has output result with shape {1, 3, 224, 224} and `NHWC` layout. User may need to transpose
 /// output picture to interleaved format {1, 224, 224, 3}. This can be done with the following code
 ///
 /// \code{.cpp}
-/// <network has output parameter with shape {1, 3, 224, 224}>
-/// auto proc =
-/// PrePostProcessor()
-///     .output(OutputInfo()
-///            .network(OutputNetworkInfo().set_layout("NCHW")
-///            .preprocess(PostProcessSteps().convert_layout("NHWC")))
-///     );
+///     <network has output result with shape {1, 3, 224, 224}>
+///     auto proc = PrePostProcessor(function);
+///     proc.output().network().set_layout("NCHW");
+///     proc.output().postprocess().convert_layout("NHWC");
+///     function = proc.build();
 /// \endcode
 class OPENVINO_API OutputNetworkInfo final {
     class OutputNetworkInfoImpl;
diff --git a/ngraph/core/include/openvino/core/preprocess/output_tensor_info.hpp b/ngraph/core/include/openvino/core/preprocess/output_tensor_info.hpp
index 385a47d729898e..77045b899d45ee 100644
--- a/ngraph/core/include/openvino/core/preprocess/output_tensor_info.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/output_tensor_info.hpp
@@ -16,13 +16,11 @@ namespace preprocess {
 /// 'element_type') according to application's data and specify appropriate conversions in post-processing steps
 ///
 /// \code{.cpp}
-/// auto proc =
-/// PrePostProcessor()
-///     .output(OutputInfo()
-///            .postprocess(<add steps + conversion to user's output element type>)
-///            .tensor(OutputTensorInfo()
-///                    .set_element_type(ov::element::u8))
-///     );
+///     auto proc = PrePostProcessor(function);
+///     auto& output = proc.output();
+///     output.postprocess().<add steps + conversion to user's output element type>;
+///     output.tensor().set_element_type(ov::element::u8);
+///     function = proc.build();
 /// \endcode
 class OPENVINO_API OutputTensorInfo final {
     class OutputTensorInfoImpl;
@@ -31,6 +29,8 @@ class OPENVINO_API OutputTensorInfo final {
 
 public:
     /// \brief Default empty constructor
+    ///
+    /// \todo Consider making this private to not allow user to create standalone object
     OutputTensorInfo();
 
     /// \brief Default move constructor
diff --git a/ngraph/core/include/openvino/core/preprocess/postprocess_steps.hpp b/ngraph/core/include/openvino/core/preprocess/postprocess_steps.hpp
index d318b8684c95ce..8d0e47db47ba4d 100644
--- a/ngraph/core/include/openvino/core/preprocess/postprocess_steps.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/postprocess_steps.hpp
@@ -17,11 +17,9 @@ namespace preprocess {
 /// \brief Postprocessing steps. Each step typically intends adding of some operation to output parameter
 /// User application can specify sequence of postprocessing steps in a builder-like manner
 /// \code{.cpp}
-/// auto proc = PrePostProcessor()
-///     .output(OutputInfo()
-///             .postprocess(PostProcessSteps()
-///                        .convert_element_type(element::u8)))
-///     );
+///     auto proc = PrePostProcessor(function);
+///     proc.output().postprocess().convert_element_type(element::u8);
+///     function = proc.build();
 /// \endcode
 class OPENVINO_API PostProcessSteps final {
     class PostProcessStepsImpl;
@@ -30,6 +28,8 @@ class OPENVINO_API PostProcessSteps final {
 
 public:
     /// \brief Default empty constructor
+    ///
+    /// \todo Consider remove it (don't allow user to create standalone objects)
     PostProcessSteps();
 
     /// \brief Default move constructor
@@ -64,7 +64,7 @@ class OPENVINO_API PostProcessSteps final {
     /// interleaved output image ('NHWC', [1, 224, 224, 3]). Post-processing may look like this:
     ///
     /// \code{.cpp} auto proc =
-    /// PrePostProcessor()
+    /// PrePostProcessor(function)
     ///     .output(OutputInfo()
     ///            .network(OutputTensorInfo().set_layout("NCHW")) // Network output is NCHW
     ///            .postprocess(PostProcessSteps()
@@ -92,7 +92,7 @@ class OPENVINO_API PostProcessSteps final {
     /// interleaved output image [1, 480, 640, 3]. Post-processing may look like this:
     ///
     /// \code{.cpp} auto proc =
-    /// PrePostProcessor()
+    /// PrePostProcessor(function)
     ///     .output(OutputInfo()
     ///            .postprocess(PostProcessSteps()
     ///                        .convert_layout({0, 2, 3, 1})
diff --git a/ngraph/core/include/openvino/core/preprocess/pre_post_process.hpp b/ngraph/core/include/openvino/core/preprocess/pre_post_process.hpp
index 54ac8b5ab4d423..18913b828f9c85 100644
--- a/ngraph/core/include/openvino/core/preprocess/pre_post_process.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/pre_post_process.hpp
@@ -17,7 +17,7 @@ namespace preprocess {
 /// \brief Main class for adding pre- and post- processing steps to existing ov::Function
 /// API has Builder-like style to allow chaining calls in client's code, like
 /// \code{.cpp}
-/// auto proc = PrePostProcessor().input(<for input1>).input(<input2>);
+/// auto proc = PrePostProcessor(function).input(<for input1>).input(<input2>);
 /// \endcode
 ///
 /// This is a helper class for writing easy pre- and post- processing operations on ov::Function object assuming that
@@ -32,7 +32,9 @@ class OPENVINO_API PrePostProcessor final {
 
 public:
     /// \brief Default constructor
-    PrePostProcessor();
+    ///
+    /// \param function Existing function representing loaded model
+    explicit PrePostProcessor(const std::shared_ptr<Function>& function);
 
     /// \brief Default move constructor
     PrePostProcessor(PrePostProcessor&&) noexcept;
@@ -43,8 +45,62 @@ class OPENVINO_API PrePostProcessor final {
     /// \brief Default destructor
     ~PrePostProcessor();
 
+    /// \brief Gets input pre-processing data structure. Should be used only if network/function has only one input
+    /// Using returned structure application's code is able to set user's tensor data (e.g layout), preprocess steps,
+    /// target model's data
+    ///
+    /// \return Reference to network's input information structure
+    InputInfo& input();
+
+    /// \brief Gets input pre-processing data structure for input identified by it's tensor name
+    ///
+    /// \param tensor_name Tensor name of specific input. Throws if tensor name is not associated with any input in a
+    /// model
+    ///
+    /// \return Reference to network's input information structure
+    InputInfo& input(const std::string& tensor_name);
+
+    /// \brief Gets input pre-processing data structure for input identified by it's order in a model
+    ///
+    /// \param input_index Input index of specific input. Throws if input index is out of range for associated function
+    ///
+    /// \return Reference to network's input information structure
+    InputInfo& input(size_t input_index);
+
+    /// \brief Gets output post-processing data structure. Should be used only if network/function has only one output
+    /// Using returned structure application's code is able to set model's output data, post-process steps, user's
+    /// tensor data (e.g layout)
+    ///
+    /// \return Reference to network's output information structure
+    OutputInfo& output();
+
+    /// \brief Gets output post-processing data structure for output identified by it's tensor name
+    ///
+    /// \param tensor_name Tensor name of specific output. Throws if tensor name is not associated with any input in a
+    /// model
+    ///
+    /// \return Reference to network's output information structure
+    OutputInfo& output(const std::string& tensor_name);
+
+    /// \brief Gets output post-processing data structure for output identified by it's order in a model
+    ///
+    /// \param output_index Output index of specific output. Throws if output index is out of range for associated
+    /// function
+    ///
+    /// \return Reference to network's output information structure
+    OutputInfo& output(size_t output_index);
+
+    /// \brief Adds pre/post-processing operations to function passed in constructor
+    ///
+    /// \return Function with added pre/post-processing operations
+    std::shared_ptr<Function> build();
+
+    //------------------ TODO: consider removal of rest --------
+
     /// \brief Adds pre-processing information and steps to input of model.
     ///
+    /// \todo TODO: Consider remove this in sake of `InputInfo& input(...)` version
+    ///
     /// \param builder Pre-processing data for input tensor of model.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -52,6 +108,8 @@ class OPENVINO_API PrePostProcessor final {
 
     /// \brief Adds pre-processing information and steps to input of model - Rvalue version.
     ///
+    /// \todo TODO: Consider remove this in sake of `InputInfo& input(...)` version
+    ///
     /// \param builder Pre-processing data for input tensor of model.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -59,6 +117,8 @@ class OPENVINO_API PrePostProcessor final {
 
     /// \brief Adds post-processing information and steps to output of model.
     ///
+    /// \todo TODO: Consider remove this in sake of `OutputInfo& output(...)` version
+    ///
     /// \param builder Post-processing data for output tensor of model.
     ///
     /// \return Reference to 'this' to allow chaining with other calls in a builder-like manner
@@ -66,17 +126,12 @@ class OPENVINO_API PrePostProcessor final {
 
     /// \brief Adds pre-processing information and steps to input of model - Rvalue version.
     ///
+    /// \todo TODO: Consider remove this in sake of `OutputInfo& output(...)` version
+    ///
     /// \param builder Post-processing data for output tensor of model.
     ///
     /// \return Rvalue reference to 'this' to allow chaining with other calls in a builder-like manner
     PrePostProcessor&& output(OutputInfo&& builder) &&;
-
-    /// \brief Adds pre/post-processing operations to existing function
-    ///
-    /// \param function Existing function representing loaded model
-    ///
-    /// \return Function with added pre/post-processing operations
-    std::shared_ptr<Function> build(const std::shared_ptr<Function>& function);
 };
 
 }  // namespace preprocess
diff --git a/ngraph/core/include/openvino/core/preprocess/preprocess_steps.hpp b/ngraph/core/include/openvino/core/preprocess/preprocess_steps.hpp
index 6f331dd72d267a..5c85489ec29968 100644
--- a/ngraph/core/include/openvino/core/preprocess/preprocess_steps.hpp
+++ b/ngraph/core/include/openvino/core/preprocess/preprocess_steps.hpp
@@ -18,7 +18,7 @@ namespace preprocess {
 /// \brief Preprocessing steps. Each step typically intends adding of some operation to input parameter
 /// User application can specify sequence of preprocessing steps in a builder-like manner
 /// \code{.cpp}
-/// auto proc = PrePostProcessor()
+/// auto proc = PrePostProcessor(function)
 ///     .input(InputInfo()
 ///            .preprocess(PreProcessSteps()
 ///                        .mean(0.2f)     // Subtract 0.2 from each element
@@ -207,7 +207,7 @@ class OPENVINO_API PreProcessSteps final {
     /// planar input image ('NCHW', [1, 3, 224, 224]). Preprocessing may look like this:
     ///
     /// \code{.cpp} auto proc =
-    /// PrePostProcessor()
+    /// PrePostProcessor(function)
     ///     .input(InputInfo()
     ///            .tensor(InputTensorInfo().set_layout("NHWC")) // User data is NHWC
     ///            .preprocess(PreProcessSteps()
@@ -228,7 +228,7 @@ class OPENVINO_API PreProcessSteps final {
     /// planar input image ('NCHW', [1, 3, 480, 640]). Preprocessing may look like this:
     ///
     /// \code{.cpp} auto proc =
-    /// PrePostProcessor()
+    /// PrePostProcessor(function)
     ///     .input(InputInfo()
     ///            .preprocess(PreProcessSteps()
     ///                        .convert_layout({0, 3, 1, 2})
@@ -249,7 +249,7 @@ class OPENVINO_API PreProcessSteps final {
     /// BGR planes order. Preprocessing may look like this:
     ///
     /// \code{.cpp} auto proc =
-    /// PrePostProcessor()
+    /// PrePostProcessor(function)
     ///     .input(InputInfo()
     ///            .tensor(InputTensorInfo().set_layout("NCHW")) // User data is NCHW
     ///            .preprocess(PreProcessSteps()
diff --git a/ngraph/core/include/openvino/core/rtti.hpp b/ngraph/core/include/openvino/core/rtti.hpp
index a39b37833b6e44..80064b985ba447 100644
--- a/ngraph/core/include/openvino/core/rtti.hpp
+++ b/ngraph/core/include/openvino/core/rtti.hpp
@@ -11,13 +11,14 @@
 
 #define _OPENVINO_RTTI_WITH_TYPE(TYPE_NAME) _OPENVINO_RTTI_WITH_TYPE_VERSION(TYPE_NAME, "util")
 
-#define _OPENVINO_RTTI_WITH_TYPE_VERSION(TYPE_NAME, VERSION_NAME)                  \
-    static const ::ov::DiscreteTypeInfo& get_type_info_static() {                  \
-        static const ::ov::DiscreteTypeInfo type_info{TYPE_NAME, 0, VERSION_NAME}; \
-        return type_info;                                                          \
-    }                                                                              \
-    const ::ov::DiscreteTypeInfo& get_type_info() const override {                 \
-        return get_type_info_static();                                             \
+#define _OPENVINO_RTTI_WITH_TYPE_VERSION(TYPE_NAME, VERSION_NAME)            \
+    static const ::ov::DiscreteTypeInfo& get_type_info_static() {            \
+        static ::ov::DiscreteTypeInfo type_info{TYPE_NAME, 0, VERSION_NAME}; \
+        type_info.hash();                                                    \
+        return type_info;                                                    \
+    }                                                                        \
+    const ::ov::DiscreteTypeInfo& get_type_info() const override {           \
+        return get_type_info_static();                                       \
     }
 
 #define _OPENVINO_RTTI_WITH_TYPE_VERSION_PARENT(TYPE_NAME, VERSION_NAME, PARENT_CLASS) \
@@ -25,10 +26,11 @@
 
 #define _OPENVINO_RTTI_WITH_TYPE_VERSIONS_PARENT(TYPE_NAME, VERSION_NAME, PARENT_CLASS, OLD_VERSION) \
     static const ::ov::DiscreteTypeInfo& get_type_info_static() {                                    \
-        static const ::ov::DiscreteTypeInfo type_info{TYPE_NAME,                                     \
-                                                      OLD_VERSION,                                   \
-                                                      VERSION_NAME,                                  \
-                                                      &PARENT_CLASS::get_type_info_static()};        \
+        static ::ov::DiscreteTypeInfo type_info{TYPE_NAME,                                           \
+                                                OLD_VERSION,                                         \
+                                                VERSION_NAME,                                        \
+                                                &PARENT_CLASS::get_type_info_static()};              \
+        type_info.hash();                                                                            \
         return type_info;                                                                            \
     }                                                                                                \
     const ::ov::DiscreteTypeInfo& get_type_info() const override {                                   \
diff --git a/ngraph/core/include/openvino/core/type.hpp b/ngraph/core/include/openvino/core/type.hpp
index 63f850e4199ca5..08eb5c240704e3 100644
--- a/ngraph/core/include/openvino/core/type.hpp
+++ b/ngraph/core/include/openvino/core/type.hpp
@@ -39,7 +39,8 @@ struct OPENVINO_API DiscreteTypeInfo {
         : name(_name),
           version(_version),
           version_id(nullptr),
-          parent(_parent) {}
+          parent(_parent),
+          hash_value(0) {}
 
     constexpr DiscreteTypeInfo(const char* _name,
                                uint64_t _version,
@@ -48,22 +49,12 @@ struct OPENVINO_API DiscreteTypeInfo {
         : name(_name),
           version(_version),
           version_id(_version_id),
-          parent(_parent) {}
+          parent(_parent),
+          hash_value(0) {}
 
-    bool is_castable(const DiscreteTypeInfo& target_type) const {
-        return *this == target_type || (parent && parent->is_castable(target_type));
-    }
+    bool is_castable(const DiscreteTypeInfo& target_type) const;
 
-    std::string get_version() const {
-        if (version_id) {
-            return std::string(version_id);
-        }
-        return std::to_string(version);
-    }
-
-    operator std::string() const {
-        return std::string(name) + "_" + get_version();
-    }
+    std::string get_version() const;
 
     // For use as a key
     bool operator<(const DiscreteTypeInfo& b) const;
@@ -72,6 +63,14 @@ struct OPENVINO_API DiscreteTypeInfo {
     bool operator>=(const DiscreteTypeInfo& b) const;
     bool operator==(const DiscreteTypeInfo& b) const;
     bool operator!=(const DiscreteTypeInfo& b) const;
+
+    operator std::string() const;
+
+    size_t hash() const;
+    size_t hash();
+
+private:
+    size_t hash_value;
 };
 
 OPENVINO_API
diff --git a/ngraph/core/include/openvino/core/variant.hpp b/ngraph/core/include/openvino/core/variant.hpp
index 2337f0639901ab..5802fe14f9f649 100644
--- a/ngraph/core/include/openvino/core/variant.hpp
+++ b/ngraph/core/include/openvino/core/variant.hpp
@@ -58,6 +58,14 @@ class VariantImpl : public Variant {
     value_type m_value;
 };
 
+template <>
+class VariantImpl<void> : public Variant {
+public:
+    using value_type = void;
+
+    VariantImpl() = default;
+};
+
 extern template class OPENVINO_API VariantImpl<std::string>;
 extern template class OPENVINO_API VariantImpl<int64_t>;
 extern template class OPENVINO_API VariantImpl<bool>;
diff --git a/ngraph/core/include/openvino/core/version.hpp b/ngraph/core/include/openvino/core/version.hpp
index 97f82366f9705c..368398ba129352 100644
--- a/ngraph/core/include/openvino/core/version.hpp
+++ b/ngraph/core/include/openvino/core/version.hpp
@@ -1,7 +1,6 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-
 #pragma once
 
 #include "openvino/core/core_visibility.hpp"
diff --git a/ngraph/core/include/openvino/op/embeddingbag_offsets_sum.hpp b/ngraph/core/include/openvino/op/embeddingbag_offsets_sum.hpp
index 42f74822bd543f..afe4386a0a5b22 100644
--- a/ngraph/core/include/openvino/op/embeddingbag_offsets_sum.hpp
+++ b/ngraph/core/include/openvino/op/embeddingbag_offsets_sum.hpp
@@ -26,7 +26,7 @@ class OPENVINO_API EmbeddingBagOffsetsSum : public util::EmbeddingBagOffsetsBase
     ///
     /// \param emb_table tensor containing the embedding lookup table of the module of
     /// shape [num_emb, emb_dim1, emb_dim2, ...] and  of type T
-    /// \param tensor of shape [num_indices] and of type T_IND. Required
+    /// \param indices tensor of shape [num_indices] and of type T_IND. Required
     /// \param offsets tensor of shape [batch] and of type T_IND containing the starting
     /// index positions of each "bag" in indices. Required.
     /// \param default_index scalar of type T_IND containing default index in embedding
diff --git a/ngraph/core/include/openvino/op/gather_nd.hpp b/ngraph/core/include/openvino/op/gather_nd.hpp
index 36f23d8970c930..5ec88f331e6017 100644
--- a/ngraph/core/include/openvino/op/gather_nd.hpp
+++ b/ngraph/core/include/openvino/op/gather_nd.hpp
@@ -4,16 +4,15 @@
 
 #pragma once
 
-#include "openvino/op/op.hpp"
+#include "openvino/op/util/gather_nd_base.hpp"
 
 namespace ov {
 namespace op {
 namespace v5 {
 /// \brief GatherND operation
-///
-class OPENVINO_API GatherND : public Op {
+class OPENVINO_API GatherND : public op::util::GatherNDBase {
 public:
-    OPENVINO_OP("GatherND", "opset5", op::Op, 5);
+    OPENVINO_OP("GatherND", "opset5", op::util::GatherNDBase, 5);
     BWDCMP_RTTI_DECLARATION;
     GatherND() = default;
 
@@ -26,16 +25,30 @@ class OPENVINO_API GatherND : public Op {
     GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims = 0);
 
     void validate_and_infer_types() override;
-    bool visit_attributes(AttributeVisitor& visitor) override;
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
+};
+}  // namespace v5
 
-    size_t get_batch_dims() const {
-        return m_batch_dims;
-    }
+namespace v8 {
+/// \brief GatherND operation
+///
+class OPENVINO_API GatherND : public op::util::GatherNDBase {
+public:
+    OPENVINO_OP("GatherND", "opset8", op::util::GatherNDBase);
+    BWDCMP_RTTI_DECLARATION;
+    GatherND() = default;
 
-private:
-    size_t m_batch_dims;
+    /// \brief Constructs a GatherND operation.
+    ///
+    /// \param data Node producing data that are gathered
+    /// \param indices Node producing indices by which the operation gathers elements
+    /// or slices from data
+    /// \param batch_dims Specifies a number of batch dimensions
+    GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims = 0);
+
+    void validate_and_infer_types() override;
+    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 };
-}  // namespace v5
+}  // namespace v8
 }  // namespace op
 }  // namespace ov
diff --git a/ngraph/core/include/openvino/op/matmul.hpp b/ngraph/core/include/openvino/op/matmul.hpp
index c14f2a366adc74..5026ebbc365595 100644
--- a/ngraph/core/include/openvino/op/matmul.hpp
+++ b/ngraph/core/include/openvino/op/matmul.hpp
@@ -50,8 +50,8 @@ class OPENVINO_API MatMul : public Op {
     }
 
 private:
-    bool m_transpose_a;
-    bool m_transpose_b;
+    bool m_transpose_a{false};
+    bool m_transpose_b{false};
 };
 }  // namespace v0
 }  // namespace op
diff --git a/ngraph/core/include/openvino/op/op.hpp b/ngraph/core/include/openvino/op/op.hpp
index c7920537c8a0e8..d1bae6d12fd283 100644
--- a/ngraph/core/include/openvino/op/op.hpp
+++ b/ngraph/core/include/openvino/op/op.hpp
@@ -31,7 +31,8 @@ class OPENVINO_API Op : public Node {
 
 public:
     static const ::ov::Node::type_info_t& get_type_info_static() {
-        static const ::ov::Node::type_info_t info{"Op", 0, "util"};
+        static ::ov::Node::type_info_t info{"Op", 0, "util"};
+        info.hash();
         return info;
     }
     const ::ov::Node::type_info_t& get_type_info() const override {
diff --git a/ngraph/core/include/openvino/op/shuffle_channels.hpp b/ngraph/core/include/openvino/op/shuffle_channels.hpp
index f8318d2bce5020..3a231a9c266fed 100644
--- a/ngraph/core/include/openvino/op/shuffle_channels.hpp
+++ b/ngraph/core/include/openvino/op/shuffle_channels.hpp
@@ -49,8 +49,8 @@ class OPENVINO_API ShuffleChannels : public Op {
 private:
     bool evaluate_shuffle_channels(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
 
-    int64_t m_axis;
-    int64_t m_group;
+    int64_t m_axis{1};
+    int64_t m_group{1};
 };
 }  // namespace v0
 }  // namespace op
diff --git a/ngraph/core/include/openvino/op/slice.hpp b/ngraph/core/include/openvino/op/slice.hpp
index f35cd715982c6b..caa5d365781329 100644
--- a/ngraph/core/include/openvino/op/slice.hpp
+++ b/ngraph/core/include/openvino/op/slice.hpp
@@ -32,6 +32,7 @@ class OPENVINO_API Slice : public Op {
 
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 
+    std::shared_ptr<ngraph::op::v0::Constant> get_default_const_axes(const Output<Node>& start) const;
     PartialShape calculate_output_shape(const std::vector<int64_t>& starts,
                                         const std::vector<int64_t>& stops,
                                         const std::vector<int64_t>& steps,
diff --git a/ngraph/core/include/openvino/op/util/embeddingbag_offsets_base.hpp b/ngraph/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
index 9c86232c5f005c..25a3fc86910909 100644
--- a/ngraph/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
+++ b/ngraph/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
@@ -26,7 +26,7 @@ class OPENVINO_API EmbeddingBagOffsetsBase : public Op {
     ///
     /// \param emb_table tensor containing the embedding lookup table of the module of
     /// shape [num_emb, emb_dim1, emb_dim2, ...] and  of type T
-    /// \param tensor of shape [num_indices] and of type T_IND. Required
+    /// \param indices tensor of shape [num_indices] and of type T_IND. Required
     /// \param offsets tensor of shape [batch] and of type T_IND containing the starting
     /// index positions of each "bag" in indices. Required.
     /// \param per_sample_weigths tensor of the same shape as indices and of type T.
diff --git a/ngraph/core/include/openvino/op/util/gather_nd_base.hpp b/ngraph/core/include/openvino/op/util/gather_nd_base.hpp
new file mode 100644
index 00000000000000..2048ac06af28de
--- /dev/null
+++ b/ngraph/core/include/openvino/op/util/gather_nd_base.hpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/op/op.hpp"
+
+namespace ov {
+namespace op {
+namespace util {
+/// \brief GatherNDBase basic class for GatherND v5 and v8
+class OPENVINO_API GatherNDBase : public Op {
+public:
+    OPENVINO_OP("GatherNDBase", "util");
+    BWDCMP_RTTI_DECLARATION;
+    GatherNDBase() = default;
+
+    /// \brief Constructs a GatherND operation.
+    ///
+    /// \param data Node producing data that are gathered
+    /// \param indices Node producing indices by which the operation gathers elements
+    /// or slices from data
+    /// \param batch_dims Specifies a leading number of dimensions representing the batches
+    GatherNDBase(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims = 0);
+
+    size_t get_batch_dims() const {
+        return m_batch_dims;
+    }
+
+    void validate_inputs_and_infer_shape();
+
+    bool visit_attributes(AttributeVisitor& visitor) override;
+
+protected:
+    size_t m_batch_dims = 0;
+};
+}  // namespace util
+}  // namespace op
+}  // namespace ov
diff --git a/ngraph/core/include/openvino/op/util/precision_sensitive_attribute.hpp b/ngraph/core/include/openvino/op/util/precision_sensitive_attribute.hpp
new file mode 100644
index 00000000000000..eb0f7308d4b0d7
--- /dev/null
+++ b/ngraph/core/include/openvino/op/util/precision_sensitive_attribute.hpp
@@ -0,0 +1,35 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/core/core_visibility.hpp"
+#include "openvino/core/node.hpp"
+#include "openvino/core/variant.hpp"
+
+namespace ov {
+
+void OPENVINO_API mark_as_precision_sensitive(ov::Input<ov::Node> node_input);
+
+void OPENVINO_API unmark_as_precision_sensitive(ov::Input<ov::Node> node_input);
+
+bool OPENVINO_API is_precision_sensitive(const ov::Input<ov::Node>& node_input);
+
+/**
+ * @brief PrecisionSensitive class represents runtime info attribute that marks
+ * input to an operation as a precision sensitive and disables compression to FP16
+ * of the subgraph before this input.
+ */
+class OPENVINO_API PrecisionSensitive : public VariantImpl<void> {
+public:
+    OPENVINO_RTTI("precision_sensitive", "0");
+
+    PrecisionSensitive() = default;
+
+    bool is_copyable() const override {
+        return false;
+    }
+};
+
+}  // namespace ov
diff --git a/ngraph/core/include/openvino/opsets/opset8_tbl.hpp b/ngraph/core/include/openvino/opsets/opset8_tbl.hpp
index 0c686bdab09213..e3e9c0a5f8383e 100644
--- a/ngraph/core/include/openvino/opsets/opset8_tbl.hpp
+++ b/ngraph/core/include/openvino/opsets/opset8_tbl.hpp
@@ -144,7 +144,6 @@ _OPENVINO_OP_REG(SoftPlus, ov::op::v4)
 _OPENVINO_OP_REG(Swish, ov::op::v4)
 
 // New operations added in opset5
-_OPENVINO_OP_REG(GatherND, ov::op::v5)
 _OPENVINO_OP_REG(GRUSequence, ov::op::v5)
 _OPENVINO_OP_REG(HSigmoid, ov::op::v5)
 _OPENVINO_OP_REG(LogSoftmax, ov::op::v5)
@@ -175,6 +174,7 @@ _OPENVINO_OP_REG(Roll, ov::op::v7)
 
 // New operations added in opset8
 _OPENVINO_OP_REG(Gather, ov::op::v8)
+_OPENVINO_OP_REG(GatherND, ov::op::v8)
 _OPENVINO_OP_REG(AdaptiveAvgPool, ov::op::v8)
 _OPENVINO_OP_REG(AdaptiveMaxPool, ov::op::v8)
 _OPENVINO_OP_REG(DeformableConvolution, ov::op::v8)
diff --git a/ngraph/core/include/openvino/pass/serialize.hpp b/ngraph/core/include/openvino/pass/serialize.hpp
index c964d74c5f21fa..67909afe80efaa 100644
--- a/ngraph/core/include/openvino/pass/serialize.hpp
+++ b/ngraph/core/include/openvino/pass/serialize.hpp
@@ -33,12 +33,14 @@ class OPENVINO_API Serialize : public ov::pass::FunctionPass {
     };
     bool run_on_function(std::shared_ptr<ov::Function> f) override;
 
+    OPENVINO_DEPRECATED("This constructor is deprecated. Please use new extension API")
     Serialize(std::ostream& xmlFile,
               std::ostream& binFile,
               std::map<std::string, ngraph::OpSet> custom_opsets,
               Version version = Version::UNSPECIFIED);
     Serialize(std::ostream& xmlFile, std::ostream& binFile, Version version = Version::UNSPECIFIED);
 
+    OPENVINO_DEPRECATED("This constructor is deprecated. Please use new extension API")
     Serialize(const std::string& xmlPath,
               const std::string& binPath,
               std::map<std::string, ngraph::OpSet> custom_opsets,
@@ -74,10 +76,14 @@ class OPENVINO_API StreamSerialize : public ov::pass::FunctionPass {
 
     bool run_on_function(std::shared_ptr<ov::Function> f) override;
 
+    OPENVINO_DEPRECATED("This constructor is deprecated. Please use new extension API")
     StreamSerialize(std::ostream& stream,
                     std::map<std::string, ngraph::OpSet>&& custom_opsets = {},
                     const std::function<void(std::ostream&)>& custom_data_serializer = {},
                     Serialize::Version version = Serialize::Version::UNSPECIFIED);
+    StreamSerialize(std::ostream& stream,
+                    const std::function<void(std::ostream&)>& custom_data_serializer = {},
+                    Serialize::Version version = Serialize::Version::UNSPECIFIED);
 
 private:
     std::ostream& m_stream;
diff --git a/ngraph/core/reference/CMakeLists.txt b/ngraph/core/reference/CMakeLists.txt
index 99c82bf32bbb93..e7b1d1bb20bb3f 100644
--- a/ngraph/core/reference/CMakeLists.txt
+++ b/ngraph/core/reference/CMakeLists.txt
@@ -15,7 +15,7 @@ set(REF_IMPL_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 source_group("src" FILES ${LIBRARY_SRC})
 source_group("include" FILES ${PUBLIC_HEADERS})
 
-# Create shared library
+# Create static library
 add_library(${TARGET_NAME} STATIC ${LIBRARY_SRC} ${PUBLIC_HEADERS})
 
 if(COMMAND ie_faster_build)
@@ -31,8 +31,13 @@ endif()
 
 target_compile_definitions(${TARGET_NAME} PRIVATE XBYAK_NO_OP_NAMES XBYAK64)
 
-# Defines macro in C++ to load backend plugin
-target_include_directories(${TARGET_NAME} PUBLIC ${REF_IMPL_INCLUDE_DIR} ${NGRAPH_INCLUDE_PATH})
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME} PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
+
+target_include_directories(${TARGET_NAME} PUBLIC
+    $<BUILD_INTERFACE:${REF_IMPL_INCLUDE_DIR}>
+    $<BUILD_INTERFACE:${NGRAPH_INCLUDE_PATH}>)
 
 link_system_libraries(${TARGET_NAME} PRIVATE xbyak)
 
@@ -41,5 +46,7 @@ add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 # Add an alias so that library can be used inside the build tree, e.g. when testing
 add_library(ngraph::reference ALIAS ${TARGET_NAME})
 
+ov_install_static_lib(${TARGET_NAME} ngraph)
+
 # developer package
 openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph::reference)
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/gather_nd.hpp b/ngraph/core/reference/include/ngraph/runtime/reference/gather_nd.hpp
index 40e921e67a297e..b3681adad4c1a3 100644
--- a/ngraph/core/reference/include/ngraph/runtime/reference/gather_nd.hpp
+++ b/ngraph/core/reference/include/ngraph/runtime/reference/gather_nd.hpp
@@ -56,11 +56,6 @@ void gather_nd(const T* const params,
     const Shape batch_shape(begin(params_shape), next(begin(params_shape), batch_dims));
     const auto batch_size = shape_size(batch_shape);
 
-    if (batch_dims && batch_size != out_shape.front()) {
-        throw std::domain_error{"out_shape should have on first dim multiplication of batch number of first"
-                                "dimensions of shape "};
-    }
-
     if (!std::equal(begin(params_shape), next(begin(params_shape), batch_dims), begin(indices_shape))) {
         throw std::domain_error{"dimensions in params and indices have to be equal on batch dimensions"};
     }
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/max_pool.hpp b/ngraph/core/reference/include/ngraph/runtime/reference/max_pool.hpp
index 74c0261112b653..72c1f2230b2ae9 100644
--- a/ngraph/core/reference/include/ngraph/runtime/reference/max_pool.hpp
+++ b/ngraph/core/reference/include/ngraph/runtime/reference/max_pool.hpp
@@ -142,12 +142,7 @@ void validate_max_pool_kernel_params(const size_t dims,
 ///        but at the same time it can represent pixel offsets in the filter itself (dilated or non-dilated)
 template <typename T>
 struct Coord : public std::vector<T> {
-    Coord(const Shape& pads_begin) {
-        std::vector<T>::reserve(pads_begin.size());
-        for (const auto axis_padding : pads_begin) {
-            std::vector<T>::push_back(0 - axis_padding);
-        }
-    }
+    Coord() = default;
 
     Coord(std::initializer_list<T>&& values) : std::vector<T>{std::move(values)} {}
 };
@@ -165,47 +160,15 @@ bool elem_in_padding_area(const Coord<int>& kernel_position,
     return false;
 }
 
-template <typename T>
-Coord<T> next_kernel_position_2D(Coord<T> kernel_position,
-                                 const Shape& kernel,
-                                 const Strides& kernel_strides,
-                                 const Strides& kernel_dilations,
-                                 const Shape& data_shape,
-                                 const Shape& pads_begin,
-                                 const Shape& pads_end) {
-    // move the kernel horizontally one stride to the right
-    kernel_position[1] += kernel_strides[1];
-
-    // if the top-right corner of the kernel is outside of the padding area,
-    // move it back to the left and one stride down
-    if (kernel_position[1] + (kernel[1] - 1) * kernel_dilations[1] >= data_shape[3] + pads_end[1]) {
-        kernel_position[1] = 0 - pads_begin[1];
-        kernel_position[0] += kernel_strides[0];
-    }
-
-    return kernel_position;
-}
-
-template <typename T>
-Coord<T> next_kernel_position_3D(Coord<T> kernel_position,
-                                 const Shape& kernel,
-                                 const Strides& kernel_strides,
-                                 const Strides& kernel_dilations,
-                                 const Shape& data_shape,
-                                 const Shape& pads_begin,
-                                 const Shape& pads_end) {
-    kernel_position[2] += kernel_strides[2];
-
-    if (kernel_position[2] + (kernel[2] - 1) * kernel_dilations[2] >= data_shape[4] + pads_end[2]) {
-        kernel_position[2] = 0 - pads_begin[2];
-        kernel_position[1] += kernel_strides[1];
-        if (kernel_position[1] + (kernel[1] - 1) * kernel_dilations[1] >= data_shape[3] + pads_end[1]) {
-            kernel_position[1] = 0 - pads_begin[1];
-            kernel_position[0] += kernel_strides[0];
-        }
+Coord<int> calculate_kernel_position(const Coord<size_t>& out_elem_coord,
+                                     const Strides& kernel_strides,
+                                     const Shape& pads_begin) {
+    Coord<int> top_left_corner;
+    top_left_corner.reserve(out_elem_coord.size());
+    for (size_t i = 0u; i < out_elem_coord.size(); ++i) {
+        top_left_corner.emplace_back(out_elem_coord[i] * kernel_strides[i] - pads_begin[i]);
     }
-
-    return kernel_position;
+    return top_left_corner;
 }
 
 namespace kernel {
@@ -255,43 +218,44 @@ void max_pool_2d(const Values_t* data,
                  const size_t indices_offset) {
     validate_max_pool_kernel_params(2, kernel, kernel_strides, kernel_dilations, pads_begin, pads_end);
 
-    Coord<int> kernel_position{pads_begin};
+    // helper constants(axes) denoting dimensions in the input data shape and kernel shape
+    constexpr size_t data_H = 2, data_W = 3;
+    constexpr size_t kernel_H = 0, kernel_W = 1;
 
     // select max elem and its index for each "placeholder" in the out buffer (pointed to by out_idx)
-    for (size_t out_idx = 0; out_idx < out_shape[2] * out_shape[3]; ++out_idx) {
-        Values_t max_elem = std::numeric_limits<Values_t>::lowest();
-        Indices_t max_elem_idx = Indices_t{0};
+    size_t out_idx = 0u;
+    for (size_t out_row = 0u; out_row < out_shape[data_H]; ++out_row) {
+        for (size_t out_col = 0u; out_col < out_shape[data_W]; ++out_col) {
+            Values_t max_elem = std::numeric_limits<Values_t>::lowest();
+            Indices_t max_elem_idx = Indices_t{0};
+
+            const auto kernel_position = calculate_kernel_position({out_row, out_col}, kernel_strides, pads_begin);
+            // find the max element in the area covered by a current position of the kernel
+            for (size_t kernel_row = 0; kernel_row < kernel[kernel_H]; ++kernel_row) {
+                for (size_t kernel_col = 0; kernel_col < kernel[kernel_W]; ++kernel_col) {
+                    // offset from the top-left corner of the kernel for a given row and col
+                    const Coord<size_t> kernel_offset{kernel_row * kernel_dilations[kernel_H],
+                                                      kernel_col * kernel_dilations[kernel_W]};
 
-        // find the max element in the area covered by a current position of the kernel
-        for (size_t kernel_row = 0; kernel_row < kernel[0]; ++kernel_row) {
-            for (size_t kernel_col = 0; kernel_col < kernel[1]; ++kernel_col) {
-                // offset from the top-left corner of the kernel for a given row and col
-                const Coord<size_t> kernel_offset{kernel_row * kernel_dilations[0], kernel_col * kernel_dilations[1]};
-
-                // ignore the elements in the padding area
-                if (!elem_in_padding_area(kernel_position, kernel_offset, data_shape)) {
-                    // index of the flattened tensor element under the current row & column of the kernel
-                    const size_t data_elem_index =
-                        data_shape[2] * (kernel_offset[0] + kernel_position[0]) + kernel_offset[1] + kernel_position[1];
-
-                    if (data[data_elem_index] > max_elem) {
-                        max_elem = data[data_elem_index];
-                        max_elem_idx = data_elem_index;
+                    // ignore the elements in the padding area
+                    if (!elem_in_padding_area(kernel_position, kernel_offset, data_shape)) {
+                        // index of the flattened tensor element under the current row & column of the kernel
+                        const size_t data_elem_index =
+                            data_shape[data_W] * (kernel_offset[kernel_H] + kernel_position[kernel_H]) +
+                            kernel_offset[kernel_W] + kernel_position[kernel_W];
+
+                        if (data[data_elem_index] > max_elem) {
+                            max_elem = data[data_elem_index];
+                            max_elem_idx = data_elem_index;
+                        }
                     }
                 }
             }
-        }
-
-        values[out_idx] = max_elem;
-        indices[out_idx] = max_elem_idx + indices_offset;
 
-        kernel_position = next_kernel_position_2D(kernel_position,
-                                                  kernel,
-                                                  kernel_strides,
-                                                  kernel_dilations,
-                                                  data_shape,
-                                                  pads_begin,
-                                                  pads_end);
+            values[out_idx] = max_elem;
+            indices[out_idx] = max_elem_idx + indices_offset;
+            ++out_idx;
+        }
     }
 }
 
@@ -309,49 +273,51 @@ void max_pool_3d(const Values_t* data,
                  const size_t indices_offset) {
     validate_max_pool_kernel_params(3, kernel, kernel_strides, kernel_dilations, pads_begin, pads_end);
 
-    Coord<int> kernel_position{pads_begin};
-
-    const size_t out_elems = shape_size(std::begin(out_shape) + 2, std::end(out_shape));
+    // helper constants(axes) denoting dimensions in the input data shape and kernel shape
+    constexpr size_t data_D = 2, data_H = 3, data_W = 4;
+    constexpr size_t kernel_D = 0, kernel_H = 1, kernel_W = 2;
 
     // select max elem and its index for each "placeholder" in the out buffer (pointed to by out_idx)
-    for (size_t out_idx = 0; out_idx < out_elems; ++out_idx) {
-        Values_t max_elem = std::numeric_limits<Values_t>::lowest();
-        Indices_t max_elem_idx = Indices_t{0};
-
-        for (size_t kernel_channel = 0; kernel_channel < kernel[0]; ++kernel_channel) {
-            for (size_t kernel_row = 0; kernel_row < kernel[1]; ++kernel_row) {
-                for (size_t kernel_col = 0; kernel_col < kernel[2]; ++kernel_col) {
-                    // offset from the top-left corner of the kernel for a given row and col
-                    const Coord<size_t> kernel_offset{kernel_channel * kernel_dilations[0],
-                                                      kernel_row * kernel_dilations[1],
-                                                      kernel_col * kernel_dilations[2]};
-
-                    // ignore the elements in the padding area
-                    if (!elem_in_padding_area(kernel_position, kernel_offset, data_shape)) {
-                        // index of the flattened tensor element under the current row & column of the kernel
-                        const size_t data_elem_index =
-                            data_shape[2] * data_shape[3] * (kernel_offset[0] + kernel_position[0]) +
-                            data_shape[3] * (kernel_offset[1] + kernel_position[1]) + kernel_offset[2] +
-                            kernel_position[2];
-
-                        if (data[data_elem_index] > max_elem) {
-                            max_elem = data[data_elem_index];
-                            max_elem_idx = data_elem_index;
+    size_t out_idx = 0u;
+    for (size_t out_channel = 0u; out_channel < out_shape[data_D]; ++out_channel) {
+        for (size_t out_row = 0u; out_row < out_shape[data_H]; ++out_row) {
+            for (size_t out_col = 0u; out_col < out_shape[data_W]; ++out_col) {
+                Values_t max_elem = std::numeric_limits<Values_t>::lowest();
+                Indices_t max_elem_idx = Indices_t{0};
+
+                const auto kernel_position =
+                    calculate_kernel_position({out_channel, out_row, out_col}, kernel_strides, pads_begin);
+
+                for (size_t kernel_channel = 0; kernel_channel < kernel[kernel_D]; ++kernel_channel) {
+                    for (size_t kernel_row = 0; kernel_row < kernel[kernel_H]; ++kernel_row) {
+                        for (size_t kernel_col = 0; kernel_col < kernel[kernel_W]; ++kernel_col) {
+                            // offset from the top-left corner of the kernel for a given row and col
+                            const Coord<size_t> kernel_offset{kernel_channel * kernel_dilations[kernel_D],
+                                                              kernel_row * kernel_dilations[kernel_H],
+                                                              kernel_col * kernel_dilations[kernel_W]};
+
+                            // ignore the elements in the padding area
+                            if (!elem_in_padding_area(kernel_position, kernel_offset, data_shape)) {
+                                // index of the flattened tensor element under the current row & column of the kernel
+                                const size_t data_elem_index =
+                                    data_shape[data_H] * data_shape[data_W] *
+                                        (kernel_offset[kernel_D] + kernel_position[kernel_D]) +
+                                    data_shape[data_W] * (kernel_offset[kernel_H] + kernel_position[kernel_H]) +
+                                    kernel_offset[kernel_W] + kernel_position[kernel_W];
+
+                                if (data[data_elem_index] > max_elem) {
+                                    max_elem = data[data_elem_index];
+                                    max_elem_idx = data_elem_index;
+                                }
+                            }
                         }
                     }
                 }
+                values[out_idx] = max_elem;
+                indices[out_idx] = max_elem_idx + indices_offset;
+                ++out_idx;
             }
         }
-        values[out_idx] = max_elem;
-        indices[out_idx] = max_elem_idx + indices_offset;
-
-        kernel_position = next_kernel_position_3D(kernel_position,
-                                                  kernel,
-                                                  kernel_strides,
-                                                  kernel_dilations,
-                                                  data_shape,
-                                                  pads_begin,
-                                                  pads_end);
     }
 }
 }  // namespace kernel
diff --git a/ngraph/core/reference/src/runtime/reference/pad.cpp b/ngraph/core/reference/src/runtime/reference/pad.cpp
index cb42847a8e6093..b261c0e35fd20c 100644
--- a/ngraph/core/reference/src/runtime/reference/pad.cpp
+++ b/ngraph/core/reference/src/runtime/reference/pad.cpp
@@ -95,7 +95,7 @@ struct ConstPad : PadBase {
         for (size_t i = 0; i != coord.size(); ++i) {
             const auto sc = static_cast<std::ptrdiff_t>(out_coord[i]);
 
-            const auto cc = sc - padding_begin[i];
+            const auto cc = sc - padding_begin.at(i);
             if (0 <= cc && cc < static_cast<std::ptrdiff_t>(data_shape[i])) {
                 coord[i] = cc;
             } else {
@@ -115,7 +115,7 @@ struct EdgePad : PadBase {
         for (size_t i = 0; i != coord.size(); ++i) {
             const auto sc = static_cast<std::ptrdiff_t>(out_coord[i]);
 
-            const auto cc = sc - padding_begin[i];
+            const auto cc = sc - padding_begin.at(i);
             coord[i] = clamp<std::ptrdiff_t>(cc, 0, data_shape[i] - 1);
         }
         return std::addressof(coord);
@@ -142,7 +142,7 @@ struct SymmetricAndReflectPad : PadBase {
             const auto shape_dim = static_cast<std::ptrdiff_t>(data_shape[i]);
             const auto sc = static_cast<std::ptrdiff_t>(out_coord[i]);
 
-            const auto cc = sc - padding_begin[i];
+            const auto cc = sc - padding_begin.at(i);
             const auto rollfront_cc = cc >= 0 ? cc : -cc - axis_correction;
             const auto rollback_cc = shape_dim - (rollfront_cc + 2 - shape_dim) + axis_correction;
             coord[i] = rollfront_cc < shape_dim ? rollfront_cc : rollback_cc;
@@ -154,9 +154,9 @@ struct SymmetricAndReflectPad : PadBase {
     void check_inputs() const override {
         for (size_t i = 0; i != padding_begin.size(); ++i) {
             const auto axis_size = static_cast<std::ptrdiff_t>(data_shape[i]);
-            NGRAPH_CHECK(padding_begin[i] - axis_correction < axis_size,
+            NGRAPH_CHECK(padding_begin.at(i) - axis_correction < axis_size,
                          "padding below should be less than data shape");
-            NGRAPH_CHECK(padding_end[i] - axis_correction < axis_size, "padding  should be less than data shape");
+            NGRAPH_CHECK(padding_end.at(i) - axis_correction < axis_size, "padding  should be less than data shape");
         }
     }
 
diff --git a/ngraph/core/shape_inference/CMakeLists.txt b/ngraph/core/shape_inference/CMakeLists.txt
index 23897095fce754..f769ae59d3ef76 100644
--- a/ngraph/core/shape_inference/CMakeLists.txt
+++ b/ngraph/core/shape_inference/CMakeLists.txt
@@ -15,11 +15,18 @@ set(SHAPE_INFER_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 source_group("src" FILES ${LIBRARY_SRC})
 source_group("include" FILES ${PUBLIC_HEADERS})
 
-# Create shared library
+# Create static library
 add_library(${TARGET_NAME} STATIC ${LIBRARY_SRC} ${PUBLIC_HEADERS})
 
-# Defines macro in C++ to load backend plugin
-target_include_directories(${TARGET_NAME} PUBLIC ${SHAPE_INFER_INCLUDE_DIR} ${NGRAPH_INCLUDE_PATH})
+target_include_directories(${TARGET_NAME} PUBLIC
+    $<BUILD_INTERFACE:${SHAPE_INFER_INCLUDE_DIR}>
+    $<BUILD_INTERFACE:${NGRAPH_INCLUDE_PATH}>)
+
+if(NOT BUILD_SHARED_LIBS)
+    target_compile_definitions(${TARGET_NAME} PUBLIC OPENVINO_STATIC_LIBRARY)
+endif()
+
+ov_install_static_lib(${TARGET_NAME} ngraph)
 
 # developer package
 openvino_developer_export_targets(COMPONENT ngraph TARGETS ${TARGET_NAME})
diff --git a/ngraph/core/src/descriptor/tensor.cpp b/ngraph/core/src/descriptor/tensor.cpp
index 2cf21ef978df65..c798bfd00c72f2 100644
--- a/ngraph/core/src/descriptor/tensor.cpp
+++ b/ngraph/core/src/descriptor/tensor.cpp
@@ -23,6 +23,7 @@ ov::descriptor::Tensor::Tensor(const element::Type& element_type,
       m_partial_shape(pshape),
       m_shape_changed(true) {}
 
+OPENVINO_SUPPRESS_DEPRECATED_START
 void ov::descriptor::Tensor::set_tensor_type(const element::Type& element_type, const PartialShape& pshape) {
     set_element_type(element_type);
     set_partial_shape(pshape);
@@ -36,6 +37,7 @@ void ov::descriptor::Tensor::set_partial_shape(const PartialShape& partial_shape
     m_partial_shape = partial_shape;
     m_shape_changed = true;
 }
+OPENVINO_SUPPRESS_DEPRECATED_END
 
 void ov::descriptor::Tensor::invalidate_values() {
     m_upper_value = nullptr;
diff --git a/ngraph/core/src/dimension.cpp b/ngraph/core/src/dimension.cpp
index 84b156ae7b4da3..1e04c40dbdd70c 100644
--- a/ngraph/core/src/dimension.cpp
+++ b/ngraph/core/src/dimension.cpp
@@ -105,3 +105,16 @@ Dimension::value_type Dimension::get_max_length() const {
 Dimension::value_type Dimension::get_min_length() const {
     return dimension_length(m_dimension.get_min_val());
 }
+
+const int64_t& ov::AttributeAdapter<ov::Dimension>::get() {
+    if (!m_buffer_valid) {
+        m_buffer = m_ref.is_dynamic() ? -1 : m_ref.get_length();
+        m_buffer_valid = true;
+    }
+    return m_buffer;
+}
+
+void ov::AttributeAdapter<ov::Dimension>::set(const int64_t& value) {
+    m_ref = value == -1 ? ov::Dimension::dynamic() : Dimension(value);
+    m_buffer_valid = false;
+}
diff --git a/ngraph/core/src/function.cpp b/ngraph/core/src/function.cpp
index 396a68f04edcaf..1732d0f4352909 100644
--- a/ngraph/core/src/function.cpp
+++ b/ngraph/core/src/function.cpp
@@ -87,6 +87,10 @@ ngraph::ParameterVector auto_detect_parameters(const std::vector<std::shared_ptr
 
 }  // namespace
 
+OPENVINO_SUPPRESS_DEPRECATED_START
+const ov::DiscreteTypeInfo ov::Function::type_info = ov::Function::get_type_info_static();
+OPENVINO_SUPPRESS_DEPRECATED_END
+
 ov::Function::Function(const ResultVector& results, const ngraph::ParameterVector& parameters, const std::string& name)
     : m_name(name),
       m_unique_name("Function_" + to_string(m_next_instance_id.fetch_add(1))),
diff --git a/ngraph/core/src/layout.cpp b/ngraph/core/src/layout.cpp
index ad16f16d7d6bd7..0de221bb89c1ed 100644
--- a/ngraph/core/src/layout.cpp
+++ b/ngraph/core/src/layout.cpp
@@ -346,8 +346,6 @@ std::int64_t width_idx(const Layout& layout) {
 
 }  // namespace layout
 
-constexpr DiscreteTypeInfo AttributeAdapter<ov::Layout>::type_info;
-
 const std::string& AttributeAdapter<ov::Layout>::get() {
     m_dump = m_ref.to_string();
     return m_dump;
@@ -364,4 +362,4 @@ bool LayoutAttribute::visit_attributes(AttributeVisitor& visitor) {
     return true;
 }
 
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/core/src/node.cpp b/ngraph/core/src/node.cpp
index 7f7c3a6cb8bbb9..2786cc6609437e 100644
--- a/ngraph/core/src/node.cpp
+++ b/ngraph/core/src/node.cpp
@@ -222,7 +222,9 @@ void ov::Node::set_input_is_relevant_to_value(size_t i, bool relevant) {
 }
 
 void ov::Node::set_output_type(size_t i, const element::Type& element_type, const PartialShape& pshape) {
+    OPENVINO_SUPPRESS_DEPRECATED_START
     get_output_descriptor(i).get_tensor_ptr()->set_tensor_type(element_type, pshape);
+    OPENVINO_SUPPRESS_DEPRECATED_END
 }
 
 std::string ov::Node::description() const {
diff --git a/ngraph/core/src/op/batch_to_space.cpp b/ngraph/core/src/op/batch_to_space.cpp
index 763a37db8cd5cb..3cee1ba4f42f59 100644
--- a/ngraph/core/src/op/batch_to_space.cpp
+++ b/ngraph/core/src/op/batch_to_space.cpp
@@ -19,6 +19,7 @@
 #include "ngraph/runtime/reference/strided_slice.hpp"
 #include "ngraph/shape.hpp"
 #include "ngraph/slice_plan.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -30,6 +31,9 @@ ngraph::op::v1::BatchToSpace::BatchToSpace(const ngraph::Output<ngraph::Node>& d
                                            const ngraph::Output<ngraph::Node>& crops_begin,
                                            const ngraph::Output<ngraph::Node>& crops_end)
     : Op({data, block_shape, crops_begin, crops_end}) {
+    ov::mark_as_precision_sensitive(input(1));
+    ov::mark_as_precision_sensitive(input(2));
+    ov::mark_as_precision_sensitive(input(3));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/constant.cpp b/ngraph/core/src/op/constant.cpp
index 2688d2296b4c2b..8fd55284055ad2 100644
--- a/ngraph/core/src/op/constant.cpp
+++ b/ngraph/core/src/op/constant.cpp
@@ -469,6 +469,7 @@ template <typename T>
 static bool test_bitwise_identical(const T* data, const size_t size) {
     bool data_is_constant = true;
     if (size > 0) {
+        OPENVINO_ASSERT(data != nullptr);
         const T compare = data[0];
         for (size_t i = 1; i < size; i++) {
             if (data[i] != compare) {
diff --git a/ngraph/core/src/op/convolution.cpp b/ngraph/core/src/op/convolution.cpp
index d4fdc345ccf4d3..a66c88ee81fdff 100644
--- a/ngraph/core/src/op/convolution.cpp
+++ b/ngraph/core/src/op/convolution.cpp
@@ -12,6 +12,7 @@
 #include "ngraph/op/reshape.hpp"
 #include "ngraph/util.hpp"
 #include "ngraph/validation_util.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -117,6 +118,7 @@ op::v1::ConvolutionBackpropData::ConvolutionBackpropData(const Output<Node>& dat
       m_pads_end(pads_end),
       m_auto_pad(auto_pad),
       m_output_padding(output_padding) {
+    ov::mark_as_precision_sensitive(input(2));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/gather_nd.cpp b/ngraph/core/src/op/gather_nd.cpp
index d4c54e5066e3d9..ceac01e68c8e30 100644
--- a/ngraph/core/src/op/gather_nd.cpp
+++ b/ngraph/core/src/op/gather_nd.cpp
@@ -15,110 +15,67 @@ using namespace ngraph;
 BWDCMP_RTTI_DEFINITION(op::v5::GatherND);
 
 op::v5::GatherND::GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims)
-    : Op({data, indices}),
-      m_batch_dims(batch_dims) {
+    : GatherNDBase(data, indices, batch_dims) {
     constructor_validate_and_infer_types();
 }
 
 void op::v5::GatherND::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v5_GatherND_validate_and_infer_types);
-    // check types of input tensors
-    const auto& data_type = get_input_element_type(0);
-    const auto& indices_type = get_input_element_type(1);
+    validate_inputs_and_infer_shape();
 
-    NODE_VALIDATION_CHECK(this,
-                          indices_type.is_integral_number(),
-                          "The indices type is expected to be an integer type. Got: ",
-                          indices_type);
+    // If we have m_batch_dims > 1 we need to fuse batch dimensions of output
+    if (m_batch_dims > 1) {
+        const auto& output_pshape = get_output_partial_shape(0);
+        const auto& data_type = get_input_element_type(0);
 
-    // check ranks of input tensors
-    const auto& data_pshape = get_input_partial_shape(0);
-    const auto& indices_pshape = get_input_partial_shape(1);
-
-    if (data_pshape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this, data_pshape.rank().get_length() > 0, "Data rank must be at least 1.");
-
-        NODE_VALIDATION_CHECK(this,
-                              data_pshape.rank().get_length() > static_cast<int64_t>(m_batch_dims),
-                              "Number of batch dimensions must not exceed a rank of data.");
-    }
-
-    if (indices_pshape.rank().is_static()) {
-        NODE_VALIDATION_CHECK(this, indices_pshape.rank().get_length() > 0, "Indices rank must be at least 1.");
-
-        NODE_VALIDATION_CHECK(this,
-                              indices_pshape.rank().get_length() > static_cast<int64_t>(m_batch_dims),
-                              "Number of batch dimensions must not exceed a rank of indices.");
-    }
-
-    if (data_pshape.rank().is_static() && indices_pshape.rank().is_static()) {
-        // check that batch dimensions of data and indices are the same
-        for (size_t batch_dim = 0; batch_dim < m_batch_dims; batch_dim++) {
-            if (data_pshape[batch_dim].is_static() && indices_pshape[batch_dim].is_static()) {
-                NODE_VALIDATION_CHECK(this,
-                                      data_pshape[batch_dim].get_length() == indices_pshape[batch_dim].get_length(),
-                                      "Batch dimensions of data and indices must be the same.");
-            }
-        }
-
-        if (indices_pshape[indices_pshape.rank().get_length() - 1].is_static()) {
-            NODE_VALIDATION_CHECK(
-                this,
-                static_cast<int64_t>(indices_pshape[indices_pshape.rank().get_length() - 1].get_length() +
-                                     m_batch_dims) <= data_pshape.rank().get_length(),
-                "Length of a tuple with indices must not exceed a rank of data tensor "
-                "excluding "
-                "batch dimensions.");
-        }
-    }
-
-    // set output shape
-    set_output_size(1);
-    if (data_pshape.rank().is_static() && indices_pshape.rank().is_static() &&
-        indices_pshape[indices_pshape.rank().get_length() - 1].is_static()) {
-        auto indices_tuple_length = indices_pshape[indices_pshape.rank().get_length() - 1].get_length();
-        int64_t slice_length = data_pshape.rank().get_length() - indices_tuple_length - m_batch_dims;
-        int64_t output_indices_length = indices_pshape.rank().get_length() - m_batch_dims - 1;
-        auto output_rank = output_indices_length + slice_length;
-        size_t delta_output_rank = 0;
-        if (m_batch_dims > 0) {
-            delta_output_rank = 1;
-        }
-        std::vector<Dimension> output_shape(output_rank + delta_output_rank);
-        if (m_batch_dims > 0) {
+        if (output_pshape.rank().is_static()) {
+            const auto& out_size = output_pshape.size();
+            std::vector<Dimension> output_shape(out_size - m_batch_dims + 1);
             output_shape[0] = 1;
             for (size_t dim = 0; dim < m_batch_dims; dim++) {
-                if (data_pshape[dim].is_static()) {
-                    output_shape[0] *= data_pshape[dim].get_length();
-                } else if (indices_pshape[dim].is_static()) {
-                    output_shape[0] *= indices_pshape[dim].get_length();
+                if (output_pshape[dim].is_static()) {
+                    output_shape[0] *= output_pshape[dim].get_length();
                 } else {
                     output_shape[0] = Dimension::dynamic();
                     break;
                 }
             }
+            size_t ind = 1;
+            for (size_t dim = m_batch_dims; dim < out_size; dim++) {
+                if (output_pshape[dim].is_static()) {
+                    output_shape[ind] = output_pshape[dim].get_length();
+                } else {
+                    output_shape[ind] = Dimension::dynamic();
+                }
+                ind++;
+            }
+
+            set_output_type(0, data_type, ov::PartialShape(output_shape));
         }
-        for (int64_t dim = 0; dim < output_indices_length; dim++) {
-            output_shape[dim + delta_output_rank] = indices_pshape[dim + m_batch_dims];
-        }
-        for (int64_t dim = 0; dim < slice_length; dim++) {
-            output_shape[output_indices_length + dim + delta_output_rank] =
-                data_pshape[m_batch_dims + indices_tuple_length + dim];
-        }
-        set_output_type(0, data_type, ov::PartialShape(output_shape));
-    } else {
-        set_output_type(0, data_type, ov::PartialShape::dynamic());
     }
 }
 
-bool op::v5::GatherND::visit_attributes(AttributeVisitor& visitor) {
-    NGRAPH_OP_SCOPE(v5_GatherND_visit_attributes);
-    visitor.on_attribute("batch_dims", m_batch_dims);
-    return true;
-}
-
 shared_ptr<Node> op::v5::GatherND::clone_with_new_inputs(const OutputVector& new_args) const {
     NGRAPH_OP_SCOPE(v5_GatherND_clone_with_new_inputs);
     check_new_args_count(this, new_args);
     return make_shared<op::v5::GatherND>(new_args.at(0), new_args.at(1), m_batch_dims);
 }
+
+// ------------------------------ V8 ------------------------------
+BWDCMP_RTTI_DEFINITION(op::v8::GatherND);
+
+op::v8::GatherND::GatherND(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims)
+    : GatherNDBase(data, indices, batch_dims) {
+    constructor_validate_and_infer_types();
+}
+
+void op::v8::GatherND::validate_and_infer_types() {
+    NGRAPH_OP_SCOPE(v8_GatherND_validate_and_infer_types);
+    validate_inputs_and_infer_shape();
+}
+
+shared_ptr<Node> op::v8::GatherND::clone_with_new_inputs(const OutputVector& new_args) const {
+    NGRAPH_OP_SCOPE(v8_GatherND_clone_with_new_inputs);
+    check_new_args_count(this, new_args);
+    return make_shared<op::v8::GatherND>(new_args.at(0), new_args.at(1), m_batch_dims);
+}
diff --git a/ngraph/core/src/op/group_conv.cpp b/ngraph/core/src/op/group_conv.cpp
index 850d8f0038d979..625110cabef0fa 100644
--- a/ngraph/core/src/op/group_conv.cpp
+++ b/ngraph/core/src/op/group_conv.cpp
@@ -14,6 +14,7 @@
 #include "ngraph/op/convolution.hpp"
 #include "ngraph/op/reshape.hpp"
 #include "ngraph/validation_util.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -277,6 +278,7 @@ op::v1::GroupConvolutionBackpropData::GroupConvolutionBackpropData(const Output<
       m_pads_end(pads_end),
       m_auto_pad(auto_pad),
       m_output_padding(output_padding) {
+    ov::mark_as_precision_sensitive(input(2));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/interpolate.cpp b/ngraph/core/src/op/interpolate.cpp
index d244f265b426c1..2aed5f92af7e9d 100644
--- a/ngraph/core/src/op/interpolate.cpp
+++ b/ngraph/core/src/op/interpolate.cpp
@@ -13,6 +13,7 @@
 #include "itt.hpp"
 #include "ngraph/op/constant.hpp"
 #include "ngraph/runtime/reference/interpolate.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -22,6 +23,7 @@ BWDCMP_RTTI_DEFINITION(op::v0::Interpolate);
 op::v0::Interpolate::Interpolate(const Output<Node>& image, const Output<Node>& output_shape, const Attributes& attrs)
     : Op({image, output_shape}),
       m_attrs(attrs) {
+    ov::mark_as_precision_sensitive(input(1));
     constructor_validate_and_infer_types();
 }
 
@@ -99,6 +101,8 @@ op::v4::Interpolate::Interpolate(const Output<Node>& image,
                                  const op::v4::Interpolate::InterpolateAttrs& attrs)
     : Op({image, output_shape, scales, axes}),
       m_attrs(attrs) {
+    ov::mark_as_precision_sensitive(input(1));
+    ov::mark_as_precision_sensitive(input(2));
     constructor_validate_and_infer_types();
 }
 
@@ -294,9 +298,20 @@ std::vector<int64_t> get_axes_vector(const HostTensorVector& args) {
     std::vector<int64_t> axes;
 
     if (num_of_inputs == max_num_of_ports) {
-        int64_t* axes_data_ptr = args[axes_port]->get_data_ptr<int64_t>();
+        auto axes_arg = args[axes_port];
         size_t num_of_axes = args[axes_port]->get_shape()[0];
-        axes.insert(axes.end(), axes_data_ptr, axes_data_ptr + num_of_axes);
+        axes.reserve(num_of_axes);
+
+        if (axes_arg->get_element_type() == ngraph::element::i64) {
+            int64_t* axes_ptr = axes_arg->get_data_ptr<int64_t>();
+            axes.insert(axes.end(), axes_ptr, axes_ptr + num_of_axes);
+        } else if (axes_arg->get_element_type() == ngraph::element::i32) {
+            int32_t* axes_ptr = axes_arg->get_data_ptr<int32_t>();
+            for (size_t i = 0; i < num_of_axes; ++i)
+                axes.push_back(axes_ptr[i]);
+        } else {
+            OPENVINO_ASSERT(false, "Failed to process ", axes_arg->get_element_type());
+        }
     } else {
         for (size_t i = 0; i < input_rank; ++i) {
             axes.push_back(i);
@@ -308,9 +323,19 @@ std::vector<int64_t> get_axes_vector(const HostTensorVector& args) {
 
 std::vector<int64_t> get_target_shape_vector(const HostTensorVector& args, size_t num_of_axes) {
     std::vector<int64_t> target_shape;
-
-    int64_t* target_shape_ptr = args[target_shape_port]->get_data_ptr<int64_t>();
-    target_shape.insert(target_shape.end(), target_shape_ptr, target_shape_ptr + num_of_axes);
+    target_shape.reserve(num_of_axes);
+
+    auto target_shape_arg = args[target_shape_port];
+    if (target_shape_arg->get_element_type() == ngraph::element::i64) {
+        int64_t* target_shape_ptr = target_shape_arg->get_data_ptr<int64_t>();
+        target_shape.insert(target_shape.end(), target_shape_ptr, target_shape_ptr + num_of_axes);
+    } else if (target_shape_arg->get_element_type() == ngraph::element::i32) {
+        int32_t* target_shape_ptr = target_shape_arg->get_data_ptr<int32_t>();
+        for (size_t i = 0; i < num_of_axes; ++i)
+            target_shape.push_back(target_shape_ptr[i]);
+    } else {
+        OPENVINO_ASSERT(false, "Failed to process ", target_shape_arg->get_element_type());
+    }
 
     return target_shape;
 }
diff --git a/ngraph/core/src/op/lstm_sequence.cpp b/ngraph/core/src/op/lstm_sequence.cpp
index c5a60507aa91c7..579f5c02f6a232 100644
--- a/ngraph/core/src/op/lstm_sequence.cpp
+++ b/ngraph/core/src/op/lstm_sequence.cpp
@@ -538,6 +538,23 @@ void op::v5::LSTMSequence::validate_and_infer_types() {
                               Dimension::merge(merged_num_directions, merged_num_directions, b_pshape[0]),
                           "Parameter num_directions not matched in LSTMSequence.");
 
+    auto check_direction_valid = [](const ov::PartialShape& pshape, size_t index) -> bool {
+        if (pshape[index].is_static())
+            return static_cast<direction>(pshape[index].get_length()) == direction::FORWARD ||
+                   static_cast<direction>(pshape[index].get_length()) == direction::REVERSE ||
+                   static_cast<direction>(pshape[index].get_length()) == direction::BIDIRECTIONAL;
+        return true;
+    };
+
+    NODE_VALIDATION_CHECK(this,
+                          check_direction_valid(ht_pshape, 1),
+                          "Parameter direction must be Forward or Reverse or Bidirectional.");
+
+    NODE_VALIDATION_CHECK(this,
+                          m_direction == direction::FORWARD || m_direction == direction::REVERSE ||
+                              m_direction == direction::BIDIRECTIONAL,
+                          "Parameter direction must be Forward or Reverse or Bidirectional.");
+
     // Validate hidden_size value for W, R, B inputs
     if (merged_hidden_size.is_static()) {
         if (w_pshape[1].is_static()) {
diff --git a/ngraph/core/src/op/max_pool.cpp b/ngraph/core/src/op/max_pool.cpp
index afb78c6fe3bd3c..ed9775c1d0c929 100644
--- a/ngraph/core/src/op/max_pool.cpp
+++ b/ngraph/core/src/op/max_pool.cpp
@@ -220,8 +220,10 @@ bool evaluate_maxpool(const HostTensorPtr& data,
     switch (indices->get_element_type()) {
     case element::Type_t::i32: {
         switch (data->get_element_type()) {
+            EVAL_MAX_POOL_8(i8, i32);
             EVAL_MAX_POOL_8(i32, i32);
             EVAL_MAX_POOL_8(i64, i32);
+            EVAL_MAX_POOL_8(u8, i32);
             EVAL_MAX_POOL_8(u32, i32);
             EVAL_MAX_POOL_8(u64, i32);
             EVAL_MAX_POOL_8(f16, i32);
@@ -233,8 +235,10 @@ bool evaluate_maxpool(const HostTensorPtr& data,
     } break;
     case element::Type_t::i64: {
         switch (data->get_element_type()) {
+            EVAL_MAX_POOL_8(i8, i64);
             EVAL_MAX_POOL_8(i32, i64);
             EVAL_MAX_POOL_8(i64, i64);
+            EVAL_MAX_POOL_8(u8, i64);
             EVAL_MAX_POOL_8(u32, i64);
             EVAL_MAX_POOL_8(u64, i64);
             EVAL_MAX_POOL_8(f16, i64);
@@ -319,8 +323,10 @@ shared_ptr<Node> op::v8::MaxPool::clone_with_new_inputs(const OutputVector& new_
 bool op::v8::MaxPool::has_evaluate() const {
     NGRAPH_OP_SCOPE(v8_MaxPool_has_evaluate);
     switch (get_input_element_type(0)) {
+    case ngraph::element::i8:
     case ngraph::element::i32:
     case ngraph::element::i64:
+    case ngraph::element::u8:
     case ngraph::element::u32:
     case ngraph::element::u64:
     case ngraph::element::f16:
diff --git a/ngraph/core/src/op/one_hot.cpp b/ngraph/core/src/op/one_hot.cpp
index 64f42cfef6feb7..e256070dbddc83 100644
--- a/ngraph/core/src/op/one_hot.cpp
+++ b/ngraph/core/src/op/one_hot.cpp
@@ -9,6 +9,7 @@
 #include "ngraph/op/util/op_types.hpp"
 #include "ngraph/runtime/reference/one_hot.hpp"
 #include "ngraph/validation_util.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -22,6 +23,7 @@ op::v1::OneHot::OneHot(const Output<Node>& indices,
                        int64_t axis)
     : Op({indices, depth, on_value, off_value}),
       m_axis(axis) {
+    ov::mark_as_precision_sensitive(input(1));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/pad.cpp b/ngraph/core/src/op/pad.cpp
index e06e83e17e2fdd..a033f74587e0bb 100644
--- a/ngraph/core/src/op/pad.cpp
+++ b/ngraph/core/src/op/pad.cpp
@@ -13,6 +13,7 @@
 #include "ngraph/op/constant.hpp"
 #include "ngraph/op/util/op_types.hpp"
 #include "ngraph/runtime/reference/pad.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -26,6 +27,8 @@ op::v1::Pad::Pad(const Output<Node>& arg,
                  PadMode pad_mode)
     : Op({arg, pads_begin, pads_end, arg_pad_value}),
       m_pad_mode{pad_mode} {
+    ov::mark_as_precision_sensitive(input(1));
+    ov::mark_as_precision_sensitive(input(2));
     constructor_validate_and_infer_types();
 }
 
@@ -35,6 +38,8 @@ op::v1::Pad::Pad(const Output<Node>& arg,
                  PadMode pad_mode)
     : Op({arg, pads_begin, pads_end, op::v0::Constant::create(arg.get_element_type(), ov::Shape{}, {0})}),
       m_pad_mode{pad_mode} {
+    ov::mark_as_precision_sensitive(input(1));
+    ov::mark_as_precision_sensitive(input(2));
     constructor_validate_and_infer_types();
 }
 
@@ -154,6 +159,18 @@ void op::v1::Pad::validate_and_infer_types() {
                                           "of at least 2 at each "
                                           "spatial axis.");
                 }
+                NODE_VALIDATION_CHECK(
+                    this,
+                    m_pad_mode != op::PadMode::REFLECT || (pads_begin_coord[i] < arg_shape[i].get_length() &&
+                                                           pads_end_coord[i] < arg_shape[i].get_length()),
+                    "REFLECT padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
+                    "must be not greater than 'data_shape[D] - 1'.");
+                NODE_VALIDATION_CHECK(
+                    this,
+                    m_pad_mode != op::PadMode::SYMMETRIC || (pads_begin_coord[i] <= arg_shape[i].get_length() &&
+                                                             pads_end_coord[i] <= arg_shape[i].get_length()),
+                    "SYMMETRIC padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
+                    "must be not greater than 'data_shape[D]'.");
             }
         }
         set_output_type(0, get_input_element_type(0), result_dims);
diff --git a/ngraph/core/src/op/reshape.cpp b/ngraph/core/src/op/reshape.cpp
index e13718a812ac5e..ed2c6e5363ecc4 100644
--- a/ngraph/core/src/op/reshape.cpp
+++ b/ngraph/core/src/op/reshape.cpp
@@ -11,6 +11,7 @@
 #include "ngraph/op/constant.hpp"
 #include "ngraph/runtime/opt_kernel/reshape.hpp"
 #include "ngraph/runtime/reference/reshape.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -44,6 +45,7 @@ BWDCMP_RTTI_DEFINITION(op::v1::Reshape);
 op::v1::Reshape::Reshape(const Output<Node>& arg, const Output<Node>& shape_pattern, bool zero_flag)
     : Op({arg, shape_pattern}),
       m_special_zero(zero_flag) {
+    ov::mark_as_precision_sensitive(input(1));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/slice.cpp b/ngraph/core/src/op/slice.cpp
index 0c4c9de122f79e..0488b1acee2bda 100644
--- a/ngraph/core/src/op/slice.cpp
+++ b/ngraph/core/src/op/slice.cpp
@@ -10,6 +10,7 @@
 #include "ngraph/attribute_visitor.hpp"
 #include "ngraph/graph_util.hpp"
 #include "ngraph/op/constant.hpp"
+#include "ngraph/runtime/reference/slice.hpp"
 #include "ngraph/validation_util.hpp"
 
 using namespace std;
@@ -34,19 +35,6 @@ op::v8::Slice::Slice(const Output<Node>& data,
 
 namespace {
 
-std::shared_ptr<ngraph::op::v0::Constant> get_default_const_axes(const Output<Node>& start) {
-    const auto start_pshape = start.get_partial_shape();
-    // Static case
-    if (start_pshape.rank().is_static() && start_pshape.rank().get_length() == 1 && start_pshape[0].is_static()) {
-        size_t axes_length = start_pshape[0].get_length();
-        std::vector<int64_t> axes(axes_length);
-        std::iota(axes.begin(), axes.end(), 0);
-        return op::v0::Constant::create(element::i64, Shape{axes_length}, axes);
-    }
-    // Dynamic case
-    return nullptr;
-}
-
 int64_t get_sliced_dim_size(int64_t start, int64_t stop, int64_t step, int64_t dim_size) {
     // Normalize index
     start = start < 0 ? dim_size + start : start;
@@ -64,7 +52,9 @@ int64_t get_sliced_dim_size(int64_t start, int64_t stop, int64_t step, int64_t d
         // Clip max stop index (last element exclusively)
         elements_in_range = std::max(int64_t(0), std::min(dim_size, stop) - start);
     }
-    const int64_t sliced_dim_size = std::ceil(elements_in_range / std::fabs(step));
+    const int64_t rest = elements_in_range % std::abs(step);
+    const int64_t integer_div = elements_in_range / std::abs(step);
+    const int64_t sliced_dim_size = !rest ? integer_div : integer_div + 1;
     return sliced_dim_size;
 }
 
@@ -75,6 +65,19 @@ bool op::v8::Slice::visit_attributes(AttributeVisitor& visitor) {
     return true;
 }
 
+std::shared_ptr<ngraph::op::v0::Constant> op::v8::Slice::get_default_const_axes(const Output<Node>& start) const {
+    const auto start_pshape = start.get_partial_shape();
+    // Static case
+    if (start_pshape.rank().is_static() && start_pshape.rank().get_length() == 1 && start_pshape[0].is_static()) {
+        size_t axes_length = start_pshape[0].get_length();
+        std::vector<int64_t> axes(axes_length);
+        std::iota(axes.begin(), axes.end(), 0);
+        return op::v0::Constant::create(element::i64, Shape{axes_length}, axes);
+    }
+    // Dynamic case
+    return nullptr;
+}
+
 void op::v8::Slice::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v8_Slice_validate_and_infer_types);
 
diff --git a/ngraph/core/src/op/space_to_batch.cpp b/ngraph/core/src/op/space_to_batch.cpp
index 3a3498d60c40fb..cd11e09bd73f45 100644
--- a/ngraph/core/src/op/space_to_batch.cpp
+++ b/ngraph/core/src/op/space_to_batch.cpp
@@ -17,6 +17,7 @@
 #include "ngraph/runtime/opt_kernel/reshape.hpp"
 #include "ngraph/runtime/reference/pad.hpp"
 #include "ngraph/shape.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -28,6 +29,9 @@ ngraph::op::v1::SpaceToBatch::SpaceToBatch(const ngraph::Output<ngraph::Node>& d
                                            const ngraph::Output<ngraph::Node>& pads_begin,
                                            const ngraph::Output<ngraph::Node>& pads_end)
     : Op({data, block_shape, pads_begin, pads_end}) {
+    ov::mark_as_precision_sensitive(input(1));
+    ov::mark_as_precision_sensitive(input(2));
+    ov::mark_as_precision_sensitive(input(3));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/strided_slice.cpp b/ngraph/core/src/op/strided_slice.cpp
index 003f909d9e7bd8..1cb0e93ee20354 100644
--- a/ngraph/core/src/op/strided_slice.cpp
+++ b/ngraph/core/src/op/strided_slice.cpp
@@ -19,6 +19,7 @@
 #include "ngraph/type/element_type_traits.hpp"
 #include "ngraph/util.hpp"
 #include "ngraph/validation_util.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -40,6 +41,9 @@ op::v1::StridedSlice::StridedSlice(const Output<Node>& data,
       m_new_axis_mask{new_axis_mask},
       m_shrink_axis_mask{shrink_axis_mask},
       m_ellipsis_mask{ellipsis_mask} {
+    ov::mark_as_precision_sensitive(input(1));
+    ov::mark_as_precision_sensitive(input(2));
+    ov::mark_as_precision_sensitive(input(3));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/tile.cpp b/ngraph/core/src/op/tile.cpp
index a77ae182fc02db..1f6d95fd650bb3 100644
--- a/ngraph/core/src/op/tile.cpp
+++ b/ngraph/core/src/op/tile.cpp
@@ -9,6 +9,7 @@
 #include "itt.hpp"
 #include "ngraph/op/constant.hpp"
 #include "ngraph/runtime/reference/tile.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -16,6 +17,7 @@ using namespace ngraph;
 BWDCMP_RTTI_DEFINITION(op::v0::Tile);
 
 op::v0::Tile::Tile(const Output<Node>& data, const Output<Node>& repeats) : Op({data, repeats}) {
+    ov::mark_as_precision_sensitive(input(1));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/topk.cpp b/ngraph/core/src/op/topk.cpp
index 93bc14a17bee8f..ef7b46f7290aaf 100644
--- a/ngraph/core/src/op/topk.cpp
+++ b/ngraph/core/src/op/topk.cpp
@@ -15,6 +15,7 @@
 #include "ngraph/runtime/reference/topk.hpp"
 #include "ngraph/shape.hpp"
 #include "ngraph/validation_util.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -154,6 +155,7 @@ op::v1::TopK::TopK(const Output<Node>& data,
       m_mode{as_enum<Mode>(mode)},
       m_sort{as_enum<SortType>(sort)},
       m_index_element_type{index_element_type} {
+    ov::mark_as_precision_sensitive(input(1));
     constructor_validate_and_infer_types();
 }
 
@@ -169,6 +171,7 @@ op::v1::TopK::TopK(const Output<Node>& data,
       m_mode{mode},
       m_sort{sort},
       m_index_element_type{index_element_type} {
+    ov::mark_as_precision_sensitive(input(1));
     constructor_validate_and_infer_types();
 }
 
diff --git a/ngraph/core/src/op/unsqueeze.cpp b/ngraph/core/src/op/unsqueeze.cpp
index ffafec5a2b3c9f..4e78f53670e094 100644
--- a/ngraph/core/src/op/unsqueeze.cpp
+++ b/ngraph/core/src/op/unsqueeze.cpp
@@ -89,8 +89,8 @@ bool evaluate_unsqueeze(const HostTensorPtr& arg0, const HostTensorPtr& arg1, co
     auto data_shape = arg0->get_shape();
     int64_t data_rank = static_cast<int64_t>(data_shape.size());
     auto axes_shape = arg1->get_shape();
-    NGRAPH_CHECK(axes_shape.size() == 1, "Axes to add must be a vector.");
-    NGRAPH_CHECK(axes_shape[0] > 0, "Axes cannot be empty.");
+    NGRAPH_CHECK(axes_shape.size() == 1 || axes_shape.empty(),
+                 "Axes to add must be a scalar or 1D tensor with 1 element");
 
     auto out_shape = data_shape;
     int64_t out_rank = data_rank + static_cast<int64_t>(shape_size(axes_shape));
diff --git a/ngraph/core/src/op/util/broadcast_base.cpp b/ngraph/core/src/op/util/broadcast_base.cpp
index 47d40d21c3c98e..8c094c739e0e87 100644
--- a/ngraph/core/src/op/util/broadcast_base.cpp
+++ b/ngraph/core/src/op/util/broadcast_base.cpp
@@ -14,6 +14,7 @@
 #include "ngraph/op/util/op_types.hpp"
 #include "ngraph/partial_shape.hpp"
 #include "ngraph/runtime/reference/broadcast.hpp"
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
 
 using namespace std;
 
@@ -24,13 +25,17 @@ ov::op::util::BroadcastBase::BroadcastBase(const Output<Node>& arg,
                                            const Output<Node>& axes_mapping,
                                            const BroadcastModeSpec& broadcast_mode)
     : Op({arg, target_shape, axes_mapping}),
-      m_mode{broadcast_mode} {}
+      m_mode{broadcast_mode} {
+    ov::mark_as_precision_sensitive(input(1));
+}
 
 ov::op::util::BroadcastBase::BroadcastBase(const Output<Node>& arg,
                                            const Output<Node>& target_shape,
                                            const BroadcastModeSpec& broadcast_mode)
     : Op({arg, target_shape}),
-      m_mode{broadcast_mode} {}
+      m_mode{broadcast_mode} {
+    ov::mark_as_precision_sensitive(input(1));
+}
 
 ov::PartialShape ov::op::util::BroadcastBase::get_result_shape_pdpd(const PartialShape& arg0_shape,
                                                                     const PartialShape& target_pshape,
diff --git a/ngraph/core/src/op/util/gather_nd_base.cpp b/ngraph/core/src/op/util/gather_nd_base.cpp
new file mode 100644
index 00000000000000..96dac95f8ac523
--- /dev/null
+++ b/ngraph/core/src/op/util/gather_nd_base.cpp
@@ -0,0 +1,114 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph/op/util/gather_nd_base.hpp"
+
+#include <ngraph/validation_util.hpp>
+
+#include "ngraph/op/concat.hpp"
+#include "ngraph/op/constant.hpp"
+#include "ngraph/op/squeeze.hpp"
+#include "ngraph/runtime/host_tensor.hpp"
+#include "ngraph/shape.hpp"
+
+using namespace std;
+
+BWDCMP_RTTI_DEFINITION(ov::op::util::GatherNDBase);
+
+ov::op::util::GatherNDBase::GatherNDBase(const Output<Node>& data, const Output<Node>& indices, const size_t batch_dims)
+    : Op({data, indices}),
+      m_batch_dims(batch_dims) {
+    constructor_validate_and_infer_types();
+}
+
+void ov::op::util::GatherNDBase::validate_inputs_and_infer_shape() {
+    // check types of input tensors
+    const auto& data_type = get_input_element_type(0);
+    const auto& indices_type = get_input_element_type(1);
+
+    NODE_VALIDATION_CHECK(this,
+                          indices_type.is_integral_number(),
+                          "The indices type is expected to be an integer type. Got: ",
+                          indices_type);
+
+    // check ranks of input tensors
+    const auto& data_pshape = get_input_partial_shape(0);
+    const auto& indices_pshape = get_input_partial_shape(1);
+
+    if (data_pshape.rank().is_static()) {
+        NODE_VALIDATION_CHECK(this, data_pshape.rank().get_length() > 0, "Data rank must be at least 1.");
+
+        NODE_VALIDATION_CHECK(this,
+                              data_pshape.rank().get_length() > static_cast<int64_t>(m_batch_dims),
+                              "Number of batch dimensions must not exceed a rank of data.");
+    }
+
+    if (indices_pshape.rank().is_static()) {
+        NODE_VALIDATION_CHECK(this, indices_pshape.rank().get_length() > 0, "Indices rank must be at least 1.");
+
+        NODE_VALIDATION_CHECK(this,
+                              indices_pshape.rank().get_length() > static_cast<int64_t>(m_batch_dims),
+                              "Number of batch dimensions must not exceed a rank of indices.");
+    }
+
+    if (data_pshape.rank().is_static() && indices_pshape.rank().is_static()) {
+        // check that batch dimensions of data and indices are the same
+        for (size_t batch_dim = 0; batch_dim < m_batch_dims; batch_dim++) {
+            if (data_pshape[batch_dim].is_static() && indices_pshape[batch_dim].is_static()) {
+                NODE_VALIDATION_CHECK(this,
+                                      data_pshape[batch_dim].get_length() == indices_pshape[batch_dim].get_length(),
+                                      "Batch dimensions of data and indices must be the same.");
+            }
+        }
+
+        if (indices_pshape[indices_pshape.rank().get_length() - 1].is_static()) {
+            NODE_VALIDATION_CHECK(
+                this,
+                static_cast<int64_t>(indices_pshape[indices_pshape.rank().get_length() - 1].get_length() +
+                                     m_batch_dims) <= data_pshape.rank().get_length(),
+                "Length of a tuple with indices must not exceed a rank of data tensor "
+                "excluding "
+                "batch dimensions.");
+        }
+    }
+
+    // set output shape
+    set_output_size(1);
+    if (data_pshape.rank().is_static() && indices_pshape.rank().is_static() &&
+        indices_pshape[indices_pshape.rank().get_length() - 1].is_static()) {
+        auto indices_tuple_length = indices_pshape[indices_pshape.rank().get_length() - 1].get_length();
+        int64_t slice_length = data_pshape.rank().get_length() - indices_tuple_length - m_batch_dims;
+        int64_t output_indices_length = indices_pshape.rank().get_length() - m_batch_dims - 1;
+        auto output_rank = output_indices_length + slice_length;
+        size_t delta_output_rank = 0;
+        delta_output_rank = m_batch_dims;
+        std::vector<Dimension> output_shape(output_rank + delta_output_rank);
+        for (size_t dim = 0; dim < m_batch_dims; dim++) {
+            output_shape[dim] = 1;
+            if (data_pshape[dim].is_static()) {
+                output_shape[dim] = data_pshape[dim].get_length();
+            } else if (indices_pshape[dim].is_static()) {
+                output_shape[dim] = indices_pshape[dim].get_length();
+            } else {
+                output_shape[dim] = Dimension::dynamic();
+                break;
+            }
+        }
+        for (int64_t dim = 0; dim < output_indices_length; dim++) {
+            output_shape[dim + delta_output_rank] = indices_pshape[dim + m_batch_dims];
+        }
+        for (int64_t dim = 0; dim < slice_length; dim++) {
+            output_shape[output_indices_length + dim + delta_output_rank] =
+                data_pshape[m_batch_dims + indices_tuple_length + dim];
+        }
+        set_output_type(0, data_type, ov::PartialShape(output_shape));
+    } else {
+        set_output_type(0, data_type, ov::PartialShape::dynamic());
+    }
+}
+
+bool ov::op::util::GatherNDBase::visit_attributes(AttributeVisitor& visitor) {
+    visitor.on_attribute("batch_dims", m_batch_dims);
+    return true;
+}
diff --git a/ngraph/core/src/op/util/precision_sensitive_attribute.cpp b/ngraph/core/src/op/util/precision_sensitive_attribute.cpp
new file mode 100644
index 00000000000000..6591699a341702
--- /dev/null
+++ b/ngraph/core/src/op/util/precision_sensitive_attribute.cpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/util/precision_sensitive_attribute.hpp"
+
+void ov::mark_as_precision_sensitive(ov::Input<ov::Node> node_input) {
+    auto& rt_info = node_input.get_rt_info();
+    rt_info[PrecisionSensitive::get_type_info_static()] = std::make_shared<PrecisionSensitive>();
+}
+
+void ov::unmark_as_precision_sensitive(ov::Input<ov::Node> node_input) {
+    auto& rt_info = node_input.get_rt_info();
+    rt_info.erase(PrecisionSensitive::get_type_info_static());
+}
+
+bool ov::is_precision_sensitive(const ov::Input<ov::Node>& node_input) {
+    const auto& rt_info = node_input.get_rt_info();
+    return rt_info.count(PrecisionSensitive::get_type_info_static());
+}
diff --git a/ngraph/core/src/pass/convert_precision.cpp b/ngraph/core/src/pass/convert_precision.cpp
index 41793842b4d8e8..b11ee9fe029f2f 100644
--- a/ngraph/core/src/pass/convert_precision.cpp
+++ b/ngraph/core/src/pass/convert_precision.cpp
@@ -10,6 +10,7 @@
 #include <ngraph/opsets/opset4.hpp>
 #include <ngraph/opsets/opset5.hpp>
 #include <ngraph/opsets/opset6.hpp>
+#include <ngraph/opsets/opset8.hpp>
 #include <ngraph/runtime/reference/convert.hpp>
 #include <vector>
 
@@ -30,6 +31,7 @@ bool fuse_type_to_nms3(const std::shared_ptr<ngraph::Node>& node, ngraph::elemen
 bool fuse_type_to_nms4(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_nms5(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_topk(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
+bool fuse_type_to_maxpool(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_nonzero(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_bucketize(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_ctc_greedy_decoder_seq_len(const std::shared_ptr<ngraph::Node>& node,
@@ -253,6 +255,7 @@ bool ngraph::pass::ConvertPrecision::run_on_function(std::shared_ptr<ngraph::Fun
         {opset5::NonMaxSuppression::get_type_info_static(), fuse_type_to_nms5},
         {opset6::CTCGreedyDecoderSeqLen::get_type_info_static(), fuse_type_to_ctc_greedy_decoder_seq_len},
         {opset4::TopK::get_type_info_static(), fuse_type_to_topk},
+        {opset8::MaxPool::get_type_info_static(), fuse_type_to_maxpool},
         {opset4::NonZero::get_type_info_static(), fuse_type_to_nonzero},
         {opset4::Bucketize::get_type_info_static(), fuse_type_to_bucketize},
         {opset4::Equal::get_type_info_static(), fuse_type_to_binary_comparision<opset4::Equal>},
@@ -392,6 +395,16 @@ bool fuse_type_to_topk(const std::shared_ptr<ngraph::Node>& node, ngraph::elemen
     return false;
 }
 
+bool fuse_type_to_maxpool(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx) {
+    if (auto maxpool = ov::as_type_ptr<opset8::MaxPool>(node)) {
+        if (idx == 1 && (to == element::i32 || to == element::i64)) {
+            maxpool->set_index_element_type(to);
+            return true;
+        }
+    }
+    return false;
+}
+
 bool fuse_type_to_ctc_greedy_decoder_seq_len(const std::shared_ptr<ngraph::Node>& node,
                                              ngraph::element::Type to,
                                              size_t idx) {
diff --git a/ngraph/core/src/pass/serialize.cpp b/ngraph/core/src/pass/serialize.cpp
index acacdb26a20796..08becb27c50085 100644
--- a/ngraph/core/src/pass/serialize.cpp
+++ b/ngraph/core/src/pass/serialize.cpp
@@ -125,21 +125,6 @@ void ngfunction_2_ir(pugi::xml_node& node,
                      int64_t version,
                      bool deterministic);
 
-// Some of the operators were added to wrong opsets. This is a mapping
-// that allows such operators to be serialized with proper opsets.
-// If new operators are discovered that have the same problem, the mapping
-// needs to be updated here. The keys contain op name and version in NodeTypeInfo.
-const std::unordered_map<ngraph::Node::type_info_t, std::string> special_operator_to_opset_assignments = {
-    {ngraph::Node::type_info_t("ShuffleChannels", 0), "opset3"}};
-
-std::string get_special_opset_for_op(const ngraph::Node::type_info_t& type_info) {
-    auto found = special_operator_to_opset_assignments.find(type_info);
-    if (found != end(special_operator_to_opset_assignments)) {
-        return found->second;
-    }
-    return "";
-}
-
 namespace rt_info {
 const std::vector<std::string> list_of_names{
     "PrimitivesPriority",
@@ -287,8 +272,6 @@ class XmlSerializer : public ngraph::AttributeVisitor {
         const std::vector<std::string>& result_mapping,
         pugi::xml_node& port_map,
         const std::string& portmap_name) {
-        NGRAPH_CHECK(!parameter_mapping.empty(), "No parameters found in body Function.");
-
         if (!m_xml_node.parent().child(portmap_name.c_str())) {
             port_map = m_xml_node.parent().insert_child_before(portmap_name.c_str(), m_xml_node.parent().first_child());
         }
@@ -415,9 +398,8 @@ class XmlSerializer : public ngraph::AttributeVisitor {
                 map_type_from_body(m_xml_node.parent(), "Parameter", m_version, body_name);
 
             pugi::xml_node port_map = m_xml_node.parent().child(portmap_name.c_str());
-
-            NGRAPH_CHECK(!parameter_mapping.empty() || !result_mapping.empty(),
-                         "No parameters or results found in body Function.");
+            // Bodies can be without parameters(dependig on constants), but can not be without results
+            NGRAPH_CHECK(!result_mapping.empty(), "No results found in body Function.");
             // TI, Loop do not have attributtes as regular ops, it is necessary to append "port_map" and
             // "back_edges" to layer above (m_xml_node.parent()) as in ngfunction_2_ir() layer (here "m_xml_node")
             // with empty attributes is removed.
@@ -574,6 +556,10 @@ const std::vector<Edge> create_edge_mapping(const std::unordered_map<ngraph::Nod
 }
 
 std::string get_opset_name(const ngraph::Node* n, const std::map<std::string, ngraph::OpSet>& custom_opsets) {
+    OPENVINO_ASSERT(n != nullptr);
+    if (n->get_type_info().version_id != nullptr) {
+        return n->get_type_info().version_id;
+    }
     // Try to find opset name from RT info
     auto opset_it = n->get_rt_info().find("opset");
     if (opset_it != n->get_rt_info().end()) {
@@ -585,26 +571,6 @@ std::string get_opset_name(const ngraph::Node* n, const std::map<std::string, ng
         }
     }
 
-    auto opsets = std::array<std::reference_wrapper<const ngraph::OpSet>, 8>{ngraph::get_opset1(),
-                                                                             ngraph::get_opset2(),
-                                                                             ngraph::get_opset3(),
-                                                                             ngraph::get_opset4(),
-                                                                             ngraph::get_opset5(),
-                                                                             ngraph::get_opset6(),
-                                                                             ngraph::get_opset7(),
-                                                                             ngraph::get_opset8()};
-
-    auto special_opset = get_special_opset_for_op(n->get_type_info());
-    if (!special_opset.empty()) {
-        return special_opset;
-    }
-    // return the oldest opset name where node type is present
-    for (size_t idx = 0; idx < opsets.size(); idx++) {
-        if (opsets[idx].get().contains_op_type(n)) {
-            return "opset" + std::to_string(idx + 1);
-        }
-    }
-
     for (const auto& custom_opset : custom_opsets) {
         std::string name = custom_opset.first;
         ngraph::OpSet opset = custom_opset.second;
@@ -1137,6 +1103,7 @@ bool pass::Serialize::run_on_function(std::shared_ptr<ngraph::Function> f) {
     return false;
 }
 
+OPENVINO_SUPPRESS_DEPRECATED_START
 pass::Serialize::Serialize(std::ostream& xmlFile,
                            std::ostream& binFile,
                            std::map<std::string, ngraph::OpSet> custom_opsets,
@@ -1147,6 +1114,7 @@ pass::Serialize::Serialize(std::ostream& xmlFile,
       m_binPath{},
       m_version{version},
       m_custom_opsets{custom_opsets} {}
+
 pass::Serialize::Serialize(std::ostream& xmlFile, std::ostream& binFile, pass::Serialize::Version version)
     : pass::Serialize::Serialize(xmlFile, binFile, std::map<std::string, ngraph::OpSet>{}, version) {}
 
@@ -1162,7 +1130,9 @@ pass::Serialize::Serialize(const std::string& xmlPath,
 
 pass::Serialize::Serialize(const std::string& xmlPath, const std::string& binPath, pass::Serialize::Version version)
     : pass::Serialize::Serialize(xmlPath, binPath, std::map<std::string, ngraph::OpSet>{}, version) {}
+OPENVINO_SUPPRESS_DEPRECATED_END
 
+OPENVINO_SUPPRESS_DEPRECATED_START
 pass::StreamSerialize::StreamSerialize(std::ostream& stream,
                                        std::map<std::string, ngraph::OpSet>&& custom_opsets,
                                        const std::function<void(std::ostream&)>& custom_data_serializer,
@@ -1177,6 +1147,12 @@ pass::StreamSerialize::StreamSerialize(std::ostream& stream,
     }
 }
 
+pass::StreamSerialize::StreamSerialize(std::ostream& stream,
+                                       const std::function<void(std::ostream&)>& custom_data_serializer,
+                                       Serialize::Version version)
+    : StreamSerialize(stream, {}, custom_data_serializer, version) {}
+OPENVINO_SUPPRESS_DEPRECATED_END
+
 bool pass::StreamSerialize::run_on_function(std::shared_ptr<ngraph::Function> f) {
     /*
         Format:
diff --git a/ngraph/core/src/preprocess/pre_post_process.cpp b/ngraph/core/src/preprocess/pre_post_process.cpp
index bccc934df671b1..678a1a31f3877e 100644
--- a/ngraph/core/src/preprocess/pre_post_process.cpp
+++ b/ngraph/core/src/preprocess/pre_post_process.cpp
@@ -165,20 +165,25 @@ struct InputInfo::InputInfoImpl {
         return m_has_name;
     }
 
-    void create_tensor_data(const element::Type& type, const Layout& layout) {
-        auto data = std::unique_ptr<InputTensorInfo::InputTensorInfoImpl>(new InputTensorInfo::InputTensorInfoImpl());
-        data->set_layout(layout);
-        data->set_element_type(type);
-        m_tensor_data = std::move(data);
+    std::unique_ptr<InputTensorInfo::InputTensorInfoImpl>& get_tensor_data() {
+        return m_tensor_info.m_impl;
+    }
+
+    std::unique_ptr<PreProcessSteps::PreProcessStepsImpl>& get_preprocess() {
+        return m_preprocess.m_impl;
+    }
+
+    std::unique_ptr<InputNetworkInfo::InputNetworkInfoImpl>& get_network() {
+        return m_network_data.m_impl;
     }
 
     bool m_has_index = false;
     size_t m_index = 0;
     bool m_has_name = false;
     std::string m_name;
-    std::unique_ptr<InputTensorInfo::InputTensorInfoImpl> m_tensor_data;
-    std::unique_ptr<PreProcessSteps::PreProcessStepsImpl> m_preprocess;
-    std::unique_ptr<InputNetworkInfo::InputNetworkInfoImpl> m_network_data;
+    InputTensorInfo m_tensor_info;
+    PreProcessSteps m_preprocess;
+    InputNetworkInfo m_network_data;
     std::shared_ptr<op::v0::Parameter> m_resolved_param;
 };
 
@@ -196,18 +201,25 @@ struct OutputInfo::OutputInfoImpl {
         return m_has_name;
     }
 
-    void create_tensor_data() {
-        m_tensor_data =
-            std::unique_ptr<OutputTensorInfo::OutputTensorInfoImpl>(new OutputTensorInfo::OutputTensorInfoImpl());
+    std::unique_ptr<OutputTensorInfo::OutputTensorInfoImpl>& get_tensor_data() {
+        return m_tensor_info.m_impl;
+    }
+
+    std::unique_ptr<PostProcessSteps::PostProcessStepsImpl>& get_postprocess() {
+        return m_postprocess.m_impl;
+    }
+
+    std::unique_ptr<OutputNetworkInfo::OutputNetworkInfoImpl>& get_network_data() {
+        return m_network_info.m_impl;
     }
 
     bool m_has_index = false;
     size_t m_index = 0;
     bool m_has_name = false;
     std::string m_name;
-    std::unique_ptr<OutputTensorInfo::OutputTensorInfoImpl> m_tensor_data;
-    std::unique_ptr<PostProcessSteps::PostProcessStepsImpl> m_postprocess;
-    std::unique_ptr<OutputNetworkInfo::OutputNetworkInfoImpl> m_network_data;
+    OutputTensorInfo m_tensor_info;
+    PostProcessSteps m_postprocess;
+    OutputNetworkInfo m_network_info;
 };
 
 //-------------- InputInfo ------------------
@@ -220,32 +232,44 @@ InputInfo& InputInfo::operator=(InputInfo&&) noexcept = default;
 InputInfo::~InputInfo() = default;
 
 InputInfo& InputInfo::tensor(InputTensorInfo&& builder) & {
-    m_impl->m_tensor_data = std::move(builder.m_impl);
+    m_impl->m_tensor_info = std::move(builder);
     return *this;
 }
 
+InputTensorInfo& InputInfo::tensor() {
+    return m_impl->m_tensor_info;
+}
+
+PreProcessSteps& InputInfo::preprocess() {
+    return m_impl->m_preprocess;
+}
+
+InputNetworkInfo& InputInfo::network() {
+    return m_impl->m_network_data;
+}
+
 InputInfo&& InputInfo::tensor(InputTensorInfo&& builder) && {
-    m_impl->m_tensor_data = std::move(builder.m_impl);
+    m_impl->m_tensor_info = std::move(builder);
     return std::move(*this);
 }
 
 InputInfo&& InputInfo::preprocess(PreProcessSteps&& builder) && {
-    m_impl->m_preprocess = std::move(builder.m_impl);
+    m_impl->m_preprocess = std::move(builder);
     return std::move(*this);
 }
 
 InputInfo& InputInfo::preprocess(PreProcessSteps&& builder) & {
-    m_impl->m_preprocess = std::move(builder.m_impl);
+    m_impl->m_preprocess = std::move(builder);
     return *this;
 }
 
 InputInfo& InputInfo::network(InputNetworkInfo&& builder) & {
-    m_impl->m_network_data = std::move(builder.m_impl);
+    m_impl->m_network_data = std::move(builder);
     return *this;
 }
 
 InputInfo&& InputInfo::network(InputNetworkInfo&& builder) && {
-    m_impl->m_network_data = std::move(builder.m_impl);
+    m_impl->m_network_data = std::move(builder);
     return std::move(*this);
 }
 
@@ -260,73 +284,202 @@ OutputInfo::OutputInfo(OutputInfo&&) noexcept = default;
 OutputInfo& OutputInfo::operator=(OutputInfo&&) noexcept = default;
 OutputInfo::~OutputInfo() = default;
 
+OutputNetworkInfo& OutputInfo::network() {
+    return m_impl->m_network_info;
+}
+
+PostProcessSteps& OutputInfo::postprocess() {
+    return m_impl->m_postprocess;
+}
+
+OutputTensorInfo& OutputInfo::tensor() {
+    return m_impl->m_tensor_info;
+}
+
+// TODO: remove this in future
 OutputInfo& OutputInfo::tensor(OutputTensorInfo&& builder) & {
-    m_impl->m_tensor_data = std::move(builder.m_impl);
+    m_impl->m_tensor_info = std::move(builder);
     return *this;
 }
 
+// TODO: remove this in future
 OutputInfo&& OutputInfo::tensor(OutputTensorInfo&& builder) && {
-    m_impl->m_tensor_data = std::move(builder.m_impl);
+    m_impl->m_tensor_info = std::move(builder);
     return std::move(*this);
 }
 
+// TODO: remove this in future
 OutputInfo&& OutputInfo::postprocess(PostProcessSteps&& builder) && {
-    m_impl->m_postprocess = std::move(builder.m_impl);
+    m_impl->m_postprocess = std::move(builder);
     return std::move(*this);
 }
 
+// TODO: remove this in future
 OutputInfo& OutputInfo::postprocess(PostProcessSteps&& builder) & {
-    m_impl->m_postprocess = std::move(builder.m_impl);
+    m_impl->m_postprocess = std::move(builder);
     return *this;
 }
 
+// TODO: remove this in future
 OutputInfo& OutputInfo::network(OutputNetworkInfo&& builder) & {
-    m_impl->m_network_data = std::move(builder.m_impl);
+    m_impl->m_network_info = std::move(builder);
     return *this;
 }
 
+// TODO: remove this in future
 OutputInfo&& OutputInfo::network(OutputNetworkInfo&& builder) && {
-    m_impl->m_network_data = std::move(builder.m_impl);
+    m_impl->m_network_info = std::move(builder);
     return std::move(*this);
 }
 
 // ------------------------ PrePostProcessor --------------------
 struct PrePostProcessor::PrePostProcessorImpl {
 public:
-    std::list<std::unique_ptr<InputInfo::InputInfoImpl>> in_contexts;
-    std::list<std::unique_ptr<OutputInfo::OutputInfoImpl>> out_contexts;
+    PrePostProcessorImpl() = default;
+    explicit PrePostProcessorImpl(const std::shared_ptr<ov::Function>& f) : m_function(f) {
+        OPENVINO_ASSERT(f, "Function can't be nullptr for PrePostProcessor");
+        m_inputs.reserve(m_function->inputs().size());
+        for (size_t i = 0; i < m_function->inputs().size(); i++) {
+            m_inputs.emplace_back(i);
+        }
+
+        m_outputs.reserve(m_function->outputs().size());
+        for (size_t i = 0; i < m_function->outputs().size(); i++) {
+            m_outputs.emplace_back(i);
+        }
+    }
+    size_t find_input_index(const std::string& tensor_name) {
+        size_t index;
+        for (index = 0; index < m_function->inputs().size(); index++) {
+            if (m_function->input(index).get_names().count(tensor_name)) {
+                break;
+            }
+        }
+        OPENVINO_ASSERT(index < m_inputs.size(), "Function doesn't have input with name ", tensor_name);
+        return index;
+    }
+    size_t find_output_index(const std::string& tensor_name) {
+        size_t index;
+        for (index = 0; index < m_function->outputs().size(); index++) {
+            if (m_function->output(index).get_names().count(tensor_name)) {
+                break;
+            }
+        }
+        OPENVINO_ASSERT(index < m_outputs.size(), "Function doesn't have output with name ", tensor_name);
+        return index;
+    }
+
+    // TODO: this is created for compatibility, consider to remove
+    void add_input_info(InputInfo&& builder) {
+        if (builder.m_impl->m_has_index) {
+            OPENVINO_ASSERT(builder.m_impl->m_index < m_inputs.size(), "Index is out of range");
+            m_inputs[builder.m_impl->m_index] = std::move(builder);
+        } else if (builder.m_impl->m_has_name) {
+            size_t index = find_input_index(builder.m_impl->m_name);
+            m_inputs[index] = std::move(builder);
+        } else {
+            OPENVINO_ASSERT(1 == m_inputs.size(), "Function shall have only one input");
+            m_inputs[0] = std::move(builder);
+        }
+    }
+
+    // TODO: this is created for compatibility, consider to remove
+    void add_output_info(OutputInfo&& builder) {
+        if (builder.m_impl->m_has_index) {
+            OPENVINO_ASSERT(builder.m_impl->m_index < m_inputs.size(), "Output index is out of range");
+            m_outputs[builder.m_impl->m_index] = std::move(builder);
+        } else if (builder.m_impl->m_has_name) {
+            size_t index = find_output_index(builder.m_impl->m_name);
+            m_outputs[index] = std::move(builder);
+        } else {
+            OPENVINO_ASSERT(1 == m_inputs.size(), "Function shall have only one output");
+            m_outputs[0] = std::move(builder);
+        }
+    }
+    std::vector<InputInfo> m_inputs;
+    std::vector<OutputInfo> m_outputs;
+    std::shared_ptr<Function> m_function = nullptr;
 };
 
-PrePostProcessor::PrePostProcessor() : m_impl(std::unique_ptr<PrePostProcessorImpl>(new PrePostProcessorImpl())) {}
+PrePostProcessor::PrePostProcessor(const std::shared_ptr<Function>& function)
+    : m_impl(std::unique_ptr<PrePostProcessorImpl>(new PrePostProcessorImpl(function))) {}
 PrePostProcessor::PrePostProcessor(PrePostProcessor&&) noexcept = default;
 PrePostProcessor& PrePostProcessor::operator=(PrePostProcessor&&) noexcept = default;
 PrePostProcessor::~PrePostProcessor() = default;
 
+InputInfo& PrePostProcessor::input() {
+    OPENVINO_ASSERT(m_impl->m_inputs.size() == 1,
+                    "PrePostProcessor::input() - function must have exactly one input, got ",
+                    m_impl->m_inputs.size());
+    return m_impl->m_inputs.front();
+}
+
+InputInfo& PrePostProcessor::input(size_t input_index) {
+    OPENVINO_ASSERT(m_impl->m_inputs.size() > input_index,
+                    "PrePostProcessor::input(size_t) - function doesn't have input with index ",
+                    input_index,
+                    ". Total number of inputs is ",
+                    m_impl->m_inputs.size());
+    return m_impl->m_inputs[input_index];
+}
+
+InputInfo& PrePostProcessor::input(const std::string& tensor_name) {
+    size_t index = m_impl->find_input_index(tensor_name);
+    return m_impl->m_inputs[index];
+}
+
+OutputInfo& PrePostProcessor::output() {
+    OPENVINO_ASSERT(m_impl->m_outputs.size() == 1,
+                    "PrePostProcessor::output() - function must have exactly one output, got ",
+                    m_impl->m_outputs.size());
+    return m_impl->m_outputs.front();
+}
+
+OutputInfo& PrePostProcessor::output(size_t output_index) {
+    OPENVINO_ASSERT(m_impl->m_outputs.size() > output_index,
+                    "PrePostProcessor::output(size_t) - function doesn't have input with index ",
+                    output_index,
+                    ". Total number of inputs is ",
+                    m_impl->m_inputs.size());
+    return m_impl->m_outputs[output_index];
+}
+
+OutputInfo& PrePostProcessor::output(const std::string& tensor_name) {
+    size_t index = m_impl->find_output_index(tensor_name);
+    return m_impl->m_outputs[index];
+}
+
+// TODO: consider to remove
 PrePostProcessor& PrePostProcessor::input(InputInfo&& builder) & {
-    m_impl->in_contexts.push_back(std::move(builder.m_impl));
+    m_impl->add_input_info(std::move(builder));
     return *this;
 }
 
+// TODO: consider to remove
 PrePostProcessor&& PrePostProcessor::input(InputInfo&& builder) && {
-    m_impl->in_contexts.push_back(std::move(builder.m_impl));
+    m_impl->add_input_info(std::move(builder));
     return std::move(*this);
 }
 
+// TODO: consider remove
 PrePostProcessor& PrePostProcessor::output(OutputInfo&& builder) & {
-    m_impl->out_contexts.push_back(std::move(builder.m_impl));
+    m_impl->add_output_info(std::move(builder));
     return *this;
 }
 
+// TODO: consider remove
 PrePostProcessor&& PrePostProcessor::output(OutputInfo&& builder) && {
-    m_impl->out_contexts.push_back(std::move(builder.m_impl));
+    m_impl->add_output_info(std::move(builder));
     return std::move(*this);
 }
 
-std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function>& function) {
+std::shared_ptr<Function> PrePostProcessor::build() {
+    auto function = m_impl->m_function;
     FunctionGuard guard(function);
     std::tuple<std::unordered_set<std::string>, bool> existing_names{std::unordered_set<std::string>{}, false};
     bool tensor_data_updated = false;
-    for (const auto& input : m_impl->in_contexts) {
+    for (const auto& input_info : m_impl->m_inputs) {
+        auto& input = input_info.m_impl;
         std::shared_ptr<op::v0::Parameter> param;
         Output<Node> node;
         OPENVINO_ASSERT(input, "Internal error: Invalid preprocessing input, please report a problem");
@@ -339,8 +492,8 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
         }
         param = std::dynamic_pointer_cast<op::v0::Parameter>(node.get_node_shared_ptr());
         // Set parameter layout from 'network' information
-        if (input->m_network_data && input->m_network_data->is_layout_set() && param->get_layout().empty()) {
-            param->set_layout(input->m_network_data->get_layout());
+        if (input->get_network()->is_layout_set() && param->get_layout().empty()) {
+            param->set_layout(input->get_network()->get_layout());
         }
         input->m_resolved_param = param;
     }
@@ -348,31 +501,29 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
     auto parameters_list = std::list<std::shared_ptr<op::v0::Parameter>>(function->get_parameters().begin(),
                                                                          function->get_parameters().end());
 
-    for (const auto& input : m_impl->in_contexts) {
+    for (const auto& input_info : m_impl->m_inputs) {
+        const auto& input = input_info.m_impl;
         auto param = input->m_resolved_param;
         auto consumers = param->output(0).get_target_inputs();
-        if (!input->m_tensor_data) {
-            input->create_tensor_data(param->get_element_type(), param->get_layout());
-        }
-        if (!input->m_tensor_data->is_element_type_set()) {
-            input->m_tensor_data->set_element_type(param->get_element_type());
+        if (!input->get_tensor_data()->is_element_type_set()) {
+            input->get_tensor_data()->set_element_type(param->get_element_type());
         }
-        auto color_info = ColorFormatInfo::get(input->m_tensor_data->get_color_format());
-        if (!input->m_tensor_data->is_layout_set()) {
+        auto color_info = ColorFormatInfo::get(input->get_tensor_data()->get_color_format());
+        if (!input->get_tensor_data()->is_layout_set()) {
             if (!color_info->default_layout().empty()) {
-                input->m_tensor_data->set_layout(color_info->default_layout());
+                input->get_tensor_data()->set_layout(color_info->default_layout());
             } else if (!param->get_layout().empty()) {
-                input->m_tensor_data->set_layout(param->get_layout());
+                input->get_tensor_data()->set_layout(param->get_layout());
             }
         }
 
         auto net_shape = param->get_partial_shape();
         auto new_param_shape = net_shape;
-        if (input->m_tensor_data->is_layout_set() && !param->get_layout().empty() &&
-            param->get_layout() != input->m_tensor_data->get_layout()) {
+        if (input->get_tensor_data()->is_layout_set() && !param->get_layout().empty() &&
+            param->get_layout() != input->get_tensor_data()->get_layout()) {
             // Find transpose between network and tensor layouts and update tensor shape
             auto net_to_tensor =
-                layout::find_permutation(param->get_layout(), net_shape.rank(), input->m_tensor_data->get_layout());
+                layout::find_permutation(param->get_layout(), net_shape.rank(), input->get_tensor_data()->get_layout());
             if (!net_to_tensor.empty()) {
                 std::vector<ov::Dimension> dims(new_param_shape.size());
                 std::transform(net_to_tensor.begin(), net_to_tensor.end(), dims.begin(), [&](int64_t v) {
@@ -380,20 +531,20 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
                 });
                 new_param_shape = PartialShape(dims);
             }
-        } else if (input->m_preprocess) {
-            new_param_shape = input->m_preprocess->calculate_param_shape(new_param_shape);
+        } else {
+            new_param_shape = input->get_preprocess()->calculate_param_shape(new_param_shape);
         }
-        if (input->m_tensor_data->is_spatial_shape_set()) {
-            auto height_idx = get_and_check_height_idx(input->m_tensor_data->get_layout(), new_param_shape);
-            auto width_idx = get_and_check_width_idx(input->m_tensor_data->get_layout(), new_param_shape);
-            if (input->m_tensor_data->is_spatial_shape_dynamic()) {
+        if (input->get_tensor_data()->is_spatial_shape_set()) {
+            auto height_idx = get_and_check_height_idx(input->get_tensor_data()->get_layout(), new_param_shape);
+            auto width_idx = get_and_check_width_idx(input->get_tensor_data()->get_layout(), new_param_shape);
+            if (input->get_tensor_data()->is_spatial_shape_dynamic()) {
                 // Use dynamic spatial dimensions
                 new_param_shape[height_idx] = Dimension::dynamic();
                 new_param_shape[width_idx] = Dimension::dynamic();
             } else {
                 // Use static spatial dimensions
-                new_param_shape[height_idx] = input->m_tensor_data->get_spatial_height();
-                new_param_shape[width_idx] = input->m_tensor_data->get_spatial_width();
+                new_param_shape[height_idx] = input->get_tensor_data()->get_spatial_height();
+                new_param_shape[width_idx] = input->get_tensor_data()->get_spatial_width();
             }
         }
 
@@ -404,11 +555,11 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
         for (size_t plane = 0; plane < color_info->planes_count(); plane++) {
             auto plane_shape = color_info->shape(plane, new_param_shape);
             auto plane_param =
-                std::make_shared<op::v0::Parameter>(input->m_tensor_data->get_element_type(), plane_shape);
-            if (plane < input->m_tensor_data->planes_sub_names().size()) {
+                std::make_shared<op::v0::Parameter>(input->get_tensor_data()->get_element_type(), plane_shape);
+            if (plane < input->get_tensor_data()->planes_sub_names().size()) {
                 std::unordered_set<std::string> plane_tensor_names;
                 std::string sub_name;
-                sub_name = std::string("/") + input->m_tensor_data->planes_sub_names()[plane];
+                sub_name = std::string("/") + input->get_tensor_data()->planes_sub_names()[plane];
                 if (!std::get<1>(existing_names)) {
                     existing_names = std::make_tuple(get_function_tensor_names(function), true);
                 }
@@ -428,26 +579,24 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
                     param->get_default_output().get_tensor().get_names());
                 plane_param->set_friendly_name(param->get_friendly_name());
             }
-            if (!input->m_tensor_data->get_layout().empty()) {
-                plane_param->set_layout(input->m_tensor_data->get_layout());
+            if (!input->get_tensor_data()->get_layout().empty()) {
+                plane_param->set_layout(input->get_tensor_data()->get_layout());
             }
             new_params.push_back(plane_param);
             nodes.emplace_back(plane_param);
         }
 
-        PreprocessingContext context(input->m_tensor_data->get_layout());
-        context.color_format() = input->m_tensor_data->get_color_format();
+        PreprocessingContext context(input->get_tensor_data()->get_layout());
+        context.color_format() = input->get_tensor_data()->get_color_format();
         context.target_layout() = param->get_layout();
         context.network_shape() = param->get_partial_shape();
         context.target_element_type() = param->get_element_type();
 
         // 2. Apply preprocessing
-        if (input->m_preprocess) {
-            for (const auto& action : input->m_preprocess->actions()) {
-                auto action_result = action(nodes, function, context);
-                nodes = std::get<0>(action_result);
-                tensor_data_updated |= std::get<1>(action_result);
-            }
+        for (const auto& action : input->get_preprocess()->actions()) {
+            auto action_result = action(nodes, function, context);
+            nodes = std::get<0>(action_result);
+            tensor_data_updated |= std::get<1>(action_result);
         }
 
         OPENVINO_ASSERT(nodes.size() == 1,
@@ -511,7 +660,8 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
     }
 
     // Post processing
-    for (const auto& output : m_impl->out_contexts) {
+    for (const auto& output_info : m_impl->m_outputs) {
+        const auto& output = output_info.m_impl;
         std::shared_ptr<op::v0::Result> result;
         Output<Node> node;
         OPENVINO_ASSERT(output, "Internal error: Invalid postprocessing output, please report a problem");
@@ -526,33 +676,30 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
         node.get_tensor().set_names({});
         result = std::dynamic_pointer_cast<op::v0::Result>(node.get_node_shared_ptr());
         // Set result layout from 'network' information
-        if (output->m_network_data && output->m_network_data->is_layout_set() && result->get_layout().empty()) {
-            result->set_layout(output->m_network_data->get_layout());
+        if (output->get_network_data()->is_layout_set() && result->get_layout().empty()) {
+            result->set_layout(output->get_network_data()->get_layout());
         }
         auto parent = result->get_input_source_output(0);
-        if (!output->m_tensor_data) {
-            output->create_tensor_data();
-        }
         PostprocessingContext context(result->get_layout());
-        if (output->m_tensor_data->is_layout_set()) {
-            context.target_layout() = output->m_tensor_data->get_layout();
+        if (output->get_tensor_data()->is_layout_set()) {
+            context.target_layout() = output->get_tensor_data()->get_layout();
         }
-        if (output->m_tensor_data->is_element_type_set()) {
-            context.target_element_type() = output->m_tensor_data->get_element_type();
+        if (output->get_tensor_data()->is_element_type_set()) {
+            context.target_element_type() = output->get_tensor_data()->get_element_type();
         }
         // Apply post-processing
         node = result->get_input_source_output(0);
-        if (output->m_postprocess) {
-            for (const auto& action : output->m_postprocess->actions()) {
-                auto action_result = action({node}, context);
-                node = std::get<0>(action_result);
-            }
+        bool post_processing_applied = false;
+        for (const auto& action : output->get_postprocess()->actions()) {
+            auto action_result = action({node}, context);
+            node = std::get<0>(action_result);
+            post_processing_applied = true;
         }
         // Implicit: Convert element type + layout to user's tensor implicitly
         PostStepsList implicit_steps;
-        if (node.get_element_type() != output->m_tensor_data->get_element_type() &&
-            output->m_tensor_data->is_element_type_set() && node.get_element_type() != element::dynamic) {
-            implicit_steps.add_convert_impl(output->m_tensor_data->get_element_type());
+        if (node.get_element_type() != output->get_tensor_data()->get_element_type() &&
+            output->get_tensor_data()->is_element_type_set() && node.get_element_type() != element::dynamic) {
+            implicit_steps.add_convert_impl(output->get_tensor_data()->get_element_type());
         }
 
         if (!context.target_layout().empty() && context.target_layout() != context.layout()) {
@@ -561,10 +708,18 @@ std::shared_ptr<Function> PrePostProcessor::build(const std::shared_ptr<Function
         for (const auto& action : implicit_steps.actions()) {
             auto action_result = action({node}, context);
             node = std::get<0>(action_result);
+            post_processing_applied = true;
         }
         node.get_node_shared_ptr()->set_friendly_name(
             result->get_input_source_output(0).get_node_shared_ptr()->get_friendly_name());
 
+        // Reset friendly name of input node to avoid names collision
+        // when there is at a new node inserted by post-processing steps
+        // If no new nodes are inserted by post-processing, then we need to preserve friendly name of input
+        // as it's required for old API correct work
+        if (post_processing_applied)
+            result->get_input_source_output(0).get_node_shared_ptr()->set_friendly_name("");
+
         // Create result
         auto new_result = std::make_shared<ov::op::v0::Result>(node);
         new_result->set_friendly_name(result->get_friendly_name());
diff --git a/ngraph/core/src/runtime/host_tensor.cpp b/ngraph/core/src/runtime/host_tensor.cpp
index 7c146736ea64c9..e7f4938b0b63b6 100644
--- a/ngraph/core/src/runtime/host_tensor.cpp
+++ b/ngraph/core/src/runtime/host_tensor.cpp
@@ -130,9 +130,11 @@ bool runtime::HostTensor::get_is_allocated() const {
 }
 
 void runtime::HostTensor::set_element_type(const element::Type& element_type) {
+    OPENVINO_SUPPRESS_DEPRECATED_START
     NGRAPH_CHECK(get_element_type().is_dynamic() || get_element_type() == element_type,
                  "Can not change a static element type");
     m_descriptor->set_element_type(element_type);
+    OPENVINO_SUPPRESS_DEPRECATED_END
 }
 
 void runtime::HostTensor::set_shape(const Shape& shape) {
@@ -141,7 +143,9 @@ void runtime::HostTensor::set_shape(const Shape& shape) {
                  shape,
                  " must be compatible with the partial shape: ",
                  get_partial_shape());
+    OPENVINO_SUPPRESS_DEPRECATED_START
     m_descriptor->set_partial_shape(shape);
+    OPENVINO_SUPPRESS_DEPRECATED_END
 }
 
 void runtime::HostTensor::set_unary(const HostTensorPtr& arg) {
diff --git a/ngraph/core/src/type.cpp b/ngraph/core/src/type.cpp
index 9efa977ccbe71d..876fa849634fc5 100644
--- a/ngraph/core/src/type.cpp
+++ b/ngraph/core/src/type.cpp
@@ -4,20 +4,47 @@
 
 #include "ngraph/type.hpp"
 
-#include "ngraph/util.hpp"
+#include "openvino/util/common_util.hpp"
 
 namespace std {
 size_t std::hash<ngraph::DiscreteTypeInfo>::operator()(const ngraph::DiscreteTypeInfo& k) const {
-    NGRAPH_SUPPRESS_DEPRECATED_START
-    size_t name_hash = hash<string>()(string(k.name));
-    size_t version_hash = hash<decltype(k.version)>()(k.version);
-    // don't use parent for hash calculation, it is not a part of type (yet)
-    return ngraph::hash_combine(vector<size_t>{name_hash, version_hash});
-    NGRAPH_SUPPRESS_DEPRECATED_END
+    return k.hash();
 }
 }  // namespace std
 
 namespace ov {
+
+size_t DiscreteTypeInfo::hash() const {
+    if (hash_value != 0)
+        return hash_value;
+    size_t name_hash = name ? std::hash<std::string>()(std::string(name)) : 0;
+    size_t version_hash = std::hash<decltype(version)>()(version);
+    size_t version_id_hash = version_id ? std::hash<std::string>()(std::string(version_id)) : 0;
+
+    return ov::util::hash_combine(std::vector<size_t>{name_hash, version_hash, version_id_hash});
+}
+
+size_t DiscreteTypeInfo::hash() {
+    if (hash_value == 0)
+        hash_value = static_cast<const DiscreteTypeInfo*>(this)->hash();
+    return hash_value;
+}
+
+bool DiscreteTypeInfo::is_castable(const DiscreteTypeInfo& target_type) const {
+    return *this == target_type || (parent && parent->is_castable(target_type));
+}
+
+std::string DiscreteTypeInfo::get_version() const {
+    if (version_id) {
+        return std::string(version_id);
+    }
+    return std::to_string(version);
+}
+
+DiscreteTypeInfo::operator std::string() const {
+    return std::string(name) + "_" + get_version();
+}
+
 std::ostream& operator<<(std::ostream& s, const DiscreteTypeInfo& info) {
     std::string version_id = info.version_id ? info.version_id : "(empty)";
     s << "DiscreteTypeInfo{name: " << info.name << ", version_id: " << version_id << ", old_version: " << info.version
@@ -33,22 +60,26 @@ std::ostream& operator<<(std::ostream& s, const DiscreteTypeInfo& info) {
 
 // parent is commented to fix type relaxed operations
 bool DiscreteTypeInfo::operator<(const DiscreteTypeInfo& b) const {
-    if (version_id == nullptr || b.version_id == nullptr)
-        return version < b.version ||
-               (version == b.version && strcmp(name, b.name) < 0);  // ||
-                                                                    // (version == b.version && strcmp(name, b.name) ==
-                                                                    // 0 && parent && b.parent && *parent < *b.parent);
-    else
-        return strcmp(version_id, b.version_id) < 0 ||
-               (strcmp(version_id, b.version_id) == 0 && strcmp(name, b.name) < 0);  // ||
-    // (strcmp(version_id, b.version_id) == 0 && strcmp(name, b.name) == 0 && parent && b.parent &&
-    //  *parent < *b.parent);
+    if (version < b.version)
+        return true;
+    if (version == b.version && name != nullptr && b.name != nullptr) {
+        int cmp_status = strcmp(name, b.name);
+        if (cmp_status < 0)
+            return true;
+        if (cmp_status == 0) {
+            std::string v_id(version_id == nullptr ? "" : version_id);
+            std::string bv_id(b.version_id == nullptr ? "" : b.version_id);
+            if (v_id < bv_id)
+                return true;
+        }
+    }
+
+    return false;
 }
 bool DiscreteTypeInfo::operator==(const DiscreteTypeInfo& b) const {
-    if (version_id == nullptr || b.version_id == nullptr)
-        return version == b.version && strcmp(name, b.name) == 0;  // && parent == b.parent;
-    else
-        return strcmp(version_id, b.version_id) == 0 && strcmp(name, b.name) == 0;  // && parent == b.parent;
+    if (hash_value != 0 && b.hash_value != 0)
+        return hash() == b.hash();
+    return version == b.version && strcmp(name, b.name) == 0;
 }
 bool DiscreteTypeInfo::operator<=(const DiscreteTypeInfo& b) const {
     return *this == b || *this < b;
diff --git a/ngraph/core/src/util.cpp b/ngraph/core/src/util.cpp
index 563c63bf7942fe..2c8b59cc5b3076 100644
--- a/ngraph/core/src/util.cpp
+++ b/ngraph/core/src/util.cpp
@@ -78,11 +78,7 @@ vector<string> ngraph::split(const string& src, char delimiter, bool do_trim) {
 }
 
 size_t ngraph::hash_combine(const std::vector<size_t>& list) {
-    size_t seed = 0;
-    for (size_t v : list) {
-        seed ^= v + 0x9e3779b9 + (seed << 6) + (seed >> 2);
-    }
-    return seed;
+    return ov::util::hash_combine(list);
 }
 
 void* ngraph::ngraph_malloc(size_t size) {
diff --git a/ngraph/core/src/validation_util.cpp b/ngraph/core/src/validation_util.cpp
index 10a1844246573a..244be50b167dd6 100644
--- a/ngraph/core/src/validation_util.cpp
+++ b/ngraph/core/src/validation_util.cpp
@@ -1371,6 +1371,37 @@ shared_ptr<op::Constant> ngraph::get_constant_min_of_type(element::Type_t t) {
     }
 }
 
+std::shared_ptr<op::Constant> ngraph::get_constant_lowest_of_type(element::Type_t t) {
+#define NGRAPH_TYPE_TO_LOWEST_CONST(t)                                                                             \
+    case t:                                                                                                        \
+        return op::Constant::create(t,                                                                             \
+                                    {},                                                                            \
+                                    {std::numeric_limits<typename element_type_traits<t>::value_type>::lowest()}); \
+        break
+
+    switch (t) {
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::boolean);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::bf16);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::f16);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::f32);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::f64);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::i8);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::i16);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::i32);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::i64);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::u1);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::u8);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::u16);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::u32);
+        NGRAPH_TYPE_TO_LOWEST_CONST(element::u64);
+
+    case element::undefined:
+    case element::dynamic:
+    default:
+        return nullptr;
+    }
+}
+
 namespace {
 
 HostTensorPtr equality_mask(const HostTensorPtr& tensor, const shared_ptr<op::Constant>& constant) {
diff --git a/ngraph/frontend/frontend_manager/CMakeLists.txt b/ngraph/frontend/frontend_manager/CMakeLists.txt
index 8ac00ebef8761a..cadaefceb59a74 100644
--- a/ngraph/frontend/frontend_manager/CMakeLists.txt
+++ b/ngraph/frontend/frontend_manager/CMakeLists.txt
@@ -22,16 +22,7 @@ source_group("src" FILES ${LIBRARY_SRC})
 source_group("include" FILES ${LIBRARY_HEADERS})
 source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
 
-# Static library
-
-add_library(${TARGET_NAME}_static STATIC ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
-add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME}_static)
-target_link_libraries(${TARGET_NAME}_static PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC ngraph)
-target_include_directories(${TARGET_NAME}_static PUBLIC ${FRONTEND_INCLUDE_DIR})
-target_include_directories(${TARGET_NAME}_static PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
-target_compile_definitions(${TARGET_NAME}_static PUBLIC USE_STATIC_FRONTEND_MANAGER)
-
-# Shared library - need to recompile object files to export necessary symbols
+# create library
 
 add_library(${TARGET_NAME} ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
 add_library(ngraph::${TARGET_NAME} ALIAS ${TARGET_NAME})
@@ -55,6 +46,21 @@ if(COMMAND ie_add_vs_version_file)
                            FILEDESCRIPTION "Manager of OpenVINO nGraph Frontends")
 endif()
 
+# create static library if needed
+
+if(BUILD_SHARED_LIBS)
+    add_library(${TARGET_NAME}_static STATIC ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
+    add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME}_static)
+    target_link_libraries(${TARGET_NAME}_static PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC ngraph)
+    target_include_directories(${TARGET_NAME}_static PUBLIC
+        $<BUILD_INTERFACE:${FRONTEND_INCLUDE_DIR}>)
+    target_include_directories(${TARGET_NAME}_static PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
+    target_compile_definitions(${TARGET_NAME}_static PUBLIC USE_STATIC_FRONTEND_MANAGER)
+    ov_install_static_lib(${TARGET_NAME}_static ngraph)
+else()
+    add_library(${TARGET_NAME}::static ALIAS ${TARGET_NAME})
+endif()
+
 # Installation rules for shared version only
 
 set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME frontend::manager)
diff --git a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend.hpp b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend.hpp
index a58bf9de640ef3..583d8eb35916f9 100644
--- a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend.hpp
+++ b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend.hpp
@@ -10,12 +10,12 @@
 
 #include "frontend_manager_defs.hpp"
 #include "input_model.hpp"
-#include "ngraph/function.hpp"
-#include "ngraph/variant.hpp"
 #include "openvino/core/extension.hpp"
+#include "openvino/core/function.hpp"
 #include "openvino/core/op_extension.hpp"
+#include "openvino/core/variant.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 /// \brief An interface for identifying a frontend for a particular framework.
 /// Provides an ability to load and convert of input model
@@ -59,11 +59,11 @@ class FRONTEND_API FrontEnd {
     /// possible
     /// \param model Input model
     /// \return fully converted nGraph function
-    virtual std::shared_ptr<ngraph::Function> convert(InputModel::Ptr model) const;
+    virtual std::shared_ptr<ov::Function> convert(InputModel::Ptr model) const;
 
     /// \brief Completely convert the remaining, not converted part of a function.
     /// \param partiallyConverted partially converted nGraph function
-    virtual void convert(std::shared_ptr<ngraph::Function> partially_converted) const;
+    virtual void convert(std::shared_ptr<ov::Function> partially_converted) const;
 
     /// \brief Convert only those parts of the model that can be converted leaving others
     /// as-is. Converted parts are not normalized by additional transformations; normalize
@@ -71,18 +71,18 @@ class FRONTEND_API FrontEnd {
     /// conversion process.
     /// \param model Input model
     /// \return partially converted nGraph function
-    virtual std::shared_ptr<ngraph::Function> convert_partially(InputModel::Ptr model) const;
+    virtual std::shared_ptr<ov::Function> convert_partially(InputModel::Ptr model) const;
 
     /// \brief Convert operations with one-to-one mapping with decoding nodes.
     /// Each decoding node is an nGraph node representing a single FW operation node with
     /// all attributes represented in FW-independent way.
     /// \param model Input model
     /// \return nGraph function after decoding
-    virtual std::shared_ptr<ngraph::Function> decode(InputModel::Ptr model) const;
+    virtual std::shared_ptr<ov::Function> decode(InputModel::Ptr model) const;
 
     /// \brief Runs normalization passes on function that was loaded with partial conversion
     /// \param function partially converted nGraph function
-    virtual void normalize(std::shared_ptr<ngraph::Function> function) const;
+    virtual void normalize(std::shared_ptr<ov::Function> function) const;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
     /// if frontend is selected automatically by FrontEndManager::load_by_model
@@ -107,7 +107,7 @@ class FRONTEND_API FrontEnd {
 #endif
 
     /// @brief Registers extension
-    /// @param extension Extension class which is inherited from ov::BaseExtension class
+    /// @param extension Extension class which is inherited from ov::BaseOpExtension class
     template <class T, typename std::enable_if<std::is_base_of<ov::Extension, T>::value, bool>::type = true>
     void add_extension(const T& extension) {
         std::shared_ptr<ov::Extension> ext = std::make_shared<T>(extension);
@@ -136,4 +136,4 @@ inline bool FrontEnd::supported(const std::vector<std::shared_ptr<Variant>>& var
 
 }  // namespace frontend
 
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_exceptions.hpp b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_exceptions.hpp
index f725b4244f8c44..f95c499cb4dc68 100644
--- a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_exceptions.hpp
+++ b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_exceptions.hpp
@@ -8,42 +8,42 @@
 #include <string>
 
 #include "frontend_manager_defs.hpp"
-#include "ngraph/check.hpp"
+#include "openvino/core/except.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
-class FRONTEND_API GeneralFailure : public CheckFailure {
+class FRONTEND_API GeneralFailure : public AssertFailure {
 public:
     GeneralFailure(const CheckLocInfo& check_loc_info, const std::string& context, const std::string& explanation)
-        : CheckFailure(check_loc_info, "FrontEnd API failed with GeneralFailure: " + context, explanation) {}
+        : AssertFailure(check_loc_info, "FrontEnd API failed with GeneralFailure: " + context, explanation) {}
 };
 
-class FRONTEND_API InitializationFailure : public CheckFailure {
+class FRONTEND_API InitializationFailure : public AssertFailure {
 public:
     InitializationFailure(const CheckLocInfo& check_loc_info,
                           const std::string& context,
                           const std::string& explanation)
-        : CheckFailure(check_loc_info, "FrontEnd API failed with InitializationFailure: " + context, explanation) {}
+        : AssertFailure(check_loc_info, "FrontEnd API failed with InitializationFailure: " + context, explanation) {}
 };
 
-class FRONTEND_API OpValidationFailure : public CheckFailure {
+class FRONTEND_API OpValidationFailure : public AssertFailure {
 public:
     OpValidationFailure(const CheckLocInfo& check_loc_info, const std::string& context, const std::string& explanation)
-        : CheckFailure(check_loc_info, "FrontEnd API failed with OpValidationFailure: " + context, explanation) {}
+        : AssertFailure(check_loc_info, "FrontEnd API failed with OpValidationFailure: " + context, explanation) {}
 };
 
-class FRONTEND_API OpConversionFailure : public CheckFailure {
+class FRONTEND_API OpConversionFailure : public AssertFailure {
 public:
     OpConversionFailure(const CheckLocInfo& check_loc_info, const std::string& context, const std::string& explanation)
-        : CheckFailure(check_loc_info, "FrontEnd API failed with OpConversionFailure: " + context, explanation) {}
+        : AssertFailure(check_loc_info, "FrontEnd API failed with OpConversionFailure: " + context, explanation) {}
 };
 
-class FRONTEND_API NotImplementedFailure : public CheckFailure {
+class FRONTEND_API NotImplementedFailure : public AssertFailure {
 public:
     NotImplementedFailure(const CheckLocInfo& check_loc_info,
                           const std::string& context,
                           const std::string& explanation)
-        : CheckFailure(check_loc_info, "FrontEnd API failed with NotImplementedFailure: " + context, explanation) {}
+        : AssertFailure(check_loc_info, "FrontEnd API failed with NotImplementedFailure: " + context, explanation) {}
 };
 
 /// \brief Macro to check whether a boolean condition holds.
@@ -51,39 +51,39 @@ class FRONTEND_API NotImplementedFailure : public CheckFailure {
 /// \param ... Additional error message info to be added to the error message via the `<<`
 ///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
 ///            i.e., only if the `cond` evalutes to `false`.
-/// \throws ::ngraph::frontend::GeneralFailure if `cond` is false.
-#define FRONT_END_GENERAL_CHECK(...) NGRAPH_CHECK_HELPER(::ngraph::frontend::GeneralFailure, "", __VA_ARGS__)
+/// \throws ::ov::frontend::GeneralFailure if `cond` is false.
+#define FRONT_END_GENERAL_CHECK(...) OPENVINO_ASSERT_HELPER(::ov::frontend::GeneralFailure, "", __VA_ARGS__)
 
 /// \brief Macro to check whether a boolean condition holds.
 /// \param cond Condition to check
 /// \param ... Additional error message info to be added to the error message via the `<<`
 ///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
 ///            i.e., only if the `cond` evalutes to `false`.
-/// \throws ::ngraph::frontend::InitializationFailure if `cond` is false.
+/// \throws ::ov::frontend::InitializationFailure if `cond` is false.
 #define FRONT_END_INITIALIZATION_CHECK(...) \
-    NGRAPH_CHECK_HELPER(::ngraph::frontend::InitializationFailure, "", __VA_ARGS__)
+    OPENVINO_ASSERT_HELPER(::ov::frontend::InitializationFailure, "", __VA_ARGS__)
 
 /// \brief Macro to check whether a boolean condition holds.
 /// \param cond Condition to check
 /// \param ... Additional error message info to be added to the error message via the `<<`
 ///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
 ///            i.e., only if the `cond` evalutes to `false`.
-/// \throws ::ngraph::frontend::OpConversionFailure if `cond` is false.
-#define FRONT_END_OP_CONVERSION_CHECK(...) NGRAPH_CHECK_HELPER(::ngraph::frontend::OpConversionFailure, "", __VA_ARGS__)
+/// \throws ::ov::frontend::OpConversionFailure if `cond` is false.
+#define FRONT_END_OP_CONVERSION_CHECK(...) OPENVINO_ASSERT_HELPER(::ov::frontend::OpConversionFailure, "", __VA_ARGS__)
 
 /// \brief Assert macro.
 /// \param NAME Name of the function that is not implemented
-/// \throws ::ngraph::frontend::NotImplementedFailure
-#define FRONT_END_NOT_IMPLEMENTED(NAME)                            \
-    NGRAPH_CHECK_HELPER(::ngraph::frontend::NotImplementedFailure, \
-                        "",                                        \
-                        false,                                     \
-                        #NAME " is not implemented for this FrontEnd class")
+/// \throws ::ov::frontend::NotImplementedFailure
+#define FRONT_END_NOT_IMPLEMENTED(NAME)                           \
+    OPENVINO_ASSERT_HELPER(::ov::frontend::NotImplementedFailure, \
+                           "",                                    \
+                           false,                                 \
+                           #NAME " is not implemented for this FrontEnd class")
 
 /// \brief Assert macro.
 /// \param MSG Error message
-/// \throws ::ngraph::frontend::GeneralFailure
+/// \throws ::ov::frontend::GeneralFailure
 #define FRONT_END_THROW(MSG) FRONT_END_GENERAL_CHECK(false, MSG)
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager.hpp b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager.hpp
index 607636e9c344d6..1414aedf19fc17 100644
--- a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager.hpp
+++ b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager.hpp
@@ -10,10 +10,10 @@
 
 #include "frontend.hpp"
 #include "frontend_manager_defs.hpp"
-#include "ngraph/variant.hpp"
+#include "openvino/core/variant.hpp"
 #include "parameters.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 // -------------- FrontEndManager -----------------
 using FrontEndFactory = std::function<FrontEnd::Ptr()>;
@@ -98,4 +98,4 @@ struct FrontEndPluginInfo {
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager_defs.hpp b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager_defs.hpp
index 662ce487923074..708fa83f52500d 100644
--- a/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager_defs.hpp
+++ b/ngraph/frontend/frontend_manager/include/frontend_manager/frontend_manager_defs.hpp
@@ -4,7 +4,7 @@
 
 #pragma once
 
-#include "ngraph/visibility.hpp"
+#include "openvino/core/visibility.hpp"
 
 // Increment each time when FrontEnd/InputModel/Place interface is changed
 #define OV_FRONTEND_API_VERSION 1
diff --git a/ngraph/frontend/frontend_manager/include/frontend_manager/input_model.hpp b/ngraph/frontend/frontend_manager/include/frontend_manager/input_model.hpp
index 6e930e4ecfba1d..c20840de6e56d0 100644
--- a/ngraph/frontend/frontend_manager/include/frontend_manager/input_model.hpp
+++ b/ngraph/frontend/frontend_manager/include/frontend_manager/input_model.hpp
@@ -9,11 +9,11 @@
 #include <vector>
 
 #include "frontend_manager_defs.hpp"
-#include "ngraph/partial_shape.hpp"
-#include "ngraph/type/element_type.hpp"
+#include "openvino/core/partial_shape.hpp"
+#include "openvino/core/type/element_type.hpp"
 #include "place.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 /// \brief InputModel class represents an original, not yet converted model graph in a
 /// framework format given services to find places of interest in a graph or specialize/edit
@@ -173,17 +173,17 @@ class FRONTEND_API InputModel {
     /// converted to ngraph.
     /// \param place Model place
     /// \param shape Partial shape for this place
-    virtual void set_partial_shape(Place::Ptr place, const ngraph::PartialShape& shape);
+    virtual void set_partial_shape(Place::Ptr place, const ov::PartialShape& shape);
 
     /// \brief Returns current partial shape used for this place
     /// \param place Model place
     /// \return Partial shape for this place
-    virtual ngraph::PartialShape get_partial_shape(Place::Ptr place) const;
+    virtual ov::PartialShape get_partial_shape(Place::Ptr place) const;
 
     /// \brief Sets new element type for a place
     /// \param place Model place
     /// \param type New element type
-    virtual void set_element_type(Place::Ptr place, const ngraph::element::Type& type);
+    virtual void set_element_type(Place::Ptr place, const ov::element::Type& type);
 
     /// \brief Freezes a tensor with statically defined value or replace existing value for
     /// already constant node or tensor
@@ -200,4 +200,4 @@ class FRONTEND_API InputModel {
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/frontend_manager/include/frontend_manager/parameters.hpp b/ngraph/frontend/frontend_manager/include/frontend_manager/parameters.hpp
index d5f1ec28e6ec8d..c24205620a8ad2 100644
--- a/ngraph/frontend/frontend_manager/include/frontend_manager/parameters.hpp
+++ b/ngraph/frontend/frontend_manager/include/frontend_manager/parameters.hpp
@@ -9,10 +9,10 @@
 #include <string>
 
 #include "frontend_manager_defs.hpp"
-#include "ngraph/op/constant.hpp"
 #include "ngraph/opsets/opset.hpp"
 #include "openvino/core/rtti.hpp"
 #include "openvino/core/variant.hpp"
+#include "openvino/op/constant.hpp"
 
 namespace ov {
 
diff --git a/ngraph/frontend/frontend_manager/include/frontend_manager/place.hpp b/ngraph/frontend/frontend_manager/include/frontend_manager/place.hpp
index 033bc01a6cbeba..1c3f2c6ef6eb31 100644
--- a/ngraph/frontend/frontend_manager/include/frontend_manager/place.hpp
+++ b/ngraph/frontend/frontend_manager/include/frontend_manager/place.hpp
@@ -10,7 +10,7 @@
 
 #include "frontend_manager_defs.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 /// \brief An interface for identifying a place in a graph and iterate over it; can refer to
 /// an operation node, tensor, port etc.
@@ -303,4 +303,4 @@ class FRONTEND_API Place {
     virtual bool is_equal_data(Ptr another) const;
 };
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/frontend_manager/src/frontend_manager.cpp b/ngraph/frontend/frontend_manager/src/frontend_manager.cpp
index 68e15dc3e1f64a..e1c82ad3bdb5d5 100644
--- a/ngraph/frontend/frontend_manager/src/frontend_manager.cpp
+++ b/ngraph/frontend/frontend_manager/src/frontend_manager.cpp
@@ -12,8 +12,8 @@
 #include "so_extension.hpp"
 #include "utils.hpp"
 
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov;
+using namespace ov::frontend;
 
 //----------- FrontEndManager ---------------------------
 class FrontEndManager::Impl {
diff --git a/ngraph/frontend/frontend_manager/src/plugin_loader.cpp b/ngraph/frontend/frontend_manager/src/plugin_loader.cpp
index cd2c6d032c471e..53426c2912d8bb 100644
--- a/ngraph/frontend/frontend_manager/src/plugin_loader.cpp
+++ b/ngraph/frontend/frontend_manager/src/plugin_loader.cpp
@@ -16,23 +16,26 @@
 
 #include <sys/stat.h>
 
+#include <ngraph/log.hpp>
 #include <string>
 #include <vector>
 
 #include "openvino/util/file_util.hpp"
 #include "plugin_loader.hpp"
 
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov;
+using namespace ov::frontend;
 
 #ifdef WIN32
 #    define DLOPEN(file_str) LoadLibrary(TEXT(file_str.c_str()))
 #    define DLSYM(obj, func) GetProcAddress(obj, func)
 #    define DLCLOSE(obj)     FreeLibrary(obj)
+#    define DLERROR()        ""
 #else
 #    define DLOPEN(file_str) dlopen(file_str.c_str(), RTLD_LAZY)
 #    define DLSYM(obj, func) dlsym(obj, func)
 #    define DLCLOSE(obj)     dlclose(obj)
+#    define DLERROR()        dlerror()
 #endif
 
 // TODO: change to std::filesystem for C++17
@@ -70,12 +73,13 @@ static std::vector<std::string> list_files(const std::string& path) {
     NGRAPH_SUPPRESS_DEPRECATED_END
 }
 
-std::vector<PluginData> ngraph::frontend::load_plugins(const std::string& dir_name) {
+std::vector<PluginData> ov::frontend::load_plugins(const std::string& dir_name) {
     auto files = list_files(dir_name);
     std::vector<PluginData> res;
     for (const auto& file : files) {
         auto shared_object = DLOPEN(file);
         if (!shared_object) {
+            NGRAPH_DEBUG << "Error loading FrontEnd " << file << " " << DLERROR() << std::endl;
             continue;
         }
 
diff --git a/ngraph/frontend/frontend_manager/src/plugin_loader.hpp b/ngraph/frontend/frontend_manager/src/plugin_loader.hpp
index 0ee22038e406cf..3319006a535552 100644
--- a/ngraph/frontend/frontend_manager/src/plugin_loader.hpp
+++ b/ngraph/frontend/frontend_manager/src/plugin_loader.hpp
@@ -14,7 +14,7 @@ static const char FileSeparator[] = "/";
 static const char PathSeparator[] = ":";
 #endif  // _WIN32
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 /// Plugin library handle wrapper. On destruction calls internal function which frees
 /// library handle
@@ -51,4 +51,4 @@ struct PluginData {
 std::vector<PluginData> load_plugins(const std::string& dir_name);
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/frontend_manager/src/utils.cpp b/ngraph/frontend/frontend_manager/src/utils.cpp
index c9b87b93c7e50e..be0daaed730c81 100644
--- a/ngraph/frontend/frontend_manager/src/utils.cpp
+++ b/ngraph/frontend/frontend_manager/src/utils.cpp
@@ -42,7 +42,7 @@ static std::string _get_frontend_library_path() {
     CHAR ie_library_path[MAX_PATH];
     HMODULE hm = NULL;
     if (!GetModuleHandleExA(GET_MODULE_HANDLE_EX_FLAG_FROM_ADDRESS | GET_MODULE_HANDLE_EX_FLAG_UNCHANGED_REFCOUNT,
-                            reinterpret_cast<LPSTR>(ngraph::frontend::get_frontend_library_path),
+                            reinterpret_cast<LPSTR>(ov::frontend::get_frontend_library_path),
                             &hm)) {
         FRONT_END_INITIALIZATION_CHECK(false, "GetModuleHandle returned ", GetLastError());
     }
@@ -50,13 +50,13 @@ static std::string _get_frontend_library_path() {
     return get_path_name(std::string(ie_library_path));
 #elif defined(__APPLE__) || defined(__linux__)
     Dl_info info;
-    dladdr(reinterpret_cast<void*>(ngraph::frontend::get_frontend_library_path), &info);
+    dladdr(reinterpret_cast<void*>(ov::frontend::get_frontend_library_path), &info);
     return get_path_name(std::string(info.dli_fname)).c_str();
 #else
 #    error "Unsupported OS"
 #endif  // _WIN32
 }
 
-std::string ngraph::frontend::get_frontend_library_path() {
+std::string ov::frontend::get_frontend_library_path() {
     return _get_frontend_library_path();
 }
diff --git a/ngraph/frontend/frontend_manager/src/utils.hpp b/ngraph/frontend/frontend_manager/src/utils.hpp
index 50ad9201240af8..d60d5b16ad982a 100644
--- a/ngraph/frontend/frontend_manager/src/utils.hpp
+++ b/ngraph/frontend/frontend_manager/src/utils.hpp
@@ -6,8 +6,8 @@
 
 #include "frontend_manager/frontend_manager_defs.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
-FRONTEND_API std::string get_frontend_library_path();
+std::string get_frontend_library_path();
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/ir/include/ir_frontend/frontend.hpp b/ngraph/frontend/ir/include/ir_frontend/frontend.hpp
index 7b1faa012ad219..0ba82d81f10f79 100644
--- a/ngraph/frontend/ir/include/ir_frontend/frontend.hpp
+++ b/ngraph/frontend/ir/include/ir_frontend/frontend.hpp
@@ -4,12 +4,11 @@
 
 #pragma once
 
-#include <frontend_manager/frontend.hpp>
-#include <ngraph/variant.hpp>
-
+#include "frontend_manager/frontend.hpp"
+#include "openvino/core/variant.hpp"
 #include "utility.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 
 class IR_API FrontEndIR : public FrontEnd {
@@ -48,4 +47,4 @@ class IR_API FrontEndIR : public FrontEnd {
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/ir/include/ir_frontend/model.hpp b/ngraph/frontend/ir/include/ir_frontend/model.hpp
index 2116a0fe50eaf4..057a9d23c26d11 100644
--- a/ngraph/frontend/ir/include/ir_frontend/model.hpp
+++ b/ngraph/frontend/ir/include/ir_frontend/model.hpp
@@ -4,15 +4,15 @@
 
 #pragma once
 
-#include <frontend_manager/frontend_manager.hpp>
-#include <ir_frontend/utility.hpp>
 #include <istream>
 #include <memory>
-#include <ngraph/ngraph.hpp>
-#include <openvino/core/op_extension.hpp>
 
-namespace ngraph {
+#include "frontend_manager/frontend_manager.hpp"
+#include "ir_frontend/utility.hpp"
+
+namespace ov {
 namespace frontend {
+
 class IR_API InputModelIR : public InputModel {
     friend class FrontEndIR;
     class InputModelIRImpl;
@@ -27,4 +27,4 @@ class IR_API InputModelIR : public InputModel {
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/ir/src/frontend.cpp b/ngraph/frontend/ir/src/frontend.cpp
index 91ffea9996a7cc..b02cb3a58edbd9 100644
--- a/ngraph/frontend/ir/src/frontend.cpp
+++ b/ngraph/frontend/ir/src/frontend.cpp
@@ -7,18 +7,18 @@
 #include <array>
 #include <vector>
 
-#include "ir_deserializer.hpp"
 #include "ir_frontend/model.hpp"
 #include "ir_frontend/utility.hpp"
-#include "ngraph/variant.hpp"
-#include "openvino/core/op_extension.hpp"
+#include "ngraph/runtime/aligned_buffer.hpp"
+#include "ngraph/runtime/shared_buffer.hpp"
+#include "openvino/core/variant.hpp"
 #include "openvino/util/file_util.hpp"
 #include "so_extension.hpp"
 #include "xml_parse_utils.h"
 
-using namespace ngraph;
+using namespace ov;
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace {
 
@@ -215,7 +215,7 @@ InputModel::Ptr FrontEndIR::load_impl(const std::vector<std::shared_ptr<Variant>
         bin_stream.read(aligned_weights_buffer->get_ptr<char>(), aligned_weights_buffer->size());
         bin_stream.close();
 
-        weights = std::make_shared<runtime::SharedBuffer<std::shared_ptr<runtime::AlignedBuffer>>>(
+        weights = std::make_shared<ngraph::runtime::SharedBuffer<std::shared_ptr<ngraph::runtime::AlignedBuffer>>>(
             aligned_weights_buffer->get_ptr<char>(),
             aligned_weights_buffer->size(),
             aligned_weights_buffer);
@@ -224,7 +224,7 @@ InputModel::Ptr FrontEndIR::load_impl(const std::vector<std::shared_ptr<Variant>
     return create_input_model();
 }
 
-std::shared_ptr<ngraph::Function> FrontEndIR::convert(InputModel::Ptr model) const {
+std::shared_ptr<ov::Function> FrontEndIR::convert(InputModel::Ptr model) const {
     auto ir_model = std::dynamic_pointer_cast<InputModelIR>(model);
     OPENVINO_ASSERT(ir_model != nullptr);
     return ir_model->convert();
@@ -234,9 +234,9 @@ std::string FrontEndIR::get_name() const {
     return "ir";
 }
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
 
-extern "C" IR_API ngraph::frontend::FrontEndVersion GetAPIVersion() {
+extern "C" IR_API ov::frontend::FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
diff --git a/ngraph/frontend/ir/src/ir_deserializer.cpp b/ngraph/frontend/ir/src/ir_deserializer.cpp
index 4ced1f2f2eb6f3..8908fb6c03a242 100644
--- a/ngraph/frontend/ir/src/ir_deserializer.cpp
+++ b/ngraph/frontend/ir/src/ir_deserializer.cpp
@@ -2,18 +2,18 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <xml_parse_utils.h>
+#include "ir_deserializer.hpp"
 
-#include <ie_ngraph_utils.hpp>
-#include <ir_deserializer.hpp>
-#include <ngraph/op/util/framework_node.hpp>
-#include <ngraph/opsets/opset1.hpp>
 #include <pugixml.hpp>
-#include <rt_info_deserializer.hpp>
-#include <transformations/rt_info/attributes.hpp>
-#include <utils.hpp>
 
+#include "ie_ngraph_utils.hpp"
 #include "ir_frontend/model.hpp"
+#include "ngraph/op/util/framework_node.hpp"
+#include "ngraph/opsets/opset1.hpp"
+#include "rt_info_deserializer.hpp"
+#include "transformations/rt_info/attributes.hpp"
+#include "utils.hpp"
+#include "xml_parse_utils.h"
 
 using namespace ov;
 
@@ -727,6 +727,9 @@ std::shared_ptr<ngraph::Node> XmlDeserializer::createNode(const std::vector<ngra
                 IE_THROW() << "rt_info attribute: " << attribute_name << " has no \"version\" field";
             }
             const auto& type_info = ov::DiscreteTypeInfo(attribute_name.c_str(), 0, attribute_version.c_str());
+            if (rt_info.count(type_info)) {
+                IE_THROW() << "multiple rt_info attributes are detected: " << type_info;
+            }
             if (auto attr = attrs_factory.create_by_type_info(type_info)) {
                 RTInfoDeserializer attribute_visitor(item);
                 if (attr->visit_attributes(attribute_visitor)) {
diff --git a/ngraph/frontend/ir/src/ir_deserializer.hpp b/ngraph/frontend/ir/src/ir_deserializer.hpp
index 90398fc42f28ad..192d8b7276d89c 100644
--- a/ngraph/frontend/ir/src/ir_deserializer.hpp
+++ b/ngraph/frontend/ir/src/ir_deserializer.hpp
@@ -4,25 +4,26 @@
 
 #pragma once
 
-#include <xml_parse_utils.h>
-
-#include <ie_ngraph_utils.hpp>
-#include <ir_frontend/model.hpp>
 #include <istream>
 #include <memory>
-#include <ngraph/ngraph.hpp>
 #include <pugixml.hpp>
-#include <utils.hpp>
 
+#include "ie_ngraph_utils.hpp"
+#include "ir_frontend/model.hpp"
+#include "openvino/core/attribute_visitor.hpp"
 #include "openvino/core/op_extension.hpp"
+#include "openvino/op/loop.hpp"
+#include "openvino/op/util/sub_graph_base.hpp"
+#include "utils.hpp"
+#include "xml_parse_utils.h"
 
 namespace ov {
 
 struct GenericLayerParams {
     struct LayerPortData {
         size_t portId;
-        std::vector<ngraph::Dimension> dims;
-        ngraph::element::Type_t precision;
+        std::vector<ov::Dimension> dims;
+        ov::element::Type_t precision;
         std::unordered_set<std::string> names;
     };
     size_t layerId;
@@ -55,13 +56,13 @@ struct GenericLayerParams {
     }
 };
 
-class XmlDeserializer : public ngraph::AttributeVisitor {
+class XmlDeserializer : public ov::AttributeVisitor {
 public:
     explicit XmlDeserializer(const pugi::xml_node& node,
                              const std::shared_ptr<ngraph::runtime::AlignedBuffer>& weights,
                              const std::unordered_map<std::string, ngraph::OpSet>& opsets,
                              const std::unordered_map<ov::DiscreteTypeInfo, ov::BaseOpExtension::Ptr>& extensions,
-                             std::unordered_map<std::string, std::shared_ptr<ngraph::Variable>>& variables,
+                             std::unordered_map<std::string, std::shared_ptr<ov::op::util::Variable>>& variables,
                              size_t version)
         : m_node(node),
           m_weights(weights),
@@ -70,13 +71,13 @@ class XmlDeserializer : public ngraph::AttributeVisitor {
           m_variables(variables),
           m_version(version) {}
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::string>& value) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::string>& value) override {
         std::string val;
         if (!getStrAttribute(m_node.child("data"), name, val))
             return;
         value.set(val);
     }
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<bool>& value) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<bool>& value) override {
         std::string val;
         if (!getStrAttribute(m_node.child("data"), name, val))
             return;
@@ -93,46 +94,45 @@ class XmlDeserializer : public ngraph::AttributeVisitor {
             return;
         value.set(is_true);
     }
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<void>& adapter) override;
+    void on_adapter(const std::string& name, ov::ValueAccessor<void>& adapter) override;
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<double>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<double>& adapter) override {
         std::string val;
         if (!getStrAttribute(m_node.child("data"), name, val))
             return;
         adapter.set(stringToType<double>(val));
     }
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<int64_t>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<int64_t>& adapter) override {
         std::string val;
         if (!getStrAttribute(m_node.child("data"), name, val))
             return;
         adapter.set(stringToType<int64_t>(val));
     }
 
-    void on_adapter(const std::string& name,
-                    ngraph::ValueAccessor<std::shared_ptr<ngraph::Function>>& adapter) override;
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::shared_ptr<ov::Function>>& adapter) override;
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<int32_t>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int32_t>>& adapter) override {
         std::vector<int32_t> value;
         if (!getParameters<int32_t>(m_node.child("data"), name, value))
             return;
         adapter.set(value);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<int64_t>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int64_t>>& adapter) override {
         std::vector<int64_t> value;
         if (!getParameters<int64_t>(m_node.child("data"), name, value))
             return;
         adapter.set(value);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<float>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<float>>& adapter) override {
         std::vector<float> value;
         if (!getParameters<float>(m_node.child("data"), name, value))
             return;
         adapter.set(value);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<std::string>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<std::string>>& adapter) override {
         std::vector<std::string> value;
         if (!getParameters<std::string>(m_node.child("data"), name, value))
             return;
@@ -149,41 +149,41 @@ class XmlDeserializer : public ngraph::AttributeVisitor {
     /// \brief Traverses port_map in order to create vector of InputDescription shared_ptrs.
     /// Shall be used only for ops which have port_map attribute.
     /// \param node xml op representation
-    std::vector<std::shared_ptr<ngraph::op::util::SubGraphOp::InputDescription>>
+    std::vector<std::shared_ptr<ov::op::util::SubGraphOp::InputDescription>>
     parseInputDescription(const pugi::xml_node& node, const std::string& body_name, const std::string& port_map_name);
     /// \brief Traverses port_map in order to create vector of OutputDescription shared_ptrs.
     /// Shall be used only for ops which have port_map attribute.
     /// \param node xml op representation
-    std::vector<std::shared_ptr<ngraph::op::util::SubGraphOp::OutputDescription>>
+    std::vector<std::shared_ptr<ov::op::util::SubGraphOp::OutputDescription>>
     parseOutputDescription(const pugi::xml_node& node, const std::string& body_name, const std::string& port_map_name);
 
     // TODO consider to call only once per layer/TI-Loop node
     IoMap updated_io_map(const pugi::xml_node& node, const pugi::xml_node& body_node);
 
-    /// \brief Traverses xml node representation in order to create nGraph function for it.
+    /// \brief Traverses xml node representation in order to create ov function for it.
     /// \param node xml node representation
     /// \param weights weights attached to current node
     /// \return shared pointer to function representing input node
-    std::shared_ptr<ngraph::Function> parse_function(const pugi::xml_node& root,
-                                                     const std::shared_ptr<ngraph::runtime::AlignedBuffer>& weights);
+    std::shared_ptr<ov::Function> parse_function(const pugi::xml_node& root,
+                                                 const std::shared_ptr<ngraph::runtime::AlignedBuffer>& weights);
     /// \brief Traverses xml node representation in order to get the purpose attribute of
     /// inputs/outputs in the body of Loop op. \param node xml node representation \return struct
     /// with value of purpuse attribute
-    ngraph::op::v5::Loop::SpecialBodyPorts parsePurposeAttribute(const pugi::xml_node& node);
+    ov::op::v5::Loop::SpecialBodyPorts parsePurposeAttribute(const pugi::xml_node& node);
 
     GenericLayerParams parseGenericParams(const pugi::xml_node& node);
 
-    std::shared_ptr<ngraph::Node> createNode(const ngraph::OutputVector& inputs,
-                                             const pugi::xml_node& node,
-                                             const ov::Weights& weights,
-                                             const GenericLayerParams& params);
+    std::shared_ptr<ov::Node> createNode(const ov::OutputVector& inputs,
+                                         const pugi::xml_node& node,
+                                         const ov::Weights& weights,
+                                         const GenericLayerParams& params);
 
     // -- DATA --
     const pugi::xml_node m_node;
     const ov::Weights& m_weights;
     const std::unordered_map<std::string, ngraph::OpSet>& m_opsets;
     const std::unordered_map<ov::DiscreteTypeInfo, ov::BaseOpExtension::Ptr>& m_extensions;
-    std::unordered_map<std::string, std::shared_ptr<ngraph::Variable>>& m_variables;
+    std::unordered_map<std::string, std::shared_ptr<ov::op::util::Variable>>& m_variables;
 
     ///
     /// store information about parameters/results order during function creation
diff --git a/ngraph/frontend/ir/src/model.cpp b/ngraph/frontend/ir/src/model.cpp
index 2fa09ab8c4a78e..270218219b69ac 100644
--- a/ngraph/frontend/ir/src/model.cpp
+++ b/ngraph/frontend/ir/src/model.cpp
@@ -11,6 +11,8 @@
 #include <openvino/op/util/framework_node.hpp>
 #include <pugixml.hpp>
 
+#include "openvino/core/validation_util.hpp"
+
 using namespace ngraph;
 using namespace InferenceEngine;
 
@@ -182,7 +184,7 @@ void ParsePreProcess(pugi::xml_node& root,
 }
 }  // namespace
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class InputModelIR::InputModelIRImpl {
     std::shared_ptr<ngraph::runtime::AlignedBuffer> m_weights;
@@ -239,4 +241,4 @@ std::shared_ptr<Function> InputModelIR::InputModelIRImpl::convert() {
     return function;
 }
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/ir/src/rt_info_deserializer.cpp b/ngraph/frontend/ir/src/rt_info_deserializer.cpp
index 739432e594f378..0b7bee7c329e7b 100644
--- a/ngraph/frontend/ir/src/rt_info_deserializer.cpp
+++ b/ngraph/frontend/ir/src/rt_info_deserializer.cpp
@@ -9,16 +9,16 @@
 
 using namespace ov;
 
-void RTInfoDeserializer::on_adapter(const std::string& name, ngraph::ValueAccessor<void>& adapter) {
+void RTInfoDeserializer::on_adapter(const std::string& name, ValueAccessor<void>& adapter) {
     check_attribute_name(name);
     std::string val;
     if (!getStrAttribute(m_node, name, val))
         return;
-    if (auto a = ngraph::as_type<ngraph::AttributeAdapter<std::set<std::string>>>(&adapter)) {
+    if (auto a = as_type<AttributeAdapter<std::set<std::string>>>(&adapter)) {
         std::set<std::string> ss;
         str_to_container(val, ss);
         a->set(ss);
     } else {
         IR_THROW("Not implemented");
     }
-}
\ No newline at end of file
+}
diff --git a/ngraph/frontend/ir/src/rt_info_deserializer.hpp b/ngraph/frontend/ir/src/rt_info_deserializer.hpp
index de9c708b78d5cb..86274edd88776c 100644
--- a/ngraph/frontend/ir/src/rt_info_deserializer.hpp
+++ b/ngraph/frontend/ir/src/rt_info_deserializer.hpp
@@ -4,18 +4,19 @@
 
 #pragma once
 
-#include <ir_frontend/utility.hpp>
 #include <istream>
 #include <memory>
-#include <ngraph/ngraph.hpp>
-#include <utils.hpp>
+
+#include "ir_frontend/utility.hpp"
+#include "openvino/core/attribute_visitor.hpp"
+#include "utils.hpp"
 
 namespace ov {
-class RTInfoDeserializer : public ngraph::AttributeVisitor {
+class RTInfoDeserializer : public ov::AttributeVisitor {
 public:
     explicit RTInfoDeserializer(const pugi::xml_node& node) : m_node(node) {}
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::string>& value) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::string>& value) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -23,7 +24,7 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
         value.set(val);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<bool>& value) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<bool>& value) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -42,16 +43,16 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
         value.set(is_true);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<void>& adapter) override;
+    void on_adapter(const std::string& name, ov::ValueAccessor<void>& adapter) override;
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<double>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<double>& adapter) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
             return;
         adapter.set(stringToType<double>(val));
     }
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<int64_t>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<int64_t>& adapter) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -59,12 +60,11 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
         adapter.set(stringToType<int64_t>(val));
     }
 
-    void on_adapter(const std::string& name,
-                    ngraph::ValueAccessor<std::shared_ptr<ngraph::Function>>& adapter) override {
-        throw ngraph::ngraph_error("Function type is unsupported for rt info deserialization");
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::shared_ptr<ov::Function>>& adapter) override {
+        throw ov::Exception("Function type is unsupported for rt info deserialization");
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<int32_t>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int32_t>>& adapter) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -74,7 +74,7 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
         adapter.set(value);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<int64_t>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int64_t>>& adapter) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -84,7 +84,7 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
         adapter.set(value);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<float>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<float>>& adapter) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -94,7 +94,7 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
         adapter.set(value);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<uint64_t>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<uint64_t>>& adapter) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -104,7 +104,7 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
         adapter.set(value);
     }
 
-    void on_adapter(const std::string& name, ngraph::ValueAccessor<std::vector<std::string>>& adapter) override {
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<std::string>>& adapter) override {
         check_attribute_name(name);
         std::string val;
         if (!getStrAttribute(m_node, name, val))
@@ -116,11 +116,11 @@ class RTInfoDeserializer : public ngraph::AttributeVisitor {
 
     void check_attribute_name(const std::string& name) const {
         if (name == "name" || name == "version") {
-            throw ngraph::ngraph_error("Attribute key with name: " + name + " is not allowed. Please use another name");
+            throw ov::Exception("Attribute key with name: " + name + " is not allowed. Please use another name");
         }
     }
 
 private:
     pugi::xml_node m_node;
 };
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/ir/src/utils.cpp b/ngraph/frontend/ir/src/utils.cpp
index 37bd3ff1315fe1..9af2f072fb012b 100644
--- a/ngraph/frontend/ir/src/utils.cpp
+++ b/ngraph/frontend/ir/src/utils.cpp
@@ -4,9 +4,12 @@
 
 #include "utils.hpp"
 
+#include "ie_ngraph_utils.hpp"
+#include "openvino/util/common_util.hpp"
+
 namespace ov {
-void operator>>(const std::stringstream& in, ngraph::element::Type& type) {
-    type = InferenceEngine::details::convertPrecision(ngraph::trim(in.str()));
+void operator>>(const std::stringstream& in, ov::element::Type& type) {
+    type = InferenceEngine::details::convertPrecision(ov::util::trim(in.str()));
 }
 
 bool getStrAttribute(const pugi::xml_node& node, const std::string& name, std::string& value) {
@@ -19,4 +22,4 @@ bool getStrAttribute(const pugi::xml_node& node, const std::string& name, std::s
     value = std::string(attr.value());
     return true;
 }
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/ir/src/utils.hpp b/ngraph/frontend/ir/src/utils.hpp
index 31486ead3a6750..0e734c16a0e0b7 100644
--- a/ngraph/frontend/ir/src/utils.hpp
+++ b/ngraph/frontend/ir/src/utils.hpp
@@ -4,16 +4,13 @@
 
 #pragma once
 
-#include <xml_parse_utils.h>
-
-#include <ie_ngraph_utils.hpp>
-#include <istream>
 #include <memory>
-#include <ngraph/ngraph.hpp>
-#include <pugixml.hpp>
+
+#include "openvino/core/type/element_type.hpp"
+#include "xml_parse_utils.h"
 
 namespace ov {
-void operator>>(const std::stringstream& in, ngraph::element::Type& type);
+void operator>>(const std::stringstream& in, ov::element::Type& type);
 
 bool getStrAttribute(const pugi::xml_node& node, const std::string& name, std::string& value);
 
@@ -49,4 +46,4 @@ T stringToType(const std::string& valStr) {
     }
     return ret;
 }
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/CMakeLists.txt b/ngraph/frontend/onnx/frontend/CMakeLists.txt
index ed1086f929e4c4..a1aabaccecd6c3 100644
--- a/ngraph/frontend/onnx/frontend/CMakeLists.txt
+++ b/ngraph/frontend/onnx/frontend/CMakeLists.txt
@@ -51,9 +51,6 @@ target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${ONNX_FRONTE
 target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
 
 target_compile_definitions(${TARGET_NAME} PRIVATE ONNX_OPSET_VERSION=${ONNX_OPSET_VERSION})
-if(NGRAPH_USE_PROTOBUF_LITE)
-    target_compile_definitions(${TARGET_NAME} PRIVATE NGRAPH_USE_PROTOBUF_LITE)
-endif()
 
 set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME frontend::onnx)
 install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_frontend/frontend.hpp b/ngraph/frontend/onnx/frontend/include/onnx_frontend/frontend.hpp
index c8b5015a5d536c..bbdec85fb78261 100644
--- a/ngraph/frontend/onnx/frontend/include/onnx_frontend/frontend.hpp
+++ b/ngraph/frontend/onnx/frontend/include/onnx_frontend/frontend.hpp
@@ -12,13 +12,13 @@
 #    define ONNX_FRONTEND_API OPENVINO_CORE_IMPORTS
 #endif
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class ONNX_FRONTEND_API FrontEndONNX : public FrontEnd {
 public:
-    std::shared_ptr<ngraph::Function> convert(InputModel::Ptr model) const override;
-    void convert(std::shared_ptr<ngraph::Function> partially_converted) const override;
-    std::shared_ptr<ngraph::Function> decode(InputModel::Ptr model) const override;
+    std::shared_ptr<ov::Function> convert(InputModel::Ptr model) const override;
+    void convert(std::shared_ptr<ov::Function> partially_converted) const override;
+    std::shared_ptr<ov::Function> decode(InputModel::Ptr model) const override;
     std::string get_name() const override;
     bool supported_impl(const std::vector<std::shared_ptr<Variant>>& variants) const override;
 
@@ -27,5 +27,4 @@ class ONNX_FRONTEND_API FrontEndONNX : public FrontEnd {
 };
 
 }  // namespace frontend
-
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/src/core/attribute.hpp b/ngraph/frontend/onnx/frontend/src/core/attribute.hpp
index fbd972444bc1f9..c2ca178573ee61 100644
--- a/ngraph/frontend/onnx/frontend/src/core/attribute.hpp
+++ b/ngraph/frontend/onnx/frontend/src/core/attribute.hpp
@@ -28,12 +28,7 @@ namespace error {
 namespace attribute {
 namespace detail {
 struct Attribute : ngraph_error {
-#ifdef NGRAPH_USE_PROTOBUF_LITE
     Attribute(const std::string& msg, AttributeProto_AttributeType type) : ngraph_error{msg} {}
-#else
-    Attribute(const std::string& msg, AttributeProto_AttributeType type)
-        : ngraph_error{msg + ": " + ONNX_NAMESPACE::AttributeProto_AttributeType_Name(type)} {}
-#endif
 };
 
 }  // namespace detail
diff --git a/ngraph/frontend/onnx/frontend/src/core/graph.cpp b/ngraph/frontend/onnx/frontend/src/core/graph.cpp
index bf4cb53d32e613..51d1d3772b5f52 100644
--- a/ngraph/frontend/onnx/frontend/src/core/graph.cpp
+++ b/ngraph/frontend/onnx/frontend/src/core/graph.cpp
@@ -260,6 +260,22 @@ OutputVector Graph::make_ng_nodes(const Node& onnx_node) const {
 }
 
 void Graph::set_friendly_names(const Node& onnx_node, const OutputVector& ng_node_vector) const {
+    if (onnx_node.op_type() == "Identity") {
+        // we eliminate Identity op (since it's a no-op) and therefore
+        // we must preserve its input name, unless Identity is connected
+        // to a graph's output - in that case Identity's input gets
+        // a new name
+        const auto& graph_outputs = m_model->get_graph().output();
+        const auto& name = onnx_node.output(0);
+        bool is_identity_on_output = std::find_if(graph_outputs.begin(),
+                                                  graph_outputs.end(),
+                                                  [&name](const ONNX_NAMESPACE::ValueInfoProto& output) -> bool {
+                                                      return output.name() == name;
+                                                  }) != graph_outputs.end();
+        if (!is_identity_on_output) {
+            return;
+        }
+    }
     for (size_t i = 0; i < ng_node_vector.size(); ++i) {
         // Trailing optional outputs may not be specified in the ONNX model.
         // Other optional outputs should have name set to an empty string.
diff --git a/ngraph/frontend/onnx/frontend/src/core/tensor.hpp b/ngraph/frontend/onnx/frontend/src/core/tensor.hpp
index 9d0982993e6d68..7656fc1f1bc997 100644
--- a/ngraph/frontend/onnx/frontend/src/core/tensor.hpp
+++ b/ngraph/frontend/onnx/frontend/src/core/tensor.hpp
@@ -29,23 +29,11 @@ using TensorProto_DataType = decltype(ONNX_NAMESPACE::TensorProto{}.data_type())
 namespace error {
 namespace tensor {
 struct invalid_data_type : ngraph_error {
-#ifdef NGRAPH_USE_PROTOBUF_LITE
     explicit invalid_data_type(TensorProto_DataType type) : ngraph_error{"invalid data type"} {}
-#else
-    explicit invalid_data_type(TensorProto_DataType type)
-        : ngraph_error{"invalid data type: " + ONNX_NAMESPACE::TensorProto_DataType_Name(
-                                                   static_cast<ONNX_NAMESPACE::TensorProto_DataType>(type))} {}
-#endif
 };
 
 struct unsupported_data_type : ngraph_error {
-#ifdef NGRAPH_USE_PROTOBUF_LITE
     explicit unsupported_data_type(TensorProto_DataType type) : ngraph_error{"unsupported data type"} {}
-#else
-    explicit unsupported_data_type(TensorProto_DataType type)
-        : ngraph_error{"unsupported data type: " + ONNX_NAMESPACE::TensorProto_DataType_Name(
-                                                       static_cast<ONNX_NAMESPACE::TensorProto_DataType>(type))} {}
-#endif
 };
 
 struct unspecified_name : ngraph_error {
diff --git a/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.cpp b/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.cpp
index f7662d1f116b9e..980669c6d54992 100644
--- a/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.cpp
+++ b/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.cpp
@@ -11,7 +11,7 @@
 
 #include "ngraph/check.hpp"
 
-using namespace ngraph::onnx_editor;
+using namespace ov::onnx_editor;
 
 enum class PortType { InputPort, OutputPort };
 
@@ -78,6 +78,10 @@ bool is_graph_initializer(const ONNX_NAMESPACE::GraphProto& graph, const std::st
 int find_source_node_idx(const ONNX_NAMESPACE::GraphProto& graph,
                          const int current_node_idx,
                          const std::string& input_name) {
+    // Some operators (e.g. Clip) have optional inputs
+    if (input_name.empty())
+        return -1;
+
     for (int i = current_node_idx - 1; i >= 0; --i) {
         const auto& outputs = graph.node(i).output();
         const auto output_found = std::any_of(std::begin(outputs), std::end(outputs), is_equal_to(input_name));
@@ -329,11 +333,11 @@ void SubgraphExtractor::extract_subgraph(std::vector<OutputEdge> subgraph_output
 SubgraphExtractor::SubgraphComponents SubgraphExtractor::discover_output_contributors(
     const OutputEdge& output_edge,
     const SubgraphComponents& already_collected) const {
-    const auto already_visited = [&already_collected](const int node_index) {
-        return already_collected.nodes.count(node_index) > 0;
+    SubgraphComponents output_contributors;
+    const auto already_visited = [&already_collected, &output_contributors](const int node_index) {
+        return already_collected.nodes.count(node_index) > 0 || output_contributors.nodes.count(node_index) > 0;
     };
 
-    SubgraphComponents output_contributors;
     const auto tensor_name = get_output_tensor_name(m_onnx_graph, output_edge);
     output_contributors.outputs.insert(tensor_name);
 
@@ -357,7 +361,7 @@ SubgraphExtractor::SubgraphComponents SubgraphExtractor::discover_output_contrib
         // and/or keep looking for more contributors further up in the graph
 
         // when an input or initializer is reached, the visitor stops the lookup
-        const auto n_inputs = m_node_inputs[n];
+        const auto& n_inputs = m_node_inputs[n];
         for (auto& input_name : n_inputs) {
             if (is_graph_input(m_onnx_graph, input_name)) {
                 output_contributors.inputs.insert(input_name);
@@ -371,7 +375,9 @@ SubgraphExtractor::SubgraphComponents SubgraphExtractor::discover_output_contrib
             } else {
                 // if an edge points to another node (source node) it should be visited
                 // in one of the future iterations
-                nodes_to_visit.push(find_source_node_idx(m_onnx_graph, n, input_name));
+                const auto node_idx = find_source_node_idx(m_onnx_graph, n, input_name);
+                if (node_idx >= 0)
+                    nodes_to_visit.push(node_idx);
             }
         }
     }
@@ -391,6 +397,7 @@ std::vector<OutputEdge> SubgraphExtractor::all_output_edges() const {
 
     for (const auto& graph_output : m_onnx_graph.output()) {
         const auto node_index = find_source_node_idx(m_onnx_graph, m_onnx_graph.node_size(), graph_output.name());
+        OPENVINO_ASSERT(node_index >= 0);
         const auto& node_outputs = m_onnx_graph.node(node_index).output();
         const auto output_port_it = std::find(std::begin(node_outputs), std::end(node_outputs), graph_output.name());
         all_outputs.emplace_back(node_index, output_port_it - std::begin(node_outputs));
diff --git a/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.hpp b/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.hpp
index 979dbde719a4e6..e273bfcfd4aedc 100644
--- a/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.hpp
+++ b/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.hpp
@@ -18,7 +18,7 @@ class NodeProto;
 class ValueInfoProto;
 }  // namespace ONNX_NAMESPACE
 
-namespace ngraph {
+namespace ov {
 namespace onnx_editor {
 /// \brief Subgraph extraction helper structure
 struct SubgraphExtractor {
@@ -95,4 +95,4 @@ struct SubgraphExtractor {
     void extract_subgraph_from_onnx_model(const SubgraphComponents& subgraph);
 };
 }  // namespace onnx_editor
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/src/edge_mapper.cpp b/ngraph/frontend/onnx/frontend/src/edge_mapper.cpp
index 4602a2db623403..4fd7e75f4938fd 100644
--- a/ngraph/frontend/onnx/frontend/src/edge_mapper.cpp
+++ b/ngraph/frontend/onnx/frontend/src/edge_mapper.cpp
@@ -11,8 +11,8 @@
 #include "ngraph/check.hpp"
 #include "ngraph/except.hpp"
 
-using namespace ngraph;
-using namespace ngraph::onnx_editor;
+using namespace ov;
+using namespace ov::onnx_editor;
 
 onnx_editor::EdgeMapper::EdgeMapper(const ONNX_NAMESPACE::GraphProto& graph_proto)
     : m_node_inputs(graph_proto.node().size()),
@@ -66,8 +66,8 @@ int onnx_editor::EdgeMapper::get_node_output_idx(int node_index, const std::stri
     const auto& node_outputs = m_node_outputs[node_index];
     const auto out_port_idx = std::find(std::begin(node_outputs), std::end(node_outputs), output_name);
     if (out_port_idx == std::end(node_outputs)) {
-        throw ngraph_error("Node with index: " + std::to_string(node_index) +
-                           " has not output with name: " + output_name);
+        throw ov::Exception("Node with index: " + std::to_string(node_index) +
+                            " has not output with name: " + output_name);
     }
     return (out_port_idx - std::begin(node_outputs));
 }
@@ -88,8 +88,8 @@ std::vector<int> onnx_editor::EdgeMapper::get_node_input_indexes(int node_index,
         ++index;
     }
     if (node_inputs_indexes.size() == 0) {
-        throw ngraph_error("Node with index: " + std::to_string(node_index) +
-                           " has not input with name: " + input_name);
+        throw ov::Exception("Node with index: " + std::to_string(node_index) +
+                            " has not input with name: " + input_name);
     }
     return node_inputs_indexes;
 }
@@ -102,9 +102,9 @@ InputEdge onnx_editor::EdgeMapper::find_input_edge(const EditorNode& node, const
         if (node_indexes.size() == 1) {
             node_index = node_indexes[0];
         } else if (node_indexes.empty()) {
-            throw ngraph_error("Node with name: " + (node.m_node_name.empty() ? "not_given" : node.m_node_name) +
-                               " and output_name: " + (node.m_output_name.empty() ? "not_given" : node.m_output_name) +
-                               " was not found");
+            throw ov::Exception("Node with name: " + (node.m_node_name.empty() ? "not_given" : node.m_node_name) +
+                                " and output_name: " + (node.m_output_name.empty() ? "not_given" : node.m_output_name) +
+                                " was not found");
         } else if (!in.m_input_name.empty())  // input indexes are not deterministic if a node name is ambiguous
         {
             // many nodes with the same name
@@ -117,16 +117,16 @@ InputEdge onnx_editor::EdgeMapper::find_input_edge(const EditorNode& node, const
                 }
             }
             if (matched_inputs_number == 0) {
-                throw ngraph_error("Input edge described by: " + node.m_node_name +
-                                   " and input name: " + in.m_input_name + " was not found");
+                throw ov::Exception("Input edge described by: " + node.m_node_name +
+                                    " and input name: " + in.m_input_name + " was not found");
             }
             if (matched_inputs_number > 1) {
-                throw ngraph_error("Given node name: " + node.m_node_name + " and input name: " + in.m_input_name +
-                                   " are ambiguous to determine input edge");
+                throw ov::Exception("Given node name: " + node.m_node_name + " and input name: " + in.m_input_name +
+                                    " are ambiguous to determine input edge");
             }
         } else {
-            throw ngraph_error("Given node name: " + node.m_node_name + " and input index: " +
-                               std::to_string(in.m_input_index) + " are ambiguous to determine input edge");
+            throw ov::Exception("Given node name: " + node.m_node_name + " and input index: " +
+                                std::to_string(in.m_input_index) + " are ambiguous to determine input edge");
         }
     } else {  // the node index is provided
         check_node_index(node_index);
@@ -139,27 +139,27 @@ InputEdge onnx_editor::EdgeMapper::find_input_edge(const EditorNode& node, const
         const auto input_indexes = get_node_input_indexes(node_index, in.m_input_name);
         if (input_indexes.size() > 1)  // more indexes with the same name
         {
-            throw ngraph_error("Node with index: " + std::to_string(node_index) +
-                               " has more than one inputs with name: " + in.m_input_name +
-                               ". You should use port indexes to distinguish them.");
+            throw ov::Exception("Node with index: " + std::to_string(node_index) +
+                                " has more than one inputs with name: " + in.m_input_name +
+                                ". You should use port indexes to distinguish them.");
         }
         return InputEdge{node_index, input_indexes[0], in.m_new_input_name};
     } else {
-        throw ngraph_error("Not enough information to determine input edge");
+        throw ov::Exception("Not enough information to determine input edge");
     }
 }
 
 OutputEdge onnx_editor::EdgeMapper::find_output_edge(const EditorNode& node, const EditorOutput& out) const {
-    int node_index = node_index = node.m_node_index;
+    int node_index = node.m_node_index;
     if (node_index == -1) {  // the node index is not provided
         // identification can be both based on node name and output name (if the node index is not provided)
         const auto& node_indexes = find_node_indexes(node.m_node_name, node.m_output_name);
         if (node_indexes.size() == 1) {
             node_index = node_indexes[0];
         } else if (node_indexes.empty()) {
-            throw ngraph_error("Node with name: " + (node.m_node_name.empty() ? "not_given" : node.m_node_name) +
-                               " and output_name: " + (node.m_output_name.empty() ? "not_given" : node.m_output_name) +
-                               " was not found");
+            throw ov::Exception("Node with name: " + (node.m_node_name.empty() ? "not_given" : node.m_node_name) +
+                                " and output_name: " + (node.m_output_name.empty() ? "not_given" : node.m_output_name) +
+                                " was not found");
         } else if (!out.m_output_name.empty())  // output indexes are not deterministic if a node name is ambiguous
         {
             // many nodes with the same name
@@ -173,12 +173,12 @@ OutputEdge onnx_editor::EdgeMapper::find_output_edge(const EditorNode& node, con
                 }
             }
             if (matched_outputs_number == 0) {
-                throw ngraph_error("Output edge described by: " + node.m_node_name +
-                                   " and output name: " + out.m_output_name + " was not found");
+                throw ov::Exception("Output edge described by: " + node.m_node_name +
+                                    " and output name: " + out.m_output_name + " was not found");
             }
         } else {
-            throw ngraph_error("Given node name: " + node.m_node_name + " and output index: " +
-                               std::to_string(out.m_output_index) + " are ambiguous to determine output edge");
+            throw ov::Exception("Given node name: " + node.m_node_name + " and output index: " +
+                                std::to_string(out.m_output_index) + " are ambiguous to determine output edge");
         }
     } else {  // the node index is provided
         check_node_index(node_index);
@@ -191,7 +191,7 @@ OutputEdge onnx_editor::EdgeMapper::find_output_edge(const EditorNode& node, con
         const auto output_idx = get_node_output_idx(node_index, out.m_output_name);
         return OutputEdge{node_index, output_idx};
     } else {
-        throw ngraph_error("Not enough information to determine output edge");
+        throw ov::Exception("Not enough information to determine output edge");
     }
 }
 
diff --git a/ngraph/frontend/onnx/frontend/src/edge_mapper.hpp b/ngraph/frontend/onnx/frontend/src/edge_mapper.hpp
index d9fe93fcaf03c9..44619d5a305407 100644
--- a/ngraph/frontend/onnx/frontend/src/edge_mapper.hpp
+++ b/ngraph/frontend/onnx/frontend/src/edge_mapper.hpp
@@ -16,7 +16,7 @@ namespace ONNX_NAMESPACE {
 class GraphProto;
 }  // namespace ONNX_NAMESPACE
 
-namespace ngraph {
+namespace ov {
 namespace onnx_editor {
 /// \brief A class which allows specifying InputEdge and OutputEdge by user-friendly ONNX
 /// names.
@@ -149,4 +149,4 @@ class EdgeMapper {
     std::multimap<std::string, int> m_output_consumers_index;
 };
 }  // namespace onnx_editor
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/src/editor.cpp b/ngraph/frontend/onnx/frontend/src/editor.cpp
index 641f43b144a1d4..3ed788fb8e9514 100644
--- a/ngraph/frontend/onnx/frontend/src/editor.cpp
+++ b/ngraph/frontend/onnx/frontend/src/editor.cpp
@@ -17,8 +17,8 @@
 #include "onnx_common/utils.hpp"
 #include "utils/onnx_internal.hpp"
 
-using namespace ngraph;
-using namespace ngraph::onnx_editor;
+using namespace ov;
+using namespace ov::onnx_editor;
 
 NGRAPH_SUPPRESS_DEPRECATED_START
 
@@ -69,25 +69,25 @@ ValueInfoProto* find_graph_value_info(GraphProto& graph, const std::string& name
 
 void modify_input_type(ValueInfoProto& onnx_input, const element::Type_t elem_type) {
     if (!onnx_input.has_type()) {
-        throw ngraph_error("The input is malformed - it doesn't contain the 'type' field. Cannot change the "
-                           "data type. Input name: " +
-                           onnx_input.name());
+        throw ov::Exception("The input is malformed - it doesn't contain the 'type' field. Cannot change the "
+                            "data type. Input name: " +
+                            onnx_input.name());
     }
 
     auto* type_proto = onnx_input.mutable_type();
     if (!type_proto->has_tensor_type()) {
-        throw ngraph_error("The input is malformed - it doesn't contain the 'tensor_type' field. Cannot "
-                           "change the data type. Input name: " +
-                           onnx_input.name());
+        throw ov::Exception("The input is malformed - it doesn't contain the 'tensor_type' field. Cannot "
+                            "change the data type. Input name: " +
+                            onnx_input.name());
     }
 
     auto* tensor_type = type_proto->mutable_tensor_type();
 
-    if (onnx_common::is_supported_ng_type(elem_type)) {
-        tensor_type->set_elem_type(onnx_common::ng_to_onnx_data_type(elem_type));
+    if (ngraph::onnx_common::is_supported_ng_type(elem_type)) {
+        tensor_type->set_elem_type(ngraph::onnx_common::ng_to_onnx_data_type(elem_type));
     } else {
-        throw ngraph_error("The input type for input '" + onnx_input.name() + "' cannot be set to: " +
-                           element::Type(elem_type).get_type_name() + ". This type is not allowed in ONNX.");
+        throw ov::Exception("The input type for input '" + onnx_input.name() + "' cannot be set to: " +
+                            element::Type(elem_type).get_type_name() + ". This type is not allowed in ONNX.");
     }
 }
 
@@ -106,16 +106,16 @@ void add_dim_to_onnx_shape(const Dimension& dim, ONNX_NAMESPACE::TensorShapeProt
 
 void modify_input_shape(ValueInfoProto& onnx_input, const PartialShape& new_shape) {
     if (!onnx_input.has_type()) {
-        throw ngraph_error("The input is malformed - it doesn't contain the 'type' field. Cannot change the "
-                           "input shape. Input name: " +
-                           onnx_input.name());
+        throw ov::Exception("The input is malformed - it doesn't contain the 'type' field. Cannot change the "
+                            "input shape. Input name: " +
+                            onnx_input.name());
     }
 
     auto* type_proto = onnx_input.mutable_type();
     if (!type_proto->has_tensor_type()) {
-        throw ngraph_error("The input is malformed - it doesn't contain the 'tensor_type' field. Cannot "
-                           "change the input shape. Input name: " +
-                           onnx_input.name());
+        throw ov::Exception("The input is malformed - it doesn't contain the 'tensor_type' field. Cannot "
+                            "change the input shape. Input name: " +
+                            onnx_input.name());
     }
 
     auto* tensor_type = type_proto->mutable_tensor_type();
@@ -144,22 +144,22 @@ void modify_initializer(TensorProto& initializer,
                         const std::shared_ptr<ngraph::op::Constant> values,
                         ValueInfoProto* input) {
     const auto elem_type = values->get_element_type();
-    if (!onnx_common::is_supported_ng_type(elem_type)) {
-        throw ngraph_error("Initializer '" + name + "' type cannot be set to: " +
-                           element::Type(elem_type).get_type_name() + ". This type is not allowed in ONNX.");
+    if (!ngraph::onnx_common::is_supported_ng_type(elem_type)) {
+        throw ov::Exception("Initializer '" + name + "' type cannot be set to: " +
+                            element::Type(elem_type).get_type_name() + ". This type is not allowed in ONNX.");
     }
 
     initializer.Clear();
 
     initializer.set_name(name);
-    initializer.set_data_type(onnx_common::ng_to_onnx_data_type(values->get_element_type()));
+    initializer.set_data_type(ngraph::onnx_common::ng_to_onnx_data_type(values->get_element_type()));
 
     for (const auto& dim : values->get_shape()) {
         initializer.add_dims(dim);
     }
 
     const auto data_size_in_bytes =
-        shape_size(values->get_shape()) * onnx_common::get_onnx_data_size(initializer.data_type());
+        shape_size(values->get_shape()) * ngraph::onnx_common::get_onnx_data_size(initializer.data_type());
     initializer.set_raw_data(values->get_data_ptr(), data_size_in_bytes);
 
     // update input with type and shape of initializer
@@ -183,8 +183,11 @@ class InferShapesAutoRelease {
         m_infer_shapes_was_run = true;
     }
     ~InferShapesAutoRelease() {
-        if (m_infer_shapes_was_run) {
-            m_model_proto->mutable_graph()->clear_value_info();
+        try {
+            if (m_infer_shapes_was_run) {
+                m_model_proto->mutable_graph()->clear_value_info();
+            }
+        } catch (...) {
         }
     }
 
@@ -203,14 +206,17 @@ struct onnx_editor::ONNXModelEditor::Impl {
     Impl() = delete;
 
     Impl(const std::string& model_path)
-        : m_model_proto{std::make_shared<ONNX_NAMESPACE::ModelProto>(onnx_common::parse_from_file(model_path))} {}
+        : m_model_proto{
+              std::make_shared<ONNX_NAMESPACE::ModelProto>(ngraph::onnx_common::parse_from_file(model_path))} {}
 
     Impl(std::istream& model_stream)
-        : m_model_proto{std::make_shared<ONNX_NAMESPACE::ModelProto>(onnx_common::parse_from_istream(model_stream))} {}
+        : m_model_proto{
+              std::make_shared<ONNX_NAMESPACE::ModelProto>(ngraph::onnx_common::parse_from_istream(model_stream))} {}
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
     Impl(const std::wstring& model_path)
-        : m_model_proto{std::make_shared<ONNX_NAMESPACE::ModelProto>(onnx_common::parse_from_file(model_path))} {}
+        : m_model_proto{
+              std::make_shared<ONNX_NAMESPACE::ModelProto>(ngraph::onnx_common::parse_from_file(model_path))} {}
 #endif
 };
 
@@ -222,7 +228,7 @@ onnx_editor::ONNXModelEditor::ONNXModelEditor(const std::string& model_path)
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
 onnx_editor::ONNXModelEditor::ONNXModelEditor(const std::wstring& model_path)
-    : m_model_path{file_util::wstring_to_string(model_path)},
+    : m_model_path{ngraph::file_util::wstring_to_string(model_path)},
       m_pimpl{new ONNXModelEditor::Impl{model_path}, [](Impl* impl) {
                   delete impl;
               }} {}
@@ -242,11 +248,11 @@ void onnx_editor::ONNXModelEditor::serialize(const std::string& out_file_path) c
     std::ofstream out_file{out_file_path, std::ios::out | std::ios::binary};
 
     if (!out_file.is_open()) {
-        throw ngraph_error("Could not open the file: " + out_file_path);
+        throw ov::Exception("Could not open the file: " + out_file_path);
     };
 
     if (!m_pimpl->m_model_proto->SerializeToOstream(&out_file)) {
-        throw ngraph_error("Could not serialize the model to: " + out_file_path);
+        throw ov::Exception("Could not serialize the model to: " + out_file_path);
     } else {
         out_file.close();
     }
@@ -260,8 +266,8 @@ void onnx_editor::ONNXModelEditor::set_input_types(const std::map<std::string, e
         if (onnx_input != nullptr) {
             modify_input_type(*onnx_input, input_desc.second);
         } else {
-            throw ngraph_error("Could not set a custom element type for input: " + input_desc.first +
-                               ". Such input was not found in the original ONNX model.");
+            throw ov::Exception("Could not set a custom element type for input: " + input_desc.first +
+                                ". Such input was not found in the original ONNX model.");
         }
     }
 }
@@ -274,8 +280,8 @@ void onnx_editor::ONNXModelEditor::set_input_shapes(const std::map<std::string,
         if (onnx_input != nullptr) {
             modify_input_shape(*onnx_input, input_desc.second);
         } else {
-            throw ngraph_error("Could not set custom shape for input: " + input_desc.first +
-                               ". Such input was not found in the original ONNX model.");
+            throw ov::Exception("Could not set custom shape for input: " + input_desc.first +
+                                ". Such input was not found in the original ONNX model.");
         }
     }
 }
@@ -312,14 +318,14 @@ PartialShape onnx_editor::ONNXModelEditor::get_tensor_shape(const std::string& t
     if (value_info != nullptr) {
         const auto& onnx_tensor_type = value_info->type().tensor_type();
         if (onnx_tensor_type.has_shape()) {
-            return onnx_common::to_ng_shape(onnx_tensor_type.shape());
+            return ngraph::onnx_common::to_ng_shape(onnx_tensor_type.shape());
         } else {
             return PartialShape::dynamic();
         }
     } else if (tensor) {
         return PartialShape{Shape{tensor->dims().cbegin(), tensor->dims().cend()}};
     } else {
-        throw ngraph_error("The tensor: " + tensor_name + " was not found in the graph");
+        throw ov::Exception("The tensor: " + tensor_name + " was not found in the graph");
     }
 }
 
@@ -402,7 +408,7 @@ std::string onnx_editor::ONNXModelEditor::model_string() const {
 }
 
 std::shared_ptr<Function> onnx_editor::ONNXModelEditor::get_function() const {
-    return onnx_import::detail::import_onnx_model(m_pimpl->m_model_proto, m_model_path);
+    return ngraph::onnx_import::detail::import_onnx_model(m_pimpl->m_model_proto, m_model_path);
 }
 
 void onnx_editor::ONNXModelEditor::set_input_values(
@@ -581,5 +587,5 @@ std::vector<std::string> onnx_editor::ONNXModelEditor::get_output_ports(const Ed
 }
 
 std::shared_ptr<Function> onnx_editor::ONNXModelEditor::decode() {
-    return onnx_import::detail::decode_to_framework_nodes(m_pimpl->m_model_proto, m_model_path);
+    return ngraph::onnx_import::detail::decode_to_framework_nodes(m_pimpl->m_model_proto, m_model_path);
 }
diff --git a/ngraph/frontend/onnx/frontend/src/editor.hpp b/ngraph/frontend/onnx/frontend/src/editor.hpp
index c288fa4ccd0676..4d997d7ae61d2a 100644
--- a/ngraph/frontend/onnx/frontend/src/editor.hpp
+++ b/ngraph/frontend/onnx/frontend/src/editor.hpp
@@ -15,7 +15,7 @@
 #include "ngraph/type/element_type.hpp"
 #include "onnx_import/onnx_importer_visibility.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace onnx_editor {
 /// \brief A class representing a set of utilities allowing modification of an ONNX model
 ///
@@ -275,4 +275,4 @@ class ONNX_IMPORTER_API ONNXModelEditor final {
     std::unique_ptr<Impl, void (*)(Impl*)> m_pimpl;
 };
 }  // namespace onnx_editor
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/src/editor_types.hpp b/ngraph/frontend/onnx/frontend/src/editor_types.hpp
index c9c7e02408f7d9..53dcf9393d6a58 100644
--- a/ngraph/frontend/onnx/frontend/src/editor_types.hpp
+++ b/ngraph/frontend/onnx/frontend/src/editor_types.hpp
@@ -7,7 +7,7 @@
 #include <string>
 #include <utility>
 
-namespace ngraph {
+namespace ov {
 enum class EdgeType { INPUT, OUTPUT };
 
 template <EdgeType>
@@ -124,4 +124,4 @@ struct EditorNode {
     int m_node_index = -1;
 };
 }  // namespace onnx_editor
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/src/frontend.cpp b/ngraph/frontend/onnx/frontend/src/frontend.cpp
index 98213c8b3e8043..113ce25a603607 100644
--- a/ngraph/frontend/onnx/frontend/src/frontend.cpp
+++ b/ngraph/frontend/onnx/frontend/src/frontend.cpp
@@ -13,8 +13,8 @@
 
 #include "onnx_common/onnx_model_validator.hpp"
 
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov;
+using namespace ov::frontend;
 
 using VariantString = VariantWrapper<std::string>;
 using VariantWString = VariantWrapper<std::wstring>;
@@ -71,7 +71,7 @@ std::shared_ptr<ngraph::Function> FrontEndONNX::convert(InputModel::Ptr model) c
 }
 
 void FrontEndONNX::convert(std::shared_ptr<ngraph::Function> partially_converted) const {
-    onnx_import::detail::convert_decoded_function(partially_converted);
+    ngraph::onnx_import::detail::convert_decoded_function(partially_converted);
 }
 
 std::shared_ptr<ngraph::Function> FrontEndONNX::decode(InputModel::Ptr model) const {
@@ -123,14 +123,14 @@ bool FrontEndONNX::supported_impl(const std::vector<std::shared_ptr<Variant>>& v
 #endif
     if (model_stream.is_open()) {
         model_stream.seekg(0, model_stream.beg);
-        const bool is_valid_model = onnx_common::is_valid_model(model_stream);
+        const bool is_valid_model = ngraph::onnx_common::is_valid_model(model_stream);
         model_stream.close();
         return is_valid_model;
     }
     if (ov::is_type<VariantIstreamPtr>(variants[0])) {
         const auto stream = ov::as_type_ptr<VariantIstreamPtr>(variants[0])->get();
         StreamRewinder rwd{*stream};
-        return onnx_common::is_valid_model(*stream);
+        return ngraph::onnx_common::is_valid_model(*stream);
     }
     return false;
 }
diff --git a/ngraph/frontend/onnx/frontend/src/input_model.cpp b/ngraph/frontend/onnx/frontend/src/input_model.cpp
index b61e8fe19c6cf9..9c0a11a34a0a09 100644
--- a/ngraph/frontend/onnx/frontend/src/input_model.cpp
+++ b/ngraph/frontend/onnx/frontend/src/input_model.cpp
@@ -5,12 +5,12 @@
 #include "input_model.hpp"
 
 #include <frontend_manager/frontend_exceptions.hpp>
-#include <ngraph/file_util.hpp>
+#include <openvino/util/file_util.hpp>
 
 #include "place.hpp"
 
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov;
+using namespace ov::frontend;
 
 NGRAPH_SUPPRESS_DEPRECATED_START
 
@@ -28,8 +28,10 @@ InputModelONNX::InputModelONNX(std::istream& model_stream)
 InputModelONNX::InputModelONNX(std::istream& model_stream, const std::string& path)
     : m_editor{std::make_shared<onnx_editor::ONNXModelEditor>(model_stream, path)} {}
 
+#ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
 InputModelONNX::InputModelONNX(std::istream& model_stream, const std::wstring& path)
-    : InputModelONNX(model_stream, file_util::wstring_to_string(path)) {}
+    : InputModelONNX(model_stream, ov::util::wstring_to_string(path)) {}
+#endif
 
 std::vector<Place::Ptr> InputModelONNX::get_inputs() const {
     const auto& inputs = m_editor->model_inputs();
diff --git a/ngraph/frontend/onnx/frontend/src/input_model.hpp b/ngraph/frontend/onnx/frontend/src/input_model.hpp
index 59bf6cb2e53676..5db8f881308289 100644
--- a/ngraph/frontend/onnx/frontend/src/input_model.hpp
+++ b/ngraph/frontend/onnx/frontend/src/input_model.hpp
@@ -8,7 +8,7 @@
 #include <frontend_manager/input_model.hpp>
 #include <fstream>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class InputModelONNX : public InputModel {
 public:
@@ -20,7 +20,9 @@ class InputModelONNX : public InputModel {
     // The path can be required even if the model is passed as a stream because it is necessary
     // for ONNX external data feature
     InputModelONNX(std::istream& model_stream, const std::string& path);
+#ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
     InputModelONNX(std::istream& model_stream, const std::wstring& path);
+#endif
 
     std::vector<Place::Ptr> get_inputs() const override;
     std::vector<Place::Ptr> get_outputs() const override;
@@ -54,7 +56,7 @@ class InputModelONNX : public InputModel {
     void extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) override;
 
 private:
-    std::shared_ptr<onnx_editor::ONNXModelEditor> m_editor;
+    std::shared_ptr<ov::onnx_editor::ONNXModelEditor> m_editor;
 };
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/src/op/clip.cpp b/ngraph/frontend/onnx/frontend/src/op/clip.cpp
index c39a53b89806f1..1f2f229fb1d0cb 100644
--- a/ngraph/frontend/onnx/frontend/src/op/clip.cpp
+++ b/ngraph/frontend/onnx/frontend/src/op/clip.cpp
@@ -9,6 +9,7 @@
 
 #include "default_opset.hpp"
 #include "ngraph/builder/make_constant.hpp"
+#include "ngraph/validation_util.hpp"
 #include "onnx_import/core/null_node.hpp"
 
 namespace ngraph {
@@ -36,19 +37,19 @@ OutputVector clip(const Node& node) {
     Output<ngraph::Node> max;
 
     // If second input is provided, assign to min input, otherwise set lowest
-    // numeric limit of double as min input.
+    // numeric limit of data type as min input.
     if (inputs.size() > 1 && !ngraph::op::is_null(inputs.at(1))) {
         min = inputs.at(1);
     } else {
-        min = builder::make_constant_from_double(data_type, Shape{}, std::numeric_limits<double>::lowest());
+        min = ngraph::get_constant_lowest_of_type(data_type);
     }
 
     // If third input is provided, assign to max input, otherwise set maximum
-    // numeric limit of double as max input.
+    // numeric limit of data type as max input.
     if (inputs.size() == 3 && !ngraph::op::is_null(inputs.at(2))) {
         max = inputs.at(2);
     } else {
-        max = builder::make_constant_from_double(data_type, Shape{}, std::numeric_limits<double>::max());
+        max = ngraph::get_constant_max_of_type(data_type);
     }
 
     const auto max_of_min_and_data = std::make_shared<default_opset::Maximum>(min, data);
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.cpp b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.cpp
new file mode 100644
index 00000000000000..f874f96bb3cf3e
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.cpp
@@ -0,0 +1,548 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op/com.microsoft/attention.hpp"
+
+#include "default_opset.hpp"
+#include "ngraph/builder/split.hpp"
+#include "onnx_import/core/null_node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace detail {
+namespace {
+NodeVector split_to_QKV(const std::shared_ptr<default_opset::Add>& node,
+                        int64_t num_heads,
+                        const std::vector<size_t>& qkv_hidden_sizes);
+
+using NodeTuple = std::tuple<std::shared_ptr<ngraph::Node>, std::shared_ptr<ngraph::Node>>;
+
+NodeTuple get_attention_mask(const OutputVector& op_inputs, bool unidirectional);
+
+std::shared_ptr<ngraph::Node> attention_softmax(const OutputVector& op_inputs,
+                                                const std::shared_ptr<ngraph::Node>& Q,
+                                                std::shared_ptr<ngraph::Node> K,
+                                                std::shared_ptr<ngraph::Node> V,
+                                                const std::shared_ptr<ngraph::Node>& attention_mask,
+                                                const std::shared_ptr<ngraph::Node>& bin_mask,
+                                                const std::shared_ptr<ngraph::Node>& head_size,
+                                                bool unidirectional);
+
+std::shared_ptr<ngraph::Node> get_present_state(const std::shared_ptr<ngraph::Node>& K,
+                                                const std::shared_ptr<ngraph::Node>& V,
+                                                const OutputVector& op_inputs);
+}  // namespace
+}  // namespace detail
+
+namespace set_1 {
+OutputVector attention(const Node& node) {
+    auto nodes = node.get_ng_inputs();
+    const auto& input = nodes[0];
+    const auto& weights = nodes[1];
+    const auto& bias = nodes[2];
+
+    // Attention is defined as:
+    // Q = input x Wq, K = input x Wk, V = input x Wv
+    // attention = softmax((Q x K') / sqrt(head_size)) x V
+    //
+    // In this operator, Wq, Wk and Wv are combined in a single input 'weights' along the second axis.
+    // So the approach here is to do a single big matrix multiply
+    // and then split the result into Q, K, V matrices
+
+    auto matmul = std::make_shared<default_opset::MatMul>(input, weights);
+    auto add = std::make_shared<default_opset::Add>(matmul, bias);
+
+    const auto num_heads = node.get_attribute_value<int64_t>("num_heads");
+    const auto qkv_hidden_sizes = node.get_attribute_value<std::vector<size_t>>("qkv_hidden_sizes", {});
+    const auto split_result = detail::split_to_QKV(add, num_heads, qkv_hidden_sizes);
+
+    bool unidirectional = static_cast<bool>(node.get_attribute_value<int64_t>("unidirectional", 0));
+    // mask has values either 0 or -10000 and its shape must be
+    // broadcastable to (batch_size, num_heads, sequence_length, past_sequence_length + sequence_length)
+    // so it can be added to Q x K' later
+    // past_sequence_length can be 0 if 'past' input is not available
+    std::shared_ptr<ngraph::Node> attention_mask = nullptr, bin_mask = nullptr;
+    std::tie(attention_mask, bin_mask) = detail::get_attention_mask(nodes, unidirectional);
+
+    const auto& Q = split_result[0];
+    const auto& K = split_result[1];
+    const auto& V = split_result[2];
+    const auto& head_size = split_result[3];
+
+    // compute softmax((Q x K' + mask) / sqrt(head_size))
+    const auto output = detail::attention_softmax(nodes, Q, K, V, attention_mask, bin_mask, head_size, unidirectional);
+
+    // present = concat(K, V) if 'past' input is unavailable
+    // or
+    // present = concat(past, K, V)
+    const auto present = detail::get_present_state(K, V, nodes);
+
+    return {output, present};
+}
+}  // namespace set_1
+
+namespace detail {
+namespace {
+
+std::shared_ptr<ngraph::Node> get_dimensions(const std::shared_ptr<default_opset::ShapeOf>& shape,
+                                             const std::vector<int>& dims) {
+    static const auto zero = default_opset::Constant::create(element::i32, Shape{}, {0});
+    const auto dims_const = default_opset::Constant::create(element::i32, Shape{dims.size()}, dims);
+    return std::make_shared<default_opset::Gather>(shape, dims_const, zero);
+}
+
+std::shared_ptr<ngraph::Node> get_dimensions(const std::shared_ptr<ngraph::Node>& node, const std::vector<int>& dims) {
+    return get_dimensions(std::make_shared<default_opset::ShapeOf>(node), dims);
+}
+
+std::shared_ptr<ngraph::Node> get_hidden_size(const std::shared_ptr<default_opset::ShapeOf>& node_shape) {
+    // node has shape (batch_size, sequence_length, 3 * hidden_size)
+    const auto zero = default_opset::Constant::create(element::i32, Shape{}, {0});
+    const auto hidden_size_x3 = get_dimensions(node_shape, {2});
+    const auto three = default_opset::Constant::create(element::i64, Shape{}, {3});
+    const auto hidden_size = std::make_shared<default_opset::Divide>(hidden_size_x3, three);
+    return hidden_size;
+}
+
+NodeVector split_to_QKV(const std::shared_ptr<default_opset::Add>& node,
+                        int64_t num_heads,
+                        const std::vector<size_t>& qkv_hidden_sizes) {
+    OutputVector split;
+    std::shared_ptr<ngraph::Node> head_size = nullptr;
+    const auto& node_type = node->get_element_type();
+    const auto node_shape = std::make_shared<default_opset::ShapeOf>(node);
+    // node has shape (batch_size, sequence_length, 3 * hidden_size)
+    // fetch the first two dimensions
+    const auto batch_size_seq_len = get_dimensions(node_shape, {0, 1});
+    const auto num_heads_node = default_opset::Constant::create(element::i64, Shape{1}, {num_heads});
+    if (qkv_hidden_sizes.size() == 0) {
+        const auto hidden_size = get_hidden_size(node_shape);
+        // head_size = hidden_size / num_heads
+        head_size = std::make_shared<default_opset::Divide>(hidden_size, num_heads_node);
+        // split the node into 3 even parts Q, K, V with shape (batch_size, sequence_len, hidden_size)
+        split = ngraph::builder::opset1::split(node, 3, 2);
+        // and reshape each part to new shape (batch_size, sequence_len, num_heads, head_size)
+        auto new_shape =
+            std::make_shared<default_opset::Concat>(NodeVector{batch_size_seq_len, num_heads_node, head_size}, 0);
+        for (size_t i = 0; i < split.size(); i++) {
+            split[i] = std::make_shared<default_opset::Reshape>(split[i], new_shape, false);
+        }
+        head_size = std::make_shared<default_opset::Convert>(head_size, node_type);
+    } else {
+        // in this case, weights have shape
+        // (input_hidden_size, qkv_hidden_sizes[0] + qkv_hidden_sizes[1] + qkv_hidden_sizes[2])
+        // so user specified hidden_sizes for Q, K and V
+        NGRAPH_CHECK(qkv_hidden_sizes.size() == 3, "qkv_hidden_sizes attribute needs to have 3 values");
+        NGRAPH_CHECK(qkv_hidden_sizes[0] == qkv_hidden_sizes[1],
+                     "qkv_hidden_sizes first element should be same as the second");
+        // split the node into 3 parts Q, K, V with shapes
+        // Q: (batch_size, sequence_len, qkv_hidden_sizes[0])
+        // K: (batch_size, sequence_len, qkv_hidden_sizes[1])
+        // V: (batch_size, sequence_len, qkv_hidden_sizes[2])
+        split = ngraph::builder::opset1::split(node, qkv_hidden_sizes, 2);
+        // and reshape each part to new shape (batch_size, sequence_len, num_heads, head_size)
+        for (size_t i = 0; i < split.size(); i++) {
+            auto new_shape = std::make_shared<default_opset::Concat>(
+                NodeVector{batch_size_seq_len,
+                           num_heads_node,
+                           default_opset::Constant::create(element::i64, Shape{1}, {qkv_hidden_sizes[i] / num_heads})},
+                0);
+            split[i] = std::make_shared<default_opset::Reshape>(split[i], new_shape, false);
+        }
+        float head_size_val = qkv_hidden_sizes[0] > 0 ? static_cast<float>(qkv_hidden_sizes[0]) / num_heads
+                                                      : static_cast<float>(qkv_hidden_sizes[2]) / num_heads;
+        head_size = default_opset::Constant::create(node_type, Shape{1}, {head_size_val});
+    }
+
+    // transpose Q, K and V to (batch_size, num_heads, sequence_len, head_size)
+    auto perm = default_opset::Constant::create(element::i64, Shape{4}, {0, 2, 1, 3});
+    auto Q = std::make_shared<default_opset::Transpose>(split[0], perm);
+    auto K = std::make_shared<default_opset::Transpose>(split[1], perm);
+    auto V = std::make_shared<default_opset::Transpose>(split[2], perm);
+
+    return {Q, K, V, head_size};
+}
+
+// This function handles the case when mask_index rank is 1 - so its shape is (batch_size) or (2 * batch_size).
+// The returned mask consists of 0 and -10000 and has shape (batch_size, 1, 1, all_seq_len). 'mask_index' input contains
+// positions from where the -10000 values start appearing in the final mask per batch (if shape is (batch_size)) or if
+// shape is (2 * batch_size), user can define two ranges of -10000 values appearing in the final mask. For example:
+//
+// batch_size = 3, all_seq_len = 5, mask_index = [2, 4, 3]
+// the function returns following mask with shape (3, 1, 1, 5):
+// 0,  0,  -10000,  -10000,  -10000
+// 0,  0,       0,       0,  -10000
+// 0,  0,       0,  -10000,  -10000
+//
+// e.g., for batch = 2, -10000 values appear within range [mask_index[2]:5] (or [3:5])
+//
+// Another example, but with mask_index shape (2 * batch_size)
+// batch_size = 3, all_seq_len = 5, mask_index = [2, 4, 3, 1, 2, 2]
+// the function returns following mask with shape (3, 1, 1, 5):
+// -10000,       0,  -10000,  -10000,  -10000
+// -10000,  -10000,       0,       0,  -10000
+// -10000,  -10000,       0,  -10000,  -10000
+//
+// e.g., for batch = 1, -10000 values appear within two ranges [0, mask_index[4]] and [mask_index[1]:5] (or [0:2],[4:5])
+//
+//
+// This is how it's done with nGraph operations:
+//
+//  First the 'base' is generated by range + broadcast:
+//     base = range(0, all_seq_len)
+//     base = broadcast(base, shape=(batch_size, all_seq_len))
+//
+//  With batch_size = 3 and all_seq_len = 5, 'base' looks as follows:
+//       [[0, 1, 2, 3, 4],
+//        [0, 1, 2, 3, 4],
+//        [0, 1, 2, 3, 4]]
+//
+//  Next step is to reshape mask_index:
+//     mask_index = reshape(mask_index, shape=(-1, batch_size))
+//
+//  With the second example above (mask_index = [2, 4, 3, 1, 2, 2]), now it looks like:
+//     mask_index = [[2, 4, 3],
+//                 [1, 2, 2]]
+//
+//  Now we get the first row and reshape it to (batch_size, 1) to have indices laid out in column:
+//     tail_range_indices = gather(mask_index, indices=[0], axis=0)  # tail_range_indices = [2, 4, 3]
+//     tail_range_indices = reshape(tail_range_indices, shape=(batch_size, 1)
+//     # tail_range_indices = [[2],
+//     #                       [4],
+//     #                       [3]]
+//
+//  Then the base is compared with the indices
+//     tail_range_mask = base >= tail_range_indices
+//
+//  Thanks to autobroadcast in elementwise operators, the comparison conceptually happens between:
+//       [[0, 1, 2, 3, 4],      [[2, 2, 2, 2, 2],
+//        [0, 1, 2, 3, 4],  >=   [4, 4, 4, 4, 4],
+//        [0, 1, 2, 3, 4]]       [3, 3, 3, 3, 3]]
+//
+//   and the result is:
+//               [[0, 0, 1, 1, 1],
+//                [0, 0, 0, 0, 1],
+//                [0, 0, 0, 1, 1]]
+//
+// So we get the final tail range mask by multiplying this by -10000
+//
+// Similarly we process with head range - we fetch the second row from reshaped mask_index,
+// compare it with 'base' (but with 'Less' operator instead of 'GreaterEqual') and combine it
+// with tail_range_mask.
+//
+// Handling both mask_index variants (so (batch_size) and (2 * batch_size)) is tricky since we don't
+// know its dimensions upfront. So we compute both variants and use Select operator to select
+// the right one in the runtime (unless it gets constantfolded before).
+std::shared_ptr<ngraph::Node> attention_mask_from_indices(const Output<ngraph::Node>& mask_index,
+                                                          const element::Type_t& type,
+                                                          const std::shared_ptr<ngraph::Node>& batch_size,
+                                                          const std::shared_ptr<ngraph::Node>& all_seq_len) {
+    const auto zero = default_opset::Constant::create(element::i64, Shape{}, {0});
+    const auto one = default_opset::Constant::create(element::i64, Shape{}, {1});
+    const auto stop = std::make_shared<default_opset::Squeeze>(all_seq_len, zero);
+    std::shared_ptr<ngraph::Node> base =
+        std::make_shared<default_opset::Range>(zero, stop, one, mask_index.get_element_type());
+    const auto target_shape = std::make_shared<default_opset::Concat>(NodeVector{batch_size, all_seq_len}, 0);
+    // broadcast 'base' to (batch_size, all_seq_len)
+    base = std::make_shared<default_opset::Broadcast>(base, target_shape);
+    const auto indices_shape = std::make_shared<default_opset::Concat>(
+        NodeVector{default_opset::Constant::create(element::i64, Shape{1}, {-1}), batch_size},
+        0);
+    std::shared_ptr<ngraph::Node> indices = std::make_shared<default_opset::Reshape>(mask_index, indices_shape, false);
+    // fetch first row from indices
+    std::shared_ptr<ngraph::Node> tail_range_indices = std::make_shared<default_opset::Gather>(indices, zero, zero);
+    tail_range_indices =
+        std::make_shared<default_opset::Reshape>(tail_range_indices,
+                                                 default_opset::Constant::create(element::i32, Shape{2}, {-1, 1}),
+                                                 false);
+    const auto greater_eq = std::make_shared<default_opset::GreaterEqual>(base, tail_range_indices);
+    std::shared_ptr<ngraph::Node> tail_range_mask =
+        std::make_shared<default_opset::Multiply>(std::make_shared<default_opset::Convert>(greater_eq, type),
+                                                  default_opset::Constant::create(type, Shape{}, {-10000}));
+    tail_range_mask =
+        std::make_shared<default_opset::Unsqueeze>(tail_range_mask,
+                                                   default_opset::Constant::create(element::i64, Shape{2}, {1, 2}));
+
+    const auto gather_index =
+        std::make_shared<default_opset::FloorMod>(default_opset::Constant::create(element::i64, Shape{}, {1}),
+                                                  get_dimensions(indices, {0}));
+    // fetch indices from the second row (or first if not available)
+    std::shared_ptr<ngraph::Node> head_range_indices =
+        std::make_shared<default_opset::Gather>(indices, gather_index, zero);
+    head_range_indices =
+        std::make_shared<default_opset::Reshape>(head_range_indices,
+                                                 default_opset::Constant::create(element::i32, Shape{2}, {-1, 1}),
+                                                 false);
+    const auto less = std::make_shared<default_opset::Less>(base, head_range_indices);
+    std::shared_ptr<ngraph::Node> mask = std::make_shared<default_opset::LogicalOr>(less, greater_eq);
+    mask = std::make_shared<default_opset::Multiply>(std::make_shared<default_opset::Convert>(mask, type),
+                                                     default_opset::Constant::create(type, Shape{}, {-10000}));
+    // reshape from (batch_size, all_seq_len) to (batch_size, 1, 1, all_seq_len)
+    mask = std::make_shared<default_opset::Unsqueeze>(mask,
+                                                      default_opset::Constant::create(element::i64, Shape{2}, {1, 2}));
+
+    const auto mask_index_first_dim = get_dimensions(mask_index.get_node_shared_ptr(), {0});
+    // compare mask_index.shape[0] with batch_size value
+    // if they're equal - select tail_range_mask
+    // else select full mask
+    mask = std::make_shared<default_opset::Select>(
+        std::make_shared<default_opset::Equal>(batch_size, mask_index_first_dim),
+        tail_range_mask,
+        mask);
+
+    return mask;
+}
+
+// Prepare unidirectional_mask like it's done in
+// https://github.com/microsoft/onnxruntime/blob/851554536ca8185b3413ee57449ea5ac93370193/onnxruntime/contrib_ops/cpu/bert/attention_helper.h#L87-L96
+//
+// Function returns two masks - one attention mask with values 0 or -10000 with shape (seq_len, all_seq_len),
+// the second one is a binary mask where it has 0 on positions where attention mask has -10000 values and 1 otherwise.
+//
+// For example:
+// seq_len = 4, all_seq_len = 7, past_seq_len = 3. Returned attention mask has shape (4, 7) and contains:
+// 0  0  0  0  -10000  -10000  -10000
+// 0  0  0  0       0  -10000  -10000
+// 0  0  0  0       0       0  -10000
+// 0  0  0  0       0       0       0
+//
+// Returned binary mask has the shape (4, 7) and following values:
+// 1  1  1  1  0  0  0
+// 1  1  1  1  1  0  0
+// 1  1  1  1  1  1  0
+// 1  1  1  1  1  1  1
+//
+// Binary mask is used later before softmax to achieve
+// https://github.com/microsoft/onnxruntime/blob/851554536ca8185b3413ee57449ea5ac93370193/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h#L158-L166
+//
+// The approach used to generate those masks is similar to one from attention_mask_from_indices function (see comments
+// there).
+NodeTuple unidirectional_mask(const element::Type_t& type,
+                              const std::shared_ptr<ngraph::Node>& seq_len,
+                              const std::shared_ptr<ngraph::Node>& all_seq_len,
+                              const std::shared_ptr<ngraph::Node>& past_seq_len) {
+    const auto zero = default_opset::Constant::create(element::i64, Shape{}, {0});
+    const auto one = default_opset::Constant::create(element::i64, Shape{}, {1});
+    const auto stop = std::make_shared<default_opset::Squeeze>(all_seq_len, zero);
+    std::shared_ptr<ngraph::Node> bin_mask = std::make_shared<default_opset::Range>(zero, stop, one, element::i32);
+    auto target_shape = std::make_shared<default_opset::Concat>(NodeVector{seq_len, all_seq_len}, 0);
+    bin_mask = std::make_shared<default_opset::Broadcast>(bin_mask, target_shape);
+    auto start =
+        std::make_shared<default_opset::Squeeze>(std::make_shared<default_opset::Add>(past_seq_len, one), zero);
+    auto end = std::make_shared<default_opset::Squeeze>(std::make_shared<default_opset::Add>(all_seq_len, one), zero);
+    auto indices = std::make_shared<default_opset::Unsqueeze>(
+        std::make_shared<default_opset::Range>(start, end, one, element::i32),
+        default_opset::Constant::create(element::i32, Shape{1}, {1}));
+    bin_mask = std::make_shared<default_opset::GreaterEqual>(bin_mask, indices);
+    std::shared_ptr<ngraph::Node> attention_mask =
+        std::make_shared<default_opset::Multiply>(std::make_shared<default_opset::Convert>(bin_mask, type),
+                                                  default_opset::Constant::create(type, Shape{}, {-10000}));
+    bin_mask = std::make_shared<default_opset::Convert>(std::make_shared<default_opset::LogicalNot>(bin_mask), type);
+    return NodeTuple{attention_mask, bin_mask};
+}
+
+// This is the easiest variant of 'mask_index' input - the input consists of 0 or 1 values
+// and we transform them to:
+// * -10000 for positions where mask_index == 0
+// * 0 for positions where mask_index == 1
+//
+// It handles mask_index with shapes:
+// (batch_size, past_sequence_length + sequence_length) or
+// (batch_size, sequence_length, past_sequence_length + sequence_length)
+//
+// Shape (batch_size, 1, max_sequence_length, max_sequence_length) is not supported in onnxruntime:
+// https://github.com/microsoft/onnxruntime/blob/851554536ca8185b3413ee57449ea5ac93370193/onnxruntime/contrib_ops/cpu/bert/attention_helper.h#L78
+std::shared_ptr<ngraph::Node> raw_mask(const Output<ngraph::Node>& mask_index,
+                                       Dimension::value_type mask_rank,
+                                       const element::Type_t& type) {
+    std::shared_ptr<ngraph::Node> mask = std::make_shared<default_opset::Convert>(mask_index, type);
+    mask = std::make_shared<default_opset::Convert>(mask, type);
+    mask = std::make_shared<default_opset::Subtract>(default_opset::Constant::create(type, Shape{}, {1}), mask);
+    mask = std::make_shared<default_opset::Multiply>(mask, default_opset::Constant::create(type, Shape{}, {-10000}));
+    switch (mask_rank) {
+    // Handle mask_index with (batch_size, past_sequence_length + sequence_length) shape
+    // Reshape it to (batch_size, 1, 1, past_sequence_length + sequence_length)
+    case 2:
+        mask = std::make_shared<default_opset::Reshape>(
+            mask,
+            default_opset::Constant::create(element::i64, Shape{4}, {0, 1, 1, -1}),
+            true);
+        break;
+    // Handle mask_index with (batch_size, sequence_length, past_sequence_length + sequence_length) shape
+    // Reshape it to (batch_size, 1, sequence_length, past_sequence_length + sequence_length)
+    case 3:
+        mask = std::make_shared<default_opset::Reshape>(
+            mask,
+            default_opset::Constant::create(element::i64, Shape{4}, {0, 1, 0, -1}),
+            true);
+        break;
+    }
+    return mask;
+}
+
+bool is_past_input_available(const OutputVector& op_inputs) {
+    return op_inputs.size() > 4 && !ngraph::op::is_null(op_inputs[4]);
+}
+
+NodeTuple get_attention_mask(const OutputVector& op_inputs, bool unidirectional) {
+    const auto zero = default_opset::Constant::create(element::i64, Shape{1}, {0});
+    const auto one = default_opset::Constant::create(element::i64, Shape{1}, {1});
+
+    std::shared_ptr<ngraph::Node> past_seq_len;
+    // get the value of past_sequence_length
+    if (is_past_input_available(op_inputs)) {
+        const auto& past = op_inputs[4];
+        // 'past' node has shape (2, batch_size, num_heads, past_sequence_length, head_size)
+        past_seq_len = get_dimensions(past.get_node_shared_ptr(), {3});
+    } else {
+        past_seq_len = zero;
+    }
+
+    // 'input' node has shape (batch_size, sequence_length, input_hidden_size)
+    auto input_shape = std::make_shared<default_opset::ShapeOf>(op_inputs[0]);
+    auto seq_len = get_dimensions(input_shape, {1});
+    auto all_seq_len = std::make_shared<default_opset::Add>(seq_len, past_seq_len);
+    const auto& type = op_inputs[0].get_element_type();
+    std::shared_ptr<ngraph::Node> attention_mask = nullptr;
+    std::shared_ptr<ngraph::Node> bin_mask = nullptr;
+    if (unidirectional) {
+        std::tie(attention_mask, bin_mask) = unidirectional_mask(type, seq_len, all_seq_len, past_seq_len);
+    }
+    if (op_inputs.size() > 3 && !ngraph::op::is_null(op_inputs[3])) {
+        const auto& mask_index = op_inputs[3];
+        NGRAPH_CHECK(mask_index.get_element_type() == element::i32, "'mask_index' type must be int32");
+        auto batch_size = get_dimensions(input_shape, {0});
+        const auto mask_rank = mask_index.get_partial_shape().rank();
+        NGRAPH_CHECK(mask_rank.is_static(), "'mask_index' rank must be static");
+        auto mask_rank_val = mask_rank.get_length();
+        std::shared_ptr<ngraph::Node> mask;
+        if (mask_rank_val == 1) {
+            // case when mask_index has shape (batch_size) or (2 * batch_size)
+            // so it contains positions that specify how mask should be generated
+            mask = attention_mask_from_indices(mask_index, type, batch_size, all_seq_len);
+        } else if (mask_rank_val < 4) {
+            mask = raw_mask(mask_index, mask_rank.get_length(), type);
+        } else {
+            NGRAPH_CHECK(false, "mask_index with rank " + std::to_string(mask_rank_val) + " is not supported");
+        }
+        // add the mask with unidirectional mask if available
+        if (attention_mask) {
+            attention_mask = std::make_shared<default_opset::Add>(attention_mask, mask);
+        } else {
+            attention_mask = mask;
+        }
+    }
+    return NodeTuple{attention_mask, bin_mask};
+}
+
+// Compute softmax(Q x K' / sqrt(head_size)) x V
+std::shared_ptr<ngraph::Node> attention_softmax(const OutputVector& op_inputs,
+                                                const std::shared_ptr<ngraph::Node>& Q,
+                                                std::shared_ptr<ngraph::Node> K,
+                                                std::shared_ptr<ngraph::Node> V,
+                                                const std::shared_ptr<ngraph::Node>& attention_mask,
+                                                const std::shared_ptr<ngraph::Node>& bin_mask,
+                                                const std::shared_ptr<ngraph::Node>& head_size,
+                                                bool unidirectional) {
+    auto zero = default_opset::Constant::create(element::i64, Shape{}, {0});
+    if (is_past_input_available(op_inputs)) {
+        // concat past K and V with present ones
+        const auto& past = op_inputs[4];
+        // 'past' input has two matrices K and V with shape (1, batch_size, num_heads, past_sequence_length, head_size)
+        // concatenated along first axis to a single
+        // (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)
+        // so we need to split it into two parts, remove first dimension from each part and concatenate first part
+        // with current K and second part with current V
+        const auto split = ngraph::builder::opset1::split(past, 2, 0);
+        const auto past_K = std::make_shared<default_opset::Squeeze>(split[0], zero);
+        K = std::make_shared<default_opset::Concat>(NodeVector{past_K, K}, 2);
+        const auto past_V = std::make_shared<default_opset::Squeeze>(split[1], zero);
+        V = std::make_shared<default_opset::Concat>(NodeVector{past_V, V}, 2);
+    }
+    // perform Q x K'
+    std::shared_ptr<ngraph::Node> softmax_input = std::make_shared<default_opset::MatMul>(Q, K, false, true);
+    // Q x K' + mask
+    if (attention_mask) {
+        if (unidirectional) {
+            // Perform the equivalent of
+            // https://github.com/microsoft/onnxruntime/blob/851554536ca8185b3413ee57449ea5ac93370193/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h#L158-L166
+            // For positions where unidirectional_mask has -10000 values - attention_mask is moved to softmax input
+            softmax_input = std::make_shared<default_opset::Multiply>(softmax_input, bin_mask);
+        }
+        softmax_input = std::make_shared<default_opset::Add>(softmax_input, attention_mask);
+    }
+    const auto sqrt = std::make_shared<default_opset::Sqrt>(head_size);
+    // (Q x K' + mask) / sqrt(head_size)
+    softmax_input = std::make_shared<default_opset::Divide>(softmax_input, sqrt);
+    // handle 'extra_add' input
+    if (op_inputs.size() > 5 && !ngraph::op::is_null(op_inputs[5])) {
+        NGRAPH_CHECK(!is_past_input_available(op_inputs),
+                     "Cannot use both 'past' and 'extra_add' inputs in the same node");
+        const auto& extra_add = op_inputs[5];
+        softmax_input = std::make_shared<default_opset::Add>(softmax_input, extra_add);
+    }
+    // softmax((Q x K' + mask) / sqrt(head_size))
+    const auto softmax = std::make_shared<default_opset::Softmax>(softmax_input, 3);
+
+    // softmax((Q x K' + mask) / sqrt(head_size)) x V
+    std::shared_ptr<ngraph::Node> output = std::make_shared<default_opset::MatMul>(softmax, V);
+    // transpose the result from (batch_size, num_heads, sequence_length, head_size)
+    // to (batch_size, sequence_length, num_heads, head_size)
+    const auto perm = default_opset::Constant::create(element::i64, Shape{4}, {0, 2, 1, 3});
+    output = std::make_shared<default_opset::Transpose>(output, perm);
+    auto new_shape = default_opset::Constant::create(element::i32, Shape{3}, {0, 0, -1});
+    // reshape the result from (batch_size, sequence_length, num_heads, head_size) to (batch_size, sequence_length,
+    // num_heads * head_size)
+    output = std::make_shared<default_opset::Reshape>(output, new_shape, true);
+
+    return output;
+}
+
+// Make present state from K and V matrices by reshaping them from:
+// (batch_size, num_heads, sequence_length, head_size) to (1, batch_size, num_heads, sequence_length, head_size)
+// and concatenating them along first axis to make 'present' output.
+// If fifth input ('past') is available, it gets concatenated with 'present' output along fourth axis.
+std::shared_ptr<ngraph::Node> get_present_state(const std::shared_ptr<ngraph::Node>& K,
+                                                const std::shared_ptr<ngraph::Node>& V,
+                                                const OutputVector& op_inputs) {
+    auto zero = default_opset::Constant::create(element::i64, Shape{1}, {0});
+    // expand K shape (batch_size, num_heads, sequence_length, head_size) to
+    // (1, batch_size, num_heads, sequence_length, head_size)
+    auto K_unsqueezed = std::make_shared<default_opset::Unsqueeze>(K, zero);
+    // similarly expand V shape
+    auto V_unsqueezed = std::make_shared<default_opset::Unsqueeze>(V, zero);
+
+    // add padding in case K and V have different shapes (it happens when used provided uneven qkv_hidden_sizes)
+    // if the shapes are equal (so padding will be zero), Pad gets eliminated in NopElimination pass
+    const auto K_shape = std::make_shared<default_opset::ShapeOf>(K_unsqueezed);
+    const auto V_shape = std::make_shared<default_opset::ShapeOf>(V_unsqueezed);
+    const auto K_pads_end =
+        std::make_shared<default_opset::Maximum>(std::make_shared<default_opset::Subtract>(V_shape, K_shape), zero);
+    const auto V_pads_end =
+        std::make_shared<default_opset::Maximum>(std::make_shared<default_opset::Subtract>(K_shape, V_shape), zero);
+    const auto pads_begin =
+        std::make_shared<default_opset::Broadcast>(zero, std::make_shared<default_opset::ShapeOf>(K_shape));
+    const auto K_padded =
+        std::make_shared<default_opset::Pad>(K_unsqueezed, pads_begin, K_pads_end, ngraph::op::PadMode::CONSTANT);
+    const auto V_padded =
+        std::make_shared<default_opset::Pad>(V_unsqueezed, pads_begin, V_pads_end, ngraph::op::PadMode::CONSTANT);
+
+    // concat key and value tensors along first axis to make 'present' state
+    // after that operation, 'present' has shape (2, batch_size, num_heads, sequence_length, head_size)
+    std::shared_ptr<ngraph::Node> present = std::make_shared<default_opset::Concat>(NodeVector{K_padded, V_padded}, 0);
+    if (is_past_input_available(op_inputs)) {
+        const auto& past = op_inputs[4];
+        // concat 'past' to 'present' output along fourth axis
+        // after that operation, 'present' has shape:
+        // (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)
+        present = std::make_shared<default_opset::Concat>(OutputVector{past, present}, 3);
+    }
+    return present;
+}
+}  // namespace
+}  // namespace detail
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.hpp b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.hpp
new file mode 100644
index 00000000000000..50ecbd82ef57fe
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.hpp
@@ -0,0 +1,17 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "onnx_import/core/node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+OutputVector attention(const Node& node);
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/max_pool.cpp b/ngraph/frontend/onnx/frontend/src/op/max_pool.cpp
index 18004d6bf43739..df02d83943e695 100644
--- a/ngraph/frontend/onnx/frontend/src/op/max_pool.cpp
+++ b/ngraph/frontend/onnx/frontend/src/op/max_pool.cpp
@@ -25,8 +25,14 @@ OutputVector max_pool(const Node& node) {
 
 }  // namespace set_1
 
+namespace set_8 {
+OutputVector max_pool(const Node& node) {
+    return pooling::PoolingFactory(node).make_max_pool_with_indices();
+}
+}  // namespace set_8
+
 }  // namespace op
 
 }  // namespace onnx_import
 
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/max_pool.hpp b/ngraph/frontend/onnx/frontend/src/op/max_pool.hpp
index 82c10cc45c5a6f..b7fda5e4058c17 100644
--- a/ngraph/frontend/onnx/frontend/src/op/max_pool.hpp
+++ b/ngraph/frontend/onnx/frontend/src/op/max_pool.hpp
@@ -23,6 +23,19 @@ OutputVector max_pool(const Node& node);
 
 }  // namespace set_1
 
+namespace set_8 {
+///
+/// \brief Convert ONNX MaxPool operation to an nGraph node.
+///
+/// \param node   The ONNX node object representing this operation.
+///
+/// \return The vector containing Ngraph nodes producing output of ONNX MaxPool
+///         operation.
+///
+OutputVector max_pool(const Node& node);
+
+}  // namespace set_8
+
 }  // namespace op
 
 }  // namespace onnx_import
diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal.cpp b/ngraph/frontend/onnx/frontend/src/op/random_normal.cpp
new file mode 100644
index 00000000000000..574a44f177cece
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/random_normal.cpp
@@ -0,0 +1,36 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "utils/random_normal.hpp"
+
+#include "exceptions.hpp"
+#include "ngraph/shape.hpp"
+#include "utils/common.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+
+OutputVector random_normal(const Node& node) {
+    CHECK_VALID_NODE(node, node.has_attribute("shape"), "RandomNormal operator must specify a 'shape' attribute.");
+
+    const auto dtype =
+        node.get_attribute_value<int64_t>("dtype", static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT));
+    const auto target_type = common::get_ngraph_element_type(dtype);
+
+    const auto mean = node.get_attribute_value<float>("mean", 0.0f);
+    const auto scale = node.get_attribute_value<float>("scale", 1.0f);
+    const auto seed = node.get_attribute_value<float>("seed", 0);
+
+    const auto shape_dims = node.get_attribute_value<std::vector<int64_t>>("shape");
+    const auto shape = default_opset::Constant::create(element::i64, {shape_dims.size()}, shape_dims);
+
+    return detail::make_random_normal(shape, target_type, mean, scale, seed);
+}
+
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal.hpp b/ngraph/frontend/onnx/frontend/src/op/random_normal.hpp
new file mode 100644
index 00000000000000..80e95e34dcb9fe
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/random_normal.hpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "ngraph/node.hpp"
+#include "onnx_import/core/node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+
+OutputVector random_normal(const Node& node);
+
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal_like.cpp b/ngraph/frontend/onnx/frontend/src/op/random_normal_like.cpp
new file mode 100644
index 00000000000000..5648643c79b6b8
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/random_normal_like.cpp
@@ -0,0 +1,37 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph/shape.hpp"
+#include "op/random_uniform_like.hpp"
+#include "utils/common.hpp"
+#include "utils/random_normal.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+
+OutputVector random_normal_like(const Node& node) {
+    const auto input = node.get_ng_inputs().at(0);
+
+    ngraph::element::Type target_type;
+    if (node.has_attribute("dtype")) {
+        const auto dtype = node.get_attribute_value<int64_t>("dtype");
+        target_type = common::get_ngraph_element_type(dtype);
+    } else {
+        target_type = input.get_element_type();
+    }
+
+    const auto shape = std::make_shared<default_opset::ShapeOf>(input);
+    const auto mean = node.get_attribute_value<float>("mean", 0.0f);
+    const auto scale = node.get_attribute_value<float>("scale", 1.0f);
+    const auto seed = node.get_attribute_value<float>("seed", 0.0f);
+
+    return detail::make_random_normal(shape, target_type, mean, scale, seed);
+}
+
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal_like.hpp b/ngraph/frontend/onnx/frontend/src/op/random_normal_like.hpp
new file mode 100644
index 00000000000000..4d608bd61dda15
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/random_normal_like.hpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "ngraph/node.hpp"
+#include "onnx_import/core/node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+
+OutputVector random_normal_like(const Node& node);
+
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/random_uniform.cpp b/ngraph/frontend/onnx/frontend/src/op/random_uniform.cpp
index cf01a8edef8677..da14edbe18d6e5 100644
--- a/ngraph/frontend/onnx/frontend/src/op/random_uniform.cpp
+++ b/ngraph/frontend/onnx/frontend/src/op/random_uniform.cpp
@@ -23,7 +23,7 @@ OutputVector random_uniform(const Node& node) {
         node.get_attribute_value<int64_t>("dtype", static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT));
     const auto high = node.get_attribute_value<float>("high", 1.0f);
     const auto low = node.get_attribute_value<float>("low", 0.0f);
-    const auto seed = node.get_attribute_value<int64_t>("seed", 0);
+    const auto seed = node.get_attribute_value<float>("seed", 0.0f);
     const auto shape = node.get_attribute_value<std::vector<int64_t>>("shape");
 
     const auto target_shape_const = default_opset::Constant::create(ngraph::element::i64, Shape{shape.size()}, shape);
@@ -32,19 +32,17 @@ OutputVector random_uniform(const Node& node) {
 
     const auto target_type = common::get_ngraph_element_type(dtype);
     const uint64_t global_seed = 0;
+    const auto seed_uint64 = static_cast<uint64_t>(seed * 1000);
 
     return {std::make_shared<ngraph::opset8::RandomUniform>(target_shape_const,
                                                             low_const,
                                                             high_const,
                                                             target_type,
                                                             global_seed,
-                                                            seed)};
+                                                            seed_uint64)};
 }
 
 }  // namespace set_1
-
 }  // namespace op
-
 }  // namespace onnx_import
-
 }  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/random_uniform_like.cpp b/ngraph/frontend/onnx/frontend/src/op/random_uniform_like.cpp
index 0ca673622478b4..08835a0429dec8 100644
--- a/ngraph/frontend/onnx/frontend/src/op/random_uniform_like.cpp
+++ b/ngraph/frontend/onnx/frontend/src/op/random_uniform_like.cpp
@@ -18,7 +18,7 @@ namespace set_1 {
 
 OutputVector random_uniform_like(const Node& node) {
     OutputVector inputs{node.get_ng_inputs()};
-    auto input = inputs.at(0);
+    const auto input = inputs.at(0);
 
     ngraph::element::Type target_type;
     if (node.has_attribute("dtype")) {
@@ -32,25 +32,23 @@ OutputVector random_uniform_like(const Node& node) {
 
     const auto high = node.get_attribute_value<float>("high", 1.0f);
     const auto low = node.get_attribute_value<float>("low", 0.0f);
-    const auto seed = node.get_attribute_value<int64_t>("seed", 0);
+    const auto seed = node.get_attribute_value<float>("seed", 0.f);
 
     const auto high_const = default_opset::Constant::create(ngraph::element::f32, Shape{1}, {high});
     const auto low_const = default_opset::Constant::create(ngraph::element::f32, Shape{1}, {low});
 
     const uint64_t global_seed = 0;
+    const auto seed_uint64 = static_cast<uint64_t>(seed * 1000);
 
     return {std::make_shared<ngraph::opset8::RandomUniform>(target_shape,
                                                             low_const,
                                                             high_const,
                                                             target_type,
                                                             global_seed,
-                                                            seed)};
+                                                            seed_uint64)};
 }
 
 }  // namespace set_1
-
 }  // namespace op
-
 }  // namespace onnx_import
-
 }  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp b/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
index 93badc431fa806..7d12281d44674e 100644
--- a/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
+++ b/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
@@ -31,6 +31,7 @@
 #include "op/cast_like.hpp"
 #include "op/ceil.hpp"
 #include "op/clip.hpp"
+#include "op/com.microsoft/attention.hpp"
 #include "op/com.microsoft/bias_gelu.hpp"
 #include "op/com.microsoft/embed_layer_normalization.hpp"
 #include "op/com.microsoft/skip_layer_normalization.hpp"
@@ -116,6 +117,8 @@
 #include "op/qlinear_conv.hpp"
 #include "op/qlinear_matmul.hpp"
 #include "op/quantize_linear.hpp"
+#include "op/random_normal.hpp"
+#include "op/random_normal_like.hpp"
 #include "op/random_uniform.hpp"
 #include "op/random_uniform_like.hpp"
 #include "op/range.hpp"
@@ -359,6 +362,7 @@ OperatorsBridge::OperatorsBridge() {
     REGISTER_OPERATOR("MatMulInteger", 1, matmul_integer);
     REGISTER_OPERATOR("MatMul", 1, matmul);
     REGISTER_OPERATOR("MaxPool", 1, max_pool);
+    REGISTER_OPERATOR("MaxPool", 8, max_pool);
     REGISTER_OPERATOR("Max", 1, max);
     REGISTER_OPERATOR("Max", 8, max);
     REGISTER_OPERATOR("Mean", 1, mean);
@@ -384,6 +388,8 @@ OperatorsBridge::OperatorsBridge() {
     REGISTER_OPERATOR("QuantizeLinear", 1, quantize_linear);
     REGISTER_OPERATOR("QuantizeLinear", 13, quantize_linear);
     REGISTER_OPERATOR("Range", 1, range);
+    REGISTER_OPERATOR("RandomNormal", 1, random_normal);
+    REGISTER_OPERATOR("RandomNormalLike", 1, random_normal_like);
     REGISTER_OPERATOR("RandomUniform", 1, random_uniform);
     REGISTER_OPERATOR("RandomUniformLike", 1, random_uniform_like);
     REGISTER_OPERATOR("Reciprocal", 1, reciprocal);
@@ -485,6 +491,7 @@ OperatorsBridge::OperatorsBridge() {
     REGISTER_OPERATOR_WITH_DOMAIN(OPENVINO_ONNX_DOMAIN, "PriorBoxClustered", 1, prior_box_clustered);
     REGISTER_OPERATOR_WITH_DOMAIN(OPENVINO_ONNX_DOMAIN, "Swish", 1, swish);
 
+    REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "Attention", 1, attention);
     REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "BiasGelu", 1, bias_gelu);
     REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "EmbedLayerNormalization", 1, embed_layer_normalization);
     REGISTER_OPERATOR_WITH_DOMAIN(MICROSOFT_DOMAIN, "SkipLayerNormalization", 1, skip_layer_normalization);
diff --git a/ngraph/frontend/onnx/frontend/src/place.cpp b/ngraph/frontend/onnx/frontend/src/place.cpp
index c8f20e65add056..b143edbf32f6e9 100644
--- a/ngraph/frontend/onnx/frontend/src/place.cpp
+++ b/ngraph/frontend/onnx/frontend/src/place.cpp
@@ -6,8 +6,8 @@
 
 #include <frontend_manager/frontend_exceptions.hpp>
 
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov;
+using namespace ov::frontend;
 
 PlaceInputEdgeONNX::PlaceInputEdgeONNX(const onnx_editor::InputEdge& edge,
                                        std::shared_ptr<onnx_editor::ONNXModelEditor> editor)
diff --git a/ngraph/frontend/onnx/frontend/src/place.hpp b/ngraph/frontend/onnx/frontend/src/place.hpp
index a9d7b2a78dd4be..79344974e0f3bc 100644
--- a/ngraph/frontend/onnx/frontend/src/place.hpp
+++ b/ngraph/frontend/onnx/frontend/src/place.hpp
@@ -9,7 +9,7 @@
 #include <memory>
 #include <sstream>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class PlaceInputEdgeONNX : public Place {
 public:
@@ -126,4 +126,4 @@ class PlaceOpONNX : public Place {
     std::shared_ptr<onnx_editor::ONNXModelEditor> m_editor;
 };
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.cpp b/ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.cpp
index 5edcf2f4267a66..e32dd2bbf41a78 100644
--- a/ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.cpp
+++ b/ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.cpp
@@ -53,11 +53,17 @@ std::shared_ptr<ngraph::Node> ArgMinMaxFactory::make_topk_subgraph(default_opset
         // res_index = dims_on_axis - topk->output(1) = 6 - 3 = 3
         // result = res_index - 1 = 3 - 1 = 2
 
-        const auto axis_node = default_opset::Constant::create(ngraph::element::i64, Shape{1}, {m_axis});
+        const int64_t normalized_axis =
+            normalize_axis(m_input_node.get_node(), m_axis, m_input_node.get_partial_shape().rank());
+
+        const auto axis_node = default_opset::Constant::create(ngraph::element::i64, Shape{1}, {normalized_axis});
         const auto reverse = std::make_shared<opset1::Reverse>(m_input_node, axis_node, opset1::Reverse::Mode::INDEX);
 
-        const auto topk =
-            std::make_shared<default_opset::TopK>(reverse, k_node, m_axis, mode, default_opset::TopK::SortType::NONE);
+        const auto topk = std::make_shared<default_opset::TopK>(reverse,
+                                                                k_node,
+                                                                normalized_axis,
+                                                                mode,
+                                                                default_opset::TopK::SortType::NONE);
 
         const auto data_shape = std::make_shared<default_opset::ShapeOf>(m_input_node);
         const auto dims_on_axis = std::make_shared<default_opset::Gather>(
diff --git a/ngraph/frontend/onnx/frontend/src/utils/common.cpp b/ngraph/frontend/onnx/frontend/src/utils/common.cpp
index e0d5aea94cc056..691addbe8611d7 100644
--- a/ngraph/frontend/onnx/frontend/src/utils/common.cpp
+++ b/ngraph/frontend/onnx/frontend/src/utils/common.cpp
@@ -43,13 +43,7 @@ const ngraph::element::Type& get_ngraph_element_type(int64_t onnx_type) {
     case ONNX_NAMESPACE::TensorProto_DataType_BFLOAT16:
         return element::bf16;
     }
-#ifdef NGRAPH_USE_PROTOBUF_LITE
     throw ngraph_error("unsupported element type");
-#else
-    throw ngraph_error(
-        "unsupported element type: " +
-        ONNX_NAMESPACE::TensorProto_DataType_Name(static_cast<ONNX_NAMESPACE::TensorProto_DataType>(onnx_type)));
-#endif
 }
 
 std::shared_ptr<ngraph::Node> get_monotonic_range_along_node_rank(const Output<ngraph::Node>& value,
diff --git a/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.cpp b/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.cpp
index fff79f9988f948..4984698bbacd7c 100644
--- a/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.cpp
+++ b/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.cpp
@@ -14,6 +14,27 @@
 namespace ngraph {
 namespace onnx_import {
 namespace pooling {
+
+namespace {
+std::shared_ptr<default_opset::Constant> transposition_axis_order(const Rank& input_rank) {
+    NGRAPH_CHECK(input_rank.is_static(),
+                 "Generating column-major MaxPool results is supported only for inputs with static rank.");
+
+    const auto rank = static_cast<size_t>(input_rank.get_length());
+
+    std::vector<int32_t> axes(rank);
+    std::iota(axes.begin(), axes.end(), 0);
+    std::reverse(axes.begin() + 2, axes.end());
+
+    return std::make_shared<default_opset::Constant>(element::i32, Shape{rank}, axes);
+}
+
+std::shared_ptr<ngraph::Node> identity(Output<ngraph::Node> node_output) {
+    const auto zero = default_opset::Constant::create(node_output.get_element_type(), {}, {0});
+    return std::make_shared<default_opset::Add>(node_output, zero);
+}
+}  // namespace
+
 PoolingFactory::PoolingFactory(const Node& node)
     : m_onnx_node{node},
       m_inputs{node.get_ng_inputs()},
@@ -27,6 +48,7 @@ PoolingFactory::PoolingFactory(const Node& node)
     const CoordinateDiff& padding_below{paddings.first};
     m_padding_below = Shape{std::begin(padding_below), std::end(padding_below)};
     m_padding_above = Shape{std::begin(padding_above), std::end(padding_above)};
+    m_storage_order = static_cast<StorageOrder>(node.get_attribute_value<int64_t>("storage_order", 0));
 }
 
 OutputVector PoolingFactory::make_avg_pool() const {
@@ -50,6 +72,26 @@ OutputVector PoolingFactory::make_max_pool() const {
                                                      m_rounding_type,
                                                      m_auto_pad)};
 }
+
+OutputVector PoolingFactory::make_max_pool_with_indices() const {
+    const auto max_pool = std::make_shared<op::v8::MaxPool>(m_inputs.at(0),
+                                                            m_strides,
+                                                            m_dilations,
+                                                            m_padding_below,
+                                                            m_padding_above,
+                                                            m_kernel_shape,
+                                                            m_rounding_type,
+                                                            m_auto_pad);
+    if (m_storage_order == StorageOrder::COLUMN_MAJOR) {
+        const auto transposition_axes = transposition_axis_order(m_inputs.at(0).get_partial_shape().rank());
+        const auto transposed_indices =
+            std::make_shared<default_opset::Transpose>(max_pool->output(1), transposition_axes);
+
+        return {max_pool->output(0), transposed_indices};
+    } else {
+        return {identity(max_pool->output(0)), identity(max_pool->output(1))};
+    }
+}
 }  // namespace pooling
 }  // namespace onnx_import
 }  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.hpp b/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.hpp
index 133f21a6c36194..4e4f750db2528b 100644
--- a/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.hpp
+++ b/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.hpp
@@ -46,6 +46,9 @@ class PoolingFactory {
     ///
     OutputVector make_max_pool() const;
 
+    /// \brief Creates max pooling ONNX operation with 2 outputs (values and indices).
+    OutputVector make_max_pool_with_indices() const;
+
 protected:
     Node m_onnx_node;
     const OutputVector m_inputs;
@@ -56,6 +59,10 @@ class PoolingFactory {
     Shape m_padding_above;
     ngraph::op::PadType m_auto_pad;
     ngraph::op::RoundingType m_rounding_type;
+
+    enum class StorageOrder : int64_t { ROW_MAJOR = 0, COLUMN_MAJOR = 1 };
+
+    StorageOrder m_storage_order;
 };
 }  // namespace pooling
 }  // namespace onnx_import
diff --git a/ngraph/frontend/onnx/frontend/src/utils/random_normal.cpp b/ngraph/frontend/onnx/frontend/src/utils/random_normal.cpp
new file mode 100644
index 00000000000000..66cad4983fc6d0
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/utils/random_normal.cpp
@@ -0,0 +1,68 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "random_normal.hpp"
+
+#include <random>
+
+#include "default_opset.hpp"
+#include "ngraph/opsets/opset8.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace detail {
+
+OutputVector make_random_normal(const Output<ngraph::Node>& shape,
+                                element::Type target_type,
+                                float mean,
+                                float scale,
+                                float seed) {
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<uint64_t> distrib(0, 9999);
+
+    // We start by generating two random series from a uniform distribution
+    const uint64_t global_seed = 0;
+
+    // ONNX specifies the seed as a float, but OpenVINO uses uint64_t
+    const auto op_seed = static_cast<uint64_t>(seed * 1000);
+
+    // We need to use two op_seeds to make sure we get different results for two RandomUniform series
+    const uint64_t seed_1 = (op_seed == 0 ? distrib(gen) : op_seed);
+    const uint64_t seed_2 = (op_seed == 0 ? distrib(gen) : op_seed + 10000);
+
+    const auto min_val = default_opset::Constant::create(target_type, Shape{1}, {0});
+    const auto max_val = default_opset::Constant::create(target_type, Shape{1}, {1});
+
+    const auto uniform_1 =
+        std::make_shared<ngraph::opset8::RandomUniform>(shape, min_val, max_val, target_type, global_seed, seed_1);
+    const auto uniform_2 =
+        std::make_shared<ngraph::opset8::RandomUniform>(shape, min_val, max_val, target_type, global_seed, seed_2);
+
+    // Compute Box–Muller transform
+    // random_normal = scale * ng.sqrt(-2.0 * ng.log(uniform_1)) * ng.cos(2.0 * np.pi * uniform_2) + mean
+    const auto pi = default_opset::Constant::create(target_type, Shape{1}, {3.141592653589793});
+    const auto minus_two = default_opset::Constant::create(target_type, Shape{1}, {-2.0});
+    const auto two = default_opset::Constant::create(target_type, Shape{1}, {2.0});
+
+    const auto log = std::make_shared<default_opset::Log>(uniform_1);
+    const auto multiply_minus_two_log = std::make_shared<default_opset::Multiply>(log, minus_two);
+    const auto sqrt = std::make_shared<default_opset::Sqrt>(multiply_minus_two_log);
+
+    const auto multiply_two_pi = std::make_shared<default_opset::Multiply>(uniform_2, pi);
+    const auto multiply_two_pi_uniform_2 = std::make_shared<default_opset::Multiply>(multiply_two_pi, uniform_2);
+    auto const cos = std::make_shared<default_opset::Cos>(multiply_two_pi_uniform_2);
+
+    auto const scale_const = default_opset::Constant::create(target_type, Shape{1}, {scale});
+    auto const mean_const = default_opset::Constant::create(target_type, Shape{1}, {mean});
+    auto const product =
+        std::make_shared<default_opset::Multiply>(scale_const, std::make_shared<default_opset::Multiply>(sqrt, cos));
+    auto const sum = std::make_shared<default_opset::Add>(product, mean_const);
+
+    return {sum};
+}
+
+}  // namespace detail
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/utils/random_normal.hpp b/ngraph/frontend/onnx/frontend/src/utils/random_normal.hpp
new file mode 100644
index 00000000000000..7e9226d6ca0590
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/utils/random_normal.hpp
@@ -0,0 +1,29 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "ngraph/op/reshape.hpp"
+#include "ngraph/output_vector.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace detail {
+
+/// \brief Creates a random normal tensor with the given shape and type.
+/// \details Uses Box-Mueller algorithm to generate random numbers from a Gauassian distribution
+/// \param shape Shape of the output tensor
+/// \param type Type of the output tensor
+/// \param mean Mean of the distribution
+/// \param scale Standard deviation of the distribution
+/// \param seed Seed for the random number generator
+OutputVector make_random_normal(const Output<ngraph::Node>& shape,
+                                element::Type type,
+                                float mean,
+                                float scale,
+                                float seed);
+
+}  // namespace detail
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/onnx_common/CMakeLists.txt b/ngraph/frontend/onnx/onnx_common/CMakeLists.txt
index bc63b28893d89b..1c4d330290dacd 100644
--- a/ngraph/frontend/onnx/onnx_common/CMakeLists.txt
+++ b/ngraph/frontend/onnx/onnx_common/CMakeLists.txt
@@ -29,9 +29,4 @@ target_link_libraries(${TARGET_NAME} PRIVATE ngraph)
 link_system_libraries(${TARGET_NAME} PUBLIC onnx_proto onnx ${Protobuf_LITE_LIBRARIES})
 
 target_include_directories(${TARGET_NAME} PRIVATE ${ONNX_COMMON_SRC_DIR})
-
-if(NGRAPH_USE_PROTOBUF_LITE)
-    target_compile_definitions(${TARGET_NAME} PRIVATE NGRAPH_USE_PROTOBUF_LITE)
-endif()
-
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
diff --git a/ngraph/frontend/onnx/onnx_common/src/onnx_model_validator.cpp b/ngraph/frontend/onnx/onnx_common/src/onnx_model_validator.cpp
index ee75cf47991311..b6395ddf3f8c82 100644
--- a/ngraph/frontend/onnx/onnx_common/src/onnx_model_validator.cpp
+++ b/ngraph/frontend/onnx/onnx_common/src/onnx_model_validator.cpp
@@ -22,7 +22,8 @@ enum Field {
     GRAPH = 7,
     OPSET_IMPORT = 8,
     METADATA_PROPS = 14,
-    TRAINING_INFO = 20
+    TRAINING_INFO = 20,
+    FUNCTIONS = 25
 };
 
 enum WireType { VARINT = 0, BITS_64 = 1, LENGTH_DELIMITED = 2, START_GROUP = 3, END_GROUP = 4, BITS_32 = 5 };
@@ -46,6 +47,7 @@ bool is_correct_onnx_field(const PbKey& decoded_key) {
         {OPSET_IMPORT, LENGTH_DELIMITED},
         {METADATA_PROPS, LENGTH_DELIMITED},
         {TRAINING_INFO, LENGTH_DELIMITED},
+        {FUNCTIONS, LENGTH_DELIMITED},
     };
 
     if (!onnx_fields.count(static_cast<Field>(decoded_key.first))) {
diff --git a/ngraph/frontend/onnx/onnx_common/src/parser.cpp b/ngraph/frontend/onnx/onnx_common/src/parser.cpp
index 430c569d399dfc..c913a21d723233 100644
--- a/ngraph/frontend/onnx/onnx_common/src/parser.cpp
+++ b/ngraph/frontend/onnx/onnx_common/src/parser.cpp
@@ -53,20 +53,8 @@ ONNX_NAMESPACE::ModelProto parse_from_istream(std::istream& model_stream) {
 
     ONNX_NAMESPACE::ModelProto model_proto;
     if (!model_proto.ParseFromIstream(&model_stream)) {
-#ifdef NGRAPH_USE_PROTOBUF_LITE
         throw ngraph_error("Error during import of ONNX model provided as input stream "
                            " with binary protobuf message.");
-#else
-        // Rewind to the beginning and clear stream state.
-        model_stream.clear();
-        model_stream.seekg(0);
-        google::protobuf::io::IstreamInputStream iistream(&model_stream);
-        // Try parsing input as a prototxt message
-        if (!google::protobuf::TextFormat::Parse(&iistream, &model_proto)) {
-            throw ngraph_error("Error during import of ONNX model provided as input stream with prototxt "
-                               "protobuf message.");
-        }
-#endif
     }
 
     return model_proto;
diff --git a/ngraph/frontend/onnx/onnx_common/src/utils.cpp b/ngraph/frontend/onnx/onnx_common/src/utils.cpp
index 5bc6a79459ac25..5cb45a3a97ecf9 100644
--- a/ngraph/frontend/onnx/onnx_common/src/utils.cpp
+++ b/ngraph/frontend/onnx/onnx_common/src/utils.cpp
@@ -45,13 +45,7 @@ size_t get_onnx_data_size(int32_t onnx_type) {
     case ONNX_NAMESPACE::TensorProto_DataType_BFLOAT16:
         return sizeof(uint16_t);
     }
-#ifdef NGRAPH_USE_PROTOBUF_LITE
     throw ngraph_error("unsupported element type");
-#else
-    throw ngraph_error(
-        "unsupported element type: " +
-        ONNX_NAMESPACE::TensorProto_DataType_Name(static_cast<ONNX_NAMESPACE::TensorProto_DataType>(onnx_type)));
-#endif
 }
 namespace {
 using namespace ONNX_NAMESPACE;
diff --git a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/exceptions.hpp b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/exceptions.hpp
index d2fd144022db7f..4de8e7286c0ce2 100644
--- a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/exceptions.hpp
+++ b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/exceptions.hpp
@@ -5,9 +5,8 @@
 #pragma once
 
 #include <frontend_manager/frontend_exceptions.hpp>
-#include <ngraph/node.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 class NodeContext;
@@ -31,7 +30,7 @@ class OpValidationFailurePDPD : public OpValidationFailure {
 /// \param ... Additional error message info to be added to the error message via the `<<`
 ///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
 ///            i.e., only if the `cond` evalutes to `false`.
-/// \throws ::ngraph::OpValidationFailurePDPD if `cond` is false.
+/// \throws ::ov::OpValidationFailurePDPD if `cond` is false.
 #define PDPD_OP_VALIDATION_CHECK(node_context, ...) \
-    NGRAPH_CHECK_HELPER(::ngraph::frontend::pdpd::OpValidationFailurePDPD, (node_context), __VA_ARGS__)
-}  // namespace ngraph
+    OPENVINO_ASSERT_HELPER(::ov::frontend::pdpd::OpValidationFailurePDPD, (node_context), __VA_ARGS__)
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
index cf2fa3ae9e455e..857c5035097b88 100644
--- a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
+++ b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
@@ -9,7 +9,7 @@
 #include "exceptions.hpp"
 #include "model.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class OpPlacePDPD;
 
@@ -69,4 +69,4 @@ class PDPD_API FrontEndPDPD : public FrontEnd {
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp
index 83f63e38751745..4eff6120552b45 100644
--- a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp
+++ b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp
@@ -7,7 +7,7 @@
 #include <frontend_manager/frontend_manager.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class OpPlacePDPD;
 class TensorPlacePDPD;
@@ -32,11 +32,11 @@ class PDPD_API InputModelPDPD : public InputModel {
     void override_all_outputs(const std::vector<Place::Ptr>& outputs) override;
     void override_all_inputs(const std::vector<Place::Ptr>& inputs) override;
     void extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) override;
-    void set_partial_shape(Place::Ptr place, const ngraph::PartialShape&) override;
-    ngraph::PartialShape get_partial_shape(Place::Ptr place) const override;
-    void set_element_type(Place::Ptr place, const ngraph::element::Type&) override;
+    void set_partial_shape(Place::Ptr place, const ov::PartialShape&) override;
+    ov::PartialShape get_partial_shape(Place::Ptr place) const override;
+    void set_element_type(Place::Ptr place, const ov::element::Type&) override;
     void set_tensor_value(Place::Ptr place, const void* value) override;
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp
index 28a99d1dd2adf0..d4657a97d8ba96 100644
--- a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp
+++ b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp
@@ -17,7 +17,7 @@ class VarDesc;
 }  // namespace framework
 }  // namespace paddle
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class TensorPlacePDPD;
 class OpPlacePDPD;
@@ -196,4 +196,4 @@ class TensorPlacePDPD : public PlacePDPD {
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/decoder.cpp b/ngraph/frontend/paddlepaddle/src/decoder.cpp
index 9a8abb718e3fdc..d157a349a8fc99 100644
--- a/ngraph/frontend/paddlepaddle/src/decoder.cpp
+++ b/ngraph/frontend/paddlepaddle/src/decoder.cpp
@@ -15,7 +15,7 @@
 
 #include "framework.pb.h"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 using namespace paddle::framework;
 
@@ -153,4 +153,4 @@ std::map<std::string, OutputVector> DecoderPDPDProto::map_for_each_output(
 }
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/decoder.hpp b/ngraph/frontend/paddlepaddle/src/decoder.hpp
index db98b1c3bfdebe..a87bf04f591383 100644
--- a/ngraph/frontend/paddlepaddle/src/decoder.hpp
+++ b/ngraph/frontend/paddlepaddle/src/decoder.hpp
@@ -9,7 +9,6 @@
 #include <fstream>
 #include <map>
 #include <memory>
-#include <ngraph/ngraph.hpp>
 #include <paddlepaddle_frontend/frontend.hpp>
 #include <paddlepaddle_frontend/place.hpp>
 #include <string>
@@ -19,7 +18,7 @@
 #include "framework.pb.h"
 #include "node_context.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 extern std::map<paddle::framework::proto::VarType_Type, ngraph::element::Type> TYPE_MAP;
 
@@ -51,4 +50,4 @@ class DecoderPDPDProto : public pdpd::DecoderBase {
 };
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/default_opset.hpp b/ngraph/frontend/paddlepaddle/src/default_opset.hpp
index 4a7aa3259c6402..677e48045cf8bc 100644
--- a/ngraph/frontend/paddlepaddle/src/default_opset.hpp
+++ b/ngraph/frontend/paddlepaddle/src/default_opset.hpp
@@ -4,7 +4,7 @@
 
 #include "ngraph/opsets/opset8.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -13,4 +13,4 @@ namespace default_opset = ngraph::opset8;
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/exceptions.cpp b/ngraph/frontend/paddlepaddle/src/exceptions.cpp
index caf328b45d0df1..0d172c6f4b537c 100644
--- a/ngraph/frontend/paddlepaddle/src/exceptions.cpp
+++ b/ngraph/frontend/paddlepaddle/src/exceptions.cpp
@@ -6,7 +6,7 @@
 
 #include "node_context.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 std::string OpValidationFailurePDPD::get_error_msg_prefix_pdpd(const pdpd::NodeContext& node) {
@@ -16,4 +16,4 @@ std::string OpValidationFailurePDPD::get_error_msg_prefix_pdpd(const pdpd::NodeC
 }
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/frontend.cpp b/ngraph/frontend/paddlepaddle/src/frontend.cpp
index ea68793fcc6e1a..a551fecdafb4a1 100644
--- a/ngraph/frontend/paddlepaddle/src/frontend.cpp
+++ b/ngraph/frontend/paddlepaddle/src/frontend.cpp
@@ -5,8 +5,8 @@
 #include <fstream>
 #include <map>
 #include <ngraph/ngraph.hpp>
-#include <ngraph/opsets/opset7.hpp>
 #include <ngraph/variant.hpp>
+#include <openvino/opsets/opset7.hpp>
 #include <paddlepaddle_frontend/exceptions.hpp>
 #include <paddlepaddle_frontend/frontend.hpp>
 #include <paddlepaddle_frontend/model.hpp>
@@ -21,11 +21,11 @@
 #include "pdpd_fw_node.hpp"
 #include "pdpd_utils.hpp"
 
-using namespace ngraph::opset7;
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::opset7;
+using namespace ov;
+using namespace ov::frontend;
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace {
@@ -84,7 +84,7 @@ NamedOutputs make_framework_node(const std::map<pdpd::TensorName, Output<Node>>&
     }
 
     auto node =
-        std::make_shared<ngraph::frontend::PDPDFrameworkNode>(DecoderPDPDProto(op_place), inputs_vector, inputs_names);
+        std::make_shared<ov::frontend::PDPDFrameworkNode>(DecoderPDPDProto(op_place), inputs_vector, inputs_names);
 
     return node->return_named_outputs();
 }
@@ -326,7 +326,7 @@ std::string FrontEndPDPD::get_name() const {
     return "paddle";
 }
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
 
 extern "C" PDPD_API FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
diff --git a/ngraph/frontend/paddlepaddle/src/model.cpp b/ngraph/frontend/paddlepaddle/src/model.cpp
index 171c596f48efb7..0cbb9038e87ea5 100644
--- a/ngraph/frontend/paddlepaddle/src/model.cpp
+++ b/ngraph/frontend/paddlepaddle/src/model.cpp
@@ -3,7 +3,7 @@
 //
 
 #include <fstream>
-#include <ngraph/opsets/opset7.hpp>
+#include <openvino/opsets/opset7.hpp>
 #include <paddlepaddle_frontend/exceptions.hpp>
 #include <paddlepaddle_frontend/model.hpp>
 #include <paddlepaddle_frontend/place.hpp>
@@ -19,7 +19,7 @@
 #    include <locale>
 #endif
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 using namespace paddle::framework::proto;
 
@@ -463,4 +463,4 @@ void InputModelPDPD::set_tensor_value(Place::Ptr place, const void* value) {
 }
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/node_context.hpp b/ngraph/frontend/paddlepaddle/src/node_context.hpp
index 34af177d47f5d0..2e02119c485bc3 100644
--- a/ngraph/frontend/paddlepaddle/src/node_context.hpp
+++ b/ngraph/frontend/paddlepaddle/src/node_context.hpp
@@ -3,10 +3,10 @@
 //
 
 #pragma once
-#include <ngraph/compatibility.hpp>
-#include <ngraph/variant.hpp>
-#include <paddlepaddle_frontend/exceptions.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+#include "ngraph/compatibility.hpp"
+#include "openvino/core/variant.hpp"
+#include "paddlepaddle_frontend/exceptions.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 #define NGRAPH_VARIANT_DECLARATION(TYPE, info)                                            \
     template <>                                                                           \
@@ -24,9 +24,6 @@ NGRAPH_VARIANT_DECLARATION(std::vector<float>, "Variant::float_vector");
 NGRAPH_VARIANT_DECLARATION(bool, "Variant::bool");
 NGRAPH_VARIANT_DECLARATION(ngraph::element::Type, "Variant::element_type");
 NGRAPH_VARIANT_DECLARATION(std::vector<int64_t>, "Variant::int64_vector");
-}  // namespace ov
-
-namespace ngraph {
 namespace frontend {
 namespace pdpd {
 using InPortName = std::string;
@@ -202,4 +199,4 @@ inline NamedOutputs NodeContext::default_single_output_mapping(
 
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/argmax.cpp b/ngraph/frontend/paddlepaddle/src/op/argmax.cpp
index 1194bf25f62746..e2ad9a8fe6d0bd 100644
--- a/ngraph/frontend/paddlepaddle/src/op/argmax.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/argmax.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -38,4 +38,4 @@ NamedOutputs argmax(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp b/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp
index 4f4f424f169cae..ad5482f8d67a35 100644
--- a/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp
@@ -2,9 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-namespace ngraph {
+#include <openvino/opsets/opset6.hpp>
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -46,4 +46,4 @@ NamedOutputs assign_value(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp b/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp
index 869087ba963cba..1d52fecd0d3be8 100644
--- a/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -47,4 +47,4 @@ NamedOutputs batch_norm(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/cast.cpp b/ngraph/frontend/paddlepaddle/src/op/cast.cpp
index 2d0181d41bc169..75cf919a12c35e 100644
--- a/ngraph/frontend/paddlepaddle/src/op/cast.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/cast.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -19,4 +19,4 @@ NamedOutputs cast(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/clip.cpp b/ngraph/frontend/paddlepaddle/src/op/clip.cpp
index 1fd39ad00c8d18..551548f89b84c2 100644
--- a/ngraph/frontend/paddlepaddle/src/op/clip.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/clip.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -21,4 +21,4 @@ NamedOutputs clip(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/concat.cpp b/ngraph/frontend/paddlepaddle/src/op/concat.cpp
index b65043c0444d92..13fb8b45318bbd 100644
--- a/ngraph/frontend/paddlepaddle/src/op/concat.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/concat.cpp
@@ -2,20 +2,20 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
+#include <openvino/opsets/opset6.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs concat(const NodeContext& node) {
     auto data = node.get_ng_inputs("X");
     auto axis = node.get_attribute<int>("axis");
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Concat>(data, axis)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Concat>(data, axis)}, {"Out"});
 }
 
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp b/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp
index aa1ea7d928da56..87b0b9f53f9368 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp
@@ -2,11 +2,11 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
+#include <openvino/opsets/opset6.hpp>
 
 #include "conv2d_utils.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -17,4 +17,4 @@ NamedOutputs conv2d(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp b/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp
index f25f483ffb1601..57133dd626c3bb 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp
@@ -2,12 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
+#include <openvino/opsets/opset6.hpp>
 
 #include "conv2d_utils.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -18,4 +18,4 @@ NamedOutputs conv2d_transpose(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp
index b4475a195913ed..31dd422f53fa37 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp
@@ -4,11 +4,11 @@
 
 #include "conv2d_utils.hpp"
 
-#include <ngraph/opsets/opset6.hpp>
+#include <openvino/opsets/opset6.hpp>
 
 #include "node_context.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -93,4 +93,4 @@ std::shared_ptr<Node> get_reshaped_filter(const Output<Node>& filters, const int
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp
index 589a19279c896b..0dc62aec2d758a 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp
@@ -5,7 +5,7 @@
 #pragma once
 #include "node_context.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -57,4 +57,4 @@ NamedOutputs conv2d_base(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp b/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp
index 56a3a12707e4e2..13916a8efd2b1c 100644
--- a/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -34,4 +34,4 @@ NamedOutputs cumsum(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp b/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp
index dd02c81fc755b1..5c5b5a7689d4fb 100644
--- a/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp
@@ -6,7 +6,7 @@
 
 #include "conv2d_utils.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -64,4 +64,4 @@ NamedOutputs deformable_conv(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/dropout.cpp b/ngraph/frontend/paddlepaddle/src/op/dropout.cpp
index 3ce9ffeee48888..3548e63b226326 100644
--- a/ngraph/frontend/paddlepaddle/src/op/dropout.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/dropout.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -30,4 +30,4 @@ NamedOutputs dropout(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp b/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp
index 78c6da2c35359f..dd08bdc7b1552d 100644
--- a/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp
@@ -6,7 +6,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -83,4 +83,4 @@ NamedOutputs elementwise_greater_equal(const NodeContext& node_context) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/embedding.cpp b/ngraph/frontend/paddlepaddle/src/op/embedding.cpp
index a8387dd62b45e0..7a2c337b753b3d 100644
--- a/ngraph/frontend/paddlepaddle/src/op/embedding.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/embedding.cpp
@@ -2,10 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset8.hpp>
 #include <node_context.hpp>
+#include <openvino/opsets/opset8.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -52,4 +52,4 @@ NamedOutputs embedding(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/exp.cpp b/ngraph/frontend/paddlepaddle/src/op/exp.cpp
index c767ab67692c01..4af6f32a6b18ab 100644
--- a/ngraph/frontend/paddlepaddle/src/op/exp.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/exp.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -17,4 +17,4 @@ NamedOutputs exp(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp b/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp
index 0619f4fd850818..c9ab3347d1e0c6 100644
--- a/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp
@@ -6,7 +6,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -50,4 +50,4 @@ NamedOutputs expand_v2(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp b/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp
index 2a7e9f3facdbb5..11a2371c0d44ca 100644
--- a/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -18,7 +18,11 @@ NamedOutputs fill_any_like(const NodeContext& node) {
         // when type does not define, use the input type
         dtype = x.get_element_type();
     }
-    const auto supported_type = {element::i32, element::i64, element::f16, element::f32, element::f64};
+    const std::vector<element::Type> supported_type = {element::i32,
+                                                       element::i64,
+                                                       element::f16,
+                                                       element::f32,
+                                                       element::f64};
     const bool valid_type =
         std::any_of(supported_type.begin(), supported_type.end(), [dtype](const element::Type& type) {
             return dtype == type;
@@ -34,4 +38,4 @@ NamedOutputs fill_any_like(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp b/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp
index 963c9863aa28dc..ffc92c3397baa0 100644
--- a/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp
@@ -2,10 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
+#include <openvino/opsets/opset6.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -41,11 +41,11 @@ NamedOutputs fill_constant(const NodeContext& node) {
         shape_node = opset6::Constant::create(element::i64, {shape.size()}, shape);
     }
 
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Broadcast>(value_node, shape_node)},
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Broadcast>(value_node, shape_node)},
                                               {"Out"});
 }
 
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp b/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
index 5883dae573b99e..04676913e74b6c 100644
--- a/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
@@ -8,7 +8,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -122,4 +122,4 @@ NamedOutputs fill_constant_batch_size_like(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp b/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp
index 5c1920ea310038..0a32a251ac52b8 100644
--- a/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp
@@ -3,10 +3,10 @@
 //
 
 #include <ngraph/builder/reshape.hpp>
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
+#include <openvino/opsets/opset6.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -44,4 +44,4 @@ NamedOutputs flatten_contiguous_range(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/gelu.cpp b/ngraph/frontend/paddlepaddle/src/op/gelu.cpp
index 496a037b91205a..6f0593e9215999 100644
--- a/ngraph/frontend/paddlepaddle/src/op/gelu.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/gelu.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -20,4 +20,4 @@ NamedOutputs gelu(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp b/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp
index 5155f8a3b78048..b3a840c46184ec 100644
--- a/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp
@@ -6,7 +6,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -24,4 +24,4 @@ NamedOutputs hard_sigmoid(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp b/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp
index e2cecdc81c0f2e..618d29ae3bcfc3 100644
--- a/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp
@@ -6,7 +6,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -30,4 +30,4 @@ NamedOutputs hard_swish(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/interp.cpp b/ngraph/frontend/paddlepaddle/src/op/interp.cpp
index b8b739b4c96c4e..e1a6fdca36e9d4 100644
--- a/ngraph/frontend/paddlepaddle/src/op/interp.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/interp.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -175,4 +175,4 @@ NamedOutputs bicubic_interp_v2(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp b/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp
index 4dda7426f453c7..f28468a152318c 100644
--- a/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -56,4 +56,4 @@ NamedOutputs layer_norm(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp b/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp
index f5267bd31c305e..c5ab5b74f215d1 100644
--- a/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -17,4 +17,4 @@ NamedOutputs leaky_relu(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/log.cpp b/ngraph/frontend/paddlepaddle/src/op/log.cpp
index 9a46758790b7af..8b0e18c5826ead 100644
--- a/ngraph/frontend/paddlepaddle/src/op/log.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/log.cpp
@@ -6,7 +6,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -17,4 +17,4 @@ NamedOutputs log(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp b/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp
index 12b2521c2a4b25..71d571d56c8b86 100644
--- a/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -16,4 +16,4 @@ NamedOutputs logical_not(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/lstm.cpp b/ngraph/frontend/paddlepaddle/src/op/lstm.cpp
index 039f5c061bb197..3f34629b39472e 100644
--- a/ngraph/frontend/paddlepaddle/src/op/lstm.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/lstm.cpp
@@ -2,13 +2,13 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
+#include <openvino/opsets/opset6.hpp>
 
 #include "ngraph/builder/reshape.hpp"
 #include "paddlepaddle_frontend/utility.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -28,7 +28,7 @@ enum class LSTMInput {
 
 struct LSTMNgInputMap {
     explicit LSTMNgInputMap(const NodeContext& node, Output<Node>& prev_output, int layer) {
-        auto input_x = builder::opset1::reorder_axes(prev_output, {1, 0, 2});
+        auto input_x = ngraph::builder::opset1::reorder_axes(prev_output, {1, 0, 2});
         //[begin. end)
         auto weight_list = node.get_ng_inputs("WeightList");
         auto weight_begin = weight_list.begin();
@@ -70,20 +70,20 @@ struct LSTMNgInputMap {
         auto hidden_bias = std::make_shared<opset6::Concat>(layer_hidden_bias, 0);
         auto bias = std::make_shared<opset6::Add>(weight_bias, hidden_bias);
         m_input_map[LSTMInput::LSTM_INPUT_W] =
-            ngraph::op::util::convert_lstm_node_format(input_weight,
-                                                       ngraph::op::util::LSTMWeightsFormat::IFCO,
-                                                       ngraph::op::util::LSTMWeightsFormat::FICO,
-                                                       1);
+            ov::op::util::convert_lstm_node_format(input_weight,
+                                                   ov::op::util::LSTMWeightsFormat::IFCO,
+                                                   ov::op::util::LSTMWeightsFormat::FICO,
+                                                   1);
         m_input_map[LSTMInput::LSTM_INPUT_R] =
-            ngraph::op::util::convert_lstm_node_format(hidden_weight,
-                                                       ngraph::op::util::LSTMWeightsFormat::IFCO,
-                                                       ngraph::op::util::LSTMWeightsFormat::FICO,
-                                                       1);
+            ov::op::util::convert_lstm_node_format(hidden_weight,
+                                                   ov::op::util::LSTMWeightsFormat::IFCO,
+                                                   ov::op::util::LSTMWeightsFormat::FICO,
+                                                   1);
         m_input_map[LSTMInput::LSTM_INPUT_B] =
-            ngraph::op::util::convert_lstm_node_format(bias,
-                                                       ngraph::op::util::LSTMWeightsFormat::IFCO,
-                                                       ngraph::op::util::LSTMWeightsFormat::FICO,
-                                                       1);
+            ov::op::util::convert_lstm_node_format(bias,
+                                                   ov::op::util::LSTMWeightsFormat::IFCO,
+                                                   ov::op::util::LSTMWeightsFormat::FICO,
+                                                   1);
 
         // Get dimensions needed for default inputs creation
         // Parsing init hidden state
@@ -115,19 +115,19 @@ struct LSTMNgInputMap {
         auto c_end = opset6::Constant::create(element::i64, {1}, {layer * bidirect_len + bidirect_len});
 
         m_input_map[LSTMInput::LSTM_INPUT_INIT_H] =
-            builder::opset1::reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[0],
-                                                                                 h_begin,
-                                                                                 h_end,
-                                                                                 std::vector<int64_t>{0},
-                                                                                 std::vector<int64_t>{0}),
-                                          {1, 0, 2});
+            ngraph::builder::opset1::reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[0],
+                                                                                         h_begin,
+                                                                                         h_end,
+                                                                                         std::vector<int64_t>{0},
+                                                                                         std::vector<int64_t>{0}),
+                                                  {1, 0, 2});
         m_input_map[LSTMInput::LSTM_INPUT_INIT_C] =
-            builder::opset1::reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[1],
-                                                                                 c_begin,
-                                                                                 c_end,
-                                                                                 std::vector<int64_t>{0},
-                                                                                 std::vector<int64_t>{0}),
-                                          {1, 0, 2});
+            ngraph::builder::opset1::reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[1],
+                                                                                         c_begin,
+                                                                                         c_end,
+                                                                                         std::vector<int64_t>{0},
+                                                                                         std::vector<int64_t>{0}),
+                                                  {1, 0, 2});
     }
 
     Output<ngraph::Node>& at(const LSTMInput& key) {
@@ -172,12 +172,12 @@ NamedOutputs lstm(const NodeContext& node) {
                                                                     input_map.at(LSTMInput::LSTM_INPUT_B),
                                                                     attrs.m_hidden_size,
                                                                     attrs.m_direction);
-        prev_output = builder::opset1::reorder_axes(lstm_sequence->output(0), {2, 0, 1, 3});
+        prev_output = ngraph::builder::opset1::reorder_axes(lstm_sequence->output(0), {2, 0, 1, 3});
         auto out_shape = opset6::Constant::create(element::i64, Shape{3}, {0, 0, -1});
         prev_output = std::make_shared<opset6::Reshape>(prev_output, out_shape, true);
 
-        final_h.push_back(builder::opset1::reorder_axes(lstm_sequence->output(1), {1, 0, 2}));
-        final_c.push_back(builder::opset1::reorder_axes(lstm_sequence->output(2), {1, 0, 2}));
+        final_h.push_back(ngraph::builder::opset1::reorder_axes(lstm_sequence->output(1), {1, 0, 2}));
+        final_c.push_back(ngraph::builder::opset1::reorder_axes(lstm_sequence->output(2), {1, 0, 2}));
     }
 
     NamedOutputs named_outputs;
@@ -190,4 +190,4 @@ NamedOutputs lstm(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/matmul.cpp b/ngraph/frontend/paddlepaddle/src/op/matmul.cpp
index 255bd7d5292003..4e7f76d0e5740f 100644
--- a/ngraph/frontend/paddlepaddle/src/op/matmul.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/matmul.cpp
@@ -4,7 +4,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -27,4 +27,4 @@ NamedOutputs matmul(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/matmul_v2.cpp b/ngraph/frontend/paddlepaddle/src/op/matmul_v2.cpp
index 2ecd053f1569a0..f4e414a2d0c9fc 100644
--- a/ngraph/frontend/paddlepaddle/src/op/matmul_v2.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/matmul_v2.cpp
@@ -5,7 +5,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -21,4 +21,4 @@ NamedOutputs matmul_v2(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp b/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp
index 8ecffe5b241037..85ee304832c8eb 100644
--- a/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp
@@ -5,7 +5,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -84,4 +84,4 @@ NamedOutputs matrix_nms(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/mul.cpp b/ngraph/frontend/paddlepaddle/src/op/mul.cpp
deleted file mode 100644
index d3833b4837ea33..00000000000000
--- a/ngraph/frontend/paddlepaddle/src/op/mul.cpp
+++ /dev/null
@@ -1,45 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <ngraph/opsets/opset6.hpp>
-#include <node_context.hpp>
-
-namespace ngraph {
-namespace frontend {
-namespace pdpd {
-namespace op {
-NamedOutputs mul(const NodeContext& node) {
-    auto x = node.get_ng_input("X");
-    auto y = node.get_ng_input("Y");
-    PDPD_OP_VALIDATION_CHECK(node, x.get_partial_shape().rank().is_static(), "matmul: X rank must be static!");
-    int64_t x_rank = x.get_partial_shape().rank().get_length();
-    PDPD_OP_VALIDATION_CHECK(node,
-                             y.get_partial_shape().rank().is_static() && y.get_partial_shape().rank().get_length() == 2,
-                             "matmul: Y rank must be static, and 2!");
-    if (x_rank > 2) {
-        auto shape = std::make_shared<ngraph::opset6::ShapeOf>(x);
-        int64_t x_num_col_dims = node.get_attribute<int32_t>("x_num_col_dims");
-        auto axis = ngraph::opset6::Constant::create(ngraph::element::i64, {}, {0});
-        auto split_lengths =
-            ngraph::opset6::Constant::create(ngraph::element::i64, {2}, {x_num_col_dims, x_rank - x_num_col_dims});
-        auto split = std::make_shared<ngraph::opset6::VariadicSplit>(shape, axis, split_lengths);
-        auto f_dim_red_axis = ngraph::opset6::Constant::create(ngraph::element::i64, {}, {0});
-        auto first_dim_reduce = std::make_shared<ngraph::opset6::ReduceProd>(split->output(0), f_dim_red_axis);
-        auto f_dim_shape = ngraph::opset6::Constant::create(ngraph::element::i64, {1}, {1});
-        auto first_dim = std::make_shared<ngraph::opset6::Reshape>(first_dim_reduce, f_dim_shape, false);
-        auto s_dim_red_axis = ngraph::opset6::Constant::create(ngraph::element::i64, {}, {0});
-        auto second_dim_reduce = std::make_shared<ngraph::opset6::ReduceProd>(split->output(1), s_dim_red_axis);
-        auto s_dim_shape = ngraph::opset6::Constant::create(ngraph::element::i64, {1}, {1});
-        auto second_dim = std::make_shared<ngraph::opset6::Reshape>(second_dim_reduce, s_dim_shape, false);
-        auto out_shape = std::make_shared<ngraph::opset6::Concat>(ngraph::NodeVector{first_dim, second_dim}, 0);
-        auto x_reshaped = std::make_shared<ngraph::opset6::Reshape>(x, out_shape, false);
-        return node.default_single_output_mapping({std::make_shared<ngraph::opset6::MatMul>(x_reshaped, y)}, {"Out"});
-    }
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::MatMul>(x, y)}, {"Out"});
-}
-
-}  // namespace op
-}  // namespace pdpd
-}  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
diff --git a/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp b/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp
index 62ee84628c0608..ced2a3271076fe 100644
--- a/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp
@@ -5,7 +5,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -66,4 +66,4 @@ NamedOutputs multiclass_nms(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp b/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp
index 173a215faad433..5c92ad7d1e9fb2 100644
--- a/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -84,4 +84,4 @@ NamedOutputs pad3d(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp b/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp
index a2c22983f016a5..8869a0c387b787 100644
--- a/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp
@@ -8,7 +8,7 @@
 #include <ngraph/opsets/opset8.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -48,12 +48,12 @@ static void get_paddings(const NodeContext& node,
     // TODO: need to support NHWC input #55483
     switch (paddings.size()) {
     case 2:
-        pad_begin = Shape{static_cast<uint64_t>(paddings[0]), static_cast<uint64_t>(paddings[1])};
+        pad_begin = Shape{static_cast<size_t>(paddings[0]), static_cast<size_t>(paddings[1])};
         pad_end = pad_begin;
         break;
     case 4:
-        pad_begin = Shape{static_cast<uint64_t>(paddings[0]), static_cast<uint64_t>(paddings[2])};
-        pad_end = Shape{static_cast<uint64_t>(paddings[1]), static_cast<uint64_t>(paddings[3])};
+        pad_begin = Shape{static_cast<size_t>(paddings[0]), static_cast<size_t>(paddings[2])};
+        pad_end = Shape{static_cast<size_t>(paddings[1]), static_cast<size_t>(paddings[3])};
         break;
     default:
         throw std::runtime_error("Unsupported pooling paddings " + std::to_string(paddings.size()));
@@ -131,7 +131,7 @@ NamedOutputs pool2d(const NodeContext& node) {
         auto strides = node.get_attribute<std::vector<int32_t>>("strides");
         auto paddings = node.get_attribute<std::vector<int32_t>>("paddings");
 
-        uint64_t kernel_h, kernel_w;
+        size_t kernel_h, kernel_w;
         if (kernel_shape.size() == 1) {
             // Not tested: implemented according to spec, but can't generate real
             // model to test
@@ -185,4 +185,4 @@ NamedOutputs pool2d(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/pow.cpp b/ngraph/frontend/paddlepaddle/src/op/pow.cpp
index bac27d078ca9ae..52182946a6ed6b 100644
--- a/ngraph/frontend/paddlepaddle/src/op/pow.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/pow.cpp
@@ -5,7 +5,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -16,7 +16,7 @@ NamedOutputs pow(const NodeContext& node) {
     if (node.has_ng_input("FactorTensor")) {
         factor_node = node.get_ng_input("FactorTensor");
         if (factor_node.get_element_type() != dtype)
-            factor_node = std::make_shared<opset6::Convert>(factor_node, dtype);
+            factor_node = std::make_shared<ngraph::opset6::Convert>(factor_node, dtype);
     } else {
         factor_node = ngraph::opset6::Constant::create(dtype, Shape{1}, {node.get_attribute<float>("factor")});
     }
@@ -27,4 +27,4 @@ NamedOutputs pow(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp b/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp
index fcdae08eaccf48..a6ffb1a90bc88f 100644
--- a/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp
@@ -8,7 +8,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -109,4 +109,4 @@ NamedOutputs prior_box(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/range.cpp b/ngraph/frontend/paddlepaddle/src/op/range.cpp
index ebbf108fc4fefa..a90f60d0cb5cf6 100644
--- a/ngraph/frontend/paddlepaddle/src/op/range.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/range.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -28,4 +28,4 @@ NamedOutputs range(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/relu.cpp b/ngraph/frontend/paddlepaddle/src/op/relu.cpp
index 3eefc1939ab8ef..4aa63bda1dc0fe 100644
--- a/ngraph/frontend/paddlepaddle/src/op/relu.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/relu.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -17,4 +17,4 @@ NamedOutputs relu(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/relu6.cpp b/ngraph/frontend/paddlepaddle/src/op/relu6.cpp
index c5ae51a2d97ac8..65aa1354cdd07b 100644
--- a/ngraph/frontend/paddlepaddle/src/op/relu6.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/relu6.cpp
@@ -6,7 +6,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -19,4 +19,4 @@ NamedOutputs relu6(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp b/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp
index a16678a24cc255..6cffb7557ecfc6 100644
--- a/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp
@@ -6,7 +6,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -39,4 +39,4 @@ NamedOutputs reshape2(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/rnn.cpp b/ngraph/frontend/paddlepaddle/src/op/rnn.cpp
index 2115acd95e8324..f0e594079b9735 100644
--- a/ngraph/frontend/paddlepaddle/src/op/rnn.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/rnn.cpp
@@ -7,7 +7,7 @@
 
 #include "paddlepaddle_frontend/utility.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -24,4 +24,4 @@ NamedOutputs rnn(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/scale.cpp b/ngraph/frontend/paddlepaddle/src/op/scale.cpp
index 7398df6845c66e..9b67eedc70a322 100644
--- a/ngraph/frontend/paddlepaddle/src/op/scale.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/scale.cpp
@@ -3,10 +3,10 @@
 //
 
 #include <ngraph/builder/make_constant.hpp>
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
+#include <openvino/opsets/opset6.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -25,20 +25,20 @@ NamedOutputs scale(const NodeContext& node) {
             scale = std::make_shared<opset6::Convert>(scale_tensor_node, dtype);
     } else {
         auto scale_val = node.get_attribute<float>("scale");
-        scale = ngraph::opset6::Constant::create(dtype, Shape{1}, {scale_val});
+        scale = ov::opset6::Constant::create(dtype, Shape{1}, {scale_val});
     }
 
     auto bias_val = node.get_attribute<float>("bias");
-    bias = ngraph::opset6::Constant::create(dtype, Shape{1}, {bias_val});
+    bias = ov::opset6::Constant::create(dtype, Shape{1}, {bias_val});
     auto bias_after_scale = node.get_attribute<bool>("bias_after_scale");
 
     std::shared_ptr<Node> result_node;
     if (!bias_after_scale) {
-        auto node_add = std::make_shared<ngraph::opset6::Add>(data, bias);
-        result_node = std::make_shared<ngraph::opset6::Multiply>(node_add, scale);
+        auto node_add = std::make_shared<ov::opset6::Add>(data, bias);
+        result_node = std::make_shared<ov::opset6::Multiply>(node_add, scale);
     } else {
-        auto node_multiply = std::make_shared<ngraph::opset6::Multiply>(data, scale);
-        result_node = std::make_shared<ngraph::opset6::Add>(node_multiply, bias);
+        auto node_multiply = std::make_shared<ov::opset6::Multiply>(data, scale);
+        result_node = std::make_shared<ov::opset6::Add>(node_multiply, bias);
     }
 
     return node.default_single_output_mapping({result_node}, {"Out"});
@@ -47,4 +47,4 @@ NamedOutputs scale(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/shape.cpp b/ngraph/frontend/paddlepaddle/src/op/shape.cpp
index e6d77876fcf91e..3c777cb87a9c41 100644
--- a/ngraph/frontend/paddlepaddle/src/op/shape.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/shape.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -18,4 +18,4 @@ NamedOutputs shape(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp b/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp
index db2f3e8677225d..f9122d2562f259 100644
--- a/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -17,4 +17,4 @@ NamedOutputs sigmoid(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/slice.cpp b/ngraph/frontend/paddlepaddle/src/op/slice.cpp
index 8874ca18d7cb0b..873695a8fc8e8c 100644
--- a/ngraph/frontend/paddlepaddle/src/op/slice.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/slice.cpp
@@ -8,7 +8,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -77,9 +77,24 @@ NamedOutputs slice(const NodeContext& node) {
     auto decrease_axis = node.get_attribute<std::vector<int32_t>>("decrease_axis");
 
     if (decrease_axis.size() > 0) {
-        auto squeeze_index_node = Constant::create(element::i32, {}, decrease_axis);
+        // according to paddle slice_op, when all axes are decreased, output shape is [1], instead of scalar.
+        // Ref: paddle/fluid/operators/slice_op.h
+        PartialShape input_shape = data.get_partial_shape();
+        PDPD_OP_VALIDATION_CHECK(node,
+                                 input_shape.rank().is_static(),
+                                 "input rank of slice must be static when decrease_axis is set.");
+
+        auto squeeze_index_node = Constant::create(element::i32, {decrease_axis.size()}, decrease_axis);
         auto decreased_node = std::make_shared<Squeeze>(stride_slice_node, squeeze_index_node);
 
+        auto input_rank = input_shape.rank().get_length();
+        if (input_rank == decrease_axis.size()) {
+            auto restore_node = std::make_shared<Reshape>(decreased_node,
+                                                          std::make_shared<Constant>(element::i64, Shape{1}, 1),
+                                                          false);  // restore to shape (1,)
+            return node.default_single_output_mapping({restore_node}, {"Out"});
+        }
+
         return node.default_single_output_mapping({decreased_node}, {"Out"});
     }
 
@@ -88,4 +103,4 @@ NamedOutputs slice(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/softmax.cpp b/ngraph/frontend/paddlepaddle/src/op/softmax.cpp
index 0e994765c42280..0646df0b704624 100644
--- a/ngraph/frontend/paddlepaddle/src/op/softmax.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/softmax.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -22,4 +22,4 @@ NamedOutputs softmax(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/split.cpp b/ngraph/frontend/paddlepaddle/src/op/split.cpp
index 85cc660b259c30..e4fff9fd0ccc31 100644
--- a/ngraph/frontend/paddlepaddle/src/op/split.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/split.cpp
@@ -6,7 +6,7 @@
 #include <node_context.hpp>
 #include <paddlepaddle_frontend/utility.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -58,4 +58,4 @@ NamedOutputs split(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp b/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp
index d47fc784c63b13..ec52228e43dd1c 100644
--- a/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -28,4 +28,4 @@ NamedOutputs squeeze(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/stack.cpp b/ngraph/frontend/paddlepaddle/src/op/stack.cpp
index d6924290329ad8..1b11e3715f6b6b 100644
--- a/ngraph/frontend/paddlepaddle/src/op/stack.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/stack.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -38,4 +38,4 @@ NamedOutputs stack(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/tanh.cpp b/ngraph/frontend/paddlepaddle/src/op/tanh.cpp
index 11b666cff8c0ca..96ead42fa1b6cd 100644
--- a/ngraph/frontend/paddlepaddle/src/op/tanh.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/tanh.cpp
@@ -6,7 +6,7 @@
 
 #include "default_opset.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -19,4 +19,4 @@ NamedOutputs tanh(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp b/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp
index e86598fb59cd6d..ece4118d602702 100644
--- a/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -20,4 +20,4 @@ NamedOutputs transpose2(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp b/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp
index 55edccd9fd184c..34970c70639d76 100644
--- a/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp
@@ -5,7 +5,7 @@
 #include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -26,4 +26,4 @@ NamedOutputs unsqueeze(const NodeContext& node) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp b/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp
index 4ada564a51cc94..c2ad5f91b1a3a7 100644
--- a/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp
@@ -2,11 +2,11 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 #include <limits>  // std::numeric_limits
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 #include <numeric>
+#include <openvino/opsets/opset6.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -325,4 +325,4 @@ NamedOutputs yolo_box(const NodeContext& node_context) {
 }  // namespace op
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op_table.cpp b/ngraph/frontend/paddlepaddle/src/op_table.cpp
index cf02ab0bb32ff3..36b978258f6108 100644
--- a/ngraph/frontend/paddlepaddle/src/op_table.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op_table.cpp
@@ -3,7 +3,7 @@
 //
 #include "op_table.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
@@ -47,7 +47,6 @@ OP_CONVERTER(log);
 OP_CONVERTER(logical_not);
 OP_CONVERTER(matmul);
 OP_CONVERTER(matmul_v2);
-OP_CONVERTER(mul);
 OP_CONVERTER(matrix_nms);
 OP_CONVERTER(multiclass_nms);
 OP_CONVERTER(nearest_interp_v2);
@@ -74,13 +73,6 @@ OP_CONVERTER(trilinear_interp_v2);
 OP_CONVERTER(unsqueeze);
 OP_CONVERTER(yolo_box);
 }  // namespace op
-}  // namespace pdpd
-}  // namespace frontend
-}  // namespace ngraph
-
-namespace ngraph {
-namespace frontend {
-namespace pdpd {
 std::map<std::string, CreatorFunction> get_supported_ops() {
     return {{"arg_max", op::argmax},
             {"assign_value", op::assign_value},
@@ -127,7 +119,6 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"matmul", op::matmul},
             {"matmul_v2", op::matmul_v2},
             {"max_pool2d_with_index", op::pool2d},
-            {"mul", op::mul},
             {"matrix_nms", op::matrix_nms},
             {"multiclass_nms3", op::multiclass_nms},
             {"nearest_interp_v2", op::nearest_interp_v2},
@@ -159,4 +150,4 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
 
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op_table.hpp b/ngraph/frontend/paddlepaddle/src/op_table.hpp
index 0c7d9fd8ca17b3..76ee7d5e3c33ef 100644
--- a/ngraph/frontend/paddlepaddle/src/op_table.hpp
+++ b/ngraph/frontend/paddlepaddle/src/op_table.hpp
@@ -11,7 +11,7 @@
 
 #include "node_context.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 using CreatorFunction = std::function<NamedOutputs(const NodeContext&)>;
@@ -20,4 +20,4 @@ std::map<std::string, CreatorFunction> get_supported_ops();
 
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp
index 905bf631329a85..e91df788b85ec2 100644
--- a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp
+++ b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp
@@ -4,7 +4,7 @@
 
 #include <pdpd_fw_node.hpp>
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 NGRAPH_RTTI_DEFINITION(PDPDFrameworkNode, "PDPDFrameworkNode", 1);
 
@@ -36,4 +36,4 @@ std::map<std::string, OutputVector> PDPDFrameworkNode::return_named_outputs() {
 }
 
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp
index cb429b520cda04..558b29b0e75ec4 100644
--- a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp
+++ b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp
@@ -8,7 +8,7 @@
 
 #include "decoder.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 class PDPDFrameworkNode : public ov::op::util::FrameworkNode {
 public:
@@ -50,4 +50,4 @@ class PDPDFrameworkNode : public ov::op::util::FrameworkNode {
     std::vector<std::string> m_inputs_names;
 };
 }  // namespace frontend
-}  // namespace ngraph
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp b/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp
index 7133f96fa7a719..1bf1a9a813d1b6 100644
--- a/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp
+++ b/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp
@@ -6,7 +6,7 @@
 
 #include "frontend_manager/frontend_exceptions.hpp"
 
-namespace ngraph {
+namespace ov {
 namespace frontend {
 namespace pdpd {
 #ifdef _WIN32
@@ -40,4 +40,4 @@ bool endsWith(const std::basic_string<T>& str, const std::basic_string<T>& suffi
 
 }  // namespace pdpd
 }  // namespace frontend
-}  // namespace ngraph
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/place.cpp b/ngraph/frontend/paddlepaddle/src/place.cpp
index 9f5cbb6dcaea38..873eca5a24e4ae 100644
--- a/ngraph/frontend/paddlepaddle/src/place.cpp
+++ b/ngraph/frontend/paddlepaddle/src/place.cpp
@@ -7,7 +7,7 @@
 #include "decoder.hpp"
 #include "framework.pb.h"
 
-using namespace ngraph;
+using namespace ov;
 using namespace frontend;
 
 bool PlacePDPD::is_input() const {
diff --git a/ngraph/frontend/tensorflow/CMakeLists.txt b/ngraph/frontend/tensorflow/CMakeLists.txt
index db2a3686ff3208..28aacbfaf413c8 100644
--- a/ngraph/frontend/tensorflow/CMakeLists.txt
+++ b/ngraph/frontend/tensorflow/CMakeLists.txt
@@ -48,7 +48,7 @@ set_source_files_properties(${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES GENERATED TRU
 set_source_files_properties(${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES COMPILE_OPTIONS -w)
 
 # Create library
-add_library(${TARGET_NAME} ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS} ${PROTO_SRCS} ${PROTO_HDRS})
+add_library(${TARGET_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS} ${PROTO_SRCS} ${PROTO_HDRS})
 add_library(openvino::frontend::tensorflow ALIAS ${TARGET_NAME})
 
 add_dependencies(${TARGET_NAME} tensorflow_ngraph_frontend_proto)
diff --git a/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp b/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp
index 5f0996035b03ab..788946fa473bb0 100644
--- a/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp
+++ b/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp
@@ -4,13 +4,14 @@
 
 #pragma once
 
-#include <frontend_manager/frontend.hpp>
-#include <frontend_manager/input_model.hpp>
 #include <functional>
 #include <map>
-#include <openvino/core/node_vector.hpp>
-#include <openvino/core/variant.hpp>
-#include <tensorflow_frontend/utility.hpp>
+
+#include "frontend_manager/frontend.hpp"
+#include "frontend_manager/input_model.hpp"
+#include "openvino/core/node_vector.hpp"
+#include "openvino/core/variant.hpp"
+#include "tensorflow_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -22,7 +23,7 @@ class NodeContext;
 
 namespace ov {
 namespace frontend {
-class TF_API FrontEndTF : public ngraph::frontend::FrontEnd {
+class TF_API FrontEndTF : public ov::frontend::FrontEnd {
 public:
     using CreatorFunction = std::function<::ov::OutputVector(const ::ov::frontend::tf::NodeContext&)>;
     using TranslatorDictionaryType = std::map<const std::string, const CreatorFunction>;
@@ -34,11 +35,11 @@ class TF_API FrontEndTF : public ngraph::frontend::FrontEnd {
     FrontEndTF();
 
     /// \brief Completely convert the model
-    /// \return fully converted nGraph function
-    std::shared_ptr<Function> convert(ngraph::frontend::InputModel::Ptr model) const override;
+    /// \return fully converted ov function
+    std::shared_ptr<Function> convert(ov::frontend::InputModel::Ptr model) const override;
 
     /// \brief Completely convert the remaining, not converted part of a function.
-    /// \param partiallyConverted partially converted nGraph function
+    /// \param partiallyConverted partially converted ov function
     void convert(std::shared_ptr<Function> partiallyConverted) const override;
 
     /// \brief Convert only those parts of the model that can be converted leaving others
@@ -46,18 +47,18 @@ class TF_API FrontEndTF : public ngraph::frontend::FrontEnd {
     /// function or another form of convert function should be called to finalize the
     /// conversion process.
     /// \param model Input model
-    /// \return partially converted nGraph function
-    std::shared_ptr<Function> convert_partially(ngraph::frontend::InputModel::Ptr model) const override;
+    /// \return partially converted ov function
+    std::shared_ptr<Function> convert_partially(ov::frontend::InputModel::Ptr model) const override;
 
     /// \brief Convert operations with one-to-one mapping with decoding nodes.
-    /// Each decoding node is an nGraph node representing a single FW operation node with
+    /// Each decoding node is an ov node representing a single FW operation node with
     /// all attributes represented in FW-independent way.
     /// \param model Input model
-    /// \return nGraph function after decoding
-    std::shared_ptr<Function> decode(ngraph::frontend::InputModel::Ptr model) const override;
+    /// \return ov function after decoding
+    std::shared_ptr<Function> decode(ov::frontend::InputModel::Ptr model) const override;
 
     /// \brief Runs normalization passes on function that was loaded with partial conversion
-    /// \param function partially converted nGraph function
+    /// \param function partially converted ov function
     void normalize(std::shared_ptr<ov::Function> function) const override;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
@@ -69,11 +70,10 @@ class TF_API FrontEndTF : public ngraph::frontend::FrontEnd {
     /// \brief Check if FrontEndTensorflow can recognize model from given parts
     bool supported_impl(const std::vector<std::shared_ptr<ov::Variant>>& variants) const override;
 
-    ngraph::frontend::InputModel::Ptr load_impl(
-        const std::vector<std::shared_ptr<ov::Variant>>& variants) const override;
+    ov::frontend::InputModel::Ptr load_impl(const std::vector<std::shared_ptr<ov::Variant>>& variants) const override;
 
 private:
-    void translate_graph(const ngraph::frontend::InputModel::Ptr& model,
+    void translate_graph(const ov::frontend::InputModel::Ptr& model,
                          const std::string& model_name,
                          bool fail_fast,
                          bool no_conversion,
diff --git a/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp b/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
index aa5f7399bd5d68..16fe15017078df 100644
--- a/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
+++ b/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
@@ -4,9 +4,9 @@
 
 #pragma once
 
-#include <openvino/core/variant.hpp>
-#include <tensorflow_frontend/decoder.hpp>
-#include <tensorflow_frontend/utility.hpp>
+#include "openvino/core/variant.hpp"
+#include "tensorflow_frontend/decoder.hpp"
+#include "tensorflow_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/tensorflow/include/tensorflow_frontend/utility.hpp b/ngraph/frontend/tensorflow/include/tensorflow_frontend/utility.hpp
index 9a08adbfa3885c..ff814e90398185 100644
--- a/ngraph/frontend/tensorflow/include/tensorflow_frontend/utility.hpp
+++ b/ngraph/frontend/tensorflow/include/tensorflow_frontend/utility.hpp
@@ -4,12 +4,10 @@
 
 #pragma once
 
-#include <frontend_manager/frontend_exceptions.hpp>
+#include "frontend_manager/frontend_exceptions.hpp"
 
 #ifdef tensorflow_ngraph_frontend_EXPORTS
 #    define TF_API OPENVINO_CORE_EXPORTS
 #else
 #    define TF_API OPENVINO_CORE_IMPORTS
 #endif  // tensorflow_ngraph_frontend_EXPORTS
-
-#define NGRAPH_VLOG(I) std::ostringstream()
diff --git a/ngraph/frontend/tensorflow/src/decoder_proto.cpp b/ngraph/frontend/tensorflow/src/decoder_proto.cpp
index 0464314b5af5cc..784d352df6fb9a 100644
--- a/ngraph/frontend/tensorflow/src/decoder_proto.cpp
+++ b/ngraph/frontend/tensorflow/src/decoder_proto.cpp
@@ -6,6 +6,8 @@
 
 #include "node_context.hpp"
 
+using namespace std;
+
 namespace ov {
 namespace frontend {
 namespace tf {
@@ -27,59 +29,69 @@ const std::map<::tensorflow::DataType, ov::element::Type>& TYPE_MAP() {
 }
 }  // namespace
 
-std::shared_ptr<ov::Variant> DecoderTFProto::get_attribute(const std::string& name,
-                                                           const VariantTypeInfo& type_info) const {
+template <class T>
+bool is_type(const VariantTypeInfo& type_info) {
+    return type_info == VariantWrapper<T>::get_type_info_static();
+}
+
+template <class T>
+shared_ptr<VariantWrapper<T>> create_variant(const T& data) {
+    return make_shared<VariantWrapper<T>>(data);
+}
+
+shared_ptr<Variant> DecoderTFProto::get_attribute(const string& name, const VariantTypeInfo& type_info) const {
     auto attrs = decode_attribute_helper(name);
     if (attrs.empty()) {
         return nullptr;
     }
 
-    if (type_info == VariantWrapper<std::string>::get_type_info_static()) {
-        return std::make_shared<VariantWrapper<std::string>>(attrs[0].s());
-    } else if (type_info == VariantWrapper<int64_t>::get_type_info_static()) {
-        return std::make_shared<VariantWrapper<int64_t>>(attrs[0].i());
-    } else if (type_info == VariantWrapper<std::vector<int64_t>>::get_type_info_static()) {
-        std::vector<int64_t> longs;
+    if (is_type<string>(type_info)) {
+        return create_variant<string>(attrs[0].s());
+    }
+    if (is_type<int64_t>(type_info)) {
+        return create_variant<int64_t>(attrs[0].i());
+    } else if (is_type<vector<int64_t>>(type_info)) {
+        vector<int64_t> longs;
         longs.reserve(attrs[0].list().i_size());
         for (size_t idx = 0; idx < attrs[0].list().i_size(); ++idx) {
             longs.push_back(attrs[0].list().i(idx));
         }
-        return std::make_shared<VariantWrapper<std::vector<int64_t>>>(longs);
-    } else if (type_info == VariantWrapper<int32_t>::get_type_info_static()) {
-        return std::make_shared<VariantWrapper<int32_t>>(static_cast<int32_t>(attrs[0].i()));
-    } else if (type_info == VariantWrapper<std::vector<int32_t>>::get_type_info_static()) {
-        std::vector<int32_t> ints;
+        return create_variant<vector<int64_t>>(longs);
+    } else if (is_type<int32_t>(type_info)) {
+        return create_variant<int32_t>(static_cast<int32_t>(attrs[0].i()));
+    } else if (is_type<vector<int32_t>>(type_info)) {
+        vector<int32_t> ints;
         ints.reserve(attrs[0].list().i_size());
         for (size_t idx = 0; idx < attrs[0].list().i_size(); ++idx) {
             ints.push_back(static_cast<int32_t>(attrs[0].list().i(idx)));
         }
-        return std::make_shared<VariantWrapper<std::vector<int32_t>>>(ints);
-    } else if (type_info == VariantWrapper<float>::get_type_info_static()) {
-        return std::make_shared<VariantWrapper<float>>(attrs[0].f());
-    } else if (type_info == VariantWrapper<std::vector<float>>::get_type_info_static()) {
-        std::vector<float> floats;
+        return create_variant<vector<int32_t>>(ints);
+    } else if (is_type<float>(type_info)) {
+        return create_variant<float>(attrs[0].f());
+    } else if (is_type<vector<float>>(type_info)) {
+        vector<float> floats;
         floats.reserve(attrs[0].list().i_size());
         for (size_t idx = 0; idx < attrs[0].list().i_size(); ++idx) {
             floats.push_back(attrs[0].list().f(idx));
         }
-        return std::make_shared<VariantWrapper<std::vector<float>>>(floats);
-    } else if (type_info == VariantWrapper<ov::element::Type>::get_type_info_static()) {
+        return create_variant<vector<float>>(floats);
+    } else if (is_type<ov::element::Type>(type_info)) {
         auto data_type = attrs[0].type();
-        return std::make_shared<VariantWrapper<ov::element::Type>>(TYPE_MAP().at(data_type));
-    } else if (type_info == VariantWrapper<bool>::get_type_info_static()) {
-        return std::make_shared<VariantWrapper<bool>>(attrs[0].b());
-    } else if (type_info == VariantWrapper<::tensorflow::DataType>::get_type_info_static()) {
-        return std::make_shared<VariantWrapper<::tensorflow::DataType>>(attrs[0].type());
-    } else if (type_info == VariantWrapper<::tensorflow::TensorProto>::get_type_info_static()) {
-        return std::make_shared<VariantWrapper<::tensorflow::TensorProto>>(attrs[0].tensor());
-    } else if (type_info == VariantWrapper<::ov::PartialShape>::get_type_info_static()) {
-        std::vector<ov::Dimension> dims;
+        return create_variant<ov::element::Type>(TYPE_MAP().at(data_type));
+    } else if (is_type<bool>(type_info)) {
+        return create_variant<bool>(attrs[0].b());
+    } else if (is_type<::tensorflow::DataType>(type_info)) {
+        return create_variant<::tensorflow::DataType>(attrs[0].type());
+    } else if (is_type<::tensorflow::TensorProto>(type_info)) {
+        return create_variant<::tensorflow::TensorProto>(attrs[0].tensor());
+    } else if (is_type<::ov::PartialShape>(type_info)) {
+        vector<ov::Dimension> dims;
         auto tf_shape = attrs[0].shape();
         for (int i = 0; i < tf_shape.dim_size(); i++) {
-            dims.push_back(tf_shape.dim(i).size());
+            dims.emplace_back(tf_shape.dim(i).size());
         }
         auto pshape = ov::PartialShape(dims);
-        return std::make_shared<VariantWrapper<::ov::PartialShape>>(pshape);
+        return create_variant<::ov::PartialShape>(pshape);
     }
 
     // type is not supported by decoder
@@ -91,14 +103,14 @@ size_t DecoderTFProto::get_input_size() const {
 }
 
 void DecoderTFProto::get_input_node(size_t input_port_idx,
-                                    std::string& producer_name,
+                                    string& producer_name,
                                     size_t& producer_output_port_index) const {
     // TODO: handle body graph nodes with a couple of columns
-    std::string producer_port_name = m_node_def->input(input_port_idx);
+    string producer_port_name = m_node_def->input(input_port_idx);
     auto delim_pos = producer_port_name.find(':');
-    if (delim_pos != std::string::npos) {
+    if (delim_pos != string::npos) {
         producer_name = producer_port_name.substr(0, delim_pos);
-        producer_output_port_index = std::stoi(producer_port_name.substr(delim_pos));
+        producer_output_port_index = stoi(producer_port_name.substr(delim_pos));
         return;
     }
     producer_name = producer_port_name;
@@ -113,16 +125,11 @@ const std::string& DecoderTFProto::get_op_name() const {
     return m_node_def->name();
 }
 
-std::vector<::tensorflow::AttrValue> DecoderTFProto::decode_attribute_helper(const std::string& name) const {
+vector<::tensorflow::AttrValue> DecoderTFProto::decode_attribute_helper(const string& name) const {
     auto attr_map = m_node_def->attr();
-    FRONT_END_GENERAL_CHECK(attr_map.contains(name),
-                            "An error occurred while parsing the ",
-                            name,
-                            " attribute of ",
-                            this->get_op_type(),
-                            "node");
-    auto value = m_node_def->attr().at(name);
-    return {value};
+    if (attr_map.contains(name))
+        return {m_node_def->attr().at(name)};
+    return {};
 }
 }  // namespace tf
 }  // namespace frontend
diff --git a/ngraph/frontend/tensorflow/src/exceptions.hpp b/ngraph/frontend/tensorflow/src/exceptions.hpp
index d8c9744bff5833..211cebbed10ad3 100644
--- a/ngraph/frontend/tensorflow/src/exceptions.hpp
+++ b/ngraph/frontend/tensorflow/src/exceptions.hpp
@@ -4,7 +4,8 @@
 #pragma once
 
 #include <frontend_manager/frontend_exceptions.hpp>
-#include <openvino/core/node.hpp>
+
+#include "openvino/core/node.hpp"
 
 namespace ov {
 namespace frontend {
@@ -12,7 +13,7 @@ namespace tf {
 
 class NodeContext;
 
-class OpValidationFailureTF : public ngraph::frontend::OpValidationFailure {
+class OpValidationFailureTF : public ov::frontend::OpValidationFailure {
 public:
     OpValidationFailureTF(const CheckLocInfo& check_loc_info, const NodeContext& node, const std::string& explanation)
         : OpValidationFailure(check_loc_info, get_error_msg_prefix_tf(node), explanation) {}
@@ -31,5 +32,5 @@ class OpValidationFailureTF : public ngraph::frontend::OpValidationFailure {
 ///            i.e., only if the `cond` evalutes to `false`.
 /// \throws ::ov::OpValidationFailureTF if `cond` is false.
 #define TF_OP_VALIDATION_CHECK(node_context, ...) \
-    NGRAPH_CHECK_HELPER(::ov::frontend::tf::OpValidationFailureTF, (node_context), __VA_ARGS__)
+    OPENVINO_ASSERT_HELPER(::ov::frontend::tf::OpValidationFailureTF, (node_context), __VA_ARGS__)
 }  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/frontend.cpp b/ngraph/frontend/tensorflow/src/frontend.cpp
index 5ff7358cf02b62..694b04be1fa565 100644
--- a/ngraph/frontend/tensorflow/src/frontend.cpp
+++ b/ngraph/frontend/tensorflow/src/frontend.cpp
@@ -2,12 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <openvino/util/common_util.hpp>
 #include <tensorflow_frontend/frontend.hpp>
 #include <tensorflow_frontend/graph_iterator.hpp>
 
 #include "model.hpp"
 #include "op_table.hpp"
+#include "openvino/util/common_util.hpp"
 #include "pass/transpose_sinking.hpp"
 #include "tf_framework_node.hpp"
 #include "utils.hpp"
@@ -34,7 +34,6 @@ void translate_framework_node(const std::shared_ptr<TFFrameworkNode>& node,
 
     NodeContext node_ctx(*node->get_decoder(), named_inputs);
     auto new_node_outputs = translator_it->second(node_ctx);
-    SetTracingInfo(node_ctx.get_name(), new_node_outputs.front());
 
     auto new_output = new_node_outputs.begin();
     auto old_outputs = node->outputs();
@@ -48,7 +47,7 @@ void translate_framework_node(const std::shared_ptr<TFFrameworkNode>& node,
 
 FrontEndTF::FrontEndTF() : m_op_translators(tf::op::get_supported_ops()) {}
 
-void FrontEndTF::translate_graph(const ngraph::frontend::InputModel::Ptr& model,
+void FrontEndTF::translate_graph(const ov::frontend::InputModel::Ptr& model,
                                  const std::string& model_name,
                                  bool fail_fast,
                                  bool no_conversion,
@@ -64,12 +63,11 @@ void FrontEndTF::translate_graph(const ngraph::frontend::InputModel::Ptr& model,
     const auto& model_inputs = model_tf->get_inputs();
     const auto& model_outputs = model_tf->get_outputs();
     const auto& model_frozen_inputs = model_tf->get_tensor_values();
-
     std::map<const std::string, const std::function<ov::OutputVector(const NodeContext&)>> translate_map;
 
     const auto& TRANSLATE_OP_MAP = m_op_translators;
     if (no_conversion) {
-        const std::set<std::string> required_types{"Placeholder", "_Retval", "NoOp"};
+        const std::set<std::string> required_types{"Placeholder", "NoOp"};
         for (const auto& name : required_types) {
             translate_map.emplace(name, TRANSLATE_OP_MAP.at(name));
         }
@@ -85,7 +83,6 @@ void FrontEndTF::translate_graph(const ngraph::frontend::InputModel::Ptr& model,
                                 "Input with frozen value has been already met: " + frozen_input_name);
         ng_op_map[frozen_input_name] = {frozen_input_value};
     }
-
     // create parameter nodes for all tensor places corresponding to inputs
     for (const auto& input_place : model_inputs) {
         FRONT_END_GENERAL_CHECK(input_place->get_names().size() == 1, "Input place must have one name.");
@@ -98,17 +95,16 @@ void FrontEndTF::translate_graph(const ngraph::frontend::InputModel::Ptr& model,
         auto input_shape = input_tensor_place->get_partial_shape();
         auto input_type = input_tensor_place->get_element_type();
 
-        auto input_ng_output = ConstructNgNode<ov::opset8::Parameter>(input_name, input_type, input_shape);
-        auto input_ng_node = std::dynamic_pointer_cast<ov::opset8::Parameter>(input_ng_output.get_node_shared_ptr());
-        params.push_back(input_ng_node);
-        ng_op_map[input_name] = {input_ng_output};
+        auto param = std::make_shared<ov::opset8::Parameter>(input_type, input_shape);
+        set_node_name(input_name, param);
+        params.push_back(param);
+        ng_op_map[input_name] = {param};
     }
 
     // create the nGraph ops from TensorFlow ops
     for (const auto& operation_place : operation_places) {
         auto operation_decoder = operation_place->get_decoder();
         auto operation_name = operation_place->get_names()[0];
-
         // output for parameter nodes has been already generated
         if (ng_op_map.count(operation_name)) {
             continue;
@@ -180,7 +176,7 @@ void FrontEndTF::translate_graph(const ngraph::frontend::InputModel::Ptr& model,
                 auto ng_node = std::make_shared<TFFrameworkNode>(operation_decoder,
                                                                  ng_inputs,
                                                                  operation_place->get_output_ports().size());
-                SetTracingInfo(operation_name, ng_node);
+                set_node_name(operation_name, ng_node);
                 ng_outputs = ng_node->outputs();
             }
         }
@@ -251,7 +247,6 @@ void FrontEndTF::translate_graph(const ngraph::frontend::InputModel::Ptr& model,
             results.push_back(std::make_shared<ov::opset8::Result>(node_outputs[producer_port_idx]));
         }
     }
-
     // find all terminal nodes in ngraph graph to complete list of results
     if (results.empty()) {
         for (const auto& node_output_vector : ng_op_map) {
@@ -268,8 +263,7 @@ void FrontEndTF::translate_graph(const ngraph::frontend::InputModel::Ptr& model,
 
     // create the nGraph function
     ng_function = std::make_shared<ov::Function>(results, params, model_name);
-
-    NGRAPH_VLOG(5) << "Done with translations";
+    NGRAPH_DEBUG << "Done with translations";
 }
 
 /// \brief Check if FrontEndTensorflow can recognize model from given parts
@@ -291,8 +285,7 @@ bool FrontEndTF::supported_impl(const std::vector<std::shared_ptr<ov::Variant>>&
     return false;
 }
 
-ngraph::frontend::InputModel::Ptr FrontEndTF::load_impl(
-    const std::vector<std::shared_ptr<ov::Variant>>& variants) const {
+ov::frontend::InputModel::Ptr FrontEndTF::load_impl(const std::vector<std::shared_ptr<ov::Variant>>& variants) const {
     // TODO: Support other TensorFlow formats: SavedModel, .meta, checkpoint, pbtxt
     if (variants.size() == 1) {
         // a case when binary protobuf format is provided
@@ -311,19 +304,17 @@ ngraph::frontend::InputModel::Ptr FrontEndTF::load_impl(
     return nullptr;
 }
 
-std::shared_ptr<ov::Function> FrontEndTF::convert(ngraph::frontend::InputModel::Ptr model) const {
+std::shared_ptr<ov::Function> FrontEndTF::convert(ov::frontend::InputModel::Ptr model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
-
     std::shared_ptr<ov::Function> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", true, false, f);
     normalize(f);
-
     // TODO: check that nGraph function does not contain operations which are not in the opset
 
     return f;
 }
 
-std::shared_ptr<ov::Function> FrontEndTF::convert_partially(ngraph::frontend::InputModel::Ptr model) const {
+std::shared_ptr<ov::Function> FrontEndTF::convert_partially(ov::frontend::InputModel::Ptr model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
     std::shared_ptr<ov::Function> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", false, false, f);
@@ -331,7 +322,7 @@ std::shared_ptr<ov::Function> FrontEndTF::convert_partially(ngraph::frontend::In
     return f;
 }
 
-std::shared_ptr<ov::Function> FrontEndTF::decode(ngraph::frontend::InputModel::Ptr model) const {
+std::shared_ptr<ov::Function> FrontEndTF::decode(ov::frontend::InputModel::Ptr model) const {
     auto model_tf = std::dynamic_pointer_cast<InputModelTF>(model);
     std::shared_ptr<ov::Function> f;
     translate_graph(model_tf, "here_should_be_a_graph_name", false, true, f);
diff --git a/ngraph/frontend/tensorflow/src/model.cpp b/ngraph/frontend/tensorflow/src/model.cpp
index effc82b029da69..98a922a4d3cbe1 100644
--- a/ngraph/frontend/tensorflow/src/model.cpp
+++ b/ngraph/frontend/tensorflow/src/model.cpp
@@ -54,18 +54,18 @@ void extract_operation_name_and_port(const std::string& port_name,
 }  // namespace tf
 class InputModelTF::InputModelTFImpl {
 public:
-    InputModelTFImpl(const GraphIterator::Ptr& graph_iterator, const ngraph::frontend::InputModel& input_model);
-    std::vector<ngraph::frontend::Place::Ptr> getInputs() const;
-    std::vector<ngraph::frontend::Place::Ptr> getOutputs() const;
-    ngraph::frontend::Place::Ptr getPlaceByTensorName(const std::string& tensorName) const;
-    void overrideAllOutputs(const std::vector<ngraph::frontend::Place::Ptr>& outputs);
-    void overrideAllInputs(const std::vector<ngraph::frontend::Place::Ptr>& inputs);
-    void extractSubgraph(const std::vector<ngraph::frontend::Place::Ptr>& inputs,
-                         const std::vector<ngraph::frontend::Place::Ptr>& outputs);
-    void setPartialShape(ngraph::frontend::Place::Ptr place, const ov::PartialShape&);
-    ov::PartialShape getPartialShape(ngraph::frontend::Place::Ptr place) const;
-    void setElementType(ngraph::frontend::Place::Ptr place, const ov::element::Type&);
-    void setTensorValue(ngraph::frontend::Place::Ptr place, const void* value);
+    InputModelTFImpl(const GraphIterator::Ptr& graph_iterator, const ov::frontend::InputModel& input_model);
+    std::vector<ov::frontend::Place::Ptr> getInputs() const;
+    std::vector<ov::frontend::Place::Ptr> getOutputs() const;
+    ov::frontend::Place::Ptr getPlaceByTensorName(const std::string& tensorName) const;
+    void overrideAllOutputs(const std::vector<ov::frontend::Place::Ptr>& outputs);
+    void overrideAllInputs(const std::vector<ov::frontend::Place::Ptr>& inputs);
+    void extractSubgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                         const std::vector<ov::frontend::Place::Ptr>& outputs);
+    void setPartialShape(ov::frontend::Place::Ptr place, const ov::PartialShape&);
+    ov::PartialShape getPartialShape(ov::frontend::Place::Ptr place) const;
+    void setElementType(ov::frontend::Place::Ptr place, const ov::element::Type&);
+    void setTensorValue(ov::frontend::Place::Ptr place, const void* value);
 
     std::vector<std::shared_ptr<OpPlaceTF>> get_op_places() const;
     std::map<std::string, std::shared_ptr<TensorPlaceTF>> get_tensor_places() const {
@@ -82,12 +82,12 @@ class InputModelTF::InputModelTFImpl {
     std::vector<std::shared_ptr<OpPlaceTF>> m_op_places;
     std::map<std::string, std::shared_ptr<OpPlaceTF>> m_op_places_map;
     mutable std::map<std::string, std::shared_ptr<TensorPlaceTF>> m_tensor_places;
-    std::vector<ngraph::frontend::Place::Ptr> m_inputs;
-    std::vector<ngraph::frontend::Place::Ptr> m_outputs;
+    std::vector<ov::frontend::Place::Ptr> m_inputs;
+    std::vector<ov::frontend::Place::Ptr> m_outputs;
     std::map<std::string, Output<Node>> m_tensor_values;
 
     std::shared_ptr<GraphIterator> m_graph_iterator;
-    const ngraph::frontend::InputModel& m_input_model;
+    const ov::frontend::InputModel& m_input_model;
 
     // shows if some nodes might be deleted from graph
     bool m_graph_changed = false;
@@ -235,22 +235,22 @@ std::vector<std::shared_ptr<OpPlaceTF>> InputModelTF::InputModelTFImpl::determin
 }
 
 InputModelTF::InputModelTFImpl::InputModelTFImpl(const GraphIterator::Ptr& graph_iterator,
-                                                 const ngraph::frontend::InputModel& input_model)
+                                                 const ov::frontend::InputModel& input_model)
     : m_input_model(input_model),
       m_graph_iterator(graph_iterator) {
     FRONT_END_GENERAL_CHECK(m_graph_iterator, "Null pointer specified for GraphIterator");
     loadPlaces();
 }
 
-std::vector<ngraph::frontend::Place::Ptr> InputModelTF::InputModelTFImpl::getInputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModelTF::InputModelTFImpl::getInputs() const {
     return m_inputs;
 }
 
-std::vector<ngraph::frontend::Place::Ptr> InputModelTF::InputModelTFImpl::getOutputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModelTF::InputModelTFImpl::getOutputs() const {
     return m_outputs;
 }
 
-ngraph::frontend::Place::Ptr InputModelTF::InputModelTFImpl::getPlaceByTensorName(const std::string& tensorName) const {
+ov::frontend::Place::Ptr InputModelTF::InputModelTFImpl::getPlaceByTensorName(const std::string& tensorName) const {
     if (m_tensor_places.find(tensorName) != m_tensor_places.end())
         return m_tensor_places.at(tensorName);
 
@@ -270,7 +270,7 @@ ngraph::frontend::Place::Ptr InputModelTF::InputModelTFImpl::getPlaceByTensorNam
     return nullptr;
 }
 
-std::shared_ptr<TensorPlaceTF> castToTensorPlace(const ngraph::frontend::Place::Ptr& place) {
+std::shared_ptr<TensorPlaceTF> castToTensorPlace(const ov::frontend::Place::Ptr& place) {
     if (auto var_place = std::dynamic_pointer_cast<TensorPlaceTF>(place)) {
         return var_place;
     } else if (auto in_port_place = std::dynamic_pointer_cast<InPortPlaceTF>(place)) {
@@ -281,7 +281,7 @@ std::shared_ptr<TensorPlaceTF> castToTensorPlace(const ngraph::frontend::Place::
     FRONT_END_GENERAL_CHECK(false, "Cannot cast this Place to TensorPlaceTF.");
 }
 
-void InputModelTF::InputModelTFImpl::overrideAllInputs(const std::vector<ngraph::frontend::Place::Ptr>& inputs) {
+void InputModelTF::InputModelTFImpl::overrideAllInputs(const std::vector<ov::frontend::Place::Ptr>& inputs) {
     m_graph_changed = true;
     m_inputs.clear();
     for (const auto& input_place : inputs) {
@@ -289,7 +289,7 @@ void InputModelTF::InputModelTFImpl::overrideAllInputs(const std::vector<ngraph:
     }
 }
 
-void InputModelTF::InputModelTFImpl::overrideAllOutputs(const std::vector<ngraph::frontend::Place::Ptr>& outputs) {
+void InputModelTF::InputModelTFImpl::overrideAllOutputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
     m_graph_changed = true;
     m_outputs.clear();
     for (const auto& output_place : outputs) {
@@ -297,27 +297,26 @@ void InputModelTF::InputModelTFImpl::overrideAllOutputs(const std::vector<ngraph
     }
 }
 
-void InputModelTF::InputModelTFImpl::extractSubgraph(const std::vector<ngraph::frontend::Place::Ptr>& inputs,
-                                                     const std::vector<ngraph::frontend::Place::Ptr>& outputs) {
+void InputModelTF::InputModelTFImpl::extractSubgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                                                     const std::vector<ov::frontend::Place::Ptr>& outputs) {
     m_graph_changed = true;
     overrideAllInputs(inputs);
     overrideAllOutputs(outputs);
 }
 
-void InputModelTF::InputModelTFImpl::setPartialShape(ngraph::frontend::Place::Ptr place,
-                                                     const ov::PartialShape& p_shape) {
+void InputModelTF::InputModelTFImpl::setPartialShape(ov::frontend::Place::Ptr place, const ov::PartialShape& p_shape) {
     castToTensorPlace(place)->set_partial_shape(p_shape);
 }
 
-ov::PartialShape InputModelTF::InputModelTFImpl::getPartialShape(ngraph::frontend::Place::Ptr place) const {
+ov::PartialShape InputModelTF::InputModelTFImpl::getPartialShape(ov::frontend::Place::Ptr place) const {
     return castToTensorPlace(place)->get_partial_shape();
 }
 
-void InputModelTF::InputModelTFImpl::setElementType(ngraph::frontend::Place::Ptr place, const ov::element::Type& type) {
+void InputModelTF::InputModelTFImpl::setElementType(ov::frontend::Place::Ptr place, const ov::element::Type& type) {
     castToTensorPlace(place)->set_element_type(type);
 }
 
-void InputModelTF::InputModelTFImpl::setTensorValue(ngraph::frontend::Place::Ptr place, const void* value) {
+void InputModelTF::InputModelTFImpl::setTensorValue(ov::frontend::Place::Ptr place, const void* value) {
     m_graph_changed = true;
     auto tensor_place = castToTensorPlace(place);
     auto p_shape = tensor_place->get_partial_shape();
@@ -343,44 +342,44 @@ std::map<std::string, Output<Node>> InputModelTF::get_tensor_values() const {
     return _impl->get_tensor_values();
 }
 
-std::vector<ngraph::frontend::Place::Ptr> InputModelTF::get_inputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModelTF::get_inputs() const {
     return _impl->getInputs();
 }
 
-std::vector<ngraph::frontend::Place::Ptr> InputModelTF::get_outputs() const {
+std::vector<ov::frontend::Place::Ptr> InputModelTF::get_outputs() const {
     return _impl->getOutputs();
 }
 
-ngraph::frontend::Place::Ptr InputModelTF::get_place_by_tensor_name(const std::string& tensorName) const {
+ov::frontend::Place::Ptr InputModelTF::get_place_by_tensor_name(const std::string& tensorName) const {
     return _impl->getPlaceByTensorName(tensorName);
 }
 
-void InputModelTF::override_all_outputs(const std::vector<ngraph::frontend::Place::Ptr>& outputs) {
+void InputModelTF::override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
     _impl->overrideAllOutputs(outputs);
 }
 
-void InputModelTF::override_all_inputs(const std::vector<ngraph::frontend::Place::Ptr>& inputs) {
+void InputModelTF::override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) {
     _impl->overrideAllInputs(inputs);
 }
 
-void InputModelTF::extract_subgraph(const std::vector<ngraph::frontend::Place::Ptr>& inputs,
-                                    const std::vector<ngraph::frontend::Place::Ptr>& outputs) {
+void InputModelTF::extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                                    const std::vector<ov::frontend::Place::Ptr>& outputs) {
     _impl->extractSubgraph(inputs, outputs);
 }
 
-void InputModelTF::set_partial_shape(ngraph::frontend::Place::Ptr place, const ov::PartialShape& p_shape) {
+void InputModelTF::set_partial_shape(ov::frontend::Place::Ptr place, const ov::PartialShape& p_shape) {
     _impl->setPartialShape(place, p_shape);
 }
 
-ov::PartialShape InputModelTF::get_partial_shape(ngraph::frontend::Place::Ptr place) const {
+ov::PartialShape InputModelTF::get_partial_shape(ov::frontend::Place::Ptr place) const {
     return _impl->getPartialShape(place);
 }
 
-void InputModelTF::set_element_type(ngraph::frontend::Place::Ptr place, const ov::element::Type& type) {
+void InputModelTF::set_element_type(ov::frontend::Place::Ptr place, const ov::element::Type& type) {
     _impl->setElementType(place, type);
 }
 
-void InputModelTF::set_tensor_value(ngraph::frontend::Place::Ptr place, const void* value) {
+void InputModelTF::set_tensor_value(ov::frontend::Place::Ptr place, const void* value) {
     _impl->setTensorValue(place, value);
 }
 }  // namespace frontend
diff --git a/ngraph/frontend/tensorflow/src/model.hpp b/ngraph/frontend/tensorflow/src/model.hpp
index e3b9d49a599e5d..94936e0684caae 100644
--- a/ngraph/frontend/tensorflow/src/model.hpp
+++ b/ngraph/frontend/tensorflow/src/model.hpp
@@ -14,7 +14,7 @@ namespace frontend {
 class OpPlaceTF;
 class TensorPlaceTF;
 
-class InputModelTF : public ngraph::frontend::InputModel {
+class InputModelTF : public ov::frontend::InputModel {
     friend class FrontEndTF;
     class InputModelTFImpl;
     std::shared_ptr<InputModelTFImpl> _impl;
@@ -26,17 +26,17 @@ class InputModelTF : public ngraph::frontend::InputModel {
 public:
     explicit InputModelTF(const GraphIterator::Ptr& graph_iterator);
 
-    std::vector<ngraph::frontend::Place::Ptr> get_inputs() const override;
-    std::vector<ngraph::frontend::Place::Ptr> get_outputs() const override;
-    ngraph::frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensorName) const override;
-    void override_all_outputs(const std::vector<ngraph::frontend::Place::Ptr>& outputs) override;
-    void override_all_inputs(const std::vector<ngraph::frontend::Place::Ptr>& inputs) override;
-    void extract_subgraph(const std::vector<ngraph::frontend::Place::Ptr>& inputs,
-                          const std::vector<ngraph::frontend::Place::Ptr>& outputs) override;
-    void set_partial_shape(ngraph::frontend::Place::Ptr place, const ov::PartialShape&) override;
-    ov::PartialShape get_partial_shape(ngraph::frontend::Place::Ptr place) const override;
-    void set_element_type(ngraph::frontend::Place::Ptr place, const ov::element::Type&) override;
-    void set_tensor_value(ngraph::frontend::Place::Ptr place, const void* value) override;
+    std::vector<ov::frontend::Place::Ptr> get_inputs() const override;
+    std::vector<ov::frontend::Place::Ptr> get_outputs() const override;
+    ov::frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensorName) const override;
+    void override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) override;
+    void override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) override;
+    void extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
+                          const std::vector<ov::frontend::Place::Ptr>& outputs) override;
+    void set_partial_shape(ov::frontend::Place::Ptr place, const ov::PartialShape&) override;
+    ov::PartialShape get_partial_shape(ov::frontend::Place::Ptr place) const override;
+    void set_element_type(ov::frontend::Place::Ptr place, const ov::element::Type&) override;
+    void set_tensor_value(ov::frontend::Place::Ptr place, const void* value) override;
 };
 }  // namespace frontend
 }  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/ngraph_conversions.cpp b/ngraph/frontend/tensorflow/src/ngraph_conversions.cpp
index 0a3f88b20cfc17..8f1814da8eb2c6 100644
--- a/ngraph/frontend/tensorflow/src/ngraph_conversions.cpp
+++ b/ngraph/frontend/tensorflow/src/ngraph_conversions.cpp
@@ -10,7 +10,7 @@ namespace ov {
 namespace frontend {
 namespace tf {
 
-void NHWCtoNCHW(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& node) {
+void convert_nhwc_to_nchw(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& node) {
     if (need_convert) {
         auto rank = node.get_shape().size();
         if (rank == 4) {
@@ -18,11 +18,10 @@ void NHWCtoNCHW(const std::string& op_name, bool need_convert, ov::Output<ov::No
         } else if (rank == 5) {
             Transpose3D<0, 4, 1, 2, 3>(node);
         }
-        SetTracingInfo(op_name, node);
     }
 }
 
-void NCHWtoNHWC(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& node) {
+void convert_nchw_to_nhwc(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& node) {
     if (need_convert) {
         auto rank = node.get_shape().size();
         if (rank == 4) {
@@ -30,7 +29,6 @@ void NCHWtoNHWC(const std::string& op_name, bool need_convert, ov::Output<ov::No
         } else if (rank == 5) {
             Transpose3D<0, 2, 3, 4, 1>(node);
         }
-        SetTracingInfo(op_name, node);
     }
 }
 
diff --git a/ngraph/frontend/tensorflow/src/ngraph_conversions.hpp b/ngraph/frontend/tensorflow/src/ngraph_conversions.hpp
index 9a411630695795..fb44b2472fff1e 100644
--- a/ngraph/frontend/tensorflow/src/ngraph_conversions.hpp
+++ b/ngraph/frontend/tensorflow/src/ngraph_conversions.hpp
@@ -4,11 +4,11 @@
 
 #pragma once
 
-#include <openvino/opsets/opset8.hpp>
 #include <string>
 #include <tensorflow_frontend/utility.hpp>
 
 #include "graph.pb.h"
+#include "openvino/opsets/opset8.hpp"
 #include "types.pb.h"
 
 namespace ov {
@@ -17,8 +17,6 @@ namespace tf {
 
 using ::tensorflow::DataType;
 
-void TFTensorShapeToNGraphShape(const ::tensorflow::TensorShapeProto& tf_shape, ov::PartialShape* ng_shape);
-
 template <size_t a, size_t b, size_t c, size_t d>
 void Transpose(ov::Output<ov::Node>& node) {
     static_assert(a < 4 && b < 4 && c < 4 && d < 4, "Number of dimensions cannot exceed 4");
@@ -56,7 +54,7 @@ void Transpose3D(std::shared_ptr<ov::Node>& node) {
 
 namespace detail {
 template <typename T>
-void NHWCtoHW(const std::vector<T>& src, std::vector<size_t>& dst) {
+void convert_nhwc_to_hw(const std::vector<T>& src, std::vector<size_t>& dst) {
     if (dst.size() >= 2) {
         dst[0] = src[1];
         dst[1] = src[2];
@@ -67,7 +65,7 @@ void NHWCtoHW(const std::vector<T>& src, std::vector<size_t>& dst) {
 }
 
 template <typename T>
-void NCHWtoHW(const std::vector<T>& src, std::vector<size_t>& dst) {
+void convert_nchw_to_hw(const std::vector<T>& src, std::vector<size_t>& dst) {
     if (dst.size() >= 2) {
         dst[0] = src[2];
         dst[1] = src[3];
@@ -78,16 +76,16 @@ void NCHWtoHW(const std::vector<T>& src, std::vector<size_t>& dst) {
 }
 }  // namespace detail
 
-void NHWCtoNCHW(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& ng_input);
+void convert_nhwc_to_nchw(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& ng_input);
 
-void NCHWtoNHWC(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& ng_node);
+void convert_nchw_to_nhwc(const std::string& op_name, bool need_convert, ov::Output<ov::Node>& ng_node);
 
 template <typename T>
-void NHWCtoHW(bool is_nhwc, const std::vector<T>& src, std::vector<size_t>& dst) {
+void convert_nhwc_to_hw(bool is_nhwc, const std::vector<T>& src, std::vector<size_t>& dst) {
     if (is_nhwc) {
-        detail::NHWCtoHW(src, dst);
+        detail::convert_nhwc_to_hw(src, dst);
     } else {
-        detail::NCHWtoHW(src, dst);
+        detail::convert_nchw_to_hw(src, dst);
     }
 }
 
diff --git a/ngraph/frontend/tensorflow/src/node_context.hpp b/ngraph/frontend/tensorflow/src/node_context.hpp
index f5a44e78b5cd3c..e2f92e53884d4b 100644
--- a/ngraph/frontend/tensorflow/src/node_context.hpp
+++ b/ngraph/frontend/tensorflow/src/node_context.hpp
@@ -3,10 +3,10 @@
 //
 
 #pragma once
-#include <openvino/core/variant.hpp>
 #include <tensorflow_frontend/utility.hpp>
 
 #include "exceptions.hpp"
+#include "openvino/core/variant.hpp"
 #include "place.hpp"
 #include "tensor.pb.h"
 #include "types.pb.h"
@@ -80,7 +80,7 @@ class NodeContext {
     }
 
     /// Detects if there is at least one input attached with a given name
-    bool has_ng_input(const size_t& port_index) const {
+    bool has_input(const size_t& port_index) const {
         auto found = m_name_map.find(port_index);
         if (found != m_name_map.end())
             return !found->second.empty();
@@ -89,19 +89,19 @@ class NodeContext {
 
     /// Returns exactly one input with a given name; throws if there is no inputs or
     /// there are more than one input
-    Output<Node> get_ng_input(const size_t& port_index) const {
+    Output<Node> get_input(const size_t& port_index) const {
         FRONT_END_GENERAL_CHECK(m_name_map.at(port_index).size() == 1);
         return m_name_map.at(port_index).at(0);
     }
 
     /// Returns all inputs with a given name
-    OutputVector get_ng_inputs(const size_t& port_index) const {
+    OutputVector get_inputs(const size_t& port_index) const {
         return m_name_map.at(port_index);
     }
 
     /// Returns all inputs in order they appear in map. This is used for FrameworkNode
     /// creation
-    OutputVector get_all_ng_inputs() const {
+    OutputVector get_all_inputs() const {
         OutputVector res;
         for (const auto& entry : m_name_map) {
             res.insert(res.end(), entry.second.begin(), entry.second.end());
@@ -110,7 +110,7 @@ class NodeContext {
     }
 
     /// Get a number of inputs
-    size_t get_ng_input_size() const {
+    size_t get_input_size() const {
         return m_name_map.size();
     }
 
diff --git a/ngraph/frontend/tensorflow/src/op/addN.cpp b/ngraph/frontend/tensorflow/src/op/addN.cpp
index 89d2e80e0e0806..7b08abde1773e1 100644
--- a/ngraph/frontend/tensorflow/src/op/addN.cpp
+++ b/ngraph/frontend/tensorflow/src/op/addN.cpp
@@ -3,8 +3,9 @@
 //
 
 #include <numeric>
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -14,20 +15,18 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateAddNOp(const NodeContext& node) {
-    OutputVector ng_arg_vec = node.get_all_ng_inputs();
-
-    auto ng_addn = std::accumulate(std::next(ng_arg_vec.begin()),
-                                   ng_arg_vec.end(),
-                                   ng_arg_vec.at(0),
-                                   [&node](Output<Node> a, Output<Node> b) {
-                                       return ConstructNgNode<Add>(node.get_name(), a, b);
-                                   });  // accumulation: start with
-    // first element. default op is
-    // addition
-    return {ng_addn};
+OutputVector translate_add_n_op(const NodeContext& node) {
+    OutputVector ng_arg_vec = node.get_all_inputs();
+    auto res = std::accumulate(std::next(ng_arg_vec.begin()),
+                               ng_arg_vec.end(),
+                               ng_arg_vec.at(0),
+                               [](const Output<Node>& a, const Output<Node>& b) -> shared_ptr<Node> {
+                                   return make_shared<Add>(a, b);
+                               });
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/arg_min_max.cpp b/ngraph/frontend/tensorflow/src/op/arg_min_max.cpp
index cbd0246f859309..50146a386c6adf 100644
--- a/ngraph/frontend/tensorflow/src/op/arg_min_max.cpp
+++ b/ngraph/frontend/tensorflow/src/op/arg_min_max.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -14,46 +14,43 @@ namespace tf {
 namespace op {
 
 OutputVector TranslateArgMinMax(const NodeContext& node, std::string mode) {
-    Output<Node> ng_input = node.get_ng_input(0);
+    Output<Node> ng_input = node.get_input(0);
 
     std::vector<int64_t> tf_dim;
-    GetStaticInputVector(node, 1, &tf_dim);
+    get_static_input_vec(node, 1, &tf_dim);
 
     Shape input_shape = ng_input.get_shape();
     size_t input_rank = input_shape.size();
 
-    if (tf_dim.size() != 1) {
-        throw errors::InvalidArgument("ArgMax Op: dimension must be scalar, operates on a single axis");
-    }
+    TF_OP_VALIDATION_CHECK(node, tf_dim.size() == 1, "ArgMax Op: dimension must be scalar, operates on a single axis");
 
     // If input dimension is negative, make it positive
     if (tf_dim[0] < 0) {
-        NGRAPH_VLOG(3) << "Input dimension is negative, make it positive " << tf_dim[0];
+        NGRAPH_DEBUG << "Input dimension is negative, make it positive " << tf_dim[0];
         tf_dim[0] = (int64_t)input_rank + tf_dim[0];
     }
-    NGRAPH_VLOG(3) << "Axis along which to compute " << tf_dim[0];
+    NGRAPH_DEBUG << "Axis along which to compute " << tf_dim[0];
     size_t k_axis = tf_dim[0];
 
     auto ng_et = node.get_attribute<element::Type>("output_type");
 
-    auto ng_k = ConstructNgNode<Constant>(node.get_name(), element::i64, Shape{}, std::vector<int64_t>({1}));
+    auto ng_k = make_shared<Constant>(element::i64, Shape{}, std::vector<int64_t>({1}));
 
     std::string sort = "none";
     auto ng_topk = std::make_shared<TopK>(ng_input, ng_k, k_axis, mode, sort, ng_et);
     auto ng_indices = ng_topk->output(1);
     int axis = ng_topk->get_axis();
-    auto axis_to_remove =
-        ConstructNgNode<Constant>(node.get_name(), element::i64, Shape{1}, std::vector<int64_t>({axis}));
-    auto reshaped_indices = ConstructNgNode<Squeeze>(node.get_name(), ng_indices, axis_to_remove);
-    SetTracingInfo(node.get_name(), reshaped_indices);
-    return {reshaped_indices};
+    auto axis_to_remove = make_shared<Constant>(element::i64, Shape{1}, std::vector<int64_t>({axis}));
+    auto res = make_shared<Squeeze>(ng_indices, axis_to_remove);
+    set_node_name(node.get_name(), res);
+    return {res};
 }
 
-OutputVector TranslateArgMaxOp(const NodeContext& node) {
+OutputVector translate_arg_max_op(const NodeContext& node) {
     return (TranslateArgMinMax(node, "max"));
 }
 
-OutputVector TranslateArgMinOp(const NodeContext& node) {
+OutputVector translate_arg_min_op(const NodeContext& node) {
     return (TranslateArgMinMax(node, "min"));
 }
 }  // namespace op
diff --git a/ngraph/frontend/tensorflow/src/op/avg_pool.cpp b/ngraph/frontend/tensorflow/src/op/avg_pool.cpp
index c4d5fd5f297836..ef34cd07cdefd8 100644
--- a/ngraph/frontend/tensorflow/src/op/avg_pool.cpp
+++ b/ngraph/frontend/tensorflow/src/op/avg_pool.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,57 +13,58 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateAvgPoolOp(const NodeContext& node) {
-    Output<Node> ng_input = node.get_ng_input(0);
+OutputVector translate_avg_pool_op(const NodeContext& node) {
+    Output<Node> ng_input = node.get_input(0);
 
     auto tf_strides = node.get_attribute<std::vector<int32_t>>("strides");
     auto tf_ksize = node.get_attribute<std::vector<int32_t>>("ksize");
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
-    if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-        throw errors::InvalidArgument("AvgPool data format is neither NHWC nor NCHW");
-    }
+
+    TF_OP_VALIDATION_CHECK(node,
+                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                           "AvgPool data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
     Strides ng_strides(2);
     Shape ng_image_shape(2);
     Shape ng_kernel_shape(2);
-    NHWCtoHW(is_nhwc, tf_strides, ng_strides);
-    NHWCtoHW(is_nhwc, ng_input.get_shape(), ng_image_shape);
-    NHWCtoHW(is_nhwc, tf_ksize, ng_kernel_shape);
-    NHWCtoNCHW(node.get_name(), is_nhwc, ng_input);
+    convert_nhwc_to_hw(is_nhwc, tf_strides, ng_strides);
+    convert_nhwc_to_hw(is_nhwc, ng_input.get_shape(), ng_image_shape);
+    convert_nhwc_to_hw(is_nhwc, tf_ksize, ng_kernel_shape);
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, ng_input);
 
     CoordinateDiff padding_below;
     CoordinateDiff padding_above;
     Shape ng_dilations{1, 1};
-    MakePadding(tf_padding_type,
-                ng_image_shape,
-                ng_kernel_shape,
-                ng_strides,
-                ng_dilations,
-                padding_below,
-                padding_above);
+    make_padding(tf_padding_type,
+                 ng_image_shape,
+                 ng_kernel_shape,
+                 ng_strides,
+                 ng_dilations,
+                 padding_below,
+                 padding_above);
 
     // TODO: remove this once nGraph supports negative padding
     // (CoordinateDiff) for AvgPool
     Shape ng_padding_below(padding_below.begin(), padding_below.end());
     Shape ng_padding_above(padding_above.begin(), padding_above.end());
 
-    Output<Node> ng_avgpool = ConstructNgNode<AvgPool>(node.get_name(),
-                                                       ng_input,
-                                                       ng_strides,
-                                                       ng_padding_below,
-                                                       ng_padding_above,
-                                                       ng_kernel_shape,
-                                                       true,
-                                                       ov::op::RoundingType::FLOOR);
-
-    NCHWtoNHWC(node.get_name(), is_nhwc, ng_avgpool);
+    auto res_node = make_shared<AvgPool>(ng_input,
+                                         ng_strides,
+                                         ng_padding_below,
+                                         ng_padding_above,
+                                         ng_kernel_shape,
+                                         true,
+                                         ov::op::RoundingType::FLOOR);
+    auto res = res_node->output(0);
 
-    return {ng_avgpool};
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/bias_add.cpp b/ngraph/frontend/tensorflow/src/op/bias_add.cpp
index f12f1630aedc22..518116c86c857e 100644
--- a/ngraph/frontend/tensorflow/src/op/bias_add.cpp
+++ b/ngraph/frontend/tensorflow/src/op/bias_add.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,20 +13,18 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateBiasAddOp(const NodeContext& node) {
-    Output<Node> ng_input = node.get_ng_input(0), ng_bias = node.get_ng_input(1);
+OutputVector translate_bias_add_op(const NodeContext& node) {
+    Output<Node> ng_input = node.get_input(0), ng_bias = node.get_input(1);
 
     std::string tf_data_format = node.get_attribute<std::string>("data_format", "NHWC");
 
-    if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-        throw errors::InvalidArgument("BiasAdd data format is neither NHWC nor NCHW");
-    }
+    TF_OP_VALIDATION_CHECK(node,
+                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                           "BiasAdd data format is neither NHWC nor NCHW");
 
     auto ng_input_shape = ng_input.get_shape();
     auto ng_bias_shape = ng_bias.get_shape();
-    if (ng_bias_shape.size() != 1) {
-        throw errors::InvalidArgument("Bias argument to BiasAdd does not have one dimension");
-    }
+    TF_OP_VALIDATION_CHECK(node, ng_bias_shape.size() == 1, "Bias argument to BiasAdd does not have one dimension");
 
     // We'll choose reshape over broadcast
     // Reshape the bias to (1, C, 1, ...) if input is channels-first.
@@ -42,14 +40,14 @@ OutputVector TranslateBiasAddOp(const NodeContext& node) {
             }
         }
         auto target_shape_node = make_shared<Constant>(element::i64, Shape{ng_input_shape.size()}, target_shape);
-        ng_bias_reshaped = ConstructNgNode<Reshape>(node.get_name(), ng_bias, target_shape_node, false);
+        ng_bias_reshaped = make_shared<Reshape>(ng_bias, target_shape_node, false);
     }
 
-    Output<Node> ng_add = ConstructNgNode<Add>(node.get_name(), ng_input, ng_bias_reshaped);
-
-    return {ng_add};
+    auto res = make_shared<Add>(ng_input, ng_bias_reshaped);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/binary_op.cpp b/ngraph/frontend/tensorflow/src/op/binary_op.cpp
index 8c31910d537140..97c824aac504c2 100644
--- a/ngraph/frontend/tensorflow/src/op/binary_op.cpp
+++ b/ngraph/frontend/tensorflow/src/op/binary_op.cpp
@@ -2,96 +2,59 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
 
-// Helper function to translate a binary op
-// Parameters:
-//
-//    TFNodeDecoder* op               - TF op being translated. Must have only two
-//    inputs.
-//    const std::vector<const ov::frontend::tf::detail::TensorWrapper*>& static_input_map - the static input
-//    map Builder::OpMap& ng_op_map  - The TF-to-nGraph op map. std::function<Output<Node>(Output<Node>,
-//    Output<Node>)>
-//    create_binary_op           - Function to construct the graph implementing
-//                                 the binary op, given the 2 ng_inputs to the
-//                                 binaryop
-// Example Usage:
-//
-// if (op->type_string() == "SquaredDifference") {
-//      TF_RETURN_IF_ERROR(TranslateBinaryOp(op, ng_op_map,
-//         [](Output<Node> ng_input1, Output<Node>
-//         ng_input2) {
-//           auto ng_diff = Output<Subtract>(input1,
-//           input2);
-//           return Output<Multiply>(ng_diff,ng_diff);
-//         }));
-//    }
-//
-
 namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
-OutputVector TranslateBinaryOp(const NodeContext& node,
-                               std::function<Output<Node>(Output<Node>&, Output<Node>&)> create_binary_op) {
-    Output<Node> ng_lhs = node.get_ng_input(0), ng_rhs = node.get_ng_input(1);
-    auto ng_node = create_binary_op(ng_lhs, ng_rhs);
 
-    // TODO do we need it?
-    /*    if (ng_node != ng_lhs && ng_node != ng_rhs) {
-            Builder::SetTracingInfo(node.get_name(), ng_node);
-        }*/
+OutputVector translate_binary_op(const NodeContext& node,
+                                 const std::function<Output<Node>(Output<Node>&, Output<Node>&)>& create_binary_op) {
+    auto ng_lhs = node.get_input(0);
+    auto ng_rhs = node.get_input(1);
+    auto ng_node = create_binary_op(ng_lhs, ng_rhs);
+    set_node_name(node.get_name(), ng_node.get_node_shared_ptr());
     return {ng_node};
 }
 
-OutputVector TranslateFloorDivOp(const NodeContext& node) {
-    auto floordiv_fn = [&node](Output<Node> x, Output<Node> y) {
-        return ConstructNgNode<Floor>(node.get_name(), ConstructNgNode<Divide>(node.get_name(), x, y));
+OutputVector translate_floor_div_op(const NodeContext& node) {
+    auto floordiv_fn = [](const Output<Node>& x, const Output<Node>& y) {
+        return make_shared<Floor>(make_shared<Divide>(x, y));
     };
-    return TranslateBinaryOp(node, floordiv_fn);
+    return translate_binary_op(node, floordiv_fn);
 }
 
-// Helper function to translate a binary op in cases where there is a one-to-one
-// mapping from TensorFlow ops to nGraph ops.
-//
-// Example usage:
-//
-//  if (n->type_string == "Add") {
-//    TF_RETURN_IF_ERROR(TranslateBinaryOp<Add>(op,
-//    static_input_map,
-//    ng_op_map));
-//  }
-//
-
 template <typename T>
-OutputVector TranslateBinaryOp(const NodeContext& node) {
-    return TranslateBinaryOp(node, [&node](Output<Node>& ng_lhs, Output<Node>& ng_rhs) {
-        return ConstructNgNode<T>(node.get_name(), ng_lhs, ng_rhs);
+OutputVector translate_binary_op(const NodeContext& node) {
+    return translate_binary_op(node, [](Output<Node>& ng_lhs, Output<Node>& ng_rhs) {
+        return make_shared<T>(ng_lhs, ng_rhs);
     });
 }
 
-template OutputVector TranslateBinaryOp<Add>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Equal>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<FloorMod>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Greater>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<GreaterEqual>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Less>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<LessEqual>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<LogicalAnd>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<LogicalOr>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Maximum>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Minimum>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Multiply>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Mod>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<NotEqual>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Power>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Divide>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<SquaredDifference>(const NodeContext& node);
-template OutputVector TranslateBinaryOp<Subtract>(const NodeContext& node);
+template OutputVector translate_binary_op<Add>(const NodeContext& node);
+template OutputVector translate_binary_op<Equal>(const NodeContext& node);
+template OutputVector translate_binary_op<FloorMod>(const NodeContext& node);
+template OutputVector translate_binary_op<Greater>(const NodeContext& node);
+template OutputVector translate_binary_op<GreaterEqual>(const NodeContext& node);
+template OutputVector translate_binary_op<Less>(const NodeContext& node);
+template OutputVector translate_binary_op<LessEqual>(const NodeContext& node);
+template OutputVector translate_binary_op<LogicalAnd>(const NodeContext& node);
+template OutputVector translate_binary_op<LogicalOr>(const NodeContext& node);
+template OutputVector translate_binary_op<LogicalXor>(const NodeContext& node);
+template OutputVector translate_binary_op<Maximum>(const NodeContext& node);
+template OutputVector translate_binary_op<Minimum>(const NodeContext& node);
+template OutputVector translate_binary_op<Multiply>(const NodeContext& node);
+template OutputVector translate_binary_op<Mod>(const NodeContext& node);
+template OutputVector translate_binary_op<NotEqual>(const NodeContext& node);
+template OutputVector translate_binary_op<Power>(const NodeContext& node);
+template OutputVector translate_binary_op<Divide>(const NodeContext& node);
+template OutputVector translate_binary_op<SquaredDifference>(const NodeContext& node);
+template OutputVector translate_binary_op<Subtract>(const NodeContext& node);
 
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/cast.cpp b/ngraph/frontend/tensorflow/src/op/cast.cpp
index e85195969e61b4..9fe2b177c0fe98 100644
--- a/ngraph/frontend/tensorflow/src/op/cast.cpp
+++ b/ngraph/frontend/tensorflow/src/op/cast.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,14 +13,16 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateCastOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0);
+OutputVector translate_cast_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0);
 
     auto ng_et = node.get_attribute<element::Type>("DstT");
-    return {ConstructNgNode<Convert>(node.get_name(), ng_input, ng_et)};
+    auto res = make_shared<Convert>(ng_input, ng_et);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/concat.cpp b/ngraph/frontend/tensorflow/src/op/concat.cpp
index 61355df5aea7fd..513770039aa576 100644
--- a/ngraph/frontend/tensorflow/src/op/concat.cpp
+++ b/ngraph/frontend/tensorflow/src/op/concat.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov;
@@ -16,29 +16,35 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateConcatV2Op(const NodeContext& node) {
-    ValidateInputCountMin(node, 2);
+OutputVector translate_concat_op(const NodeContext& node) {
+    size_t axis_idx, concat_idx_start, concat_idx_stop;
+    if (node.get_op_type() == "ConcatV2") {
+        axis_idx = node.get_input_size() - 1;
+        concat_idx_start = 0;
+        concat_idx_stop = node.get_input_size() - 1;
+    } else if (node.get_op_type() == "Concat") {
+        axis_idx = 0;
+        concat_idx_start = 1;
+        concat_idx_stop = node.get_input_size();
+    } else {
+        TF_OP_VALIDATION_CHECK(node, false, "Incorrect operation type.");
+    }
 
     std::vector<int64_t> tf_concat_axis_vec;
-    GetStaticInputVector(node, node.get_ng_input_size() - 1, &tf_concat_axis_vec);
-
+    get_static_input_vec(node, axis_idx, &tf_concat_axis_vec);
     int64_t concat_axis = tf_concat_axis_vec[0];
 
-    if (concat_axis < 0) {
-        auto ng_first_arg = node.get_ng_input(0);
-        concat_axis += int64_t(ng_first_arg.get_shape().size());
-    }
-
     OutputVector ng_args;
-
-    for (int i = 0; i < node.get_ng_input_size() - 1; i++) {
-        Output<Node> ng_arg = node.get_ng_input(i);
+    for (int i = concat_idx_start; i < concat_idx_stop; i++) {
+        Output<Node> ng_arg = node.get_input(i);
         ng_args.push_back(ng_arg);
     }
 
-    return {ConstructNgNode<Concat>(node.get_name(), ng_args, size_t(concat_axis))};
+    auto res = make_shared<Concat>(ng_args, size_t(concat_axis));
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/const.cpp b/ngraph/frontend/tensorflow/src/op/const.cpp
index c142ea1ad40d1e..f0dee56b3016f0 100644
--- a/ngraph/frontend/tensorflow/src/op/const.cpp
+++ b/ngraph/frontend/tensorflow/src/op/const.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -15,54 +15,56 @@ namespace op {
 
 namespace {
 using ConstMap = std::map<ov::element::Type,
-                          std::pair<std::function<Status(const NodeContext&, ov::element::Type, ov::Output<ov::Node>&)>,
+                          std::pair<std::function<void(const NodeContext&, ov::element::Type, ov::Output<ov::Node>&)>,
                                     const ov::element::Type>>;
 
 const ConstMap& TF_NGRAPH_CONST_MAP() {
     static const ConstMap the_map = {
-        {ov::element::f32, make_pair(MakeConstOp<float>, ov::element::f32)},
-        {ov::element::f64, make_pair(MakeConstOp<double>, ov::element::f64)},
-        {ov::element::i8, make_pair(MakeConstOp<int8_t>, ov::element::i8)},
-        {ov::element::i16, make_pair(MakeConstOp<int16_t>, ov::element::i16)},
+        {ov::element::f32, make_pair(make_const_op<float>, ov::element::f32)},
+        {ov::element::f64, make_pair(make_const_op<double>, ov::element::f64)},
+        {ov::element::i8, make_pair(make_const_op<int8_t>, ov::element::i8)},
+        {ov::element::i16, make_pair(make_const_op<int16_t>, ov::element::i16)},
 #if 0
-      {DataType::DT_QINT8, make_pair(MakeConstOp<qint8>, ov::element::i8)},
-      {DataType::DT_QUINT8, make_pair(MakeConstOp<quint8>, ov::element::u8)},
-      {DataType::DT_QUINT16, make_pair(MakeConstOp<quint16>, ov::element::u16)},
+      {DataType::DT_QINT8, make_pair(make_const_op<qint8>, ov::element::i8)},
+      {DataType::DT_QUINT8, make_pair(make_const_op<quint8>, ov::element::u8)},
+      {DataType::DT_QUINT16, make_pair(make_const_op<quint16>, ov::element::u16)},
 #endif
-        {ov::element::i32, make_pair(MakeConstOp<int32_t>, ov::element::i32)},
-        {ov::element::i64, make_pair(MakeConstOp<int64_t>, ov::element::i64)},
-        {ov::element::u8, make_pair(MakeConstOp<uint8_t>, ov::element::u8)},
-        {ov::element::u16, make_pair(MakeConstOp<uint16_t>, ov::element::u16)},
-        {ov::element::boolean, make_pair(MakeConstOp<bool, char>, ov::element::boolean)}
+        {ov::element::i32, make_pair(make_const_op<int32_t>, ov::element::i32)},
+        {ov::element::i64, make_pair(make_const_op<int64_t>, ov::element::i64)},
+        {ov::element::u8, make_pair(make_const_op<uint8_t>, ov::element::u8)},
+        {ov::element::u16, make_pair(make_const_op<uint16_t>, ov::element::u16)},
+        {ov::element::boolean, make_pair(make_const_op<bool, char>, ov::element::boolean)}
     };
     return the_map;
 }
 }  // namespace
 
-OutputVector TranslateConstOp(const NodeContext& node) {
+OutputVector translate_const_op(const NodeContext& node) {
     auto dt = node.get_attribute<ov::element::Type>("dtype");
-    Output<Node> ng_node;
+    Output<Node> res;
 
     // For some reason the following do not work (no specialization of
     // tensorflow::checkpoint::SavedTypeTraits...)
     // case DataType::DT_UINT32:
-    //   TF_RETURN_IF_ERROR(MakeConstOp<uint32>(op, element::u32,
+    //   TF_RETURN_IF_ERROR(make_const_op<uint32>(op, element::u32,
     //   &ng_node));
     //   break;
     // case DataType::DT_UINT64:
-    //   TF_RETURN_IF_ERROR(MakeConstOp<uint64>(op, element::u64,
+    //   TF_RETURN_IF_ERROR(make_const_op<uint64>(op, element::u64,
     //   &ng_node));
     //   break;
     try {
         const auto& func_param = TF_NGRAPH_CONST_MAP().at(dt);
-        TF_RETURN_IF_ERROR(func_param.first(node, func_param.second, ng_node));
+        func_param.first(node, func_param.second, res);
     } catch (const std::out_of_range&) {
-        throw errors::Unimplemented("Failed to translate Constant with target ngraph type:" + dt.get_type_name());
+        TF_OP_VALIDATION_CHECK(node,
+                               false,
+                               "Failed to translate Constant with target ngraph type:" + dt.get_type_name());
     }
-
-    return {ng_node};
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/conv_2d.cpp b/ngraph/frontend/tensorflow/src/op/conv_2d.cpp
index f797bd5c096ac2..599354781a9c76 100644
--- a/ngraph/frontend/tensorflow/src/op/conv_2d.cpp
+++ b/ngraph/frontend/tensorflow/src/op/conv_2d.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,24 +13,26 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateConv2DOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0), ng_filter = node.get_ng_input(1);
+OutputVector translate_conv_2d_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0), ng_filter = node.get_input(1);
 
     auto tf_strides = node.get_attribute<std::vector<int32_t>>("strides");
     auto tf_dilations = node.get_attribute<std::vector<int32_t>>("dilations");
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-        throw errors::InvalidArgument("Conv2D data format is neither NHWC nor NCHW");
-    }
+    TF_OP_VALIDATION_CHECK(node,
+                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                           "Conv2D data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
     // TF Kernel Test Checks
     // Strides in the batch and depth dimension is not supported
     if (tf_strides[0] != 1 || tf_strides[is_nhwc ? 3 : 1] != 1) {
-        throw errors::InvalidArgument("Strides in batch and depth dimensions is not supported: " + node.get_op_type());
+        TF_OP_VALIDATION_CHECK(node,
+                               false,
+                               "Strides in batch and depth dimensions is not supported: " + node.get_op_type());
     }
 
     Strides ng_strides(2);
@@ -38,37 +40,32 @@ OutputVector TranslateConv2DOp(const NodeContext& node) {
     Shape ng_image_shape(2);
     Shape ng_kernel_shape(2);
 
-    NHWCtoHW(is_nhwc, tf_strides, ng_strides);
-    NHWCtoHW(is_nhwc, ng_input.get_shape(), ng_image_shape);
-    NHWCtoHW(is_nhwc, tf_dilations, ng_dilations);
-    NHWCtoNCHW(node.get_name(), is_nhwc, ng_input);
+    convert_nhwc_to_hw(is_nhwc, tf_strides, ng_strides);
+    convert_nhwc_to_hw(is_nhwc, ng_input.get_shape(), ng_image_shape);
+    convert_nhwc_to_hw(is_nhwc, tf_dilations, ng_dilations);
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, ng_input);
 
     auto& ng_filter_shape = ng_filter.get_shape();
     ng_kernel_shape[0] = ng_filter_shape[0];
     ng_kernel_shape[1] = ng_filter_shape[1];
     Transpose<3, 2, 0, 1>(ng_filter);
-    SetTracingInfo(node.get_name(), ng_filter);
 
     CoordinateDiff ng_padding_below;
     CoordinateDiff ng_padding_above;
-    MakePadding(tf_padding_type,
-                ng_image_shape,
-                ng_kernel_shape,
-                ng_strides,
-                ng_dilations,
-                ng_padding_below,
-                ng_padding_above);
+    make_padding(tf_padding_type,
+                 ng_image_shape,
+                 ng_kernel_shape,
+                 ng_strides,
+                 ng_dilations,
+                 ng_padding_below,
+                 ng_padding_above);
 
-    Output<Node> ng_conv = ConstructNgNode<Convolution>(node.get_name(),
-                                                        ng_input,
-                                                        ng_filter,
-                                                        ng_strides,
-                                                        ng_padding_below,
-                                                        ng_padding_above,
-                                                        ng_dilations);
+    Output<Node> res =
+        make_shared<Convolution>(ng_input, ng_filter, ng_strides, ng_padding_below, ng_padding_above, ng_dilations);
 
-    NCHWtoNHWC(node.get_name(), is_nhwc, ng_conv);
-    return {ng_conv};
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/conv_2d_backprop.cpp b/ngraph/frontend/tensorflow/src/op/conv_2d_backprop.cpp
index 43491428c8bc8a..6c40767a904368 100644
--- a/ngraph/frontend/tensorflow/src/op/conv_2d_backprop.cpp
+++ b/ngraph/frontend/tensorflow/src/op/conv_2d_backprop.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,8 +13,8 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateConv2DBackpropInputOp(const NodeContext& node) {
-    auto ng_filter = node.get_ng_input(1), ng_out_backprop = node.get_ng_input(2);
+OutputVector translate_conv_2d_backprop_input_op(const NodeContext& node) {
+    auto ng_filter = node.get_input(1), ng_out_backprop = node.get_input(2);
 
     // TODO: refactor me to be less redundant with other convolution ops
     auto tf_strides = node.get_attribute<std::vector<int32_t>>("strides");
@@ -22,17 +22,17 @@ OutputVector TranslateConv2DBackpropInputOp(const NodeContext& node) {
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-        throw errors::InvalidArgument("Conv2DBackpropInput data format is neither NHWC nor NCHW: %s" + tf_data_format);
-    }
+    TF_OP_VALIDATION_CHECK(node,
+                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                           "Conv2DBackpropInput data format is neither NHWC nor NCHW");
 
     std::vector<int64_t> tf_input_sizes;
-    GetStaticInputVector(node, 0, &tf_input_sizes);
+    get_static_input_vec(node, 0, &tf_input_sizes);
 
     if (std::any_of(tf_input_sizes.begin(), tf_input_sizes.end(), [](int32_t size) {
             return size <= 0;
         })) {
-        throw errors::InvalidArgument("Conv2DBackpropInput input sizes must be positive integers");
+        FRONT_END_THROW("Conv2DBackpropInput input sizes must be positive integers");
     }
 
     bool is_nhwc = (tf_data_format == "NHWC");
@@ -43,10 +43,10 @@ OutputVector TranslateConv2DBackpropInputOp(const NodeContext& node) {
     Shape ng_kernel_shape(2);
     Shape ng_batch_shape(4);
 
-    NHWCtoHW(is_nhwc, tf_strides, ng_strides);
-    NHWCtoHW(is_nhwc, tf_dilations, ng_dilations);
-    NHWCtoHW(is_nhwc, tf_input_sizes, ng_image_shape);
-    NHWCtoNCHW(node.get_name(), is_nhwc, ng_out_backprop);
+    convert_nhwc_to_hw(is_nhwc, tf_strides, ng_strides);
+    convert_nhwc_to_hw(is_nhwc, tf_dilations, ng_dilations);
+    convert_nhwc_to_hw(is_nhwc, tf_input_sizes, ng_image_shape);
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, ng_out_backprop);
     if (is_nhwc) {
         ng_batch_shape = {static_cast<unsigned long>(tf_input_sizes[0]),
                           static_cast<unsigned long>(tf_input_sizes[3]),
@@ -63,36 +63,35 @@ OutputVector TranslateConv2DBackpropInputOp(const NodeContext& node) {
     ng_kernel_shape[0] = ng_filter_shape[0];
     ng_kernel_shape[1] = ng_filter_shape[1];
     Transpose<3, 2, 0, 1>(ng_filter);
-    SetTracingInfo(node.get_name(), ng_filter);
 
     CoordinateDiff ng_padding_below;
     CoordinateDiff ng_padding_above;
-    MakePadding(tf_padding_type,
-                ng_image_shape,
-                ng_kernel_shape,
-                ng_strides,
-                ng_dilations,
-                ng_padding_below,
-                ng_padding_above);
-
-    auto ng_output_shape = ConstructNgNode<Constant>(node.get_name(),
-                                                     element::i64,
-                                                     Shape{ng_batch_shape.size() - 2},
-                                                     vector<size_t>(ng_batch_shape.begin() + 2, ng_batch_shape.end()));
-
-    auto ng_data = ConstructNgNode<ConvolutionBackpropData>(node.get_name(),
-                                                            ng_out_backprop,
-                                                            ng_filter,
-                                                            ng_output_shape,
-                                                            ng_strides,
-                                                            ng_padding_below,
-                                                            ng_padding_above,
-                                                            ng_dilations);
-
-    NCHWtoNHWC(node.get_name(), is_nhwc, ng_data);
-    return {ng_data};
+    make_padding(tf_padding_type,
+                 ng_image_shape,
+                 ng_kernel_shape,
+                 ng_strides,
+                 ng_dilations,
+                 ng_padding_below,
+                 ng_padding_above);
+
+    auto ng_output_shape = make_shared<Constant>(element::i64,
+                                                 Shape{ng_batch_shape.size() - 2},
+                                                 vector<size_t>(ng_batch_shape.begin() + 2, ng_batch_shape.end()));
+
+    auto res_node = make_shared<ConvolutionBackpropData>(ng_out_backprop,
+                                                         ng_filter,
+                                                         ng_output_shape,
+                                                         ng_strides,
+                                                         ng_padding_below,
+                                                         ng_padding_above,
+                                                         ng_dilations);
+    auto res = res_node->output(0);
+
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/conv_3d.cpp b/ngraph/frontend/tensorflow/src/op/conv_3d.cpp
index d40220edd4b725..1a76e9419ab544 100644
--- a/ngraph/frontend/tensorflow/src/op/conv_3d.cpp
+++ b/ngraph/frontend/tensorflow/src/op/conv_3d.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -14,17 +14,17 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateConv3DOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0), ng_filter = node.get_ng_input(1);
+OutputVector translate_conv_3d_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0), ng_filter = node.get_input(1);
 
     auto tf_strides = node.get_attribute<std::vector<int32_t>>("strides");
     auto tf_dilations = node.get_attribute<std::vector<int32_t>>("dilations");
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    if (tf_data_format != "NDHWC" && tf_data_format != "NCDHW") {
-        throw errors::InvalidArgument("Conv3D data format is neither NDHWC nor NCDHW");
-    }
+    TF_OP_VALIDATION_CHECK(node,
+                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                           "Conv3D data format is neither NHWC nor NCHW");
 
     bool is_ndhwc = (tf_data_format == "NDHWC");
 
@@ -42,40 +42,36 @@ OutputVector TranslateConv3DOp(const NodeContext& node) {
     Shape ng_image_shape(3);
     Shape ng_kernel_shape(3);
 
-    NHWCtoHW(is_ndhwc, tf_strides, ng_strides);
-    NHWCtoHW(is_ndhwc, ng_input.get_shape(), ng_image_shape);
-    NHWCtoHW(is_ndhwc, tf_dilations, ng_dilations);
-    NHWCtoNCHW(node.get_name(), is_ndhwc, ng_input);
+    convert_nhwc_to_hw(is_ndhwc, tf_strides, ng_strides);
+    convert_nhwc_to_hw(is_ndhwc, ng_input.get_shape(), ng_image_shape);
+    convert_nhwc_to_hw(is_ndhwc, tf_dilations, ng_dilations);
+    convert_nhwc_to_nchw(node.get_name(), is_ndhwc, ng_input);
 
     auto& ng_filter_shape = ng_filter.get_shape();
     ng_kernel_shape[0] = ng_filter_shape[0];
     ng_kernel_shape[1] = ng_filter_shape[1];
     ng_kernel_shape[2] = ng_filter_shape[2];
     Transpose3D<4, 3, 0, 1, 2>(ng_filter);
-    SetTracingInfo(node.get_name(), ng_filter);
 
     CoordinateDiff ng_padding_below;
     CoordinateDiff ng_padding_above;
-    MakePadding(tf_padding_type,
-                ng_image_shape,
-                ng_kernel_shape,
-                ng_strides,
-                ng_dilations,
-                ng_padding_below,
-                ng_padding_above);
+    make_padding(tf_padding_type,
+                 ng_image_shape,
+                 ng_kernel_shape,
+                 ng_strides,
+                 ng_dilations,
+                 ng_padding_below,
+                 ng_padding_above);
 
-    Output<Node> ng_conv = ConstructNgNode<Convolution>(node.get_name(),
-                                                        ng_input,
-                                                        ng_filter,
-                                                        ng_strides,
-                                                        ng_padding_below,
-                                                        ng_padding_above,
-                                                        ng_dilations);
+    auto res_node =
+        make_shared<Convolution>(ng_input, ng_filter, ng_strides, ng_padding_below, ng_padding_above, ng_dilations);
+    auto res = res_node->output(0);
 
-    NCHWtoNHWC(node.get_name(), is_ndhwc, ng_conv);
-    return {ng_conv};
+    convert_nchw_to_nhwc(node.get_name(), is_ndhwc, res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/crop_and_resize.cpp b/ngraph/frontend/tensorflow/src/op/crop_and_resize.cpp
new file mode 100644
index 00000000000000..763e9dea540a7b
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/crop_and_resize.cpp
@@ -0,0 +1,142 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_crop_and_resize_op(const NodeContext& node) {
+    /// ng_input: [batch, image_height, image_width, depth]
+    /// ng_boxes: [num_boxes, 4]; each box is a normalized [0.to 1.] co-ordinate
+    /// [y1,
+    /// x1, y2, x2]
+    /// ng_box_ind: [num_boxes]; i-th ng_box_ind refers to the image to crop and
+    /// ranges from 0 to batch
+    /// ng_crop_size: [crop_height, crop_width];
+
+    /// for each box b specified in ng_boxes:
+    ///  1. crop ng_input[ng_box_ind[b]] w/ co-ordinates in ng_boxes
+    ///  2. resize according to method
+
+    auto ng_input = node.get_input(0);
+    auto ng_boxes = node.get_input(1);
+    auto ng_box_ind = node.get_input(2);
+    auto ng_size = node.get_input(3);
+
+    auto resize_method = node.get_attribute<string>("method");
+
+    TF_OP_VALIDATION_CHECK(node,
+                           ng_input.get_partial_shape().is_static() && ng_boxes.get_partial_shape().is_static() &&
+                               ng_box_ind.get_partial_shape().is_static() && ng_size.get_partial_shape().is_static(),
+                           "Dynamic shapes are not supported.");
+
+    auto spatial_shape = ng_input.get_shape();
+    auto image_height = spatial_shape[1];
+    auto image_width = spatial_shape[2];
+    auto image_depth = spatial_shape[3];
+
+    auto const_boxes = dynamic_pointer_cast<Constant>(ng_boxes.get_node_shared_ptr());
+    auto const_box_ind = dynamic_pointer_cast<Constant>(ng_boxes.get_node_shared_ptr());
+    auto const_crop_size = dynamic_pointer_cast<Constant>(ng_boxes.get_node_shared_ptr());
+
+    TF_OP_VALIDATION_CHECK(node,
+                           const_boxes && const_box_ind && const_crop_size,
+                           "Boxes, BoxIndexes, CropSize inputs must be constant.");
+
+    auto boxes = const_boxes->cast_vector<float>();
+    auto box_ind = const_box_ind->cast_vector<int64_t>();
+    auto crop_size = const_crop_size->cast_vector<int64_t>();
+
+    OutputVector ng_crop_outputs(box_ind.size());
+    if (box_ind.empty()) {
+        return make_shared<Constant>(element::f32,
+                                     Shape{0,
+                                           static_cast<unsigned long>(crop_size.at(0)),
+                                           static_cast<unsigned long>(crop_size.at(1)),
+                                           image_depth},
+                                     vector<float>({}))
+            ->outputs();
+    } else {
+        for (int i = 0; i < box_ind.size(); i++) {
+            int y1, x1, y2, x2;
+            y1 = boxes.at(0 + i * 4) * (image_height - 1.);
+            x1 = boxes.at(1 + i * 4) * (image_width - 1.);
+            y2 = boxes.at(2 + i * 4) * (image_height - 1.);
+            x2 = boxes.at(3 + i * 4) * (image_width - 1.);
+
+            int crop_height = abs(y2 - y1);
+            int crop_width = abs(x2 - x1);
+
+            // account for flip crops when y1>y2 or x1>x2 with negative striding
+            int stride_height = 1, stride_width = 1;
+            if (y1 > y2) {
+                y1 = y1 - image_height;
+                y2 = y2 - image_height - 2;
+                stride_height = -1;
+            }
+            if (x1 > x2) {
+                x1 = x1 - image_height;
+                x2 = x2 - image_height - 2;
+                stride_width = -1;
+            }
+
+            auto begin = make_shared<Constant>(element::i64,
+                                               Shape{4},
+                                               vector<int64_t>({static_cast<int64_t>(box_ind[i]), y1, x1, 0}));
+            auto end = make_shared<Constant>(
+                element::i64,
+                Shape{4},
+                vector<int64_t>(
+                    {static_cast<int64_t>(box_ind[i]) + 1, y2 + 1, x2 + 1, static_cast<int64_t>(image_depth + 1)}));
+            auto strides =
+                make_shared<Constant>(element::i64, Shape{4}, vector<int64_t>({1, stride_height, stride_width, 1}));
+
+            // crop
+            auto ng_crop_node =
+                make_shared<StridedSlice>(ng_input, begin, end, strides, vector<int64_t>{}, vector<int64_t>{});
+            auto ng_crop = ng_crop_node->output(0);
+
+            Interpolate::InterpolateAttrs interpolate_attrs;
+            // always corner aligned
+            interpolate_attrs.coordinate_transformation_mode = Interpolate::CoordinateTransformMode::ALIGN_CORNERS;
+
+            // TODO: handle the case when extrapolation value is greatger than 1.0
+            // arguments for resizing
+            auto ng_spatial_shape =
+                make_shared<Constant>(element::i32, Shape{2}, vector<int32_t>{crop_height, crop_width});
+            auto ng_input_shape = make_shared<Convert>(ng_spatial_shape, element::f32);
+            auto ng_crop_size = make_shared<Convert>(ng_size, element::f32);
+            auto ng_scales = make_shared<Divide>(ng_crop_size, ng_input_shape);
+            auto ng_axes = make_shared<Constant>(element::i32, Shape{2}, vector<int>({2, 3}));
+
+            if (resize_method == "bilinear") {
+                interpolate_attrs.mode = Interpolate::InterpolateMode::LINEAR;
+            } else {  // nearest
+                interpolate_attrs.mode = Interpolate::InterpolateMode::NEAREST;
+            }
+
+            Transpose<0, 3, 1, 2>(ng_crop);
+            auto ng_output =
+                make_shared<Interpolate>(ng_crop, ng_size, ng_scales, ng_axes, interpolate_attrs)->output(0);
+            Transpose<0, 2, 3, 1>(ng_output);
+            ng_crop_outputs.at(i) = ng_output;
+        }
+
+        auto res = make_shared<Concat>(ng_crop_outputs, 0);
+        set_node_name(node.get_name(), res);
+        return res->outputs();
+    }
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/cumsum.cpp b/ngraph/frontend/tensorflow/src/op/cumsum.cpp
index fef02fcbd09570..92738c0ef24039 100644
--- a/ngraph/frontend/tensorflow/src/op/cumsum.cpp
+++ b/ngraph/frontend/tensorflow/src/op/cumsum.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,13 +13,17 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateCumsumOp(const NodeContext& node) {
-    auto ng_x = node.get_ng_input(0), ng_axis = node.get_ng_input(1);
-    auto exclusive = node.get_attribute<bool>("exclusive"), reverse = node.get_attribute<bool>("reverse");
+OutputVector translate_cumsum_op(const NodeContext& node) {
+    auto ng_x = node.get_input(0);
+    auto ng_axis = node.get_input(1);
+    auto exclusive = node.get_attribute<bool>("exclusive");
+    auto reverse = node.get_attribute<bool>("reverse");
 
-    return {ConstructNgNode<CumSum>(node.get_name(), ng_x, ng_axis, exclusive, reverse)};
+    auto res = make_shared<CumSum>(ng_x, ng_axis, exclusive, reverse);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/depth_to_space.cpp b/ngraph/frontend/tensorflow/src/op/depth_to_space.cpp
index f9703ca31af23e..5a5757ea0c7bb2 100644
--- a/ngraph/frontend/tensorflow/src/op/depth_to_space.cpp
+++ b/ngraph/frontend/tensorflow/src/op/depth_to_space.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -14,26 +14,27 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateDepthToSpaceOp(const NodeContext& node) {
-    Output<Node> ng_input = node.get_ng_input(0);
+OutputVector translate_depth_to_space_op(const NodeContext& node) {
+    Output<Node> ng_input = node.get_input(0);
 
     // Get the attributes
     auto block_size = node.get_attribute<int64_t>("block_size");
     std::string tf_data_format = node.get_attribute<std::string>("data_format");
 
-    if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-        throw errors::InvalidArgument("DepthToSpace data format is neither NHWC nor NCHW");
-    }
+    TF_OP_VALIDATION_CHECK(node,
+                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                           "DepthToSpace data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
-    NHWCtoNCHW(node.get_name(), is_nhwc, ng_input);
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, ng_input);
     auto ng_mode = DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST;
-    Output<Node> depth_to_space = ConstructNgNode<DepthToSpace>(node.get_name(), ng_input, ng_mode, block_size);
-    NCHWtoNHWC(node.get_name(), is_nhwc, depth_to_space);
-    return {depth_to_space};
+    Output<Node> res = make_shared<DepthToSpace>(ng_input, ng_mode, block_size)->output(0);
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/depthwise_conv_2d.cpp b/ngraph/frontend/tensorflow/src/op/depthwise_conv_2d.cpp
index 4e1561c82429a8..6506b3c5ff8740 100644
--- a/ngraph/frontend/tensorflow/src/op/depthwise_conv_2d.cpp
+++ b/ngraph/frontend/tensorflow/src/op/depthwise_conv_2d.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,17 +13,18 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateDepthwiseConv2dNativeOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0), ng_filter = node.get_ng_input(1);
+OutputVector translate_depthwise_conv_2d_native_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0);
+    auto ng_filter = node.get_input(1);
 
     auto tf_strides = node.get_attribute<std::vector<int32_t>>("strides");
     auto tf_dilations = node.get_attribute<std::vector<int32_t>>("dilations");
     auto tf_padding_type = node.get_attribute<std::string>("padding");
     auto tf_data_format = node.get_attribute<std::string>("data_format");
 
-    if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-        throw errors::InvalidArgument("DepthwiseConv2D data format is neither NHWC nor NCHW");
-    }
+    TF_OP_VALIDATION_CHECK(node,
+                           tf_data_format == "NHWC" || tf_data_format == "NCHW",
+                           "DepthwiseConv2D data format is neither NHWC nor NCHW");
 
     bool is_nhwc = (tf_data_format == "NHWC");
 
@@ -32,10 +33,10 @@ OutputVector TranslateDepthwiseConv2dNativeOp(const NodeContext& node) {
     Shape ng_image_shape(2);
     Shape ng_kernel_shape(2);
 
-    NHWCtoHW(is_nhwc, ng_input.get_shape(), ng_image_shape);
-    NHWCtoHW(is_nhwc, tf_strides, ng_strides);
-    NHWCtoHW(is_nhwc, tf_dilations, ng_dilations);
-    NHWCtoNCHW(node.get_name(), is_nhwc, ng_input);
+    convert_nhwc_to_hw(is_nhwc, ng_input.get_shape(), ng_image_shape);
+    convert_nhwc_to_hw(is_nhwc, tf_strides, ng_strides);
+    convert_nhwc_to_hw(is_nhwc, tf_dilations, ng_dilations);
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, ng_input);
 
     auto& ng_filter_shape = ng_filter.get_shape();
     ng_kernel_shape[0] = ng_filter_shape[0];
@@ -43,38 +44,38 @@ OutputVector TranslateDepthwiseConv2dNativeOp(const NodeContext& node) {
 
     CoordinateDiff ng_padding_below;
     CoordinateDiff ng_padding_above;
-    MakePadding(tf_padding_type,
-                ng_image_shape,
-                ng_kernel_shape,
-                ng_strides,
-                ng_dilations,
-                ng_padding_below,
-                ng_padding_above);
+    make_padding(tf_padding_type,
+                 ng_image_shape,
+                 ng_kernel_shape,
+                 ng_strides,
+                 ng_dilations,
+                 ng_padding_below,
+                 ng_padding_above);
 
     // H W I M -> H W I 1 M
-    auto filter_shape = ConstructNgNode<Constant>(
-        node.get_name(),
+    auto filter_shape = make_shared<Constant>(
         element::u64,
         Shape{5},
         ov::Shape{ng_filter_shape[0], ng_filter_shape[1], ng_filter_shape[2], 1, ng_filter_shape[3]});
-    auto reshaped_filter = ConstructNgNode<Reshape>(node.get_name(), ng_filter, filter_shape, false);
+    auto reshaped_filter = make_shared<Reshape>(ng_filter, filter_shape, false);
 
     // H W I 1 M -> I M 1 H W
-    auto order = ConstructNgNode<Constant>(node.get_name(), element::i64, Shape{5}, vector<int64_t>{2, 4, 3, 0, 1});
-    auto transposed_filter = ConstructNgNode<ov::opset8::Transpose>(node.get_name(), reshaped_filter, order);
+    auto order = make_shared<Constant>(element::i64, Shape{5}, vector<int64_t>{2, 4, 3, 0, 1});
+    auto transposed_filter = make_shared<opset8::Transpose>(reshaped_filter, order);
 
-    auto ng_conv = ConstructNgNode<GroupConvolution>(node.get_name(),
-                                                     ng_input,
-                                                     transposed_filter,
-                                                     ng_strides,
-                                                     ng_padding_below,
-                                                     ng_padding_above,
-                                                     ng_dilations);
+    auto ng_conv_node = make_shared<GroupConvolution>(ng_input,
+                                                      transposed_filter,
+                                                      ng_strides,
+                                                      ng_padding_below,
+                                                      ng_padding_above,
+                                                      ng_dilations);
+    auto ng_conv = ng_conv_node->output(0);
 
-    NCHWtoNHWC(node.get_name(), is_nhwc, ng_conv);
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, ng_conv);
+    set_node_name(node.get_name(), ng_conv.get_node_shared_ptr());
     return {ng_conv};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/elu.cpp b/ngraph/frontend/tensorflow/src/op/elu.cpp
index 237768ba1300c3..7241c1549f165c 100644
--- a/ngraph/frontend/tensorflow/src/op/elu.cpp
+++ b/ngraph/frontend/tensorflow/src/op/elu.cpp
@@ -2,10 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
-
 #include "node_context.hpp"
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov;
@@ -16,10 +15,12 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateEluOp(const NodeContext& node) {
-    auto input = node.get_ng_input(0);
-    auto alpha = 1.0;  // node.get_attribute<float>("alpha");
-    return {ConstructNgNode<Elu>(node.get_name(), input, alpha)};
+OutputVector translate_elu_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto alpha = node.get_attribute<float>("alpha", 1.0);
+    auto res = make_shared<Elu>(input, alpha);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/expand_dims.cpp b/ngraph/frontend/tensorflow/src/op/expand_dims.cpp
index 600ab95c613060..11d1a0efd1a64a 100644
--- a/ngraph/frontend/tensorflow/src/op/expand_dims.cpp
+++ b/ngraph/frontend/tensorflow/src/op/expand_dims.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,14 +13,14 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateExpandDimsOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0);
-    std::vector<int64_t> dims;
-    GetStaticInputVector(node, 1, &dims);
-    auto ng_dims = ConstructNgNode<Constant>(node.get_name(), element::i64, ov::Shape{dims.size()}, dims);
-    return {ConstructNgNode<Unsqueeze>(node.get_name(), ng_input, ng_dims)};
+OutputVector translate_expand_dims_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto dims = node.get_input(1);
+    auto res = make_shared<Unsqueeze>(input, dims);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/fake_quant_min_max_vars.cpp b/ngraph/frontend/tensorflow/src/op/fake_quant_min_max_vars.cpp
new file mode 100644
index 00000000000000..bdb2595225f952
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/fake_quant_min_max_vars.cpp
@@ -0,0 +1,66 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_fake_quant_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0);
+    auto ng_min = node.get_input(1);
+    auto ng_max = node.get_input(2);
+
+    auto narrow_range = node.get_attribute<bool>("narrow_range");
+    auto num_bits = node.get_attribute<int64_t>("num_bits");
+
+    auto levels = std::pow(2, num_bits) - int(narrow_range);
+    auto min_less_max = make_shared<Less>(ng_min, ng_max);
+    auto minimum = make_shared<Select>(min_less_max, ng_min, ng_max);
+    auto maximum = make_shared<Select>(min_less_max, ng_max, ng_min);
+
+    auto zero = make_shared<Constant>(ng_min.get_element_type(), Shape{}, std::vector<int>({0}));
+
+    auto min_greater_zero = make_shared<Greater>(minimum, zero);
+    auto max_minus_min = make_shared<Subtract>(maximum, minimum);
+    minimum = make_shared<Select>(min_greater_zero, zero, minimum);
+    maximum = make_shared<Select>(min_greater_zero, max_minus_min, maximum);
+
+    auto max_less_zero = make_shared<Less>(maximum, zero);
+    auto min_minus_max = make_shared<Subtract>(minimum, maximum);
+    minimum = make_shared<Select>(max_less_zero, min_minus_max, minimum);
+    maximum = make_shared<Select>(max_less_zero, zero, maximum);
+
+    auto float_range = make_shared<Subtract>(maximum, minimum);
+    auto quant_min_value = int(narrow_range);
+    auto quant_max_value = std::pow(2, num_bits) - 1;
+    auto value = static_cast<float>(quant_max_value - quant_min_value);
+    auto int_range = make_shared<Constant>(element::f32, Shape{}, std::vector<float>({value}));
+    auto scale = make_shared<Divide>(float_range, int_range);
+    auto descaled_min = make_shared<Divide>(minimum, scale);
+    auto rounded_descaled_min = make_shared<Round>(descaled_min, Round::RoundMode::HALF_TO_EVEN);
+    auto min_adj = make_shared<Multiply>(scale, rounded_descaled_min);
+    auto adjustment = make_shared<Subtract>(min_adj, minimum);
+    auto max_adj = make_shared<Add>(maximum, adjustment);
+
+    auto ng_input_shape = ng_input.get_shape();
+    if (ng_input_shape.size() == 4)
+        Transpose<0, 3, 1, 2>(ng_input);
+    auto res = make_shared<FakeQuantize>(ng_input, min_adj, max_adj, min_adj, max_adj, levels)->output(0);
+    if (ng_input_shape.size() == 4)
+        Transpose<0, 2, 3, 1>(res);
+
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/fill.cpp b/ngraph/frontend/tensorflow/src/op/fill.cpp
index 24c4e54ce46281..28e9ed65cf3c55 100644
--- a/ngraph/frontend/tensorflow/src/op/fill.cpp
+++ b/ngraph/frontend/tensorflow/src/op/fill.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,9 +13,12 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateFillOp(const NodeContext& node) {
-    auto ng_dims = node.get_ng_input(0), ng_value = node.get_ng_input(1);
-    return {ConstructNgNode<Broadcast>(node.get_name(), ng_value, ng_dims)};
+OutputVector translate_fill_op(const NodeContext& node) {
+    auto ng_dims = node.get_input(0);
+    auto ng_value = node.get_input(1);
+    auto res = make_shared<Broadcast>(ng_value, ng_dims);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/fused_batch_norm.cpp b/ngraph/frontend/tensorflow/src/op/fused_batch_norm.cpp
index 92a58c429c2526..93542365b16341 100644
--- a/ngraph/frontend/tensorflow/src/op/fused_batch_norm.cpp
+++ b/ngraph/frontend/tensorflow/src/op/fused_batch_norm.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,35 +13,32 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateFusedBatchNormOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0), ng_scale = node.get_ng_input(1), ng_offset = node.get_ng_input(2),
-         ng_mean = node.get_ng_input(3), ng_variance = node.get_ng_input(4);
-    bool is_v3 = node.get_op_type() == "FusedBatchNormV3";
+OutputVector translate_fused_batch_norm_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0);
+    auto ng_scale = node.get_input(1);
+    auto ng_offset = node.get_input(2);
+    auto ng_mean = node.get_input(3);
+    auto ng_variance = node.get_input(4);
 
-    auto tf_data_format = node.get_attribute<std::string>("data_format");
+    bool is_v3 = node.get_op_type() == "FusedBatchNormV3";
 
-    if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-        throw errors::InvalidArgument("Conv2D data format is neither NHWC nor NCHW");
-    }
+    auto data_format = node.get_attribute<std::string>("data_format");
+    TF_OP_VALIDATION_CHECK(node, data_format == "NHWC" || data_format == "NCHW", "Unsupported data format");
 
-    bool is_nhwc = (tf_data_format == "NHWC");
+    bool is_nhwc = (data_format == "NHWC");
 
-    NGRAPH_VLOG(3) << "data_format: " << tf_data_format;
+    NGRAPH_DEBUG << "data_format: " << data_format;
 
-    auto tf_epsilon = node.get_attribute<float>("epsilon", 0.0001);  // TODO: where does 0.0001 come from?
+    // TODO: where does 0.0001 come from?
+    auto tf_epsilon = node.get_attribute<float>("epsilon", 0.0001);
 
-    NGRAPH_VLOG(3) << "epsilon: " << tf_epsilon;
+    NGRAPH_DEBUG << "epsilon: " << tf_epsilon;
 
-    NHWCtoNCHW(node.get_name(), is_nhwc, ng_input);
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, ng_input);
 
-    auto ng_batch_norm = ConstructNgNode<BatchNormInference>(node.get_name(),
-                                                             ng_input,
-                                                             ng_scale,
-                                                             ng_offset,
-                                                             ng_mean,
-                                                             ng_variance,
-                                                             tf_epsilon);
-    NCHWtoNHWC(node.get_name(), is_nhwc, ng_batch_norm);
+    auto ng_batch_norm =
+        make_shared<BatchNormInference>(ng_input, ng_scale, ng_offset, ng_mean, ng_variance, tf_epsilon)->output(0);
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, ng_batch_norm);
 
     // TODO: Why are there so many? Is it correct?
     OutputVector result = {ng_batch_norm, ng_mean, ng_variance, ng_mean, ng_variance};
@@ -49,9 +46,10 @@ OutputVector TranslateFusedBatchNormOp(const NodeContext& node) {
         // FusedBatchNormV3 has 6 outputs
         result.push_back(ng_mean);  // reserve_space_3
     }
+    set_node_name(node.get_name(), ng_batch_norm.get_node_shared_ptr());
     return result;
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/fused_conv_2d.cpp b/ngraph/frontend/tensorflow/src/op/fused_conv_2d.cpp
deleted file mode 100644
index 3b8ba2d31abb64..00000000000000
--- a/ngraph/frontend/tensorflow/src/op/fused_conv_2d.cpp
+++ /dev/null
@@ -1,148 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
-
-using namespace std;
-using namespace ov::opset8;
-
-namespace ov {
-namespace frontend {
-namespace tf {
-namespace op {
-
-OutputVector TranslateFusedConv2DOp(const NodeContext& node) {
-    auto num_args = node.get_attribute<int>("num_args");
-    auto fused_ops = node.get_attribute<std::vector<string>>("fused_ops");
-
-    auto tf_data_format = node.get_attribute<std::string>("data_format");
-    bool is_nhwc = (tf_data_format == "NHWC");
-
-    auto CreateNgConv = [&](Output<Node>& ng_input, Output<Node>& ng_filter) {
-        auto tf_strides = node.get_attribute<std::vector<int32_t>>("strides");
-        auto tf_dilations = node.get_attribute<std::vector<int32_t>>("dilations");
-        auto tf_padding_type = node.get_attribute<std::string>("padding");
-
-        if (tf_data_format != "NHWC" && tf_data_format != "NCHW") {
-            throw errors::InvalidArgument("Conv2D data format is neither NHWC nor NCHW");
-        }
-
-        // TF Kernel Test Checks
-        // Strides in the batch and depth dimension is not supported
-        if (tf_strides[0] != 1 || tf_strides[is_nhwc ? 3 : 1] != 1) {
-            throw errors::InvalidArgument("Strides in batch and depth dimensions is not supported: " +
-                                          node.get_op_type());
-        }
-
-        Strides ng_strides(2);
-        Strides ng_dilations(2);
-        Shape ng_image_shape(2);
-        Shape ng_kernel_shape(2);
-
-        NHWCtoHW(is_nhwc, tf_strides, ng_strides);
-        NHWCtoHW(is_nhwc, ng_input.get_shape(), ng_image_shape);
-        NHWCtoHW(is_nhwc, tf_dilations, ng_dilations);
-        NHWCtoNCHW(node.get_name(), is_nhwc, ng_input);
-
-        auto& ng_filter_shape = ng_filter.get_shape();
-        ng_kernel_shape[0] = ng_filter_shape[0];
-        ng_kernel_shape[1] = ng_filter_shape[1];
-        Transpose<3, 2, 0, 1>(ng_filter);
-        SetTracingInfo(node.get_name(), ng_filter);
-
-        CoordinateDiff ng_padding_below;
-        CoordinateDiff ng_padding_above;
-        MakePadding(tf_padding_type,
-                    ng_image_shape,
-                    ng_kernel_shape,
-                    ng_strides,
-                    ng_dilations,
-                    ng_padding_below,
-                    ng_padding_above);
-
-        return ConstructNgNode<Convolution>(node.get_name() + "_FusedConv2D_Conv",
-                                            ng_input,
-                                            ng_filter,
-                                            ng_strides,
-                                            ng_padding_below,
-                                            ng_padding_above,
-                                            ng_dilations);
-    };
-
-    if (VecStrCmp(fused_ops, {"BiasAdd"}) || VecStrCmp(fused_ops, {"BiasAdd", "Relu"}) ||
-        VecStrCmp(fused_ops, {"BiasAdd", "Relu6"})) {
-        if (num_args != 1) {
-            throw errors::InvalidArgument("FusedConv2DBiasAdd has incompatible num_args");
-        }
-
-        auto ng_input = node.get_ng_input(0), ng_filter = node.get_ng_input(1), ng_bias = node.get_ng_input(2),
-             ng_conv = CreateNgConv(ng_input, ng_filter);
-
-        auto ng_conv_shape = ng_conv.get_shape();
-        auto ng_bias_shape = ng_bias.get_shape();
-        if (ng_bias_shape.size() != 1) {
-            throw errors::InvalidArgument("Bias argument to BiasAdd does not have one dimension");
-        }
-
-        std::vector<size_t> reshape_pattern_values(ng_conv_shape.size(), 1U);
-        reshape_pattern_values[1] = ng_bias.get_shape().front();
-        auto reshape_pattern =
-            make_shared<Constant>(element::u64, Shape{reshape_pattern_values.size()}, reshape_pattern_values);
-        auto ng_bias_reshaped = ConstructNgNode<Reshape>(node.get_name(), ng_bias, reshape_pattern, false);
-
-        auto ng_add = ConstructNgNode<Add>(node.get_name() + "_FusedConv2D_BiasAdd", ng_conv, ng_bias_reshaped);
-
-        if (VecStrCmp(fused_ops, {"BiasAdd", "Relu"})) {
-            auto ng_relu = ConstructNgNode<Relu>(node.get_name() + "_FusedConv2D_Relu", ng_add);
-            NCHWtoNHWC(node.get_name(), is_nhwc, ng_relu);
-            return {ng_relu};
-        } else if (VecStrCmp(fused_ops, {"BiasAdd", "Relu6"})) {
-            auto ng_relu6 = ConstructNgNode<Clamp>(node.get_name() + "_FusedConv2D_Relu6", ng_add, 0, 6);
-            NCHWtoNHWC(node.get_name(), is_nhwc, ng_relu6);
-            return {ng_relu6};
-        } else {
-            NCHWtoNHWC(node.get_name(), is_nhwc, ng_add);
-            return {ng_add};
-        }
-    } else if (VecStrCmp(fused_ops, {"FusedBatchNorm"}) || VecStrCmp(fused_ops, {"FusedBatchNorm", "Relu"}) ||
-               VecStrCmp(fused_ops, {"FusedBatchNorm", "Relu6"})) {
-        if (num_args != 4) {
-            throw errors::InvalidArgument("FusedConv2D with FusedBatchNorm has incompatible num_args");
-        }
-
-        auto ng_input = node.get_ng_input(0), ng_filter = node.get_ng_input(1), ng_scale = node.get_ng_input(2),
-             ng_offset = node.get_ng_input(3), ng_mean = node.get_ng_input(4), ng_variance = node.get_ng_input(5),
-             ng_conv = CreateNgConv(ng_input, ng_filter);
-
-        auto tf_epsilon = node.get_attribute<float>("epsilon");
-
-        auto ng_batch_norm = ConstructNgNode<BatchNormInference>(node.get_name() + "_FusedConv2D_BatchNorm",
-                                                                 ng_conv,
-                                                                 ng_scale,
-                                                                 ng_offset,
-                                                                 ng_mean,
-                                                                 ng_variance,
-                                                                 tf_epsilon);
-
-        if (VecStrCmp(fused_ops, {"FusedBatchNorm", "Relu"})) {
-            auto ng_relu = ConstructNgNode<Relu>(node.get_name() + "_FusedConv2D_BatchNormRelu", ng_batch_norm);
-            NCHWtoNHWC(node.get_name(), is_nhwc, ng_relu);
-            return {ng_relu};
-        } else if (VecStrCmp(fused_ops, {"FusedBatchNorm", "Relu6"})) {
-            auto ng_relu6 = ConstructNgNode<Clamp>(node.get_name() + "_FusedConv2D_BatchNormRelu", ng_batch_norm, 0, 6);
-            NCHWtoNHWC(node.get_name(), is_nhwc, ng_relu6);
-            return {ng_relu6};
-        } else {
-            NCHWtoNHWC(node.get_name(), is_nhwc, ng_batch_norm);
-            return {ng_batch_norm};
-        }
-    } else {
-        FRONT_END_THROW("Unsupported _FusedConv2D ");
-    }
-}
-}  // namespace op
-}  // namespace tf
-}  // namespace frontend
-}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/fused_mat_mul.cpp b/ngraph/frontend/tensorflow/src/op/fused_mat_mul.cpp
deleted file mode 100644
index b7f9884c1b8c41..00000000000000
--- a/ngraph/frontend/tensorflow/src/op/fused_mat_mul.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
-
-using namespace std;
-using namespace ov::opset8;
-
-namespace ov {
-namespace frontend {
-namespace tf {
-namespace op {
-
-OutputVector TranslateFusedMatMulOp(const NodeContext& node) {
-    // auto num_args = node.get_attribute<int>("num_args"); // TODO: it is unused but why?
-    auto fused_ops = node.get_attribute<std::vector<string>>("fused_ops");
-
-    // Transpose arguments if requested.
-    auto transpose_a = node.get_attribute<bool>("transpose_a", false);
-    auto transpose_b = node.get_attribute<bool>("transpose_b", false);
-
-    auto ng_lhs = node.get_ng_input(0), ng_rhs = node.get_ng_input(1), ng_bias = node.get_ng_input(2);
-
-    Output<Node> ng_matmul = ConstructNgNode<MatMul>(node.get_name(), ng_lhs, ng_rhs, transpose_a, transpose_b);
-
-    auto ng_matmul_shape = ng_matmul.get_shape();
-    auto ng_bias_shape = ng_bias.get_shape();
-
-    if (ng_bias_shape.size() != 1) {
-        throw errors::InvalidArgument("Bias argument to BiasAdd does not have one dimension");
-    }
-
-    auto ng_add = ConstructNgNode<Add>(node.get_name(), ng_matmul, ng_bias);
-    if (fused_ops.size() == 1) {  // Only fusing BiasAdd
-        return {ng_add};
-    } else if (fused_ops.size() == 2) {  // Also has activation
-        if (fused_ops[1] == "Relu") {
-            return {ConstructNgNode<Relu>(node.get_name(), ng_add)};
-        } else if (fused_ops[1] == "Relu6") {
-            return {ConstructNgNode<Clamp>(node.get_name(), ng_add, 0, 6)};
-        } else {
-            throw errors::Internal("Expected activation to be Relu or Relu6 but got " + fused_ops[1]);
-        }
-    } else {
-        // Adding this here to catch future changes in _FusedMatMul
-        throw errors::Internal("Unsupported combination");
-    }
-}
-}  // namespace op
-}  // namespace tf
-}  // namespace frontend
-}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/gather.cpp b/ngraph/frontend/tensorflow/src/op/gather.cpp
index 64996d8028f637..c26cef15c08b26 100644
--- a/ngraph/frontend/tensorflow/src/op/gather.cpp
+++ b/ngraph/frontend/tensorflow/src/op/gather.cpp
@@ -2,65 +2,46 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
 
-// See .../tensorflow/include/tensorflow/cc/ops/array_ops.h
-// and .../openvino/ngraph/core/include/ngraph/op/gather.hpp
 namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateGatherOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0), ng_input_indices = node.get_ng_input(1);
-
-    auto ng_axis = ConstructNgNode<Constant>(node.get_name(), element::i64, Shape{}, 0);
-
-    auto gather_op = ConstructNgNode<Gather>(node.get_name(), ng_input, ng_input_indices, ng_axis);
-
-    return {gather_op};
+OutputVector translate_gather_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0);
+    auto ng_input_indices = node.get_input(1);
+    auto ng_axis = make_shared<Constant>(element::i64, Shape{}, 0);
+    auto res = make_shared<Gather>(ng_input, ng_input_indices, ng_axis);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 
-OutputVector TranslateGatherV2Op(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0), ng_input_coords = node.get_ng_input(1);
-
-    std::vector<int64_t> tf_axis;
-    GetStaticInputVector(node, 2, &tf_axis);
-
-    if (tf_axis.size() > 1) {
-        std::ostringstream buf;
-        buf << "Found axis in GatherV2 op (" << node.get_name() << ") translation to be non scalar, of size "
-            << tf_axis.size();
-        throw errors::Internal(buf.str());
-    }
-
-    // Negative axis is supported. Accounting for that
-    auto ng_input_shape = ng_input.get_shape();
-    size_t ng_input_rank = ng_input_shape.size();
-    int axis;
-    if (tf_axis[0] >= 0) {
-        axis = tf_axis[0];
-    } else {
-        axis = tf_axis[0] + ng_input_rank;
-    }
-    if (axis < 0 || axis >= ng_input_rank) {
-    std:
-        ostringstream buf;
-        buf << "Expected axis in the range [-" << ng_input_rank << ", " << ng_input_rank << "), but got " << tf_axis[0];
-        throw errors::InvalidArgument(buf.str());
-    }
-
-    auto ng_axis = ConstructNgNode<Constant>(node.get_name(), element::i64, Shape{tf_axis.size()}, tf_axis);
-
-    auto gather_op = ConstructNgNode<Gather>(node.get_name(), ng_input, ng_input_coords, ng_axis);
+OutputVector translate_gather_v2_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0);
+    auto ng_input_coords = node.get_input(1);
+    auto ng_axis = node.get_input(2);
+    auto batch_dims = node.get_attribute<int64_t>("batch_dims", 0);
+    auto res = make_shared<Gather>(ng_input, ng_input_coords, ng_axis, batch_dims);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
 
-    return {gather_op};
+OutputVector translate_gather_nd_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto input_indices = node.get_input(1);
+    auto batch_dims = node.get_attribute<int64_t>("batch_dims", 0);
+    auto res = make_shared<GatherND>(input, input_indices, batch_dims);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
+
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/identity.cpp b/ngraph/frontend/tensorflow/src/op/identity.cpp
index 8509a4c658efb8..cc6bd45463bf16 100644
--- a/ngraph/frontend/tensorflow/src/op/identity.cpp
+++ b/ngraph/frontend/tensorflow/src/op/identity.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,11 +13,14 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateIdentityOp(const NodeContext& node) {
-    return {node.get_ng_input(0)};
+OutputVector translate_identity_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    set_out_name(node.get_name(), input);
+    set_out_name(node.get_name() + ":" + "0", input);
+    return {input};
 }
 
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/interpolate.cpp b/ngraph/frontend/tensorflow/src/op/interpolate.cpp
new file mode 100644
index 00000000000000..ed98d112ae7524
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/interpolate.cpp
@@ -0,0 +1,48 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+ov::OutputVector translate_interpolate_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto input_sizes = node.get_input(1);
+
+    Interpolate::InterpolateAttrs interpolate_attrs;
+    interpolate_attrs.mode = Interpolate::InterpolateMode::LINEAR;
+    interpolate_attrs.shape_calculation_mode = Interpolate::ShapeCalcMode::SIZES;
+    if (node.get_attribute<bool>("align_corners", false))
+        interpolate_attrs.coordinate_transformation_mode = Interpolate::CoordinateTransformMode::ALIGN_CORNERS;
+
+    if (node.get_op_type() == "ResizeNearestNeighbor") {
+        interpolate_attrs.mode = Interpolate::InterpolateMode::NEAREST;
+        interpolate_attrs.nearest_mode = Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+    }
+
+    // TODO: do we need this .get_shape() actually?
+    auto input_shape = input.get_shape();
+    std::vector<float> spatial_shape = {static_cast<float>(input_shape[1]), static_cast<float>(input_shape[2])};
+    auto ng_spatial_shape = make_shared<Constant>(element::f32, Shape{2}, spatial_shape);
+
+    auto ng_sizes = make_shared<Convert>(input_sizes, element::f32);
+    auto ng_scales = make_shared<Divide>(ng_sizes, ng_spatial_shape);
+    auto ng_axes = make_shared<Constant>(element::i32, Shape{2}, std::vector<int>({2, 3}));
+
+    Transpose<0, 3, 1, 2>(input);
+    auto res = make_shared<Interpolate>(input, input_sizes, ng_scales, ng_axes, interpolate_attrs)->output(0);
+    Transpose<0, 2, 3, 1>(res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/is_finite.cpp b/ngraph/frontend/tensorflow/src/op/is_finite.cpp
new file mode 100644
index 00000000000000..626d3aec9bc24f
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/is_finite.cpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_is_finite_op(const NodeContext& node) {
+    // TODO: Refactor this code. Not sure about in == in.
+    // Implemented tf.is_finite by checking:
+    // (in != inf) && (in != -inf) && (in == in)
+    //                                 ^^^^^^^^ checks for NaN's
+
+    auto input = node.get_input(0);
+    auto el_type = input.get_element_type();
+
+    auto inf = make_shared<Constant>(el_type, Shape{}, vector<float>{numeric_limits<float>::infinity()});
+    auto neg_inf = make_shared<Constant>(el_type, Shape{}, vector<float>{-numeric_limits<float>::infinity()});
+
+    auto neq_inf = make_shared<NotEqual>(input, inf);
+    auto neq_neg_inf = make_shared<NotEqual>(input, neg_inf);
+    auto eq_nan = make_shared<Equal>(input, input);
+
+    auto neq_inf_and_neq_neg_inf = make_shared<LogicalAnd>(neq_inf, neq_neg_inf);
+    auto res = make_shared<LogicalAnd>(neq_inf_and_neq_neg_inf, eq_nan);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/l2_loss.cpp b/ngraph/frontend/tensorflow/src/op/l2_loss.cpp
new file mode 100644
index 00000000000000..8cad1e58958241
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/l2_loss.cpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_l2_loss_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+
+    vector<float> val;
+    val.push_back(2.0);
+    auto const_2 = make_shared<Constant>(input.get_element_type(), Shape{}, 2);
+    auto pow = make_shared<Multiply>(input, input);
+    auto p_shape = input.get_partial_shape();
+
+    Output<Node> reduction_axes;
+    if (p_shape.rank().is_static()) {
+        vector<int64_t> axes(p_shape.size());
+        iota(axes.begin(), axes.end(), 0);
+        reduction_axes = make_shared<Constant>(element::i64, Shape{axes.size()}, axes);
+    } else {
+        auto shape = make_shared<ShapeOf>(input);
+        auto rank = make_shared<ShapeOf>(shape);
+        auto start = make_shared<Constant>(element::i64, Shape{1}, 0);
+        auto step = make_shared<Constant>(element::i64, Shape{1}, 1);
+        reduction_axes = make_shared<Range>(start, rank, step, element::i64);
+    }
+
+    auto sum = make_shared<ReduceSum>(pow, reduction_axes);
+    auto res = make_shared<Divide>(sum, const_2);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/leaky_relu.cpp b/ngraph/frontend/tensorflow/src/op/leaky_relu.cpp
new file mode 100644
index 00000000000000..ecba626e8550d9
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/leaky_relu.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+ov::OutputVector translate_leaky_relu_op(const NodeContext& node) {
+    auto in = node.get_input(0);
+    auto alpha_attr = node.get_attribute<float>("alpha", 0.f);
+    auto alpha_const = make_shared<Constant>(element::f32, Shape{1}, alpha_attr);
+
+    auto leaky_relu = make_shared<PRelu>(in, alpha_const);
+    return leaky_relu->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/log1p.cpp b/ngraph/frontend/tensorflow/src/op/log1p.cpp
new file mode 100644
index 00000000000000..84d15a4d45ec58
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/log1p.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_log_1p_op(const NodeContext& node) {
+    auto n = node.get_input(0);
+    auto const_1 = make_shared<Constant>(n.get_element_type(), Shape{}, 1);
+    auto add = make_shared<Add>(n, const_1);
+    auto res = make_shared<Log>(add);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/log_softmax.cpp b/ngraph/frontend/tensorflow/src/op/log_softmax.cpp
index 2d725d09d1b544..eaf6ed06869129 100644
--- a/ngraph/frontend/tensorflow/src/op/log_softmax.cpp
+++ b/ngraph/frontend/tensorflow/src/op/log_softmax.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,13 +13,11 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateLogSoftmaxOp(const NodeContext& node) {
-    auto ng_inp = node.get_ng_input(0);
-    auto inp_shape = ng_inp.get_shape();
-    size_t rank = inp_shape.size();
-    int64_t axes = rank - 1;
-
-    return {ConstructNgNode<LogSoftmax>(node.get_name(), ng_inp, axes)};
+OutputVector translate_log_softmax_op(const NodeContext& node) {
+    auto ng_inp = node.get_input(0);
+    auto res = make_shared<LogSoftmax>(ng_inp, -1);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/lrn.cpp b/ngraph/frontend/tensorflow/src/op/lrn.cpp
new file mode 100644
index 00000000000000..ea4005e761e5db
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/lrn.cpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_lrn_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto alpha = node.get_attribute<float>("alpha");
+    auto beta = node.get_attribute<float>("beta");
+    auto bias = node.get_attribute<float>("bias");
+    auto depth_radius = node.get_attribute<int64_t>("depth_radius");
+
+    // OV: Each input value is divided by (bias+(alpha/size)*sum(xi^2 for every xi
+    // in the local region))^beta
+    // TF: sqr_sum[a, b, c, d] = sum(input[a, b, c, d - depth_radius : d +
+    // depth_radius + 1] ** 2)
+    //     output = input / (bias + alpha * sqr_sum) ** beta
+    int64_t size = depth_radius * 2 + 1;
+    alpha = alpha * size;
+    // todo: input is in NHWC, need to apply NHWC to NCHW?
+    auto res = make_shared<LRN>(input, alpha, beta, bias, static_cast<size_t>(size));
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/matmul.cpp b/ngraph/frontend/tensorflow/src/op/matmul.cpp
new file mode 100644
index 00000000000000..f05c95d64b4c15
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/matmul.cpp
@@ -0,0 +1,29 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_mat_mul_op(const NodeContext& node) {
+    auto a = node.get_input(0);
+    auto b = node.get_input(1);
+    auto transpose_a = node.get_attribute<bool>("transpose_a", false);
+    auto transpose_b = node.get_attribute<bool>("transpose_b", false);
+
+    auto res = make_shared<MatMul>(a, b, transpose_a, transpose_b);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/max_pool.cpp b/ngraph/frontend/tensorflow/src/op/max_pool.cpp
index 8ecb356767d4cc..82c72a5263c82d 100644
--- a/ngraph/frontend/tensorflow/src/op/max_pool.cpp
+++ b/ngraph/frontend/tensorflow/src/op/max_pool.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
 #include <openvino/opsets/opset7.hpp>
 
+#include "op_table.hpp"
+
 using namespace std;
 using namespace ov;
 using namespace ov::frontend::tf::detail;
@@ -14,8 +15,8 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateMaxPoolOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0);
+OutputVector translate_max_pool_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0);
 
     auto tf_strides = node.get_attribute<std::vector<int32_t>>("strides");
     auto tf_ksize = node.get_attribute<std::vector<int32_t>>("ksize");
@@ -33,39 +34,40 @@ OutputVector TranslateMaxPoolOp(const NodeContext& node) {
     Shape ng_kernel_shape(N);
     Shape ng_dilations(N, 1);
 
-    NHWCtoHW(is_nhwc, tf_strides, ng_strides);
-    NHWCtoHW(is_nhwc, ng_input.get_shape(), ng_image_shape);
-    NHWCtoHW(is_nhwc, tf_ksize, ng_kernel_shape);
-    NHWCtoNCHW(node.get_name(), is_nhwc, ng_input);
+    convert_nhwc_to_hw(is_nhwc, tf_strides, ng_strides);
+    convert_nhwc_to_hw(is_nhwc, ng_input.get_shape(), ng_image_shape);
+    convert_nhwc_to_hw(is_nhwc, tf_ksize, ng_kernel_shape);
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, ng_input);
 
     CoordinateDiff padding_below;
     CoordinateDiff padding_above;
-    MakePadding(tf_padding_type,
-                ng_image_shape,
-                ng_kernel_shape,
-                ng_strides,
-                ng_dilations,
-                padding_below,
-                padding_above);
+    make_padding(tf_padding_type,
+                 ng_image_shape,
+                 ng_kernel_shape,
+                 ng_strides,
+                 ng_dilations,
+                 padding_below,
+                 padding_above);
 
     // TODO: remove this once nGraph supports negative padding
     // (CoordinateDiff) for MaxPool
     Shape ng_padding_below(padding_below.begin(), padding_below.end());
     Shape ng_padding_above(padding_above.begin(), padding_above.end());
 
-    auto ng_maxpool = ConstructNgNode<ov::opset7::MaxPool>(node.get_name(),
-                                                           ng_input,
-                                                           ng_strides,
-                                                           ng_padding_below,
-                                                           ng_padding_above,
-                                                           ng_kernel_shape,
-                                                           ov::op::RoundingType::FLOOR);
+    auto res_node = make_shared<ov::opset7::MaxPool>(ng_input,
+                                                     ng_strides,
+                                                     ng_padding_below,
+                                                     ng_padding_above,
+                                                     ng_kernel_shape,
+                                                     ov::op::RoundingType::FLOOR);
+    auto res = res_node->output(0);
 
-    NCHWtoNHWC(node.get_name(), is_nhwc, ng_maxpool);
-    return {ng_maxpool};
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/no_op.cpp b/ngraph/frontend/tensorflow/src/op/no_op.cpp
index 57f09a73410b5b..135b7c3a323ca8 100644
--- a/ngraph/frontend/tensorflow/src/op/no_op.cpp
+++ b/ngraph/frontend/tensorflow/src/op/no_op.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,16 +13,21 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector NoOp(const NodeContext& node) {
-    if (node.get_ng_input_size() == 0) {
+OutputVector translate_no_op(const NodeContext& node) {
+    if (node.get_input_size() == 0) {
         return OutputVector{};
     }
-    if (node.get_ng_input_size() != 1) {
-        throw errors::InvalidArgument("NoOp has " + to_string(node.get_ng_input_size()) + " inputs, should have 1");
-    }
-    return OutputVector{node.get_ng_input(0)};
+
+    TF_OP_VALIDATION_CHECK(node,
+                           node.get_input_size() == 1,
+                           "NoOp has " + to_string(node.get_input_size()) + " inputs, should have 1");
+
+    auto input = node.get_input(0);
+    set_out_name(node.get_name(), input);
+    set_out_name(node.get_name() + ":" + "0", input);
+    return {input};
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/non_max_suppression.cpp b/ngraph/frontend/tensorflow/src/op/non_max_suppression.cpp
new file mode 100644
index 00000000000000..4f527cce2a958a
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/non_max_suppression.cpp
@@ -0,0 +1,85 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_non_max_suppression_op(const NodeContext& node) {
+    auto boxes = node.get_input(0);
+    auto scores = node.get_input(1);
+    auto max_output_size = node.get_input(2);
+    auto iou_threshold = node.get_input(3);
+
+    auto axis = make_shared<Constant>(element::i64, Shape{1}, 0);
+    auto boxes_unsqueezed = make_shared<Unsqueeze>(boxes, axis);
+
+    auto axis_scores = make_shared<Constant>(element::i64, Shape{2}, vector<int64_t>{0, 1});
+    auto scores_unsqueezed = make_shared<Unsqueeze>(scores, axis_scores);
+
+    const auto& op_type = node.get_op_type();
+    if (op_type == "NonMaxSuppressionV5") {
+        auto score_threshold = node.get_input(4);
+        auto soft_nms_sigma = node.get_input(5);
+        // todo: pad_to_max_output_size
+        auto res = make_shared<NonMaxSuppression>(boxes_unsqueezed,
+                                                  scores_unsqueezed,
+                                                  max_output_size,
+                                                  iou_threshold,
+                                                  score_threshold,
+                                                  soft_nms_sigma,
+                                                  NonMaxSuppression::BoxEncodingType::CORNER,
+                                                  false,
+                                                  element::Type_t::i32);
+        set_node_name(node.get_name(), res);
+        return res->outputs();
+    } else if (op_type == "NonMaxSuppressionV4") {
+        auto score_threshold = node.get_input(4);
+        // todo: pad_to_max_output_size
+        auto res = make_shared<NonMaxSuppression>(boxes_unsqueezed,
+                                                  scores_unsqueezed,
+                                                  max_output_size,
+                                                  iou_threshold,
+                                                  score_threshold,
+                                                  NonMaxSuppression::BoxEncodingType::CORNER,
+                                                  false,
+                                                  element::Type_t::i32);
+        set_node_name(node.get_name(), res);
+        return res->outputs();
+    } else if (op_type == "NonMaxSuppressionV3") {
+        auto score_threshold = node.get_input(4);
+        auto res = make_shared<NonMaxSuppression>(boxes_unsqueezed,
+                                                  scores_unsqueezed,
+                                                  max_output_size,
+                                                  iou_threshold,
+                                                  score_threshold,
+                                                  NonMaxSuppression::BoxEncodingType::CORNER,
+                                                  false,
+                                                  element::Type_t::i32);
+        set_node_name(node.get_name(), res);
+        return {res->output(0)};
+    } else if (op_type == "NonMaxSuppressionV2" || op_type == "NonMaxSuppression") {
+        auto res = make_shared<NonMaxSuppression>(boxes_unsqueezed,
+                                                  scores_unsqueezed,
+                                                  max_output_size,
+                                                  iou_threshold,
+                                                  NonMaxSuppression::BoxEncodingType::CORNER,
+                                                  false,
+                                                  element::Type_t::i32);
+        set_node_name(node.get_name(), res);
+        return {res->output(0)};
+    }
+    TF_OP_VALIDATION_CHECK(node, false, "No translator found.");
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/one_hot.cpp b/ngraph/frontend/tensorflow/src/op/one_hot.cpp
index f7c4a1520a21fc..79f4806941431d 100644
--- a/ngraph/frontend/tensorflow/src/op/one_hot.cpp
+++ b/ngraph/frontend/tensorflow/src/op/one_hot.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,16 +13,16 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateOneHotOp(const NodeContext& node) {
-    auto ng_features = node.get_ng_input(0);
-    auto ng_depth = node.get_ng_input(1);
-    auto ng_on = node.get_ng_input(2);
-    auto ng_off = node.get_ng_input(3);
+OutputVector translate_one_hot_op(const NodeContext& node) {
+    auto ng_features = node.get_input(0);
+    auto ng_depth = node.get_input(1);
+    auto ng_on = node.get_input(2);
+    auto ng_off = node.get_input(3);
 
     auto one_hot_axis = node.get_attribute<int64_t>("axis");
-    auto ng_onehot = make_shared<OneHot>(ng_features, ng_depth, ng_on, ng_off, one_hot_axis);
-    ng_onehot->set_friendly_name(ng_onehot->get_friendly_name());
-    return ng_onehot->outputs();
+    auto res = make_shared<OneHot>(ng_features, ng_depth, ng_on, ng_off, one_hot_axis);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/pack.cpp b/ngraph/frontend/tensorflow/src/op/pack.cpp
new file mode 100644
index 00000000000000..16624d8efeaf88
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/pack.cpp
@@ -0,0 +1,33 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_pack_op(const NodeContext& node) {
+    auto axis = node.get_attribute<int32_t>("axis");
+    auto axis_const = make_shared<Constant>(element::i64, Shape{}, axis);
+
+    OutputVector concat_inputs;
+    for (size_t i = 0; i < node.get_input_size(); ++i) {
+        auto in = node.get_input(i);
+        concat_inputs.push_back(make_shared<Unsqueeze>(in, axis_const));
+    }
+
+    auto res = make_shared<Concat>(concat_inputs, axis);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/pad.cpp b/ngraph/frontend/tensorflow/src/op/pad.cpp
index dedbb3a03faeee..ef4e54b39b0b96 100644
--- a/ngraph/frontend/tensorflow/src/op/pad.cpp
+++ b/ngraph/frontend/tensorflow/src/op/pad.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -17,19 +17,18 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslatePadOp(const NodeContext& node) {
-    auto ng_input = node.get_ng_input(0), ng_paddings_op = node.get_ng_input(1);
+OutputVector translate_pad_op(const NodeContext& node) {
+    auto ng_input = node.get_input(0), ng_paddings_op = node.get_input(1);
     Output<Node> pad_val_op;
 
     // Set inputs and pad_val_op
     auto op_type = node.get_op_type();
     if (op_type == "Pad" || op_type == "MirrorPad") {
-        pad_val_op =
-            ConstructNgNode<Constant>(node.get_name(), ng_input.get_element_type(), Shape(), std::vector<int>({0}));
+        pad_val_op = make_shared<Constant>(ng_input.get_element_type(), Shape(), std::vector<int>({0}));
     } else if (op_type == "PadV2") {
-        pad_val_op = node.get_ng_input(2);
+        pad_val_op = node.get_input(2);
     } else {
-        throw errors::InvalidArgument("Incorrect TF Pad OpType: " + node.get_op_type());
+        TF_OP_VALIDATION_CHECK(node, false, "Incorrect TF Pad OpType: " + node.get_op_type());
     }
 
     // Set pad_mode
@@ -41,16 +40,18 @@ OutputVector TranslatePadOp(const NodeContext& node) {
         } else if (pad_mode_str == "SYMMETRIC") {
             pad_mode = ov::op::PadMode::SYMMETRIC;
         } else {
-            throw errors::InvalidArgument(pad_mode_str + " is not an allowed padding mode.");
+            TF_OP_VALIDATION_CHECK(node, false, pad_mode_str + " is not an allowed padding mode.");
         }
     }
 
     // Set pads_begin & pads_end (from the pad_val_op)
     std::vector<int64_t> paddings;
-    GetStaticInputVector(node, 1, &paddings);
+    get_static_input_vec(node, 1, &paddings);
     if (paddings.size() % 2 != 0) {
-        throw errors::InvalidArgument("Constant node for paddings does not have an even number of "
-                                      "elements");
+        TF_OP_VALIDATION_CHECK(node,
+                               false,
+                               "Constant node for paddings does not have an even number of "
+                               "elements");
     }
     std::vector<int64_t> pad_begin(paddings.size() / 2);
     std::vector<int64_t> pad_end(paddings.size() / 2);
@@ -58,16 +59,15 @@ OutputVector TranslatePadOp(const NodeContext& node) {
         pad_begin[i] = paddings[2 * i];
         pad_end[i] = paddings[2 * i + 1];
     }
-    auto pads_begin_node = ConstructNgNode<Constant>(node.get_name(), element::i64, Shape{pad_begin.size()}, pad_begin);
-    auto pads_end_node = ConstructNgNode<Constant>(node.get_name(), element::i64, Shape{pad_end.size()}, pad_end);
+    auto pads_begin_node = make_shared<Constant>(element::i64, Shape{pad_begin.size()}, pad_begin);
+    auto pads_end_node = make_shared<Constant>(element::i64, Shape{pad_end.size()}, pad_end);
 
     // Create final Op
-    auto result_pad_op =
-        ConstructNgNode<Pad>(node.get_name(), ng_input, pads_begin_node, pads_end_node, pad_val_op, pad_mode);
-
-    return {result_pad_op};
+    auto res = make_shared<Pad>(ng_input, pads_begin_node, pads_end_node, pad_val_op, pad_mode);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/placeholder.cpp b/ngraph/frontend/tensorflow/src/op/placeholder.cpp
index c8b2f7f8712c3f..0277d5e7e3a14b 100644
--- a/ngraph/frontend/tensorflow/src/op/placeholder.cpp
+++ b/ngraph/frontend/tensorflow/src/op/placeholder.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,12 +13,15 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector PlaceholderOp(const NodeContext& node) {
+OutputVector translate_placeholder_op(const NodeContext& node) {
     auto ng_et = node.get_attribute<ov::element::Type>("dtype");
     auto ng_shape = node.get_attribute<ov::PartialShape>("shape", ov::PartialShape());
-    return {ConstructNgNode<Parameter>(node.get_name(), ng_et, ng_shape)};
+
+    auto res = std::make_shared<Parameter>(ng_et, ng_shape);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/random_uniform.cpp b/ngraph/frontend/tensorflow/src/op/random_uniform.cpp
index 2087575e8325bb..9c1a050a411fed 100644
--- a/ngraph/frontend/tensorflow/src/op/random_uniform.cpp
+++ b/ngraph/frontend/tensorflow/src/op/random_uniform.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -12,16 +12,28 @@ namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
-ov::OutputVector TranslateRandomUniformOp(const NodeContext& node) {
-    auto data = node.get_ng_input(0);
-    auto seed = node.get_attribute<int64_t>("seed");
-    auto seed2 = node.get_attribute<int64_t>("seed2");
+ov::OutputVector translate_random_uniform_op(const NodeContext& node) {
+    auto shape = node.get_input(0);
+    auto seed = node.get_attribute<int64_t>("seed", 0);
+    auto seed2 = node.get_attribute<int64_t>("seed2", 0);
     auto minval_const = make_shared<Constant>(element::f32, Shape{}, 0);
     auto maxval_const = make_shared<Constant>(element::f32, Shape{}, 1);
     auto ng_et = node.get_attribute<ov::element::Type>("dtype");
-    auto random_uniform = std::make_shared<RandomUniform>(data, minval_const, maxval_const, ng_et, seed, seed2);
-    random_uniform->set_friendly_name(node.get_name());
-    return random_uniform->outputs();
+    auto res = std::make_shared<RandomUniform>(shape, minval_const, maxval_const, ng_et, seed, seed2);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+ov::OutputVector translate_random_uniform_int_op(const NodeContext& node) {
+    auto shape = node.get_input(0);
+    auto minval = node.get_input(1);
+    auto maxval = node.get_input(2);
+    auto seed = node.get_attribute<int64_t>("seed", 0);
+    auto seed2 = node.get_attribute<int64_t>("seed2", 0);
+    auto ng_et = minval.get_element_type();
+    auto res = std::make_shared<RandomUniform>(shape, minval, maxval, ng_et, seed, seed2);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/range.cpp b/ngraph/frontend/tensorflow/src/op/range.cpp
new file mode 100644
index 00000000000000..544af501e0dac0
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/range.cpp
@@ -0,0 +1,30 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_range_op(const NodeContext& node) {
+    auto start = node.get_input(0);
+    auto stop = node.get_input(1);
+    auto step = node.get_input(2);
+    auto out_type = node.get_attribute<ov::element::Type>("Tidx");
+
+    auto res = make_shared<Range>(start, stop, step, out_type);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/rank.cpp b/ngraph/frontend/tensorflow/src/op/rank.cpp
new file mode 100644
index 00000000000000..e4fc655706cd89
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/rank.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+ov::OutputVector translate_rank_op(const NodeContext& node) {
+    auto data = node.get_input(0);
+    auto shape_of_1 = make_shared<ShapeOf>(data, ov::element::i64);
+    auto res = make_shared<ShapeOf>(shape_of_1, ov::element::i64);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/reciprocal.cpp b/ngraph/frontend/tensorflow/src/op/reciprocal.cpp
new file mode 100644
index 00000000000000..017bbddbb4c68e
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/reciprocal.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_reciprocal_op(const NodeContext& node) {
+    auto x = node.get_input(0);
+    auto ng_exponent = make_shared<Constant>(x.get_element_type(), Shape{}, -1);
+    auto res = make_shared<Power>(x, ng_exponent);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/reduce.cpp b/ngraph/frontend/tensorflow/src/op/reduce.cpp
index 5397300557481f..11c9e7f6c1b20e 100644
--- a/ngraph/frontend/tensorflow/src/op/reduce.cpp
+++ b/ngraph/frontend/tensorflow/src/op/reduce.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -15,53 +15,37 @@ namespace op {
 
 OutputVector TranslateReduceOp(const NodeContext& node,
                                std::function<Output<Node>(Output<Node>, Output<Node>, const bool)> create_ng_node) {
-    Output<Node> ng_input = node.get_ng_input(0);
+    auto input = node.get_input(0);
+    auto reduction_axes = node.get_input(1);
     auto tf_keep_dims = node.get_attribute<bool>("keep_dims", false);
-
-    std::vector<int64_t> axes;
-    GetStaticInputVector(node, 1, &axes);
-
-    Shape input_shape = ng_input.get_shape();
-    size_t input_rank = input_shape.size();
-
-    TF_RETURN_IF_ERROR(CheckAxisDimInRange(axes, input_rank));
-
-    std::vector<size_t> ng_reduction_axes_vect(axes.size());
-    std::transform(axes.begin(), axes.end(), ng_reduction_axes_vect.begin(), [input_rank](int idx) {
-        return idx + (idx < 0 ? (int)input_rank : 0);
-    });
-    auto ng_reduction_axes = ConstructNgNode<Constant>(node.get_name(),
-                                                       element::i64,
-                                                       Shape{ng_reduction_axes_vect.size()},
-                                                       ng_reduction_axes_vect);
-
-    Output<Node> ng_node = create_ng_node(ng_input, ng_reduction_axes, tf_keep_dims);
-
-    return {ng_node};
+    auto res = create_ng_node(input, reduction_axes, tf_keep_dims);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
 }
 
 template <typename T>
-OutputVector TranslateDirectReduceOp(const NodeContext& node) {
+OutputVector translate_direct_reduce_op(const NodeContext& node) {
     // ensure its either an arithmetic or a logical reduction
     if (!(std::is_base_of<ov::op::util::ArithmeticReduction, T>::value ||
           std::is_base_of<ov::op::util::LogicalReduction, T>::value)) {
-        throw errors::InvalidArgument("Expected node to be either a valid logical or arithmetic reduction "
-                                      "type");
+        TF_OP_VALIDATION_CHECK(node,
+                               false,
+                               "Expected node to be either a valid logical or arithmetic reduction "
+                               "type");
     }
-    return TranslateReduceOp(node,
-                             [&node](Output<Node> ng_input, Output<Node> ng_reduction_axes, const bool keep_dims) {
-                                 return ConstructNgNode<T>(node.get_name(), ng_input, ng_reduction_axes, keep_dims);
-                             });
+    return TranslateReduceOp(node, [](Output<Node> ng_input, Output<Node> ng_reduction_axes, const bool keep_dims) {
+        return make_shared<T>(ng_input, ng_reduction_axes, keep_dims);
+    });
 }
 
-template OutputVector TranslateDirectReduceOp<ReduceLogicalOr>(const NodeContext& node);
-template OutputVector TranslateDirectReduceOp<ReduceLogicalAnd>(const NodeContext& node);
-template OutputVector TranslateDirectReduceOp<ReduceMax>(const NodeContext& node);
-template OutputVector TranslateDirectReduceOp<ReduceMean>(const NodeContext& node);
-template OutputVector TranslateDirectReduceOp<ReduceMin>(const NodeContext& node);
-template OutputVector TranslateDirectReduceOp<ReduceProd>(const NodeContext& node);
-template OutputVector TranslateDirectReduceOp<ReduceSum>(const NodeContext& node);
+template OutputVector translate_direct_reduce_op<ReduceLogicalOr>(const NodeContext& node);
+template OutputVector translate_direct_reduce_op<ReduceLogicalAnd>(const NodeContext& node);
+template OutputVector translate_direct_reduce_op<ReduceMax>(const NodeContext& node);
+template OutputVector translate_direct_reduce_op<ReduceMean>(const NodeContext& node);
+template OutputVector translate_direct_reduce_op<ReduceMin>(const NodeContext& node);
+template OutputVector translate_direct_reduce_op<ReduceProd>(const NodeContext& node);
+template OutputVector translate_direct_reduce_op<ReduceSum>(const NodeContext& node);
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/relu_6.cpp b/ngraph/frontend/tensorflow/src/op/relu_6.cpp
index 4f9a1627213312..83692a56312b58 100644
--- a/ngraph/frontend/tensorflow/src/op/relu_6.cpp
+++ b/ngraph/frontend/tensorflow/src/op/relu_6.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -12,11 +12,11 @@ namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
-ov::OutputVector TranslateRelu6Op(const NodeContext& node) {
-    auto data = node.get_ng_input(0);
-    auto clamp = std::make_shared<Clamp>(data, 0.0, 6.0f);
-    clamp->set_friendly_name(node.get_name());
-    return clamp->outputs();
+ov::OutputVector translate_relu_6_op(const NodeContext& node) {
+    auto data = node.get_input(0);
+    auto res = std::make_shared<Clamp>(data, 0.0, 6.0f);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/reshape.cpp b/ngraph/frontend/tensorflow/src/op/reshape.cpp
new file mode 100644
index 00000000000000..b14c02b6879a5d
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/reshape.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_reshape_op(const NodeContext& node) {
+    auto data = node.get_input(0);
+    auto shape = node.get_input(1);
+    auto res = make_shared<Reshape>(data, shape, false);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/retval.cpp b/ngraph/frontend/tensorflow/src/op/retval.cpp
deleted file mode 100644
index 5adca5a0ea7920..00000000000000
--- a/ngraph/frontend/tensorflow/src/op/retval.cpp
+++ /dev/null
@@ -1,30 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
-
-using namespace std;
-using namespace ov::opset8;
-
-namespace ov {
-namespace frontend {
-namespace tf {
-namespace op {
-
-OutputVector RetvalOp(const NodeContext& node) {
-    // Make sure that this _Retval only has one input node.
-    if (node.get_ng_input_size() != 1) {
-        throw errors::InvalidArgument("_Retval has " + to_string(node.get_ng_input_size()) + " inputs, should have 1");
-    }
-
-    // auto ret_val_index = node.get_attribute<int>("index");
-    // TODO: Put ret_val_index to RT info that should be later utilized to order outpus by indices
-
-    return {ConstructNgNode<Result>(node.get_name(), node.get_ng_input(0))};
-}
-}  // namespace op
-}  // namespace tf
-}  // namespace frontend
-}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/reverse.cpp b/ngraph/frontend/tensorflow/src/op/reverse.cpp
new file mode 100644
index 00000000000000..768e1ad8f27546
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/reverse.cpp
@@ -0,0 +1,51 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_reverse_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto axes = node.get_input(1);
+
+    auto axes_const = dynamic_pointer_cast<Constant>(axes.get_node_shared_ptr());
+    TF_OP_VALIDATION_CHECK(node, axes_const != nullptr, "Axes input must be constant.");
+    TF_OP_VALIDATION_CHECK(node, axes_const->get_shape().size() == 1, "Axes input must be 1D.");
+    TF_OP_VALIDATION_CHECK(node, axes_const->get_shape()[0] == 1, "Axes input must have only one value.");
+    auto seq_axis = axes_const->cast_vector<int64_t>().at(0);
+    int64_t batch_axis = !seq_axis;
+
+    Output<Node> seq_lengths;
+    if (input.get_partial_shape().is_static()) {
+        auto in_shape = input.get_shape();
+        seq_lengths = make_shared<Constant>(element::i64, Shape{in_shape[batch_axis]}, in_shape[seq_axis]);
+    } else {
+        auto shape = make_shared<ShapeOf>(input);
+        auto one = make_shared<Constant>(element::i64, Shape{1}, 1);
+        auto gather_batch = make_shared<Gather>(shape,
+                                                make_shared<Constant>(element::i64, Shape{1}, batch_axis),
+                                                make_shared<Constant>(element::i64, Shape{1}, 0));
+        auto gather_seq = make_shared<Gather>(shape,
+                                              make_shared<Constant>(element::i64, Shape{1}, seq_axis),
+                                              make_shared<Constant>(element::i64, Shape{1}, 0));
+        auto broadcast = make_shared<Broadcast>(one, gather_batch);
+        seq_lengths = make_shared<Multiply>(broadcast, gather_seq);
+    }
+
+    auto res = make_shared<ReverseSequence>(input, seq_lengths, batch_axis, seq_axis);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/roll.cpp b/ngraph/frontend/tensorflow/src/op/roll.cpp
index 0676b205f87ab2..a004436b9afbcf 100644
--- a/ngraph/frontend/tensorflow/src/op/roll.cpp
+++ b/ngraph/frontend/tensorflow/src/op/roll.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov;
@@ -14,13 +14,13 @@ namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
-ov::OutputVector TranslateRollOp(const NodeContext& node) {
-    auto data = node.get_ng_input(0);
-    auto shift = node.get_ng_input(1);
-    auto axis = node.get_ng_input(2);
-    auto roll = std::make_shared<Roll>(data, shift, axis);
-    roll->set_friendly_name(node.get_name());
-    return roll->outputs();
+ov::OutputVector translate_roll_op(const NodeContext& node) {
+    auto data = node.get_input(0);
+    auto shift = node.get_input(1);
+    auto axis = node.get_input(2);
+    auto res = std::make_shared<Roll>(data, shift, axis);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/round.cpp b/ngraph/frontend/tensorflow/src/op/round.cpp
new file mode 100644
index 00000000000000..8c2d64bc951f7f
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/round.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_round_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    // using default round mode "half_to_even" in openvino,
+    // as TF has only that mode
+    auto round_mode = Round::RoundMode::HALF_TO_EVEN;
+    auto res = make_shared<Round>(input, round_mode);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/rsqrt.cpp b/ngraph/frontend/tensorflow/src/op/rsqrt.cpp
index a9554d46231109..a67fa255767d31 100644
--- a/ngraph/frontend/tensorflow/src/op/rsqrt.cpp
+++ b/ngraph/frontend/tensorflow/src/op/rsqrt.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,12 +13,12 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateRsqrtOp(const NodeContext& node) {
-    auto input = node.get_ng_input(0);
-    auto ng_exponent = ConstructNgNode<Constant>(node.get_name(), input.get_element_type(), Shape{1}, -0.5f);
-    auto power = make_shared<Power>(input, ng_exponent);
-    power->set_friendly_name(node.get_name());
-    return power->outputs();
+OutputVector translate_rsqrt_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto ng_exponent = make_shared<Constant>(input.get_element_type(), Shape{1}, -0.5f);
+    auto res = make_shared<Power>(input, ng_exponent);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/select.cpp b/ngraph/frontend/tensorflow/src/op/select.cpp
index 51fb1bd4435ab5..9f3d9771ff06cc 100644
--- a/ngraph/frontend/tensorflow/src/op/select.cpp
+++ b/ngraph/frontend/tensorflow/src/op/select.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -12,11 +12,11 @@ namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
-OutputVector TranslateSelectOp(const NodeContext& node) {
-    TF_OP_VALIDATION_CHECK(node, node.get_all_ng_inputs().size() == 3, "Select op cannot be converted");
-    auto in_1 = node.get_ng_input(0);
-    auto in_2 = node.get_ng_input(1);
-    auto in_3 = node.get_ng_input(2);
+OutputVector translate_select_op(const NodeContext& node) {
+    TF_OP_VALIDATION_CHECK(node, node.get_all_inputs().size() == 3, "Select op cannot be converted");
+    auto in_1 = node.get_input(0);
+    auto in_2 = node.get_input(1);
+    auto in_3 = node.get_input(2);
     if (in_1.get_partial_shape().is_static() && in_2.get_partial_shape().is_static()) {
         // select broadcast
         if (in_1.get_shape().size() == 1 && in_2.get_shape().size() > 1) {
@@ -24,14 +24,14 @@ OutputVector TranslateSelectOp(const NodeContext& node) {
             std::iota(axes.begin(), axes.end(), 1);
             auto unsqueeze_axes = make_shared<Constant>(ov::element::i64, Shape{in_2.get_shape().size() - 1}, axes);
             auto unsqueeze = make_shared<Unsqueeze>(in_1, unsqueeze_axes);
-            auto ng_select = make_shared<Select>(unsqueeze, in_2, in_3);
-            ng_select->set_friendly_name(node.get_name());
-            return ng_select->outputs();
+            auto res = make_shared<Select>(unsqueeze, in_2, in_3);
+            set_node_name(node.get_name(), res);
+            return res->outputs();
         }
     }
-    auto ng_select = make_shared<Select>(in_1, in_2, in_3);
-    ng_select->set_friendly_name(node.get_name());
-    return ng_select->outputs();
+    auto res = make_shared<Select>(in_1, in_2, in_3);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/shape.cpp b/ngraph/frontend/tensorflow/src/op/shape.cpp
new file mode 100644
index 00000000000000..511bb65bbab3af
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/shape.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+ov::OutputVector translate_shape_op(const NodeContext& node) {
+    auto data = node.get_input(0);
+    auto out_type = node.get_attribute<ov::element::Type>("out_type");
+    auto res = make_shared<ShapeOf>(data, out_type);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/size.cpp b/ngraph/frontend/tensorflow/src/op/size.cpp
new file mode 100644
index 00000000000000..79855e0991380e
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/size.cpp
@@ -0,0 +1,29 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+ov::OutputVector translate_size_op(const NodeContext& node) {
+    auto data = node.get_input(0);
+    auto out_type = node.get_attribute<ov::element::Type>("out_type");
+    auto shape_of = make_shared<ShapeOf>(data, out_type);
+    auto axis = make_shared<Constant>(ov::element::i64, Shape{}, 0);
+    auto res = make_shared<ReduceProd>(shape_of, axis);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/slice.cpp b/ngraph/frontend/tensorflow/src/op/slice.cpp
new file mode 100644
index 00000000000000..89376e9955e3cb
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/slice.cpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_slice_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto start = node.get_input(1);
+    auto size = node.get_input(2);
+
+    auto stop = make_shared<Add>(start, size);
+
+    auto one = make_shared<Constant>(element::i64, Shape{1}, 1);
+    auto shape = make_shared<ShapeOf>(start);
+    auto step = make_shared<Broadcast>(one, shape);
+
+    auto res = make_shared<Slice>(input, start, stop, step);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/softmax.cpp b/ngraph/frontend/tensorflow/src/op/softmax.cpp
index 31647e549afcf1..24fc5620858ab9 100644
--- a/ngraph/frontend/tensorflow/src/op/softmax.cpp
+++ b/ngraph/frontend/tensorflow/src/op/softmax.cpp
@@ -2,8 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include <openvino/opsets/opset7.hpp>
+
+#include "op_table.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -12,19 +13,17 @@ namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
-OutputVector TranslateSoftmaxOp(const NodeContext& node) {
-    auto ng_inp = node.get_ng_input(0);
-    auto inp_shape = ng_inp.get_shape();
-    size_t rank = inp_shape.size();
-    int64_t axes = rank - 1;
-    if (rank < 1) {
-        throw errors::InvalidArgument("TF Softmax logits must be >=1 dimension");
-    }
-
-    return {ConstructNgNode<Softmax>(node.get_name(), ng_inp, axes)};
+OutputVector translate_softmax_op(const NodeContext& node) {
+    auto ng_inp = node.get_input(0);
+    // todo: switch to opset8::Softmax when is ready and delete Dynamic rank limitation
+    TF_OP_VALIDATION_CHECK(node, ng_inp.get_partial_shape().rank().is_static(), "Dynamic rank is not supported.");
+    size_t axis = ng_inp.get_partial_shape().rank().get_length() - 1;
+    auto res = make_shared<opset7::Softmax>(ng_inp, axis);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/space_to_batch_nd.cpp b/ngraph/frontend/tensorflow/src/op/space_to_batch_nd.cpp
new file mode 100644
index 00000000000000..d1b2897cecab52
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/space_to_batch_nd.cpp
@@ -0,0 +1,81 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+OutputVector translate_batch_nd_and_space_nd_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto block_shape = node.get_input(1);
+    auto crops = node.get_input(2);
+
+    // ng_crops should be of shape N=[ng_input.get_shape()).size()]
+    // But TF's ng_crops input is limited only to the spatial dimensions (neither
+    // batch nor innermost),
+    // which would mean ngraph inputs have missing ng_crops[0] and ng_crops[N].
+    // Hence, pad ng_crops with zeros at both ends
+
+    // return with input if rank < 2 as ngraph's impl doesn't support it
+    const auto& input_pshape = input.get_partial_shape();
+    const auto& block_shape_pshape = block_shape.get_partial_shape();
+    if (input_pshape.rank().is_static() && block_shape_pshape.rank().is_static()) {
+        auto N = input_pshape.rank().get_length();
+        if (N < 2)
+            return {input};
+    } else {
+        // TODO: support dynamic rank
+        TF_OP_VALIDATION_CHECK(node, false, "Dynamic rank is not supported.");
+    }
+
+    auto N = input_pshape.rank().get_length();
+    auto M = block_shape_pshape.rank().get_length();
+    auto padded_crops =
+        make_shared<Pad>(crops,
+                         make_shared<Constant>(crops.get_element_type(), Shape{2}, std::vector<int64_t>{1, 0}),
+                         make_shared<Constant>(crops.get_element_type(), Shape{2}, std::vector<int64_t>{N - M - 1, 0}),
+                         ov::op::PadMode::CONSTANT);
+
+    // Padding needs to be done for block_shape as done for crops above but with
+    // value=1
+    auto padded_block_shape = make_shared<Pad>(
+        block_shape,
+        make_shared<Constant>(block_shape.get_element_type(), Shape{1}, std::vector<int64_t>{1}),
+        make_shared<Constant>(block_shape.get_element_type(), Shape{1}, std::vector<int64_t>{N - M - 1}),
+        make_shared<Constant>(block_shape.get_element_type(), Shape{}, 1),
+        ov::op::PadMode::CONSTANT);
+
+    auto target_axis = make_shared<Constant>(element::i64, Shape{}, 1);
+    // split into two 1-D vectors crops_begin and crops_end along axis 1
+    auto crops_split = make_shared<Split>(padded_crops, target_axis, 2);
+
+    // crops: [[0, 1], [1, 2], ...]
+    // crops_split: [[[0], [1]], [[1], [2]], ...]
+    // crops_begin: [0, 1, ...], crops_end: [1, 2, ...]
+    auto axes = make_shared<Constant>(element::i32, Shape{}, -1);
+    auto crops_begin = make_shared<Squeeze>(crops_split->outputs()[0], axes);
+    auto crops_end = make_shared<Squeeze>(crops_split->outputs()[1], axes);
+
+    if (node.get_op_type() == "BatchToSpaceND") {
+        auto res = make_shared<BatchToSpace>(input, padded_block_shape, crops_begin, crops_end);
+        set_node_name(node.get_name(), res);
+        return res->outputs();
+    } else if (node.get_op_type() == "SpaceToBatchND") {
+        auto res = make_shared<SpaceToBatch>(input, padded_block_shape, crops_begin, crops_end);
+        set_node_name(node.get_name(), res);
+        return res->outputs();
+    }
+    TF_OP_VALIDATION_CHECK(node, false, "No translator found.");
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/space_to_depth.cpp b/ngraph/frontend/tensorflow/src/op/space_to_depth.cpp
new file mode 100644
index 00000000000000..ac173f6120c3e7
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/space_to_depth.cpp
@@ -0,0 +1,35 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_space_to_depth_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+
+    auto block_size = node.get_attribute<int64_t>("block_size");
+    auto data_format = node.get_attribute<string>("data_format");
+
+    TF_OP_VALIDATION_CHECK(node, data_format == "NHWC" || data_format == "NCHW", "Unsupported data format.");
+
+    bool is_nhwc = (data_format == "NHWC");
+    convert_nhwc_to_nchw(node.get_name(), is_nhwc, input);
+    auto ng_mode = SpaceToDepth::SpaceToDepthMode::BLOCKS_FIRST;
+    auto res = make_shared<SpaceToDepth>(input, ng_mode, block_size)->output(0);
+    convert_nchw_to_nhwc(node.get_name(), is_nhwc, res);
+    set_node_name(node.get_name(), res.get_node_shared_ptr());
+    return {res};
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/split.cpp b/ngraph/frontend/tensorflow/src/op/split.cpp
new file mode 100644
index 00000000000000..20ea1ad789ee8d
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/split.cpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_split_op(const NodeContext& node) {
+    auto axes = node.get_input(0);
+    auto input = node.get_input(1);
+    auto num_split = node.get_attribute<int64_t>("num_split");
+
+    auto res = make_shared<Split>(input, axes, num_split);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+OutputVector translate_split_v_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto split_lengths = node.get_input(1);
+    auto split_dims = node.get_input(2);
+
+    // todo(itikhono): double check split_lengths and split_dims are in supported form here
+    auto res = make_shared<VariadicSplit>(input, split_dims, split_lengths);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/sqrt.cpp b/ngraph/frontend/tensorflow/src/op/sqrt.cpp
index ffa9dec52ad299..97490a4bc3c13b 100644
--- a/ngraph/frontend/tensorflow/src/op/sqrt.cpp
+++ b/ngraph/frontend/tensorflow/src/op/sqrt.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,12 +13,12 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateSqrtOp(const NodeContext& node) {
-    auto input = node.get_ng_input(0);
-    auto ng_exponent = ConstructNgNode<Constant>(node.get_name(), input.get_element_type(), Shape{1}, 0.5f);
-    auto power = make_shared<Power>(input, ng_exponent);
-    power->set_friendly_name(node.get_name());
-    return power->outputs();
+OutputVector translate_sqrt_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto ng_exponent = make_shared<Constant>(input.get_element_type(), Shape{1}, 0.5f);
+    auto res = make_shared<Power>(input, ng_exponent);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 }  // namespace op
 }  // namespace tf
diff --git a/ngraph/frontend/tensorflow/src/op/square.cpp b/ngraph/frontend/tensorflow/src/op/square.cpp
new file mode 100644
index 00000000000000..9a74550f1f8e4d
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/square.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_square_op(const NodeContext& node) {
+    auto n = node.get_input(0);
+    auto res = make_shared<Multiply>(n, n);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/squeeze.cpp b/ngraph/frontend/tensorflow/src/op/squeeze.cpp
index 70cc8201e033db..1af6b5272b6180 100644
--- a/ngraph/frontend/tensorflow/src/op/squeeze.cpp
+++ b/ngraph/frontend/tensorflow/src/op/squeeze.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,23 +13,16 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-OutputVector TranslateSqueezeOp(const NodeContext& node) {
-    Output<Node> ng_input = node.get_ng_input(0);
-    size_t input_dims = ng_input.get_shape().size();
-
-    auto tf_axis = node.get_attribute<std::vector<int32_t>>("squeeze_dims");
-
-    // If input dimension is negative, make it positive
-    for (size_t i = 0; i < tf_axis.size(); i++) {
-        tf_axis[i] = tf_axis[i] < 0 ? (int32_t)(input_dims) + tf_axis[i] : tf_axis[i];
-    }
-
-    auto ng_const = ConstructNgNode<Constant>(node.get_name(), element::i32, Shape{tf_axis.size()}, tf_axis);
-
-    return {ConstructNgNode<Squeeze>(node.get_name(), ng_input, ng_const)};
+OutputVector translate_squeeze_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto axes = node.get_attribute<std::vector<int32_t>>("squeeze_dims");
+    auto axes_const = make_shared<Constant>(element::i32, Shape{axes.size()}, axes);
+    auto res = make_shared<Squeeze>(input, axes_const);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
 }
 
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/strided_slice.cpp b/ngraph/frontend/tensorflow/src/op/strided_slice.cpp
new file mode 100644
index 00000000000000..31cc7f63d91b86
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/strided_slice.cpp
@@ -0,0 +1,58 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_strided_slice_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto begin = node.get_input(1);
+    auto end = node.get_input(2);
+    auto strides = node.get_input(3);
+
+    auto begin_mask = node.get_attribute<int32_t>("begin_mask");
+    auto end_mask = node.get_attribute<int32_t>("end_mask");
+    auto new_axis_mask = node.get_attribute<int32_t>("new_axis_mask");
+    auto ellipsis_mask = node.get_attribute<int32_t>("ellipsis_mask");
+    auto shrink_axis_mask = node.get_attribute<int32_t>("shrink_axis_mask");
+
+    auto mask_to_vec = [](int32_t mask) {
+        auto length = sizeof(mask) * CHAR_BIT;
+        vector<int64_t> vec(length, 0);
+        if (mask == 0) {
+            return vec;
+        }
+        for (auto i = 0; i < length; ++i) {
+            if (static_cast<unsigned char>(mask >> i & 0x01) == 1) {
+                vec[i] = 1;
+            }
+        }
+        return vec;
+    };
+
+    auto res = make_shared<StridedSlice>(input,
+                                         begin,
+                                         end,
+                                         strides,
+                                         mask_to_vec(begin_mask),
+                                         mask_to_vec(end_mask),
+                                         mask_to_vec(new_axis_mask),
+                                         mask_to_vec(shrink_axis_mask),
+                                         mask_to_vec(ellipsis_mask));
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/tile.cpp b/ngraph/frontend/tensorflow/src/op/tile.cpp
new file mode 100644
index 00000000000000..b3c63ddad27048
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/tile.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_tile_op(const NodeContext& node) {
+    auto data = node.get_input(0);
+    auto repeats = node.get_input(1);
+
+    auto res = make_shared<Tile>(data, repeats);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/top_k.cpp b/ngraph/frontend/tensorflow/src/op/top_k.cpp
new file mode 100644
index 00000000000000..4826a8efbcdccc
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/top_k.cpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_top_k_v2_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto k = node.get_input(1);
+
+    TF_OP_VALIDATION_CHECK(node, input.get_partial_shape().rank().is_static(), "Input rank must be static.");
+    TF_OP_VALIDATION_CHECK(node,
+                           input.get_partial_shape().rank().get_length() >= 1,
+                           "Input rank must be greater than 0.");
+    // axis along which to compute top k indices
+    int64_t k_axis = input.get_partial_shape().rank().get_length() - 1;
+    bool sorted = node.get_attribute<bool>("sorted", true);
+    auto res = std::make_shared<TopK>(input,
+                                      k,
+                                      k_axis,
+                                      TopK::Mode::MAX,
+                                      sorted ? TopK::SortType::SORT_VALUES : TopK::SortType::SORT_INDICES);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/transpose.cpp b/ngraph/frontend/tensorflow/src/op/transpose.cpp
new file mode 100644
index 00000000000000..0a07c8e43c3765
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/transpose.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_transpose_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto order = node.get_input(1);
+    auto res = make_shared<opset8::Transpose>(input, order);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/unary_op.cpp b/ngraph/frontend/tensorflow/src/op/unary_op.cpp
index 028be04342f704..e1d894027620e8 100644
--- a/ngraph/frontend/tensorflow/src/op/unary_op.cpp
+++ b/ngraph/frontend/tensorflow/src/op/unary_op.cpp
@@ -2,8 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <op_table.hpp>
-#include <openvino/opsets/opset8.hpp>
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace std;
 using namespace ov::opset8;
@@ -13,82 +13,47 @@ namespace frontend {
 namespace tf {
 namespace op {
 
-// Helper function to translate a unary op.
-//
-// Parameters:
-//
-//    TFNodeDecoder* op                   - TF op being translated. Must have one input.
-//    const std::vector<const ov::frontend::tf::detail::TensorWrapper*>& static_input_map
-//                               - the static input map
-//    Builder::OpMap& ng_op_map  - The TF-to-nGraph op map.
-//
-//    std::function<Output<Node>(Output<Node>>
-//      create_unary_op           - Function to construct the graph implementing
-//                                 the unary op, given the input to the unop
-//                                 as an argument.
-//
-// Example usage:
-//
-//  if (n->type_string == "Square") {
-//    TF_RETURN_IF_ERROR(TranslateUnaryOp(n, static_input_map, ng_op_map,
-//                       [] (Output<Node> n) {
-//                           return
-//                           (Output<Multiply>(n,n));
-//                       });
-//  }
-OutputVector TranslateUnaryOp(const NodeContext& op, std::function<Output<Node>(Output<Node>)> create_unary_op) {
-    Output<Node> ng_input = op.get_ng_input(0);
-    auto ng_node = create_unary_op(ng_input);
-    if (ng_node != ng_input) {
-        SetTracingInfo(op.get_name(), ng_node);
-    }
-    // SaveNgOp(ng_op_map, node.get_name(), ng_node);
-    // return Status::OK();
-    return {ng_node};
+OutputVector translate_unary_op(const NodeContext& op,
+                                const std::function<shared_ptr<Node>(Output<Node>)>& create_unary_op) {
+    auto ng_input = op.get_input(0);
+    auto res = create_unary_op(ng_input);
+    set_node_name(op.get_name(), res);
+    return {res};
 }
 
-// Helper function to translate a unary op in cases where there is a one-to-one
-// mapping from TensorFlow ops to nGraph ops.
-//
-// Example usage:
-//
-//  if (n->type_string == "Abs") {
-//    TF_RETURN_IF_ERROR(TranslateUnaryOp<op::Abs>(n, static_input_map,
-//    ng_op_map));
-//  }
-//
 template <typename T>
-OutputVector TranslateUnaryOp(const NodeContext& node) {
-    return TranslateUnaryOp(node, [&node](Output<Node> n) {
-        return ConstructNgNode<T>(node.get_name(), n);
+OutputVector translate_unary_op(const NodeContext& node) {
+    return translate_unary_op(node, [](Output<Node> n) {
+        return make_shared<T>(n);
     });
 }
 
-template OutputVector TranslateUnaryOp<Abs>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Acos>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Acosh>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Asin>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Asinh>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Atan>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Atanh>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Ceiling>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Cos>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Cosh>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Exp>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Floor>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Log>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<LogicalNot>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Negative>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Relu>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Sigmoid>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Sin>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Sinh>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Sign>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<SoftPlus>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Tan>(const NodeContext& node);
-template OutputVector TranslateUnaryOp<Tanh>(const NodeContext& node);
+template OutputVector translate_unary_op<Abs>(const NodeContext& node);
+template OutputVector translate_unary_op<Acos>(const NodeContext& node);
+template OutputVector translate_unary_op<Acosh>(const NodeContext& node);
+template OutputVector translate_unary_op<Asin>(const NodeContext& node);
+template OutputVector translate_unary_op<Asinh>(const NodeContext& node);
+template OutputVector translate_unary_op<Atan>(const NodeContext& node);
+template OutputVector translate_unary_op<Atanh>(const NodeContext& node);
+template OutputVector translate_unary_op<Ceiling>(const NodeContext& node);
+template OutputVector translate_unary_op<Cos>(const NodeContext& node);
+template OutputVector translate_unary_op<Cosh>(const NodeContext& node);
+template OutputVector translate_unary_op<Exp>(const NodeContext& node);
+template OutputVector translate_unary_op<Floor>(const NodeContext& node);
+template OutputVector translate_unary_op<Log>(const NodeContext& node);
+template OutputVector translate_unary_op<LogicalNot>(const NodeContext& node);
+template OutputVector translate_unary_op<Negative>(const NodeContext& node);
+template OutputVector translate_unary_op<Relu>(const NodeContext& node);
+template OutputVector translate_unary_op<Sigmoid>(const NodeContext& node);
+template OutputVector translate_unary_op<Sin>(const NodeContext& node);
+template OutputVector translate_unary_op<Sinh>(const NodeContext& node);
+template OutputVector translate_unary_op<Sign>(const NodeContext& node);
+template OutputVector translate_unary_op<SoftPlus>(const NodeContext& node);
+template OutputVector translate_unary_op<Tan>(const NodeContext& node);
+template OutputVector translate_unary_op<Tanh>(const NodeContext& node);
+template OutputVector translate_unary_op<Swish>(const NodeContext& node);
 
 }  // namespace op
 }  // namespace tf
 }  // namespace frontend
-}  // namespace ov
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/unpack.cpp b/ngraph/frontend/tensorflow/src/op/unpack.cpp
new file mode 100644
index 00000000000000..772ee493bae75b
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/unpack.cpp
@@ -0,0 +1,29 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_unpack_op(const NodeContext& node) {
+    auto input = node.get_input(0);
+    auto axis = node.get_attribute<int64_t>("axis");
+    auto num = node.get_attribute<int64_t>("num");
+
+    auto axis_const = make_shared<Constant>(element::i64, Shape{}, axis);
+    auto res = make_shared<Split>(input, axis_const, num);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/where.cpp b/ngraph/frontend/tensorflow/src/op/where.cpp
new file mode 100644
index 00000000000000..cdddb82f5dc721
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/where.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_where_op(const NodeContext& node) {
+    auto x = node.get_input(0);
+    auto non_zero = make_shared<NonZero>(x);
+    auto transpose_order = make_shared<Constant>(element::i64, Shape{2}, vector<int64_t>{1, 0});
+    auto res = make_shared<opset8::Transpose>(non_zero, transpose_order);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op/xdivy.cpp b/ngraph/frontend/tensorflow/src/op/xdivy.cpp
new file mode 100644
index 00000000000000..121efbc58ee98d
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/xdivy.cpp
@@ -0,0 +1,32 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_x_div_y_op(const NodeContext& node) {
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+
+    auto zero = make_shared<Constant>(x.get_element_type(), Shape{}, 0);
+    auto x_is_zero = make_shared<Equal>(x, zero);
+    auto one = make_shared<Constant>(x.get_element_type(), Shape{}, 1);
+    auto select = make_shared<Select>(x_is_zero, one, y);
+    auto res = make_shared<Divide>(x, select);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/ngraph/frontend/tensorflow/src/op/zeros_like.cpp b/ngraph/frontend/tensorflow/src/op/zeros_like.cpp
new file mode 100644
index 00000000000000..0ece26c16c40f9
--- /dev/null
+++ b/ngraph/frontend/tensorflow/src/op/zeros_like.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+using namespace std;
+using namespace ov::opset8;
+
+namespace ov {
+namespace frontend {
+namespace tf {
+namespace op {
+
+OutputVector translate_zeros_like_op(const NodeContext& node) {
+    auto x = node.get_input(0);
+    auto shape_of = make_shared<ShapeOf>(x);
+    auto zero = make_shared<Constant>(x.get_element_type(), Shape{1}, 0);
+    auto res = make_shared<Broadcast>(zero, shape_of);
+    set_node_name(node.get_name(), res);
+    return res->outputs();
+}
+
+}  // namespace op
+}  // namespace tf
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/op_table.cpp b/ngraph/frontend/tensorflow/src/op_table.cpp
index dc07e7c5731474..2220fd7b1d8157 100644
--- a/ngraph/frontend/tensorflow/src/op_table.cpp
+++ b/ngraph/frontend/tensorflow/src/op_table.cpp
@@ -12,214 +12,235 @@ namespace ov {
 namespace frontend {
 namespace tf {
 namespace op {
-#define OP_CONVERTER(op) ov::OutputVector op(const NodeContext& node)
+#define OP_CONVERTER(op) OutputVector op(const NodeContext& node)
 #define OP_T_CONVERTER(op) \
     template <class T>     \
-    ov::OutputVector op(const NodeContext& node)
+    OutputVector op(const NodeContext& node)
 
-OP_T_CONVERTER(TranslateUnaryOp);
-OP_T_CONVERTER(TranslateBinaryOp);
-OP_T_CONVERTER(TranslateDirectReduceOp);
+OP_T_CONVERTER(translate_unary_op);
+OP_T_CONVERTER(translate_binary_op);
+OP_T_CONVERTER(translate_direct_reduce_op);
 
-OP_CONVERTER(TranslateAddNOp);
-OP_CONVERTER(TranslateArgMaxOp);
-OP_CONVERTER(TranslateArgMinOp);
-OP_CONVERTER(TranslateAvgPoolOp);
-OP_CONVERTER(TranslateBiasAddOp);
-OP_CONVERTER(TranslateCastOp);
-OP_CONVERTER(TranslateConcatV2Op);
-OP_CONVERTER(TranslateConstOp);
-OP_CONVERTER(TranslateConv2DOp);
-OP_CONVERTER(TranslateConv2DBackpropInputOp);
-OP_CONVERTER(TranslateConv3DOp);
-OP_CONVERTER(TranslateCumsumOp);
-OP_CONVERTER(TranslateDepthToSpaceOp);
-OP_CONVERTER(TranslateDepthwiseConv2dNativeOp);
-OP_CONVERTER(TranslateEluOp);
-OP_CONVERTER(TranslateExpandDimsOp);
-OP_CONVERTER(TranslateFillOp);
-OP_CONVERTER(TranslateFloorDivOp);
-OP_CONVERTER(TranslateFusedBatchNormOp);
-OP_CONVERTER(TranslateGatherOp);
-OP_CONVERTER(TranslateGatherV2Op);
-OP_CONVERTER(TranslateFusedConv2DOp);
-OP_CONVERTER(TranslateFusedMatMulOp);
-OP_CONVERTER(TranslateIdentityOp);
-// OP_CONVERTER(TranslateIsFiniteOp);
-// OP_CONVERTER(TranslateL2LossOp);
-OP_CONVERTER(TranslateLogSoftmaxOp);
-// OP_CONVERTER(TranslateLog1pOp);
-// OP_CONVERTER(TranslateLRNOp);
-// OP_CONVERTER(TranslateMatMulOp);
-OP_CONVERTER(TranslateMaxPoolOp);
-OP_CONVERTER(TranslateNonMaxSuppressionV2Op);
-OP_CONVERTER(TranslatePadOp);
-OP_CONVERTER(PlaceholderOp);
-OP_CONVERTER(NoOp);
-OP_CONVERTER(TranslateOneHotOp);
-// OP_CONVERTER(TranslatePackOp);
-OP_CONVERTER(TranslateRangeOp);
-OP_CONVERTER(TranslateRankOp);
-OP_CONVERTER(TranslateRandomUniformOp);
-OP_CONVERTER(TranslateRelu6Op);
-// OP_CONVERTER(TranslateReciprocalOp);
-// OP_CONVERTER(TranslateReshapeOp);
-OP_CONVERTER(RetvalOp);
-OP_CONVERTER(TranslateRollOp);
-OP_CONVERTER(TranslateRsqrtOp);
-OP_CONVERTER(TranslateSelectOp);
-// OP_CONVERTER(TranslateShapeOp);
-// OP_CONVERTER(TranslateSizeOp);
-// OP_CONVERTER(TranslateSliceOp);
-// OP_CONVERTER(transpose2);
-OP_CONVERTER(TranslateSoftmaxOp);
-// OP_CONVERTER(TranslateSpaceToDepthOp);
-// OP_CONVERTER(TranslateSplitOp);
-// OP_CONVERTER(TranslateSplitOp);
-OP_CONVERTER(TranslateSqueezeOp);
-// OP_CONVERTER(TranslateStridedSliceOp);
-OP_CONVERTER(TranslateSqrtOp);
-// OP_CONVERTER(TranslateTileOp);
-// OP_CONVERTER(TranslateTopKV2Op);
-// OP_CONVERTER(TranslateTransposeOp);
-// OP_CONVERTER(TranslateUnpackOp);
-// OP_CONVERTER(TranslateWhereOp);
-// OP_CONVERTER(TranslateXdivyOp);
-// OP_CONVERTER(TranslateZerosLikeOp);
+OP_CONVERTER(translate_add_n_op);
+OP_CONVERTER(translate_arg_max_op);
+OP_CONVERTER(translate_arg_min_op);
+OP_CONVERTER(translate_avg_pool_op);
+OP_CONVERTER(translate_bias_add_op);
+OP_CONVERTER(translate_batch_nd_and_space_nd_op);
+OP_CONVERTER(translate_cast_op);
+OP_CONVERTER(translate_concat_op);
+OP_CONVERTER(translate_const_op);
+OP_CONVERTER(translate_conv_2d_op);
+OP_CONVERTER(translate_conv_2d_backprop_input_op);
+OP_CONVERTER(translate_conv_3d_op);
+OP_CONVERTER(translate_cumsum_op);
+OP_CONVERTER(translate_crop_and_resize_op);
+OP_CONVERTER(translate_depth_to_space_op);
+OP_CONVERTER(translate_depthwise_conv_2d_native_op);
+OP_CONVERTER(translate_elu_op);
+OP_CONVERTER(translate_expand_dims_op);
+OP_CONVERTER(translate_fake_quant_op);
+OP_CONVERTER(translate_fill_op);
+OP_CONVERTER(translate_floor_div_op);
+OP_CONVERTER(translate_fused_batch_norm_op);
+OP_CONVERTER(translate_gather_op);
+OP_CONVERTER(translate_gather_v2_op);
+OP_CONVERTER(translate_gather_nd_op);
+OP_CONVERTER(translate_identity_op);
+OP_CONVERTER(translate_interpolate_op);
+OP_CONVERTER(translate_is_finite_op);
+OP_CONVERTER(translate_l2_loss_op);
+OP_CONVERTER(translate_leaky_relu_op);
+OP_CONVERTER(translate_log_softmax_op);
+OP_CONVERTER(translate_log_1p_op);
+OP_CONVERTER(translate_lrn_op);
+OP_CONVERTER(translate_mat_mul_op);
+OP_CONVERTER(translate_max_pool_op);
+OP_CONVERTER(translate_non_max_suppression_op);
+OP_CONVERTER(translate_pad_op);
+OP_CONVERTER(translate_placeholder_op);
+OP_CONVERTER(translate_no_op);
+OP_CONVERTER(translate_one_hot_op);
+OP_CONVERTER(translate_pack_op);
+OP_CONVERTER(translate_range_op);
+OP_CONVERTER(translate_rank_op);
+OP_CONVERTER(translate_random_uniform_op);
+OP_CONVERTER(translate_random_uniform_int_op);
+OP_CONVERTER(translate_relu_6_op);
+OP_CONVERTER(translate_reciprocal_op);
+OP_CONVERTER(translate_reshape_op);
+OP_CONVERTER(translate_reverse_op);
+OP_CONVERTER(translate_roll_op);
+OP_CONVERTER(translate_round_op);
+OP_CONVERTER(translate_rsqrt_op);
+OP_CONVERTER(translate_select_op);
+OP_CONVERTER(translate_shape_op);
+OP_CONVERTER(translate_size_op);
+OP_CONVERTER(translate_slice_op);
+OP_CONVERTER(translate_softmax_op);
+OP_CONVERTER(translate_space_to_depth_op);
+OP_CONVERTER(translate_split_op);
+OP_CONVERTER(translate_split_v_op);
+OP_CONVERTER(translate_square_op);
+OP_CONVERTER(translate_squeeze_op);
+OP_CONVERTER(translate_strided_slice_op);
+OP_CONVERTER(translate_sqrt_op);
+OP_CONVERTER(translate_tile_op);
+OP_CONVERTER(translate_top_k_v2_op);
+OP_CONVERTER(translate_transpose_op);
+OP_CONVERTER(translate_unpack_op);
+OP_CONVERTER(translate_where_op);
+OP_CONVERTER(translate_x_div_y_op);
+OP_CONVERTER(translate_zeros_like_op);
 
 const std::map<const std::string, const CreatorFunction> get_supported_ops() {
     return {
         // note: UnaryOp translator declaration for each op must to be added in unary_op.cpp file
-        {"Abs", TranslateUnaryOp<opset8::Abs>},
-        {"Acos", TranslateUnaryOp<opset8::Acos>},
-        {"Acosh", TranslateUnaryOp<opset8::Acosh>},
-        {"Asin", TranslateUnaryOp<opset8::Asin>},
-        {"Asinh", TranslateUnaryOp<opset8::Asinh>},
-        {"Atan", TranslateUnaryOp<opset8::Atan>},
-        {"Atanh", TranslateUnaryOp<opset8::Atanh>},
-        {"Ceil", TranslateUnaryOp<opset8::Ceiling>},
-        {"Cos", TranslateUnaryOp<opset8::Cos>},
-        {"Cosh", TranslateUnaryOp<opset8::Cosh>},
-        {"Exp", TranslateUnaryOp<opset8::Exp>},
-        {"Floor", TranslateUnaryOp<opset8::Floor>},
-        {"Log", TranslateUnaryOp<opset8::Log>},
-        {"LogicalNot", TranslateUnaryOp<opset8::LogicalNot>},
-        {"Neg", TranslateUnaryOp<opset8::Negative>},
-        {"Relu", TranslateUnaryOp<opset8::Relu>},
-        {"Sigmoid", TranslateUnaryOp<opset8::Sigmoid>},
-        {"Sin", TranslateUnaryOp<opset8::Sin>},
-        {"Sinh", TranslateUnaryOp<opset8::Sinh>},
-        {"Sign", TranslateUnaryOp<opset8::Sign>},
-        {"Softplus", TranslateUnaryOp<opset8::SoftPlus>},
-        {"Tan", TranslateUnaryOp<opset8::Tan>},
-        {"Tanh", TranslateUnaryOp<opset8::Tanh>},
+        {"Abs", translate_unary_op<opset8::Abs>},
+        {"Acos", translate_unary_op<opset8::Acos>},
+        {"Acosh", translate_unary_op<opset8::Acosh>},
+        {"Asin", translate_unary_op<opset8::Asin>},
+        {"Asinh", translate_unary_op<opset8::Asinh>},
+        {"Atan", translate_unary_op<opset8::Atan>},
+        {"Atanh", translate_unary_op<opset8::Atanh>},
+        {"Ceil", translate_unary_op<opset8::Ceiling>},
+        {"Cos", translate_unary_op<opset8::Cos>},
+        {"Cosh", translate_unary_op<opset8::Cosh>},
+        {"Exp", translate_unary_op<opset8::Exp>},
+        {"Floor", translate_unary_op<opset8::Floor>},
+        {"Log", translate_unary_op<opset8::Log>},
+        {"LogicalNot", translate_unary_op<opset8::LogicalNot>},
+        {"Neg", translate_unary_op<opset8::Negative>},
+        {"Relu", translate_unary_op<opset8::Relu>},
+        {"Sigmoid", translate_unary_op<opset8::Sigmoid>},
+        {"Sin", translate_unary_op<opset8::Sin>},
+        {"Sinh", translate_unary_op<opset8::Sinh>},
+        {"Sign", translate_unary_op<opset8::Sign>},
+        {"Softplus", translate_unary_op<opset8::SoftPlus>},
+        {"Tan", translate_unary_op<opset8::Tan>},
+        {"Tanh", translate_unary_op<opset8::Tanh>},
+        {"Swish", translate_unary_op<opset8::Swish>},
 
-        // note: BinaryOp translator declaration for each op  must to be added in binary_op.cpp file
-        {"Add", TranslateBinaryOp<opset8::Add>},
-        {"AddV2", TranslateBinaryOp<opset8::Add>},
-        {"Equal", TranslateBinaryOp<opset8::Equal>},
-        {"FloorMod", TranslateBinaryOp<opset8::FloorMod>},
-        {"Greater", TranslateBinaryOp<opset8::Greater>},
-        {"GreaterEqual", TranslateBinaryOp<opset8::GreaterEqual>},
-        {"Less", TranslateBinaryOp<opset8::Less>},
-        {"LessEqual", TranslateBinaryOp<opset8::LessEqual>},
-        {"LogicalAnd", TranslateBinaryOp<opset8::LogicalAnd>},
-        {"LogicalOr", TranslateBinaryOp<opset8::LogicalOr>},
-        {"Maximum", TranslateBinaryOp<opset8::Maximum>},
-        {"Minimum", TranslateBinaryOp<opset8::Minimum>},
-        {"Mul", TranslateBinaryOp<opset8::Multiply>},
-        {"Mod", TranslateBinaryOp<opset8::Mod>},
-        {"NotEqual", TranslateBinaryOp<opset8::NotEqual>},
-        {"Pow", TranslateBinaryOp<opset8::Power>},
-        {"RealDiv", TranslateBinaryOp<opset8::Divide>},
-        {"SquaredDifference", TranslateBinaryOp<opset8::SquaredDifference>},
-        {"Sub", TranslateBinaryOp<opset8::Subtract>},
+        // note: BinaryOp translator declaration for each op must to be added in binary_op.cpp file
+        {"Add", translate_binary_op<opset8::Add>},
+        {"AddV2", translate_binary_op<opset8::Add>},
+        {"Equal", translate_binary_op<opset8::Equal>},
+        {"FloorMod", translate_binary_op<opset8::FloorMod>},
+        {"Greater", translate_binary_op<opset8::Greater>},
+        {"GreaterEqual", translate_binary_op<opset8::GreaterEqual>},
+        {"Less", translate_binary_op<opset8::Less>},
+        {"LessEqual", translate_binary_op<opset8::LessEqual>},
+        {"LogicalAnd", translate_binary_op<opset8::LogicalAnd>},
+        {"LogicalOr", translate_binary_op<opset8::LogicalOr>},
+        {"LogicalXor", translate_binary_op<opset8::LogicalXor>},
+        {"Maximum", translate_binary_op<opset8::Maximum>},
+        {"Minimum", translate_binary_op<opset8::Minimum>},
+        {"Mul", translate_binary_op<opset8::Multiply>},
+        {"Mod", translate_binary_op<opset8::Mod>},
+        {"NotEqual", translate_binary_op<opset8::NotEqual>},
+        {"Pow", translate_binary_op<opset8::Power>},
+        {"RealDiv", translate_binary_op<opset8::Divide>},
+        {"SquaredDifference", translate_binary_op<opset8::SquaredDifference>},
+        {"Sub", translate_binary_op<opset8::Subtract>},
 
         // note: ReduceOp translator declaration for each op must to be added in reduce.cpp file
-        {"Any", TranslateDirectReduceOp<opset8::ReduceLogicalOr>},
-        {"All", TranslateDirectReduceOp<opset8::ReduceLogicalAnd>},
-        {"Max", TranslateDirectReduceOp<opset8::ReduceMax>},
-        {"Mean", TranslateDirectReduceOp<opset8::ReduceMean>},
-        {"Min", TranslateDirectReduceOp<opset8::ReduceMin>},
-        {"Prod", TranslateDirectReduceOp<opset8::ReduceProd>},
-        {"Sum", TranslateDirectReduceOp<opset8::ReduceSum>},
+        {"Any", translate_direct_reduce_op<opset8::ReduceLogicalOr>},
+        {"All", translate_direct_reduce_op<opset8::ReduceLogicalAnd>},
+        {"Max", translate_direct_reduce_op<opset8::ReduceMax>},
+        {"Mean", translate_direct_reduce_op<opset8::ReduceMean>},
+        {"Min", translate_direct_reduce_op<opset8::ReduceMin>},
+        {"Prod", translate_direct_reduce_op<opset8::ReduceProd>},
+        {"Sum", translate_direct_reduce_op<opset8::ReduceSum>},
 
         // Separate translators:
-        {"AddN", TranslateAddNOp},
-        {"ArgMax", TranslateArgMaxOp},
-        {"ArgMin", TranslateArgMinOp},
-        {"AvgPool", TranslateAvgPoolOp},
-        {"BiasAdd", TranslateBiasAddOp},
-        {"Cast", TranslateCastOp},
-        {"ConcatV2", TranslateConcatV2Op},
-        {"Const", TranslateConstOp},
-        {"Conv2D", TranslateConv2DOp},
-        {"Conv2DBackpropInput", TranslateConv2DBackpropInputOp},
-        {"Conv3D", TranslateConv3DOp},
-        {"Cumsum", TranslateCumsumOp},
-        {"DepthToSpace", TranslateDepthToSpaceOp},
-        {"DepthwiseConv2dNative", TranslateDepthwiseConv2dNativeOp},
-        {"Elu", TranslateEluOp},
-        {"ExpandDims", TranslateExpandDimsOp},
-        {"Fill", TranslateFillOp},
-        {"FloorDiv", TranslateFloorDivOp},
-        {"FusedBatchNorm", TranslateFusedBatchNormOp},
-        {"FusedBatchNormV2", TranslateFusedBatchNormOp},
-        {"FusedBatchNormV3", TranslateFusedBatchNormOp},
-        {"Gather", TranslateGatherOp},
-        {"GatherV2", TranslateGatherV2Op},
-        {"_FusedConv2D", TranslateFusedConv2DOp},
-        {"_FusedMatMul", TranslateFusedMatMulOp},
-        {"Identity", TranslateIdentityOp},
-        //{"IsFinite", TranslateIsFiniteOp},
-        //{"L2Loss", TranslateL2LossOp},
-        {"LogSoftmax", TranslateLogSoftmaxOp},
-        //{"Log1p", TranslateLog1pOp},
-        //{"LRN", TranslateLRNOp},
-        //{"MatMul", TranslateMatMulOp},
-        {"MaxPool", TranslateMaxPoolOp},
-        {"MaxPool3D", TranslateMaxPoolOp},
-        //{"NonMaxSuppressionV2", TranslateNonMaxSuppressionV2Op},
-        {"MirrorPad", TranslatePadOp},
-        {"NoOp", NoOp},  // do nothing
-        {"OneHot", TranslateOneHotOp},
-        //{"Pack", TranslatePackOp},
-        {"Pad", TranslatePadOp},
-        {"PadV2", TranslatePadOp},
-        //{"_Arg", ArgOp}, // should be registered as an extension in OVTF
-        {"Placeholder", PlaceholderOp},
-        // PreventGradient is just Identity in dataflow terms, so reuse that.
-        {"PreventGradient", TranslateIdentityOp},
-        //{"Range", TranslateRangeOp},
-        //{"Rank", TranslateRankOp},
-        {"RandomUniform", TranslateRandomUniformOp},
-        //{"Reciprocal", TranslateReciprocalOp},
-        {"Relu6", TranslateRelu6Op},
-        //{"Reshape", TranslateReshapeOp},
-        {"_Retval", RetvalOp},
-        {"Roll", TranslateRollOp},
-        {"Rsqrt", TranslateRsqrtOp},
-        {"Select", TranslateSelectOp},
-        {"SelectV2", TranslateSelectOp},
-        //{"Shape", TranslateShapeOp},
-        //{"Size", TranslateSizeOp},
-        //{"Slice", TranslateSliceOp},
-        //{"Snapshot", TranslateIdentityOp},
-        {"Softmax", TranslateSoftmaxOp},
-        //{"SpaceToDepth", TranslateSpaceToDepthOp},
-        //{"Split", TranslateSplitOp},
-        //{"SplitV", TranslateSplitVOp},
-        {"Sqrt", TranslateSqrtOp},
-        //{"Square", TranslateSquareOp},
-        {"Squeeze", TranslateSqueezeOp},
-        //{"StridedSlice", TranslateStridedSliceOp},
-        //{"Tile", TranslateTileOp},
-        //{"TopKV2", TranslateTopKV2Op},
-        //{"Transpose", TranslateTransposeOp},
-        //{"Unpack", TranslateUnpackOp},
-        //{"Where", TranslateWhereOp},
-        //{"Xdivy", TranslateXdivyOp},
-        //{"ZerosLike", TranslateZerosLikeOp},
+        {"AddN", translate_add_n_op},
+        {"ArgMax", translate_arg_max_op},
+        {"ArgMin", translate_arg_min_op},
+        {"AvgPool", translate_avg_pool_op},
+        {"BatchToSpaceND", translate_batch_nd_and_space_nd_op},
+        {"BiasAdd", translate_bias_add_op},
+        {"Cast", translate_cast_op},
+        {"Concat", translate_concat_op},
+        {"ConcatV2", translate_concat_op},
+        {"Const", translate_const_op},
+        {"Conv2D", translate_conv_2d_op},
+        {"Conv2DBackpropInput", translate_conv_2d_backprop_input_op},
+        {"Conv3D", translate_conv_3d_op},
+        {"CropAndResize", translate_crop_and_resize_op},
+        {"Cumsum", translate_cumsum_op},
+        {"DepthToSpace", translate_depth_to_space_op},
+        {"DepthwiseConv2dNative", translate_depthwise_conv_2d_native_op},
+        {"Elu", translate_elu_op},
+        {"ExpandDims", translate_expand_dims_op},
+        {"FakeQuantWithMinMaxVars", translate_fake_quant_op},
+        {"Fill", translate_fill_op},
+        {"FloorDiv", translate_floor_div_op},
+        {"FusedBatchNorm", translate_fused_batch_norm_op},
+        {"FusedBatchNormV2", translate_fused_batch_norm_op},
+        {"FusedBatchNormV3", translate_fused_batch_norm_op},
+        {"Gather", translate_gather_op},
+        {"GatherV2", translate_gather_v2_op},
+        {"GatherNd", translate_gather_nd_op},
+        {"Identity", translate_identity_op},
+        {"IdentityN", translate_identity_op},
+        {"IsFinite", translate_is_finite_op},
+        {"L2Loss", translate_l2_loss_op},
+        {"LeakyRelu", translate_leaky_relu_op},
+        {"LogSoftmax", translate_log_softmax_op},
+        {"Log1p", translate_log_1p_op},
+        {"LRN", translate_lrn_op},
+        {"MatMul", translate_mat_mul_op},
+        {"MaxPool", translate_max_pool_op},
+        {"MaxPool3D", translate_max_pool_op},
+        {"MirrorPad", translate_pad_op},
+        {"NonMaxSuppression", translate_non_max_suppression_op},
+        {"NonMaxSuppressionV2", translate_non_max_suppression_op},
+        {"NonMaxSuppressionV3", translate_non_max_suppression_op},
+        {"NonMaxSuppressionV4", translate_non_max_suppression_op},
+        {"NonMaxSuppressionV5", translate_non_max_suppression_op},
+        {"NoOp", translate_no_op},  // do nothing
+        {"OneHot", translate_one_hot_op},
+        {"Pack", translate_pack_op},
+        {"Pad", translate_pad_op},
+        {"PadV2", translate_pad_op},
+        {"Placeholder", translate_placeholder_op},
+        {"PreventGradient", translate_identity_op},
+        {"Range", translate_range_op},
+        {"Rank", translate_rank_op},
+        {"RandomUniform", translate_random_uniform_op},
+        {"RandomUniformInt", translate_random_uniform_int_op},
+        {"Reciprocal", translate_reciprocal_op},
+        {"Relu6", translate_relu_6_op},
+        {"Reshape", translate_reshape_op},
+        {"Reverse", translate_reverse_op},
+        {"ReverseV2", translate_reverse_op},
+        {"ResizeBilinear", translate_interpolate_op},
+        {"ResizeNearestNeighbor", translate_interpolate_op},
+        {"Roll", translate_roll_op},
+        {"Round", translate_round_op},
+        {"Rsqrt", translate_rsqrt_op},
+        {"Select", translate_select_op},
+        {"SelectV2", translate_select_op},
+        {"Shape", translate_shape_op},
+        {"Size", translate_size_op},
+        {"Slice", translate_slice_op},
+        {"Snapshot", translate_identity_op},
+        {"Softmax", translate_softmax_op},
+        {"SpaceToDepth", translate_space_to_depth_op},
+        {"Split", translate_split_op},
+        {"SplitV", translate_split_v_op},
+        {"Sqrt", translate_sqrt_op},
+        {"Square", translate_square_op},
+        {"Squeeze", translate_squeeze_op},
+        {"SpaceToBatchND", translate_batch_nd_and_space_nd_op},
+        {"StridedSlice", translate_strided_slice_op},
+        {"Tile", translate_tile_op},
+        {"TopKV2", translate_top_k_v2_op},
+        {"Transpose", translate_transpose_op},
+        {"Unpack", translate_unpack_op},
+        {"Where", translate_where_op},
+        {"Xdivy", translate_x_div_y_op},
+        {"ZerosLike", translate_zeros_like_op},
     };
 };
 }  // namespace op
diff --git a/ngraph/frontend/tensorflow/src/op_table.hpp b/ngraph/frontend/tensorflow/src/op_table.hpp
index 81bc6b3ba492e1..7cf9f4ba47e1bb 100644
--- a/ngraph/frontend/tensorflow/src/op_table.hpp
+++ b/ngraph/frontend/tensorflow/src/op_table.hpp
@@ -6,11 +6,11 @@
 
 #include <functional>
 #include <map>
-#include <openvino/core/node_vector.hpp>
 #include <string>
 
 #include "ngraph_conversions.hpp"
 #include "node_context.hpp"
+#include "openvino/core/node_vector.hpp"
 #include "utils.hpp"
 
 namespace ov {
diff --git a/ngraph/frontend/tensorflow/src/pass/transpose_sinking.cpp b/ngraph/frontend/tensorflow/src/pass/transpose_sinking.cpp
index d74b5341694adf..cdfccebf5980de 100644
--- a/ngraph/frontend/tensorflow/src/pass/transpose_sinking.cpp
+++ b/ngraph/frontend/tensorflow/src/pass/transpose_sinking.cpp
@@ -4,8 +4,7 @@
 
 #include "transpose_sinking.hpp"
 
-#include <openvino/opsets/opset8.hpp>
-
+#include "openvino/opsets/opset8.hpp"
 #include "utils.hpp"
 
 using namespace std;
@@ -51,14 +50,14 @@ static string describe(shared_ptr<Node> node) {
 static shared_ptr<Transpose> make_transpose(const Output<Node>& arg, const AxisVector& input_order) {
     auto order = std::make_shared<Constant>(element::u64, Shape{input_order.size()}, input_order);
     auto transpose = make_shared<Transpose>(arg, order);
-    NGRAPH_VLOG() << "Make Transpose " << describe<Transpose>(transpose);
+    NGRAPH_DEBUG << "Make Transpose " << describe<Transpose>(transpose);
     return transpose;
 }
 
 static shared_ptr<Reshape> make_reshape(const Output<Node>& arg, const AxisVector& input_order) {
     auto order = std::make_shared<Constant>(element::u64, Shape{input_order.size()}, input_order);
     auto transpose = make_shared<Reshape>(arg, order, false);
-    NGRAPH_VLOG() << "Make Reshape " << describe<Reshape>(transpose);
+    NGRAPH_DEBUG << "Make Reshape " << describe<Reshape>(transpose);
     return transpose;
 }
 
@@ -66,14 +65,14 @@ static void write_transposemap(TransposeMap& reorders,
                                const Output<Node>& target,
                                const shared_ptr<Transpose>& transpose) {
     auto name = target.get_node()->get_name() + "." + to_string(target.get_index());
-    NGRAPH_VLOG() << "Write TransposeMap[" << name << "] = " << describe<Transpose>(transpose);
+    NGRAPH_DEBUG << "Write TransposeMap[" << name << "] = " << describe<Transpose>(transpose);
     reorders[name] = transpose;
 }
 
 static shared_ptr<Transpose> read_transposemap(TransposeMap& reorders, const Output<Node>& target) {
     auto name = target.get_node()->get_name() + "." + to_string(target.get_index());
     auto transpose = reorders[name];
-    NGRAPH_VLOG() << "Read TransposeMap[" << name << "]  -> " << describe<Transpose>(transpose);
+    NGRAPH_DEBUG << "Read TransposeMap[" << name << "]  -> " << describe<Transpose>(transpose);
     return transpose;
 }
 
@@ -86,31 +85,31 @@ static shared_ptr<Transpose> combine_transposes(const shared_ptr<Transpose>& t1,
     auto perm_t2 = apply_permutation(perm_t1, t2_const->get_axis_vector_val());
 
     auto combined = make_transpose(t2->input_value(0), perm_t2);
-    NGRAPH_VLOG() << "Combining " << describe<Transpose>(t1) << " and " << describe<Transpose>(t2) << " into "
-                  << describe<Transpose>(combined);
+    NGRAPH_DEBUG << "Combining " << describe<Transpose>(t1) << " and " << describe<Transpose>(t2) << " into "
+                 << describe<Transpose>(combined);
     return combined;
 }
 
 static void insert_transpose(const shared_ptr<Node>& target, const shared_ptr<Node>& transpose, size_t input_index) {
-    NGRAPH_VLOG() << "Inserting transpose at input " << target->get_name() << " input index " << input_index;
+    NGRAPH_DEBUG << "Inserting transpose at input " << target->get_name() << " input index " << input_index;
     auto arg = target->input(input_index).get_source_output();
-    NGRAPH_VLOG() << "Arg shape: " << arg.get_shape();
+    NGRAPH_DEBUG << "Arg shape: " << arg.get_shape();
     auto new_order = as_type_ptr<Constant>(transpose->input_value(1).get_node_shared_ptr());
     auto new_transpose = make_transpose(arg.get_node_shared_ptr(), new_order->get_axis_vector_val());
-    NGRAPH_VLOG() << "Inserting transpose " << describe<Transpose>(new_transpose) << " at input " << target->get_name()
-                  << " input index " << input_index;
+    NGRAPH_DEBUG << "Inserting transpose " << describe<Transpose>(new_transpose) << " at input " << target->get_name()
+                 << " input index " << input_index;
 
     target->input(input_index).replace_source_output(new_transpose->output(0));
 }
 
 static void delete_transpose(const shared_ptr<Node>& transpose) {
-    NGRAPH_VLOG() << "Removing transpose " << transpose->get_name();
+    NGRAPH_DEBUG << "Removing transpose " << transpose->get_name();
     if (!transpose->get_users().empty()) {
         Output<Node> output = transpose->output(0);
-        NGRAPH_VLOG() << "output " << output.get_node_shared_ptr()->get_name();
-        NGRAPH_VLOG() << "target input size " << output.get_target_inputs().size();
+        NGRAPH_DEBUG << "output " << output.get_node_shared_ptr()->get_name();
+        NGRAPH_DEBUG << "target input size " << output.get_target_inputs().size();
         for (auto input : output.get_target_inputs()) {
-            NGRAPH_VLOG() << "input " << input.get_node()->get_name();
+            NGRAPH_DEBUG << "input " << input.get_node()->get_name();
             input.replace_source_output(transpose->input_value(0));
         }
     }
@@ -118,7 +117,7 @@ static void delete_transpose(const shared_ptr<Node>& transpose) {
 
 static void mark_transpose_for_deletion(const shared_ptr<Node>& transpose,
                                         set<shared_ptr<Node>>& transposes_to_delete) {
-    NGRAPH_VLOG() << "Marking transpose " << transpose->get_name() << " for deletion";
+    NGRAPH_DEBUG << "Marking transpose " << transpose->get_name() << " for deletion";
     transposes_to_delete.insert(transpose);
 }
 
@@ -161,8 +160,8 @@ static void convert_binary_to_default_order(const shared_ptr<Node>& binary,
     }
     input.replace_source_output(new_node->output(0));
 
-    NGRAPH_VLOG() << "right = " << ngraph::vector_to_string(right.get_shape()) << ", "
-                  << right.get_node_shared_ptr()->get_name();
+    NGRAPH_DEBUG << "right = " << ngraph::vector_to_string(right.get_shape()) << ", "
+                 << right.get_node_shared_ptr()->get_name();
     // this should now insert transpose on right
     mark_transpose_for_deletion(right_t, transposes_to_delete);
     write_transposemap(reorders, binary, right_t);
@@ -181,8 +180,8 @@ static void materialize_shapes(const shared_ptr<Node>& n,
         // materialize all pending transposes, flush pending transposes
         auto arg = n->input_value(i);
         auto arg_transpose = read_transposemap(reorders, arg);
-        NGRAPH_VLOG() << "Materializing " << describe<Transpose>(arg_transpose) << " for "
-                      << arg.get_node_shared_ptr()->get_name();
+        NGRAPH_DEBUG << "Materializing " << describe<Transpose>(arg_transpose) << " for "
+                     << arg.get_node_shared_ptr()->get_name();
         mark_transpose_for_deletion(arg_transpose, transposes_to_delete);
         auto arg_transpose_order = as_type_ptr<Constant>(arg_transpose->input_value(1).get_node_shared_ptr());
         if (arg_transpose_order->get_axis_vector_val() != ngraph::get_default_order(arg.get_shape())) {
@@ -195,7 +194,7 @@ static void materialize_shapes(const shared_ptr<Node>& n,
 static void sink_transpose(const shared_ptr<Transpose>& transpose,
                            TransposeMap& reorders,
                            set<shared_ptr<Node>>& transposes_to_delete) {
-    NGRAPH_VLOG() << "Sinking Transpose :" << describe<Transpose>(transpose);
+    NGRAPH_DEBUG << "Sinking Transpose :" << describe<Transpose>(transpose);
     auto transpose_in = transpose->input_value(0);
     auto orig_transpose = read_transposemap(reorders, transpose_in);
     // combine both transposes
@@ -213,7 +212,7 @@ static void sink_unary(const shared_ptr<Node>& n,
                        TransposeMap& reorders,
                        set<shared_ptr<Node>>& /* transposes_to_delete */) {
     auto arg_transpose = read_transposemap(reorders, n->input_value(0));
-    NGRAPH_VLOG() << "Propagating " << describe<Transpose>(arg_transpose) << " for " << n->get_name();
+    NGRAPH_DEBUG << "Propagating " << describe<Transpose>(arg_transpose) << " for " << n->get_name();
     write_transposemap(reorders, n, arg_transpose);
 }
 
@@ -233,15 +232,15 @@ static void sink_binary(const shared_ptr<Node>& binary,
     auto left_mismatch = left_order != ngraph::get_default_order(left.get_shape());
     auto right_mismatch = right_order != ngraph::get_default_order(right.get_shape());
 
-    NGRAPH_VLOG() << "Sink binary " << binary->get_name() << " left transpose: " << ngraph::vector_to_string(left_order)
-                  << " left default: " << ngraph::vector_to_string(ngraph::get_default_order(left.get_shape()))
-                  << " right transpose: " << ngraph::vector_to_string(right_order)
-                  << " right default: " << ngraph::vector_to_string(ngraph::get_default_order(right.get_shape()));
+    NGRAPH_DEBUG << "Sink binary " << binary->get_name() << " left transpose: " << ngraph::vector_to_string(left_order)
+                 << " left default: " << ngraph::vector_to_string(ngraph::get_default_order(left.get_shape()))
+                 << " right transpose: " << ngraph::vector_to_string(right_order)
+                 << " right default: " << ngraph::vector_to_string(ngraph::get_default_order(right.get_shape()));
 
     if ((left_order.size() == right_order.size() && left_order == right_order) || (!left_mismatch && !right_mismatch)) {
         // Propagate the reshape which matches the shape of the binary node
         auto new_transpose = (binary->get_output_shape(0) == left.get_shape()) ? left_t : right_t;
-        NGRAPH_VLOG() << "Propagating " << describe<Transpose>(new_transpose) << " for " << binary->get_name();
+        NGRAPH_DEBUG << "Propagating " << describe<Transpose>(new_transpose) << " for " << binary->get_name();
         write_transposemap(reorders, binary, new_transpose);
         // at this point, both transposes will be eventually removed
         mark_transpose_for_deletion(left_t, transposes_to_delete);
@@ -283,10 +282,10 @@ static void sink_pad(shared_ptr<Pad> n, TransposeMap& reorders, set<shared_ptr<N
     auto new_end = make_shared<Constant>(element::i64, Shape{pad_end.size()}, pad_end);
     auto new_pad = make_shared<Pad>(dummy_correct_shape, new_begin, new_end, n->input_value(3), n->get_pad_mode());
     replace_node(dummy_correct_shape, n->input_value(0).get_node_shared_ptr());
-    NGRAPH_VLOG() << "Replacing " << n->get_name() << " with " << new_pad->get_name();
+    NGRAPH_DEBUG << "Replacing " << n->get_name() << " with " << new_pad->get_name();
     replace_node(n, new_pad);
     auto new_transpose = make_transpose(new_pad, order);
-    NGRAPH_VLOG() << "Propagating " << describe<Transpose>(new_transpose) << " for " << n->get_name();
+    NGRAPH_DEBUG << "Propagating " << describe<Transpose>(new_transpose) << " for " << n->get_name();
     write_transposemap(reorders, new_pad, new_transpose);
 }
 
@@ -315,7 +314,7 @@ static void sink_concat(const shared_ptr<Concat>& n,
         auto iarg_transpose_order = as_type_ptr<Constant>(iarg_transpose->input_value(1).get_node_shared_ptr());
         auto iorder = iarg_transpose_order->get_axis_vector_val();
         if (iorder != order) {
-            NGRAPH_VLOG() << " input order at " << i << "-th arg is different from first arg";
+            NGRAPH_DEBUG << " input order at " << i << "-th arg is different from first arg";
             materialize_shapes(n, reorders, transposes_to_delete);
             return;
         }
@@ -331,14 +330,14 @@ static void sink_concat(const shared_ptr<Concat>& n,
     auto new_concat = make_shared<Concat>(new_args, new_axis);
     // put back the original arguments
     for (size_t i = 0; i < new_concat->get_input_size(); i++) {
-        NGRAPH_VLOG() << "Replacing " << new_concat->get_name() << " input " << i << " with " << n->get_name()
-                      << " input " << i;
+        NGRAPH_DEBUG << "Replacing " << new_concat->get_name() << " input " << i << " with " << n->get_name()
+                     << " input " << i;
         new_concat->input(i).replace_source_output(n->input_value(i));
     }
-    NGRAPH_VLOG() << "Replacing " << n->get_name() << " with " << new_concat->get_name();
+    NGRAPH_DEBUG << "Replacing " << n->get_name() << " with " << new_concat->get_name();
     replace_node(n, new_concat);
     auto new_transpose = make_transpose(new_concat, order);
-    NGRAPH_VLOG() << "Propagating " << describe<Transpose>(new_transpose) << " for " << n->get_name();
+    NGRAPH_DEBUG << "Propagating " << describe<Transpose>(new_transpose) << " for " << n->get_name();
     write_transposemap(reorders, new_concat, new_transpose);
 }
 
@@ -359,7 +358,7 @@ bool ov::frontend::tf::pass::TransposeSinkingOVTF::run_on_function(shared_ptr<Fu
     // STEP 1 : Sink or Swim transposes away for op clusters
     try {
         for (const auto& n : f->get_ordered_ops()) {
-            NGRAPH_VLOG() << "Processing " << n->get_name();
+            NGRAPH_DEBUG << "Processing " << n->get_name();
             // collect output shape of all Result nodes for a sanity check
             if (ngraph::op::is_output(n)) {
                 orig_result_out_shape[n->get_name()] = n->get_output_shape(0);
@@ -379,19 +378,19 @@ bool ov::frontend::tf::pass::TransposeSinkingOVTF::run_on_function(shared_ptr<Fu
             }
         }
     } catch (...) {
-        NGRAPH_VLOG() << "Caught exception while sinking op";
+        NGRAPH_DEBUG << "Caught exception while sinking op";
         return false;
     }
 
     // STEP 2: purge all the transposes we either sunk or swam.
-    NGRAPH_VLOG() << "Purging transposes ";
-    for (auto r : transposes_to_delete) {
+    NGRAPH_DEBUG << "Purging transposes ";
+    for (const auto& r : transposes_to_delete) {
         delete_transpose(r);
     }
 
     // STEP 3: fix wrong shape info wholesale
-    NGRAPH_VLOG() << "Fixing wrong shape info for the whole graph";
-    for (auto n : f->get_ordered_ops()) {
+    NGRAPH_DEBUG << "Fixing wrong shape info for the whole graph";
+    for (const auto& n : f->get_ordered_ops()) {
         n->revalidate_and_infer_types();
     }
 
diff --git a/ngraph/frontend/tensorflow/src/pass/transpose_sinking.hpp b/ngraph/frontend/tensorflow/src/pass/transpose_sinking.hpp
index 53082d07d6524a..d26f64cababbf0 100644
--- a/ngraph/frontend/tensorflow/src/pass/transpose_sinking.hpp
+++ b/ngraph/frontend/tensorflow/src/pass/transpose_sinking.hpp
@@ -2,8 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <openvino/pass/pass.hpp>
+#pragma once
 
+#include "openvino/pass/pass.hpp"
 #include "tensorflow_frontend/utility.hpp"
 
 namespace ov {
diff --git a/ngraph/frontend/tensorflow/src/place.cpp b/ngraph/frontend/tensorflow/src/place.cpp
index 25f6126c1a8bce..e4cad115fb4a25 100644
--- a/ngraph/frontend/tensorflow/src/place.cpp
+++ b/ngraph/frontend/tensorflow/src/place.cpp
@@ -17,7 +17,7 @@ namespace frontend {
 bool PlaceTF::is_input() const {
     const auto& model_ins = m_input_model.get_inputs();
 
-    const auto cmp = [this](const ngraph::frontend::Place::Ptr& p) {
+    const auto cmp = [this](const ov::frontend::Place::Ptr& p) {
         return p.get() == this;
     };
     return std::find_if(model_ins.begin(), model_ins.end(), cmp) != model_ins.end();
@@ -25,13 +25,13 @@ bool PlaceTF::is_input() const {
 
 bool PlaceTF::is_output() const {
     const auto& model_outs = m_input_model.get_outputs();
-    const auto cmp = [this](const ngraph::frontend::Place::Ptr& p) {
+    const auto cmp = [this](const ov::frontend::Place::Ptr& p) {
         return p.get() == this;
     };
     return std::find_if(model_outs.begin(), model_outs.end(), cmp) != model_outs.end();
 }
 
-OpPlaceTF::OpPlaceTF(const ngraph::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder)
+OpPlaceTF::OpPlaceTF(const ov::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder)
     : PlaceTF(input_model, {op_decoder->get_op_name()}),
       m_op_decoder(op_decoder) {}
 
@@ -63,34 +63,34 @@ void OpPlaceTF::add_in_port(const std::shared_ptr<InPortPlaceTF>& input, const s
     m_input_ports[name].push_back(input);
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_input_port(const std::string& name) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_input_port(const std::string& name) const {
     FRONT_END_GENERAL_CHECK(m_input_ports.at(name).size() == 1, "Only one input port should exist.");
     return m_input_ports.at(name)[0];
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_input_port(int outputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_input_port(int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(m_input_ports.size() == 1, "Only one named input port should exist.");
     return m_input_ports.begin()->second[outputPortIndex];
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_output_port(int outputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_output_port(int outputPortIndex) const {
     FRONT_END_GENERAL_CHECK(m_output_ports.size() > outputPortIndex, "No port with index: ", outputPortIndex);
     return m_output_ports[outputPortIndex];
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_output_port() const {
+ov::frontend::Place::Ptr OpPlaceTF::get_output_port() const {
     FRONT_END_GENERAL_CHECK(m_output_ports.size() == 1, "Only one output port should exist.");
     return m_output_ports[0];
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_input_port() const {
+ov::frontend::Place::Ptr OpPlaceTF::get_input_port() const {
     FRONT_END_GENERAL_CHECK(m_input_ports.size() == 1 && m_input_ports.begin()->second.size() == 1,
                             "Only one input port should exist.");
     return m_input_ports.begin()->second[0];
 }
 
-std::vector<ngraph::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations() const {
-    std::vector<ngraph::frontend::Place::Ptr> consuming_ops;
+std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations() const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ops;
     for (const auto& out_port : m_output_ports) {
         auto consuming_ops_out = out_port->get_consuming_operations();
         consuming_ops.insert(consuming_ops.end(), consuming_ops_out.begin(), consuming_ops_out.end());
@@ -98,12 +98,12 @@ std::vector<ngraph::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations()
     return consuming_ops;
 }
 
-std::vector<ngraph::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations(int outputPortIndex) const {
+std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_operations(int outputPortIndex) const {
     return get_output_port(outputPortIndex)->get_consuming_operations();
 }
 
-std::vector<ngraph::frontend::Place::Ptr> OpPlaceTF::get_consuming_ports() const {
-    std::vector<ngraph::frontend::Place::Ptr> consuming_ports;
+std::vector<ov::frontend::Place::Ptr> OpPlaceTF::get_consuming_ports() const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ports;
     for (const auto& out_port : m_output_ports) {
         auto consuming_ops_out = out_port->get_consuming_ports();
         consuming_ports.insert(consuming_ports.end(), consuming_ops_out.begin(), consuming_ops_out.end());
@@ -111,53 +111,52 @@ std::vector<ngraph::frontend::Place::Ptr> OpPlaceTF::get_consuming_ports() const
     return consuming_ports;
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_input_port(const std::string& inputName, int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_input_port(const std::string& inputName, int inputPortIndex) const {
     FRONT_END_GENERAL_CHECK(inputPortIndex <= m_input_ports.at(inputName).size(), "inputPortIndex is out of bounds.");
     return m_input_ports.at(inputName)[inputPortIndex];
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_source_tensor() const {
+ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor() const {
     return get_input_port()->get_source_tensor();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_source_tensor(const std::string& inputName) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor(const std::string& inputName) const {
     return get_input_port(inputName)->get_source_tensor();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_source_tensor(int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor(int inputPortIndex) const {
     return get_input_port(inputPortIndex)->get_source_tensor();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_source_tensor(const std::string& inputName, int inputPortIndex) const {
     return get_input_port(inputName, inputPortIndex)->get_source_tensor();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_target_tensor() const {
+ov::frontend::Place::Ptr OpPlaceTF::get_target_tensor() const {
     return get_output_port()->get_target_tensor();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_producing_operation(const std::string& inputName) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation(const std::string& inputName) const {
     return get_input_port(inputName)->get_producing_operation();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_producing_operation(const std::string& inputName,
-                                                                int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation(const std::string& inputName, int inputPortIndex) const {
     return get_input_port(inputName, inputPortIndex)->get_producing_operation();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation() const {
     return get_input_port()->get_producing_operation();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_producing_operation(int inputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_producing_operation(int inputPortIndex) const {
     return get_input_port(inputPortIndex)->get_producing_operation();
 }
 
-ngraph::frontend::Place::Ptr OpPlaceTF::get_target_tensor(int outputPortIndex) const {
+ov::frontend::Place::Ptr OpPlaceTF::get_target_tensor(int outputPortIndex) const {
     return get_output_port(outputPortIndex)->get_target_tensor();
 }
 
-TensorPlaceTF::TensorPlaceTF(const ngraph::frontend::InputModel& input_model,
+TensorPlaceTF::TensorPlaceTF(const ov::frontend::InputModel& input_model,
                              const ov::PartialShape& pshape,
                              ov::element::Type type,
                              const std::vector<std::string>& names)
@@ -165,8 +164,8 @@ TensorPlaceTF::TensorPlaceTF(const ngraph::frontend::InputModel& input_model,
       m_pshape(pshape),
       m_type(type) {}
 
-std::vector<ngraph::frontend::Place::Ptr> TensorPlaceTF::get_consuming_ports() const {
-    std::vector<ngraph::frontend::Place::Ptr> consuming_ports;
+std::vector<ov::frontend::Place::Ptr> TensorPlaceTF::get_consuming_ports() const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ports;
     for (const auto& consuming_port : m_consuming_ports) {
         if (const auto& locked = consuming_port.lock()) {
             consuming_ports.push_back(locked);
@@ -177,7 +176,7 @@ std::vector<ngraph::frontend::Place::Ptr> TensorPlaceTF::get_consuming_ports() c
     return consuming_ports;
 }
 
-ngraph::frontend::Place::Ptr TensorPlaceTF::get_producing_port() const {
+ov::frontend::Place::Ptr TensorPlaceTF::get_producing_port() const {
     FRONT_END_GENERAL_CHECK(m_producing_ports.size() == 1, "Only one producing port is supported.");
     if (const auto& producing_port = m_producing_ports[0].lock()) {
         return producing_port;
@@ -193,8 +192,8 @@ void TensorPlaceTF::add_consuming_port(const std::shared_ptr<InPortPlaceTF>& in_
     m_consuming_ports.push_back(in_port);
 }
 
-std::vector<ngraph::frontend::Place::Ptr> TensorPlaceTF::get_consuming_operations() const {
-    std::vector<ngraph::frontend::Place::Ptr> consuming_ops;
+std::vector<ov::frontend::Place::Ptr> TensorPlaceTF::get_consuming_operations() const {
+    std::vector<ov::frontend::Place::Ptr> consuming_ops;
     for (const auto& consuming_port : m_consuming_ports) {
         if (auto port_ptr = consuming_port.lock()) {
             auto port_consuming_ops = port_ptr->get_consuming_operations();
@@ -206,7 +205,7 @@ std::vector<ngraph::frontend::Place::Ptr> TensorPlaceTF::get_consuming_operation
     return consuming_ops;
 }
 
-bool TensorPlaceTF::is_equal_data(ngraph::frontend::Place::Ptr another) const {
+bool TensorPlaceTF::is_equal_data(ov::frontend::Place::Ptr another) const {
     auto consuming_ports = get_consuming_ports();
     bool eq_to_consuming_port =
         std::any_of(consuming_ports.begin(), consuming_ports.end(), [&another](const Ptr& place) {
@@ -215,7 +214,7 @@ bool TensorPlaceTF::is_equal_data(ngraph::frontend::Place::Ptr another) const {
     return is_equal(another) || get_producing_port()->is_equal(another) || eq_to_consuming_port;
 }
 
-ngraph::frontend::Place::Ptr TensorPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr TensorPlaceTF::get_producing_operation() const {
     return get_producing_port()->get_producing_operation();
 }
 
@@ -237,29 +236,29 @@ void InPortPlaceTF::set_source_tensor(const std::weak_ptr<TensorPlaceTF>& source
     m_source_tensor = source_tensor;
 }
 
-std::vector<ngraph::frontend::Place::Ptr> InPortPlaceTF::get_consuming_operations() const {
+std::vector<ov::frontend::Place::Ptr> InPortPlaceTF::get_consuming_operations() const {
     if (const auto& consuming_op = m_op.lock()) {
         return {consuming_op};
     }
     FRONT_END_THROW("Operation has expired.");
 }
 
-ngraph::frontend::Place::Ptr InPortPlaceTF::get_source_tensor() const {
+ov::frontend::Place::Ptr InPortPlaceTF::get_source_tensor() const {
     if (const auto& tensor = m_source_tensor.lock()) {
         return tensor;
     }
     FRONT_END_THROW("Source Tensor has expired.");
 }
 
-ngraph::frontend::Place::Ptr InPortPlaceTF::get_producing_port() const {
+ov::frontend::Place::Ptr InPortPlaceTF::get_producing_port() const {
     return get_source_tensor()->get_producing_port();
 }
 
-bool InPortPlaceTF::is_equal_data(ngraph::frontend::Place::Ptr another) const {
+bool InPortPlaceTF::is_equal_data(ov::frontend::Place::Ptr another) const {
     return get_source_tensor()->is_equal_data(another);
 }
 
-ngraph::frontend::Place::Ptr InPortPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr InPortPlaceTF::get_producing_operation() const {
     return get_producing_port()->get_producing_operation();
 }
 
@@ -270,7 +269,7 @@ std::shared_ptr<TensorPlaceTF> OutPortPlaceTF::get_target_tensor_tf() const {
     FRONT_END_THROW("Target Tensor has expired.");
 }
 
-std::vector<ngraph::frontend::Place::Ptr> OutPortPlaceTF::get_consuming_operations() const {
+std::vector<ov::frontend::Place::Ptr> OutPortPlaceTF::get_consuming_operations() const {
     if (auto tensor_ptr = m_target_tensor.lock()) {
         return tensor_ptr->get_consuming_operations();
     }
@@ -281,29 +280,29 @@ void OutPortPlaceTF::set_target_tensor(const std::weak_ptr<TensorPlaceTF>& targe
     m_target_tensor = target_tensor;
 }
 
-std::vector<ngraph::frontend::Place::Ptr> OutPortPlaceTF::get_consuming_ports() const {
+std::vector<ov::frontend::Place::Ptr> OutPortPlaceTF::get_consuming_ports() const {
     if (auto tensor_ptr = m_target_tensor.lock()) {
         return tensor_ptr->get_consuming_ports();
     }
     FRONT_END_THROW("Tensor has expired.");
 }
 
-bool OutPortPlaceTF::is_equal_data(ngraph::frontend::Place::Ptr another) const {
+bool OutPortPlaceTF::is_equal_data(ov::frontend::Place::Ptr another) const {
     return get_target_tensor()->is_equal_data(another);
 }
 
-ngraph::frontend::Place::Ptr OutPortPlaceTF::get_target_tensor() const {
+ov::frontend::Place::Ptr OutPortPlaceTF::get_target_tensor() const {
     if (const auto& target_tensor = m_target_tensor.lock()) {
         return target_tensor;
     }
     FRONT_END_THROW("Target Tensor has expired.");
 }
 
-ngraph::frontend::Place::Ptr OutPortPlaceTF::get_producing_operation() const {
+ov::frontend::Place::Ptr OutPortPlaceTF::get_producing_operation() const {
     if (auto op = m_op.lock()) {
         return op;
     }
     FRONT_END_THROW("Operation has expired.");
 }
 }  // namespace frontend
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/ngraph/frontend/tensorflow/src/place.hpp b/ngraph/frontend/tensorflow/src/place.hpp
index 7547c380916e94..2fea68d2bee6be 100644
--- a/ngraph/frontend/tensorflow/src/place.hpp
+++ b/ngraph/frontend/tensorflow/src/place.hpp
@@ -13,14 +13,13 @@ namespace frontend {
 class TensorPlaceTF;
 class OpPlaceTF;
 
-class PlaceTF : public ngraph::frontend::Place {
+class PlaceTF : public ov::frontend::Place {
 public:
-    PlaceTF(const ngraph::frontend::InputModel& input_model, const std::vector<std::string>& names)
+    PlaceTF(const ov::frontend::InputModel& input_model, const std::vector<std::string>& names)
         : m_input_model(input_model),
           m_names(names) {}
 
-    explicit PlaceTF(const ngraph::frontend::InputModel& input_model)
-        : PlaceTF(input_model, std::vector<std::string>{}) {}
+    explicit PlaceTF(const ov::frontend::InputModel& input_model) : PlaceTF(input_model, std::vector<std::string>{}) {}
 
     ~PlaceTF() override = default;
 
@@ -35,13 +34,13 @@ class PlaceTF : public ngraph::frontend::Place {
     }
 
 private:
-    const ngraph::frontend::InputModel& m_input_model;
+    const ov::frontend::InputModel& m_input_model;
     std::vector<std::string> m_names;
 };
 
 class InPortPlaceTF : public PlaceTF {
 public:
-    explicit InPortPlaceTF(const ngraph::frontend::InputModel& input_model) : PlaceTF(input_model) {}
+    explicit InPortPlaceTF(const ov::frontend::InputModel& input_model) : PlaceTF(input_model) {}
 
     void set_op(const std::weak_ptr<OpPlaceTF>& op) {
         m_op = op;
@@ -55,7 +54,7 @@ class InPortPlaceTF : public PlaceTF {
     // External usage
     std::vector<Ptr> get_consuming_operations() const override;
     Ptr get_producing_operation() const override;
-    ngraph::frontend::Place::Ptr get_source_tensor() const override;
+    ov::frontend::Place::Ptr get_source_tensor() const override;
     Ptr get_producing_port() const override;
 
     bool is_equal_data(Ptr another) const override;
@@ -67,7 +66,7 @@ class InPortPlaceTF : public PlaceTF {
 
 class OutPortPlaceTF : public PlaceTF {
 public:
-    explicit OutPortPlaceTF(const ngraph::frontend::InputModel& input_model) : PlaceTF(input_model) {}
+    explicit OutPortPlaceTF(const ov::frontend::InputModel& input_model) : PlaceTF(input_model) {}
 
     void set_op(const std::weak_ptr<OpPlaceTF>& op) {
         m_op = op;
@@ -78,8 +77,8 @@ class OutPortPlaceTF : public PlaceTF {
 
     // External usage
     std::vector<Ptr> get_consuming_operations() const override;
-    ngraph::frontend::Place::Ptr get_producing_operation() const override;
-    std::vector<ngraph::frontend::Place::Ptr> get_consuming_ports() const override;
+    ov::frontend::Place::Ptr get_producing_operation() const override;
+    std::vector<ov::frontend::Place::Ptr> get_consuming_ports() const override;
     Ptr get_target_tensor() const override;
     bool is_equal_data(Ptr another) const override;
 
@@ -90,7 +89,7 @@ class OutPortPlaceTF : public PlaceTF {
 
 class OpPlaceTF : public PlaceTF {
 public:
-    OpPlaceTF(const ngraph::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder);
+    OpPlaceTF(const ov::frontend::InputModel& input_model, std::shared_ptr<DecoderBase> op_decoder);
 
     void add_in_port(const std::shared_ptr<InPortPlaceTF>& input, const std::string& name);
     void add_out_port(const std::shared_ptr<OutPortPlaceTF>& output, int idx);
@@ -102,7 +101,7 @@ class OpPlaceTF : public PlaceTF {
     std::shared_ptr<DecoderBase> get_decoder() const;
 
     // External API methods
-    std::vector<ngraph::frontend::Place::Ptr> get_consuming_ports() const override;
+    std::vector<ov::frontend::Place::Ptr> get_consuming_ports() const override;
 
     Ptr get_output_port() const override;
     Ptr get_output_port(int outputPortIndex) const override;
@@ -136,7 +135,7 @@ class OpPlaceTF : public PlaceTF {
 
 class TensorPlaceTF : public PlaceTF {
 public:
-    TensorPlaceTF(const ngraph::frontend::InputModel& input_model,
+    TensorPlaceTF(const ov::frontend::InputModel& input_model,
                   const ov::PartialShape& pshape,
                   ov::element::Type type,
                   const std::vector<std::string>& names);
@@ -160,8 +159,8 @@ class TensorPlaceTF : public PlaceTF {
 
     // External usage
     Ptr get_producing_operation() const override;
-    std::vector<ngraph::frontend::Place::Ptr> get_consuming_operations() const override;
-    std::vector<ngraph::frontend::Place::Ptr> get_consuming_ports() const override;
+    std::vector<ov::frontend::Place::Ptr> get_consuming_operations() const override;
+    std::vector<ov::frontend::Place::Ptr> get_consuming_ports() const override;
     Ptr get_producing_port() const override;
     bool is_equal_data(Ptr another) const override;
 
diff --git a/ngraph/frontend/tensorflow/src/tensorflow.cpp b/ngraph/frontend/tensorflow/src/tensorflow.cpp
index dfcd44e11aee81..ac8ff086ccaad2 100644
--- a/ngraph/frontend/tensorflow/src/tensorflow.cpp
+++ b/ngraph/frontend/tensorflow/src/tensorflow.cpp
@@ -5,12 +5,12 @@
 #include <frontend_manager/frontend_manager.hpp>
 #include <tensorflow_frontend/frontend.hpp>
 
-extern "C" OPENVINO_CORE_EXPORTS ngraph::frontend::FrontEndVersion GetAPIVersion() {
+extern "C" OPENVINO_CORE_EXPORTS ov::frontend::FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
 extern "C" OPENVINO_CORE_EXPORTS void* GetFrontEndData() {
-    auto res = new ngraph::frontend::FrontEndPluginInfo();
+    auto res = new ov::frontend::FrontEndPluginInfo();
     res->m_name = "tf";
     res->m_creator = []() {
         return std::make_shared<ov::frontend::FrontEndTF>();
diff --git a/ngraph/frontend/tensorflow/src/tf_framework_node.hpp b/ngraph/frontend/tensorflow/src/tf_framework_node.hpp
index 8074ea43f207f3..894b89b5f56254 100644
--- a/ngraph/frontend/tensorflow/src/tf_framework_node.hpp
+++ b/ngraph/frontend/tensorflow/src/tf_framework_node.hpp
@@ -5,9 +5,10 @@
 #pragma once
 
 #include <algorithm>
-#include <openvino/op/util/framework_node.hpp>
 #include <tensorflow_frontend/decoder.hpp>
 
+#include "openvino/op/util/framework_node.hpp"
+
 namespace ov {
 namespace frontend {
 namespace tf {
diff --git a/ngraph/frontend/tensorflow/src/utils.cpp b/ngraph/frontend/tensorflow/src/utils.cpp
index df423264047171..a61d49438f4c62 100644
--- a/ngraph/frontend/tensorflow/src/utils.cpp
+++ b/ngraph/frontend/tensorflow/src/utils.cpp
@@ -4,16 +4,26 @@
 
 #include "utils.hpp"
 
-void ov::frontend::tf::SetTracingInfo(const std::string& op_name, const ov::Output<ov::Node>& ng_node) {
-    auto node = ng_node.get_node_shared_ptr();
-    node->set_friendly_name(op_name);
-}
-
-void ov::frontend::tf::TFTensorShapeToNGraphShape(const tensorflow::TensorShapeProto& tf_shape,
-                                                  ov::PartialShape* ng_shape) {
+void ov::frontend::tf::tf_shape_to_ngraph_shape(const tensorflow::TensorShapeProto& tf_shape,
+                                                ov::PartialShape* ng_shape) {
     std::vector<ov::Dimension> dims;
     for (int i = 0; i < tf_shape.dim_size(); i++) {
-        dims.push_back(tf_shape.dim(i).size());
+        dims.emplace_back(tf_shape.dim(i).size());
     }
     *ng_shape = ov::PartialShape(dims);
 }
+
+void ov::frontend::tf::set_node_name(const std::string& node_name, const std::shared_ptr<Node>& node) {
+    const auto& outputs = node->outputs();
+    node->set_friendly_name(node_name);
+    if (outputs.size() == 1) {
+        set_out_name(node_name, outputs[0]);
+    }
+    for (size_t idx = 0; idx < outputs.size(); ++idx) {
+        set_out_name({node_name + ":" + std::to_string(idx)}, outputs[idx]);
+    }
+}
+
+void ov::frontend::tf::set_out_name(const std::string& out_name, const ov::Output<ov::Node>& output) {
+    output.get_tensor().add_names({out_name});
+}
diff --git a/ngraph/frontend/tensorflow/src/utils.hpp b/ngraph/frontend/tensorflow/src/utils.hpp
index 1cfb0c2491ed98..63bc29d01ce3d6 100644
--- a/ngraph/frontend/tensorflow/src/utils.hpp
+++ b/ngraph/frontend/tensorflow/src/utils.hpp
@@ -12,7 +12,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  *
- * We modified "ValuesFromConstNode" function from
+ * We modified "values_from_const_node" function from
  * tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc file
  * to integrate it with our infrastructure. The purpose and basic
  * functionality remains the same.
@@ -20,51 +20,16 @@
 
 #pragma once
 
-#include <openvino/opsets/opset8.hpp>
-
 #include "graph_iterator_proto.hpp"
+#include "ngraph/log.hpp"
 #include "ngraph/ngraph.hpp"
 #include "ngraph_conversions.hpp"
 #include "node_context.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 namespace ov {
 namespace frontend {
 namespace tf {
-namespace detail {
-// TODO: avoid using directly:
-using ::tensorflow::DataType;
-using ::tensorflow::TensorProto;
-
-// TODO: separate interface from proto implementation; here is a proto implementation
-class TensorWrapper {
-public:
-    const TensorProto* tensor_def;
-
-    TensorWrapper(const TensorProto* _tensor_def) : tensor_def(_tensor_def) {}
-
-    // a hack to minimize amount of code
-    TensorWrapper& attrs() const {
-        return const_cast<TensorWrapper&>(*this);
-    }
-
-    template <typename T>
-    std::vector<T> flat() const;
-
-    size_t NumElements() const;
-
-    DataType dtype() const;
-};
-
-}  // namespace detail
-}  // namespace tf
-}  // namespace frontend
-}  // namespace ov
-
-namespace ov {
-namespace frontend {
-namespace tf {
-using namespace ::ov::frontend::tf::detail;
-
 using OpMap = std::unordered_map<std::string, std::vector<ov::Output<ov::Node>>>;
 
 void extract_operation_name_and_port(const std::string& port_name,
@@ -72,56 +37,22 @@ void extract_operation_name_and_port(const std::string& port_name,
                                      size_t& port_index,
                                      std::string& port_type);
 
-class Status {
-public:
-    int status = 0;
-    std::string message;
-
-    static Status OK() {
-        return Status();
-    }
+void set_out_name(const std::string& out_name, const Output<Node>& output);
 
-    Status(const std::string& x) : message(x), status(1) {}
-    Status() {}
-};
+void set_node_name(const std::string& node_name, const std::shared_ptr<Node>& node);
 
-inline bool operator!=(const Status& x, const Status& y) {
-    return x.status != y.status;
-}
-
-inline std::ostream& operator<<(std::ostream& out, const Status& s) {
-    return out << s.message;
+static bool vec_str_cmp(const std::vector<std::string>& a, const std::vector<std::string>& b) {
+    return a == b;
 }
 
-#define TF_RETURN_IF_ERROR(S) \
-    if ((S).status != 0)      \
-        throw S;
-
-class errors {
-public:
-    static Status InvalidArgument(const std::string& x) {
-        return Status("InvalidArgument: " + x);
-    }
-
-    static Status Internal(const std::string& x) {
-        return Status("Internal: " + x);
-    }
-
-    static Status Unimplemented(const std::string& x) {
-        return Status("Unimplemented: " + x);
-    }
-};
-
-void SetTracingInfo(const std::string& op_name, const ov::Output<ov::Node>& ng_node);
-
 template <typename T>
-static void MakePadding(const std::string& tf_padding_type,
-                        const ov::Shape& ng_image_shape,
-                        const ov::Shape& ng_kernel_shape,
-                        const ov::Strides& ng_strides,
-                        const ov::Shape& ng_dilations,
-                        T& ng_padding_below,
-                        T& ng_padding_above) {
+void make_padding(const std::string& tf_padding_type,
+                  const ov::Shape& ng_image_shape,
+                  const ov::Shape& ng_kernel_shape,
+                  const ov::Strides& ng_strides,
+                  const ov::Shape& ng_dilations,
+                  T& ng_padding_below,
+                  T& ng_padding_above) {
     if (tf_padding_type == "SAME") {
         ov::Shape img_shape = {0, 0};
         img_shape.insert(img_shape.end(), ng_image_shape.begin(), ng_image_shape.end());
@@ -138,117 +69,16 @@ static void MakePadding(const std::string& tf_padding_type,
     }
 }
 
-template <typename Ttensor, typename Tvector>
-static void ConvertTensorDataToVector(const TensorWrapper& tensor, std::vector<Tvector>* vector) {
-    const Ttensor* data = tensor.flat<Ttensor>().data();
-    vector->resize(tensor.NumElements());
-    for (int64_t i = 0; i < tensor.NumElements(); i++) {
-        (*vector)[i] = Tvector(data[i]);
-    }
-}
-
-static bool VecStrCmp(const std::vector<std::string>& a, const std::vector<std::string>& b) {
-    return a == b;
-}
-
-static Status ValidateInputCount(const NodeContext& op, size_t count) {
-    if (op.get_ng_input_size() != count) {
-        std::ostringstream buf;
-        buf << "\"" << op.get_name() << "\" requires " << count << " input(s), got " << op.get_ng_input_size()
-            << " instead";
-        return errors::InvalidArgument(buf.str());
-    }
-    return Status::OK();
-}
-
-static void ValidateInputCountMin(const ov::frontend::tf::NodeContext& node, size_t count) {
-    if (node.get_ng_input_size() < count) {
-        std::ostringstream buf;
-        buf << "\"" << node.get_name() << "\" requires at least " << count << " input(s), got "
-            << node.get_ng_input_size() << " instead";
-        throw errors::InvalidArgument(buf.str());
-    }
-}
-
-// Check to make sure the axis dimension for reduction are in within range.
-// Returns error if axis is out of range. Otherwise returns Status::OK().
-static Status CheckAxisDimInRange(const std::vector<int64_t>& axes, size_t rank) {
-    for (auto i : axes) {
-        if (i < -(int)rank || i >= (int)rank) {
-            std::ostringstream buf;
-            buf << "Axis Dimension is out of range. Got " << i << ", should be in range [-" << rank << ", " << rank
-                << ")";
-            return errors::InvalidArgument(buf.str());
-        }
-    }
-    return Status::OK();
-}
-
-//
-// Helper for storing ops in ng_op_map.
-// For most of the cases, op would have one output so
-// std::vector ng_op_map[op_name] would contain one element.
-//
-// If storing more than one output_nodes, make sure it's in
-// the same order as tensorflow would do that.
-//
-// Parameters:
-//    Builder::OpMap& ng_op_map        - The TF-to-nGraph op map.
-//    std::string op_name              - Name of the op.
-//
-//    ov::Output<ov::Node> output_node - ov::Node to store
-//
-static void SaveNgOp(OpMap& ng_op_map, const std::string& op_name, ov::Output<ov::Node> output_node) {
-    // no need to try-catch, map[key] will create std::vector object
-    // if not exists
-    ng_op_map[op_name].push_back(output_node);
-}
-
-template <class TOpType, class... TArg>
-ov::Output<ov::Node> ConstructNgNode(const std::string& op_name, TArg&&... Args) {
-    auto ng_node = std::make_shared<TOpType>(std::forward<TArg>(Args)...);
-    SetTracingInfo(op_name, ng_node);
-    return ng_node;
-}
-
-static Status GetInputNode(const NodeContext op, size_t input_idx, ov::Output<ov::Node>& result) {
-    // Stub
-    result = op.get_ng_input(input_idx);
-    return Status::OK();
-}
-
-namespace detail {
-static Status GetInputNodes(const NodeContext&, size_t) {
-    return Status::OK();
-}
-
-template <typename... Arguments>
-static Status GetInputNodes(const NodeContext node,
-                            size_t index,
-                            ov::Output<ov::Node>& result,
-                            Arguments&... remaining) {
-    TF_RETURN_IF_ERROR(GetInputNode(node, index, result));
-    return GetInputNodes(node, index + 1, remaining...);
-}
-}  // namespace detail
-
-template <typename... Arguments>
-static Status GetInputNodes(const NodeContext& node, Arguments&... remaining) {
-    constexpr size_t args_len = sizeof...(Arguments);
-    TF_RETURN_IF_ERROR(ValidateInputCount(node, args_len));
-    return detail::GetInputNodes(node, 0, remaining...);
-}
-
-void TFTensorShapeToNGraphShape(const ::tensorflow::TensorShapeProto& tf_shape, ov::PartialShape* ng_shape);
+void tf_shape_to_ngraph_shape(const ::tensorflow::TensorShapeProto& tf_shape, ov::PartialShape* ng_shape);
 
 template <typename T>
-static void GetStaticInputVector(const ov::frontend::tf::NodeContext& node,
-                                 int64_t input_index,
-                                 std::vector<T>* vector) {
-    ov::Output<ov::Node> ng_input = node.get_ng_input(input_index);
-    auto constant = std::dynamic_pointer_cast<ov::opset8::Constant>(ng_input.get_node_shared_ptr());
-    FRONT_END_GENERAL_CHECK(constant != nullptr, "Node ", node.get_name(), " can't be casted to Constant.");
-    *vector = constant->cast_vector<T>();
+void get_static_input_vec(const NodeContext& node, int64_t input_index, std::vector<T>* vector) {
+    auto ng_input = node.get_input(input_index);
+    if (auto constant = std::dynamic_pointer_cast<opset8::Constant>(ng_input.get_node_shared_ptr())) {
+        *vector = constant->cast_vector<T>();
+        return;
+    }
+    FRONT_END_THROW("Node must be converted to Constant.");
 }
 
 // Taken from: tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -259,27 +89,24 @@ static void GetStaticInputVector(const ov::frontend::tf::NodeContext& node,
 // should be (e.g. when T is `bool`, we actually need a std::vector of `char` for
 // compatibility with nGraph).
 template <typename T, typename VecT = T>
-static Status ValuesFromConstNode(const ::ov::frontend::DecoderBase* node,
-                                  ov::Shape* const_tensor_shape,
-                                  std::vector<VecT>* values) {
-    if (node->get_op_type() != "Const") {
-        return errors::InvalidArgument("TFNodeDecoder not a Const");
-    }
-    auto dt1 = node->get_attribute("dtype", ::ov::VariantWrapper<::tensorflow::DataType>::get_type_info_static());
-    FRONT_END_GENERAL_CHECK(dt1);
+void values_from_const_node(const NodeContext& node, ov::Shape* const_tensor_shape, std::vector<VecT>* values) {
+    TF_OP_VALIDATION_CHECK(node, node.get_op_type() == "Const", "Node is expected to be Constant.");
+    const auto* decoder = node.get_decoder();
+    auto dt1 = decoder->get_attribute("dtype", ::ov::VariantWrapper<::tensorflow::DataType>::get_type_info_static());
+    FRONT_END_GENERAL_CHECK(dt1 != nullptr);
     auto dt = std::dynamic_pointer_cast<::ov::VariantWrapper<::tensorflow::DataType>>(dt1)->get();
 
     auto tensor_proto_var =
-        node->get_attribute("value", ::ov::VariantWrapper<::tensorflow::TensorProto>::get_type_info_static());
-    FRONT_END_GENERAL_CHECK(tensor_proto_var);
+        decoder->get_attribute("value", ::ov::VariantWrapper<::tensorflow::TensorProto>::get_type_info_static());
+    FRONT_END_GENERAL_CHECK(tensor_proto_var != nullptr);
     auto tensor_proto =
         std::dynamic_pointer_cast<::ov::VariantWrapper<::tensorflow::TensorProto>>(tensor_proto_var)->get();
 
     const tensorflow::TensorShapeProto& shape = tensor_proto.tensor_shape();
     ov::PartialShape pshape;
-    TFTensorShapeToNGraphShape(shape, &pshape);
+    tf_shape_to_ngraph_shape(shape, &pshape);
     *const_tensor_shape = pshape.get_shape();
-    FRONT_END_GENERAL_CHECK(!pshape.is_dynamic(), "Dynamic shapes are not supported in ValuesFromConstNode function");
+    TF_OP_VALIDATION_CHECK(node, pshape.is_static(), "Dynamic shapes are not supported in Constant conversion.");
     auto tensor_content = tensor_proto.tensor_content();
     std::vector<char> tensor_values_plain(tensor_content.begin(), tensor_content.end());
     const T* tensor_values = reinterpret_cast<const T*>(tensor_values_plain.data());
@@ -290,9 +117,9 @@ static Status ValuesFromConstNode(const ::ov::frontend::DecoderBase* node,
         // make sure no compression happens.
         // if (shape.dim_size() == 1 && shape.dim(0).size() == tensor_values_plain.size()/sizeof(T)) {
         values->insert(values->end(), tensor_values, tensor_values + tensor_values_plain.size() / sizeof(T));
-        return Status::OK();
+        return;
+        //}
     }
-
     const auto tensor_content_size = tensor_proto.tensor_content().size();
     if (tensor_content_size % sizeof(VecT)) {
         std::cerr << "[ ERROR ] tensor_content_size (" << tensor_content_size << ") is not a multiple of "
@@ -304,15 +131,14 @@ static Status ValuesFromConstNode(const ::ov::frontend::DecoderBase* node,
     if (tensor_content_size == 0) {
         int64_t n_elements = 1;
         for (auto i = 0; i < shape.dim_size(); i++) {
-            if (shape.dim(i).size() < 0) {
-                return errors::InvalidArgument("Const node has empty tensor and an unknown dimension size");
-            }
+            TF_OP_VALIDATION_CHECK(node,
+                                   shape.dim(i).size() >= 0,
+                                   "Const node has empty tensor and an unknown dimension size");
             n_elements *= shape.dim(i).size();
         }
         values->resize(n_elements);
 
         auto val_lastsaved = (T)0;  // cast
-
         for (auto i = 0; i < n_elements; i++) {
             int64_t val_size = 0;
             auto val_i = (T)0;  // cast
@@ -345,14 +171,13 @@ static Status ValuesFromConstNode(const ::ov::frontend::DecoderBase* node,
                     val_i = tensor_proto.double_val()[i];
                 break;
             default:
-                NGRAPH_VLOG(0) << "Const node has empty tensor_proto and we don't know how to "
-                                  "handle this element type";
-                return errors::Unimplemented("Encountered unknown element type " + DataType_Name(dt) +
-                                             " on an empty tensor_proto");
+                NGRAPH_DEBUG << "Const node has empty tensor_proto and we don't know how to "
+                                "handle this element type";
+                FRONT_END_THROW("Encountered unknown element type " + DataType_Name(dt) + " on an empty tensor_proto");
             }
-            if (val_size == 0) {
-                return errors::InvalidArgument("Empty values vector");
-            } else if (i < val_size) {
+            TF_OP_VALIDATION_CHECK(node, val_size != 0, "Empty values vector");
+
+            if (i < val_size) {
                 (*values)[i] = val_i;
                 val_lastsaved = val_i;
             } else {
@@ -360,22 +185,18 @@ static Status ValuesFromConstNode(const ::ov::frontend::DecoderBase* node,
             }
         }
     } else {
-        return Status::OK();
+        return;
     }
-
-    return Status::OK();
 }
 
 template <typename T, typename VecT = T>
-static Status MakeConstOp(const NodeContext& node, ov::element::Type et, ov::Output<ov::Node>& ng_node) {
+void make_const_op(const NodeContext& node, element::Type et, ov::Output<ov::Node>& ng_node) {
     std::vector<VecT> const_values;
     ov::Shape ng_shape;
 
-    TF_RETURN_IF_ERROR((ValuesFromConstNode<T, VecT>(node.get_decoder(), &ng_shape, &const_values)));
-
-    ng_node = ConstructNgNode<ov::opset8::Constant>(node.get_name(), et, ng_shape, const_values);
-    return Status::OK();
-}
+    values_from_const_node<T, VecT>(node, &ng_shape, &const_values);
+    ng_node = std::make_shared<ov::opset8::Constant>(et, ng_shape, const_values);
+};
 }  // namespace tf
 }  // namespace frontend
 }  // namespace ov
diff --git a/ngraph/test/CMakeLists.txt b/ngraph/test/CMakeLists.txt
index 969c76276117b5..4f71751f7cbc1a 100644
--- a/ngraph/test/CMakeLists.txt
+++ b/ngraph/test/CMakeLists.txt
@@ -12,6 +12,15 @@ set(NGRAPH_ONNX_NAMESPACE ngraph_onnx)
 
 add_subdirectory(runtime)
 
+if(ENABLE_TESTS)
+    add_definitions("-DTEST_FILES=\"${TEST_MODEL_ZOO}/ngraph/files\"")
+    add_subdirectory(util)
+    add_subdirectory(engines_util)
+    if (NGRAPH_ONNX_FRONTEND_ENABLE)
+        add_subdirectory(onnx_test_util)
+    endif()
+endif()
+
 if(NOT NGRAPH_UNIT_TEST_ENABLE)
     message(STATUS "nGraph unit tests disabled")
     return()
@@ -286,6 +295,8 @@ set(SRC
     visitors/op/convolution.cpp
     visitors/op/cos.cpp
     visitors/op/cosh.cpp
+    visitors/op/ctc_greedy_decoder.cpp
+    visitors/op/ctc_greedy_decoder_seq_len.cpp
     visitors/op/ctc_loss.cpp
     visitors/op/cum_sum.cpp
     visitors/op/deformable_convolution.cpp
@@ -296,11 +307,15 @@ set(SRC
     visitors/op/divide.cpp
     visitors/op/einsum.cpp
     visitors/op/elu.cpp
+    visitors/op/embedding_segments_sum.cpp
+    visitors/op/embeddingbag_offsetssum.cpp
+    visitors/op/embeddingbag_packedsum.cpp
     visitors/op/equal.cpp
     visitors/op/erf.cpp
     visitors/op/exp.cpp
     visitors/op/experimental_detectron_detection_output.cpp
     visitors/op/experimental_detectron_generate_proposals.cpp
+    visitors/op/experimental_detectron_topkrois.cpp
     visitors/op/extractimagepatches.cpp
     visitors/op/fake_quantize.cpp
     visitors/op/floor_mod.cpp
@@ -377,6 +392,7 @@ set(SRC
     visitors/op/rnn_cell.cpp
     visitors/op/rnn_sequence.cpp
     visitors/op/roi_pooling.cpp
+    visitors/op/roll.cpp
     visitors/op/round.cpp
     visitors/op/scatter_elements_update.cpp
     visitors/op/scatter_update.cpp
@@ -411,6 +427,10 @@ set(SRC
     util.cpp
 )
 
+# For type relaxed types
+set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/threading.cpp
+        PROPERTIES INCLUDE_DIRECTORIES $<TARGET_PROPERTY:inference_engine_transformations,INTERFACE_INCLUDE_DIRECTORIES>)
+
 # Add include path to so_extension.hpp
 set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/extension.cpp
                     APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/ngraph/core/src/")
@@ -449,14 +469,6 @@ if (NGRAPH_UNIT_TEST_BACKENDS_ENABLE)
     set(ACTIVE_BACKEND_LIST ${ACTIVE_BACKEND_LIST} INTERPRETER)
 endif()
 
-add_definitions("-DTEST_FILES=\"${TEST_MODEL_ZOO}/ngraph/files\"")
-add_subdirectory(util)
-add_subdirectory(engines_util)
-if (NGRAPH_ONNX_FRONTEND_ENABLE)
-    add_subdirectory(onnx_test_util)
-endif()
-
-
 # backend specific test files must meet the following requirements:
 # 1) The must be named <name>.in.cpp
 # 2) They must be in the `test/backend` directory
@@ -471,20 +483,13 @@ set(MULTI_TEST_SRC
     backend/aliased_output.in.cpp
     backend/api.in.cpp
     backend/auto_broadcast.in.cpp
-    backend/batch_to_space.in.cpp
     backend/broadcast.in.cpp
     backend/builder_reduce_ops_opset1.in.cpp
     backend/concat.in.cpp
-    backend/ctc_greedy_decoder.in.cpp
-    backend/ctc_greedy_decoder_seq_len.in.cpp
-    backend/depth_to_space.in.cpp
     backend/dyn_reshape.in.cpp
-    backend/experimental_detectron_topk_rois.in.cpp
-    backend/strided_slice.in.cpp
     backend/dynamic.in.cpp
     backend/experimental_detectron_prior_grid.in.cpp
     backend/function_name.in.cpp
-    backend/gather.in.cpp
     backend/interpolate.in.cpp
     backend/matrix_nms.in.cpp
     backend/multiclass_nms.in.cpp
@@ -492,17 +497,7 @@ set(MULTI_TEST_SRC
     backend/multiple_result.in.cpp
     backend/node_name.in.cpp
     backend/non_max_suppression.in.cpp
-    backend/one_hot.in.cpp
-    backend/pad.in.cpp
-    backend/prior_box_clustered.in.cpp
-    backend/prior_box.in.cpp
-    backend/recurrent_cells.in.cpp
     backend/region_yolo.in.cpp
-    backend/reorg_yolo.in.cpp
-    backend/roll.in.cpp
-    backend/space_to_depth.in.cpp
-    backend/shuffle_channels.in.cpp
-    backend/space_to_batch.in.cpp
     backend/sqrt.in.cpp
     backend/tile.in.cpp
     backend/topk.in.cpp
diff --git a/ngraph/test/backend/batch_to_space.in.cpp b/ngraph/test/backend/batch_to_space.in.cpp
deleted file mode 100644
index dfa748fc153fe6..00000000000000
--- a/ngraph/test/backend/batch_to_space.in.cpp
+++ /dev/null
@@ -1,167 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "util/ndarray.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-namespace {
-template <typename dataType>
-struct BatchToSpaceParams {
-    using Data = test::NDArrayBase<dataType>;
-    using BlockShape = test::NDArrayBase<int64_t>;
-    using Crops = test::NDArrayBase<int64_t>;
-
-    BatchToSpaceParams(Data in_data, BlockShape block_shape, Crops crops_begin, Crops crops_end, Data expected_output)
-        : m_data{std::move(in_data)},
-          m_block_shape{std::move(block_shape)},
-          m_crops_begin{std::move(crops_begin)},
-          m_crops_end{std::move(crops_end)},
-          m_expected_output{std::move(expected_output)} {}
-
-    Data m_data;
-    BlockShape m_block_shape;
-    Crops m_crops_begin;
-    Crops m_crops_end;
-    Data m_expected_output;
-};
-
-template <typename dataType>
-static void BatchToSpaceTestExecute(const BatchToSpaceParams<dataType>& params) {
-    const auto data = make_shared<op::Parameter>(element::from<dataType>(), params.m_data.get_shape());
-
-    const auto block_shape =
-        op::Constant::create(element::i64, params.m_block_shape.get_shape(), params.m_block_shape.get_vector());
-
-    const auto crops_begin =
-        op::Constant::create(element::i64, params.m_crops_begin.get_shape(), params.m_crops_begin.get_vector());
-
-    const auto crops_end =
-        op::Constant::create(element::i64, params.m_crops_end.get_shape(), params.m_crops_end.get_vector());
-
-    const auto batch_to_space = make_shared<op::v1::BatchToSpace>(data, block_shape, crops_begin, crops_end);
-
-    auto f = make_shared<Function>(batch_to_space, ParameterVector{data});
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input(params.m_data.get_vector());
-    test_case.add_expected_output(params.m_expected_output.get_vector());
-    test_case.run_with_tolerance_as_fp(1e-4f);
-}
-
-class BatchToSpaceTestFloat : public testing::TestWithParam<BatchToSpaceParams<float>> {};
-}  // namespace
-
-NGRAPH_TEST_P(${BACKEND_NAME}, BatchToSpaceTestFloat, BatchToSpaceTestFloatCases) {
-    BatchToSpaceTestExecute(GetParam());
-}
-
-const test::NDArray<float, 2> input_with_shape_4x3(
-    {{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}, {7.0f, 8.0f, 9.0f}, {10.0f, 11.0f, 12.0f}});
-
-const test::NDArray<int64_t, 1> zero_crops_2d({0, 0});
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(${BACKEND_NAME},
-                                batch_to_space_2d_without_crops,
-                                BatchToSpaceTestFloat,
-                                testing::Values(BatchToSpaceParams<float>{
-                                    input_with_shape_4x3,
-                                    test::NDArray<int64_t, 1>({1, 2}),
-                                    zero_crops_2d,
-                                    zero_crops_2d,
-                                    test::NDArray<float, 2>({{1.0f, 7.0f, 2.0f, 8.0f, 3.0f, 9.0f},
-                                                             {4.0f, 10.0f, 5.0f, 11.0f, 6.0f, 12.0f}})}));
-
-const test::NDArray<float, 4> input_with_shape_4x1x1x3(
-    {{{{1.0f, 2.0f, 3.0f}}}, {{{4.0f, 5.0f, 6.0f}}}, {{{7.0f, 8.0f, 9.0f}}}, {{{10.0f, 11.0f, 12.0f}}}});
-
-const test::NDArray<float, 4> input_with_shape_4x1x2x3({{{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}},
-                                                        {{{7.0f, 8.0f, 9.0f}, {10.0f, 11.0f, 12.0f}}},
-                                                        {{{13.0f, 14.0f, 15.0f}, {16.0f, 17.0f, 18.0f}}},
-                                                        {{{19.0f, 20.0f, 21.0f}, {22.0f, 23.0f, 24.0f}}}});
-
-const test::NDArray<int64_t, 1> zero_crops_4d({0, 0, 0, 0});
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(
-    ${BACKEND_NAME},
-    batch_to_space_4d_without_crops,
-    BatchToSpaceTestFloat,
-    testing::Values(
-        BatchToSpaceParams<float>{input_with_shape_4x1x1x3,
-                                  test::NDArray<int64_t, 1>({1, 1, 1, 2}),
-                                  zero_crops_4d,
-                                  zero_crops_4d,
-                                  test::NDArray<float, 4>({{{{1.0f, 7.0f, 2.0f, 8.0f, 3.0f, 9.0f}}},
-                                                           {{{4.0f, 10.0f, 5.0f, 11.0f, 6.0f, 12.0f}}}})},
-        BatchToSpaceParams<float>{input_with_shape_4x1x1x3,
-                                  test::NDArray<int64_t, 1>({1, 1, 2, 1}),
-                                  zero_crops_4d,
-                                  zero_crops_4d,
-                                  test::NDArray<float, 4>({{{{1.0f, 2.0f, 3.0f}, {7.0f, 8.0f, 9.0f}}},
-                                                           {{{4.0f, 5.0f, 6.0f}, {10.0f, 11.0f, 12.0f}}}})},
-        BatchToSpaceParams<float>{input_with_shape_4x1x1x3,
-                                  test::NDArray<int64_t, 1>({1, 1, 2, 2}),
-                                  zero_crops_4d,
-                                  zero_crops_4d,
-                                  test::NDArray<float, 4>({{{{1.0f, 4.0f, 2.0f, 5.0f, 3.0f, 6.0f},
-                                                             {7.0f, 10.0f, 8.0f, 11.0f, 9.0f, 12.0f}}}})},
-        BatchToSpaceParams<float>{
-            input_with_shape_4x1x2x3,
-            test::NDArray<int64_t, 1>({1, 1, 1, 2}),
-            zero_crops_4d,
-            zero_crops_4d,
-            test::NDArray<float, 4>(
-                {{{{1.0f, 13.0f, 2.0f, 14.0f, 3.0f, 15.0f}, {4.0f, 16.0f, 5.0f, 17.0f, 6.0f, 18.0f}}},
-                 {{{7.0f, 19.0f, 8.0f, 20.0f, 9.0f, 21.0f}, {10.0f, 22.0f, 11.0f, 23.0f, 12.0f, 24.0f}}}})},
-        BatchToSpaceParams<float>{
-            input_with_shape_4x1x2x3,
-            test::NDArray<int64_t, 1>({1, 1, 2, 1}),
-            zero_crops_4d,
-            zero_crops_4d,
-            test::NDArray<float, 4>(
-                {{{{1.0f, 2.0f, 3.0f}, {13.0f, 14.0f, 15.0f}, {4.0f, 5.0f, 6.0f}, {16.0f, 17.0f, 18.0f}}},
-                 {{{7.0f, 8.0f, 9.0f}, {19.0f, 20.0f, 21.0f}, {10.0f, 11.0f, 12.0f}, {22.0f, 23.0f, 24.0f}}}})},
-        BatchToSpaceParams<float>{input_with_shape_4x1x2x3,
-                                  test::NDArray<int64_t, 1>({1, 1, 2, 2}),
-                                  zero_crops_4d,
-                                  zero_crops_4d,
-                                  test::NDArray<float, 4>({{{{1.0f, 7.0f, 2.0f, 8.0f, 3.0f, 9.0f},
-                                                             {13.0f, 19.0f, 14.0f, 20.0f, 15.0f, 21.0f},
-                                                             {4.0f, 10.0f, 5.0f, 11.0f, 6.0f, 12.0f},
-                                                             {16.0f, 22.0f, 17.0f, 23.0f, 18.0f, 24.0f}}}})}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(
-    ${BACKEND_NAME},
-    batch_to_space_4d_crops,
-    BatchToSpaceTestFloat,
-    testing::Values(BatchToSpaceParams<float>{input_with_shape_4x1x2x3,
-                                              test::NDArray<int64_t, 1>({1, 1, 2, 2}),
-                                              test::NDArray<int64_t, 1>({0, 0, 0, 0}),
-                                              test::NDArray<int64_t, 1>({0, 0, 0, 2}),
-                                              test::NDArray<float, 4>({{{{1.0f, 7.0f, 2.0f, 8.0f},
-                                                                         {13.0f, 19.0f, 14.0f, 20.0f},
-                                                                         {4.0f, 10.0f, 5.0f, 11.0f},
-                                                                         {16.0f, 22.0f, 17.0f, 23.0f}}}})},
-                    BatchToSpaceParams<float>{input_with_shape_4x1x2x3,
-                                              test::NDArray<int64_t, 1>({1, 1, 2, 2}),
-                                              test::NDArray<int64_t, 1>({0, 0, 0, 2}),
-                                              test::NDArray<int64_t, 1>({0, 0, 0, 0}),
-                                              test::NDArray<float, 4>({{{{2.0f, 8.0f, 3.0f, 9.0f},
-                                                                         {14.0f, 20.0f, 15.0f, 21.0f},
-                                                                         {5.0f, 11.0f, 6.0f, 12.0f},
-                                                                         {17.0f, 23.0f, 18.0f, 24.0f}}}})},
-                    BatchToSpaceParams<float>{input_with_shape_4x1x2x3,
-                                              test::NDArray<int64_t, 1>({1, 1, 2, 2}),
-                                              test::NDArray<int64_t, 1>({0, 0, 1, 0}),
-                                              test::NDArray<int64_t, 1>({0, 0, 1, 0}),
-                                              test::NDArray<float, 4>({{{{13.0f, 19.0f, 14.0f, 20.0f, 15.0f, 21.0f},
-                                                                         {4.0f, 10.0f, 5.0f, 11.0f, 6.0f, 12.0f}}}})}));
diff --git a/ngraph/test/backend/ctc_greedy_decoder.in.cpp b/ngraph/test/backend/ctc_greedy_decoder.in.cpp
deleted file mode 100644
index ba39f03c423691..00000000000000
--- a/ngraph/test/backend/ctc_greedy_decoder.in.cpp
+++ /dev/null
@@ -1,177 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <algorithm>
-#include <cinttypes>
-#include <cmath>
-#include <cstdlib>
-#include <random>
-#include <string>
-
-// clang-format off
-#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#endif
-
-#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#endif
-// clang-format on
-
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "engines_util/test_engines.hpp"
-#include "engines_util/test_case.hpp"
-#include "util/test_control.hpp"
-
-NGRAPH_SUPPRESS_DEPRECATED_START
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, ctc_greedy_decoder) {
-    const int T = 3;
-    const int N = 1;
-    const int C = 3;
-    const auto data_shape = Shape{T, N, C};
-    const auto masks_shape = Shape{T, N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto masks = make_shared<op::Parameter>(element::f32, masks_shape);
-    auto decoder = make_shared<op::CTCGreedyDecoder>(data, masks, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, masks});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f});
-    test_case.add_input<float>({1.0f, 1.0f, 1.0f});
-    test_case.add_expected_output(Shape{N, T, 1, 1}, vector<float>{1.0f, 0.0f, 1.0f});
-
-    test_case.run_with_tolerance_as_fp(1.0e-4f);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, ctc_greedy_decoder_f16) {
-    const int T = 3;
-    const int N = 1;
-    const int C = 3;
-    const auto data_shape = Shape{T, N, C};
-    const auto masks_shape = Shape{T, N};
-
-    auto data = make_shared<op::Parameter>(element::f16, data_shape);
-    auto masks = make_shared<op::Parameter>(element::f16, masks_shape);
-    auto decoder = make_shared<op::CTCGreedyDecoder>(data, masks, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, masks});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float16>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f});
-    test_case.add_input<float16>({1.0f, 1.0f, 1.0f});
-    test_case.add_expected_output(Shape{N, T, 1, 1}, vector<float16>{1.0f, 0.0f, 1.0f});
-
-    test_case.run_with_tolerance_as_fp(1.0e-4f);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, ctc_greedy_decoder_multiple_batches) {
-    const int T = 3;
-    const int N = 2;
-    const int C = 3;
-    const auto data_shape = Shape{T, N, C};
-    const auto masks_shape = Shape{T, N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto masks = make_shared<op::Parameter>(element::f32, masks_shape);
-    auto decoder = make_shared<op::CTCGreedyDecoder>(data, masks, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, masks});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>(
-        {0.1f, 0.2f, 0.f, 0.15f, 0.25f, 0.f, 0.4f, 0.3f, 0.f, 0.45f, 0.35f, 0.f, 0.5f, 0.6f, 0.f, 0.55f, 0.65f, 0.f});
-
-    test_case.add_input<float>({1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
-
-    test_case.add_expected_output(Shape{N, T, 1, 1}, vector<float>{1.0f, 0.0f, 1.0f, 1.0f, 0.0f, 1.0f});
-
-    test_case.run_with_tolerance_as_fp(1.0e-4f);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, ctc_greedy_decoder_single_batch_short_sequence) {
-    const int T = 3;
-    const int N = 1;
-    const int C = 3;
-    const auto data_shape = Shape{T, N, C};
-    const auto masks_shape = Shape{T, N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto masks = make_shared<op::Parameter>(element::f32, masks_shape);
-    auto decoder = make_shared<op::CTCGreedyDecoder>(data, masks, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, masks});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f});
-    test_case.add_input<float>({1.0f, 1.0f, 0.0f});
-    test_case.add_expected_output(Shape{N, T, 1, 1}, vector<float>{1.0f, 0.0f, -1.0f});
-
-    test_case.run_with_tolerance_as_fp(1.0e-4f);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, ctc_greedy_decoder_merge) {
-    const int T = 3;
-    const int N = 1;
-    const int C = 3;
-    const auto data_shape = Shape{T, N, C};
-    const auto masks_shape = Shape{T, N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto masks = make_shared<op::Parameter>(element::f32, masks_shape);
-    auto decoder = make_shared<op::CTCGreedyDecoder>(data, masks, true);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, masks});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.3f, 0.4f, 0.f, 0.6f, 0.5f, 0.f});
-    test_case.add_input<float>({1.0f, 1.0f, 1.0f});
-    test_case.add_expected_output(Shape{N, T, 1, 1}, vector<float>{1.0f, 0.0f, -1.0f});
-
-    test_case.run_with_tolerance_as_fp(1.0e-4f);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, ctc_greedy_decoder_single_no_merge) {
-    const int T = 3;
-    const int N = 1;
-    const int C = 3;
-    const auto data_shape = Shape{T, N, C};
-    const auto masks_shape = Shape{T, N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto masks = make_shared<op::Parameter>(element::f32, masks_shape);
-    auto decoder = make_shared<op::CTCGreedyDecoder>(data, masks, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, masks});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.3f, 0.4f, 0.f, 0.6f, 0.5f, 0.f});
-    test_case.add_input<float>({1.0f, 1.0f, 1.0f});
-    test_case.add_expected_output(Shape{N, T, 1, 1}, vector<float>{1.0f, 1.0f, 0.0f});
-
-    test_case.run_with_tolerance_as_fp(1.0e-4f);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, ctc_greedy_decoder_multiple_sequences) {
-    const int T = 2;
-    const int N = 2;
-    const int C = 3;
-    const auto data_shape = Shape{T, N, C};
-    const auto masks_shape = Shape{T, N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto masks = make_shared<op::Parameter>(element::f32, masks_shape);
-    auto decoder = make_shared<op::CTCGreedyDecoder>(data, masks, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, masks});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f, 0.7f, 0.8f, 0.f});
-    test_case.add_input<float>({1.0f, 1.0f, 1.0f, 0.0f});
-    test_case.add_expected_output(Shape{N, T, 1, 1}, vector<float>{1.0f, 1.0f, 0.0f, -1.0f});
-
-    test_case.run_with_tolerance_as_fp(1.0e-4f);
-}
diff --git a/ngraph/test/backend/ctc_greedy_decoder_seq_len.in.cpp b/ngraph/test/backend/ctc_greedy_decoder_seq_len.in.cpp
deleted file mode 100644
index 459c945ccbf059..00000000000000
--- a/ngraph/test/backend/ctc_greedy_decoder_seq_len.in.cpp
+++ /dev/null
@@ -1,172 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <algorithm>
-#include <cinttypes>
-#include <cmath>
-#include <cstdlib>
-#include <random>
-#include <string>
-
-// clang-format off
-#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#endif
-
-#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#endif
-// clang-format on
-
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "engines_util/test_engines.hpp"
-#include "engines_util/test_case.hpp"
-#include "util/test_control.hpp"
-
-NGRAPH_SUPPRESS_DEPRECATED_START
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, evaluate_ctc_greedy_decoder_seq_len) {
-    const int N = 1;
-    const int T = 3;
-    const int C = 3;
-    const auto data_shape = Shape{N, T, C};
-    const auto seq_len_shape = Shape{N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto seq_len = make_shared<op::Parameter>(element::i32, seq_len_shape);
-    auto blank_index = op::Constant::create<int32_t>(element::i32, Shape{}, {2});
-    auto decoder = make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, blank_index, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, seq_len});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f});
-    test_case.add_input<int32_t>({2});
-    test_case.add_expected_output(Shape{N, T}, vector<int32_t>{1, 0, -1});
-    test_case.add_expected_output(Shape{N}, vector<int32_t>{2});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, evaluate_ctc_greedy_decoder_seq_len_merge) {
-    const int N = 1;
-    const int T = 3;
-    const int C = 3;
-    const auto data_shape = Shape{N, T, C};
-    const auto seq_len_shape = Shape{N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto seq_len = make_shared<op::Parameter>(element::i32, seq_len_shape);
-    auto blank_index = op::Constant::create<int32_t>(element::i32, Shape{}, {2});
-    auto decoder = make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, blank_index, true);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, seq_len});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f});
-    test_case.add_input<int32_t>({2});
-    test_case.add_expected_output(Shape{N, T}, vector<int32_t>{1, 0, -1});
-    test_case.add_expected_output(Shape{N}, vector<int32_t>{2});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, evaluate_ctc_greedy_decoder_seq_len_f16) {
-    const int N = 1;
-    const int T = 3;
-    const int C = 3;
-    const auto data_shape = Shape{N, T, C};
-    const auto seq_len_shape = Shape{N};
-
-    auto data = make_shared<op::Parameter>(element::f16, data_shape);
-    auto seq_len = make_shared<op::Parameter>(element::i32, seq_len_shape);
-    auto blank_index = op::Constant::create<int32_t>(element::i32, Shape{}, {2});
-    auto decoder = make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, blank_index, true);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, seq_len});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float16>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f});
-    test_case.add_input<int32_t>({2});
-    test_case.add_expected_output(Shape{N, T}, vector<int32_t>{1, 0, -1});
-    test_case.add_expected_output(Shape{N}, vector<int32_t>{2});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, evaluate_ctc_greedy_decoder_seq_len_multiple_batches) {
-    const int N = 2;
-    const int T = 3;
-    const int C = 3;
-    const auto data_shape = Shape{N, T, C};
-    const auto seq_len_shape = Shape{N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto seq_len = make_shared<op::Parameter>(element::i32, seq_len_shape);
-    auto blank_index = op::Constant::create<int32_t>(element::i32, Shape{}, {2});
-    auto decoder = make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, blank_index, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, seq_len});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>(
-        {0.1f, 0.2f, 0.f, 0.15f, 0.25f, 0.f, 0.4f, 0.3f, 0.f, 0.45f, 0.35f, 0.f, 0.5f, 0.6f, 0.f, 0.55f, 0.65f, 0.f});
-
-    test_case.add_input<int32_t>({1, 1});
-
-    test_case.add_expected_output(Shape{N, T}, vector<int32_t>{1, -1, -1, 0, -1, -1});
-    test_case.add_expected_output(Shape{N}, vector<int32_t>{1, 1});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, evaluate_ctc_greedy_decoder_seq_len_multiple_batches2) {
-    const int N = 3;
-    const int T = 3;
-    const int C = 3;
-    const auto data_shape = Shape{N, T, C};
-    const auto seq_len_shape = Shape{N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto seq_len = make_shared<op::Parameter>(element::i32, seq_len_shape);
-    auto blank_index = op::Constant::create<int32_t>(element::i32, Shape{}, {2});
-    auto decoder = make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, blank_index, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, seq_len});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f,  0.2f,  0.f, 0.15f, 0.25f, 0.f, 0.4f,  0.3f,  0.f,
-                                0.45f, 0.35f, 0.f, 0.5f,  0.6f,  0.f, 0.55f, 0.65f, 0.f,
-                                0.1f,  0.2f,  0.f, 0.15f, 0.25f, 0.f, 0.4f,  0.3f,  0.f});
-
-    test_case.add_input<int32_t>({2, 3, 1});
-
-    test_case.add_expected_output(Shape{N, T}, vector<int32_t>{1, 1, -1, 0, 1, 1, 1, -1, -1});
-    test_case.add_expected_output(Shape{N}, vector<int32_t>{2, 3, 1});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, evaluate_ctc_greedy_decoder_seq_len_no_optional_input) {
-    const int N = 1;
-    const int T = 3;
-    const int C = 3;
-    const auto data_shape = Shape{N, T, C};
-    const auto seq_len_shape = Shape{N};
-
-    auto data = make_shared<op::Parameter>(element::f32, data_shape);
-    auto seq_len = make_shared<op::Parameter>(element::i32, seq_len_shape);
-    auto decoder = make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, false);
-    auto function = make_shared<Function>(decoder, ParameterVector{data, seq_len});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.1f, 0.2f, 0.f, 0.4f, 0.3f, 0.f, 0.5f, 0.6f, 0.f});
-    test_case.add_input<int32_t>({2});
-    test_case.add_expected_output(Shape{N, T}, vector<int32_t>{1, 0, -1});
-    test_case.add_expected_output(Shape{N}, vector<int32_t>{2});
-
-    test_case.run();
-}
diff --git a/ngraph/test/backend/depth_to_space.in.cpp b/ngraph/test/backend/depth_to_space.in.cpp
deleted file mode 100644
index 85053d3b6e9560..00000000000000
--- a/ngraph/test/backend/depth_to_space.in.cpp
+++ /dev/null
@@ -1,106 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/op/depth_to_space.hpp"
-#include "util/test_control.hpp"
-
-using namespace ngraph;
-
-static std::string s_manifest = "${MANIFEST}";
-
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, depth_to_space_block_first_K1_BS2) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 8, 2});
-    auto depth_to_space = std::make_shared<op::DepthToSpace>(A, op::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST, 2);
-    auto function = std::make_shared<Function>(NodeVector{depth_to_space}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>(
-        {0.f, 2.f, 8.f, 10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f, 11.f, 17.f, 19.f, 25.f, 27.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 4, 4},
-        {0.f, 1.f, 2.f, 3.f, 8.f, 9.f, 10.f, 11.f, 16.f, 17.f, 18.f, 19.f, 24.f, 25.f, 26.f, 27.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, depth_to_space_block_first_K2_BS2) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 8, 2, 2});
-    auto depth_to_space = std::make_shared<op::DepthToSpace>(A, op::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST, 2);
-    auto function = std::make_shared<Function>(NodeVector{depth_to_space}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  2.f,  8.f,  10.f, 16.f, 18.f, 24.f, 26.f, 1.f,  3.f,  9.f,
-                                11.f, 17.f, 19.f, 25.f, 27.f, 4.f,  6.f,  12.f, 14.f, 20.f, 22.f,
-                                28.f, 30.f, 5.f,  7.f,  13.f, 15.f, 21.f, 23.f, 29.f, 31.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 2, 4, 4},
-        {0.f,  1.f,  2.f,  3.f,  4.f,  5.f,  6.f,  7.f,  8.f,  9.f,  10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
-         16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, depth_to_space_block_first_K2_BS4) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 16, 2, 1});
-    auto depth_to_space = std::make_shared<op::DepthToSpace>(A, op::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST, 4);
-    auto function = std::make_shared<Function>(NodeVector{depth_to_space}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  16.f, 1.f,  17.f, 2.f,  18.f, 3.f,  19.f, 4.f,  20.f, 5.f,
-                                21.f, 6.f,  22.f, 7.f,  23.f, 8.f,  24.f, 9.f,  25.f, 10.f, 26.f,
-                                11.f, 27.f, 12.f, 28.f, 13.f, 29.f, 14.f, 30.f, 15.f, 31.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 1, 8, 4},
-        {0.f,  1.f,  2.f,  3.f,  4.f,  5.f,  6.f,  7.f,  8.f,  9.f,  10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
-         16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, depth_to_space_depth_first_1K_BS2) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 8, 2});
-    auto depth_to_space = std::make_shared<op::DepthToSpace>(A, op::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST, 2);
-    auto function = std::make_shared<Function>(NodeVector{depth_to_space}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f, 2.f, 1.f, 3.f, 4.f, 6.f, 5.f, 7.f, 8.f, 10.f, 9.f, 11.f, 12.f, 14.f, 13.f, 15.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 4, 4},
-        {0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, depth_to_space_depth_first_2K_BS2) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 8, 2, 2});
-    auto depth_to_space = std::make_shared<op::DepthToSpace>(A, op::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST, 2);
-    auto function = std::make_shared<Function>(NodeVector{depth_to_space}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  2.f,  8.f,  10.f, 16.f, 18.f, 24.f, 26.f, 1.f,  3.f,  9.f,
-                                11.f, 17.f, 19.f, 25.f, 27.f, 4.f,  6.f,  12.f, 14.f, 20.f, 22.f,
-                                28.f, 30.f, 5.f,  7.f,  13.f, 15.f, 21.f, 23.f, 29.f, 31.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 2, 4, 4},
-        {0.f, 16.f, 2.f, 18.f, 1.f, 17.f, 3.f, 19.f, 8.f,  24.f, 10.f, 26.f, 9.f,  25.f, 11.f, 27.f,
-         4.f, 20.f, 6.f, 22.f, 5.f, 21.f, 7.f, 23.f, 12.f, 28.f, 14.f, 30.f, 13.f, 29.f, 15.f, 31.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, depth_to_space_depth_first_2K_BS4) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 16, 2, 1});
-    auto depth_to_space = std::make_shared<op::DepthToSpace>(A, op::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST, 4);
-    auto function = std::make_shared<Function>(NodeVector{depth_to_space}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  16.f, 1.f,  17.f, 2.f,  18.f, 3.f,  19.f, 4.f,  20.f, 5.f,
-                                21.f, 6.f,  22.f, 7.f,  23.f, 8.f,  24.f, 9.f,  25.f, 10.f, 26.f,
-                                11.f, 27.f, 12.f, 28.f, 13.f, 29.f, 14.f, 30.f, 15.f, 31.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 1, 8, 4},
-        {0.f,  1.f,  2.f,  3.f,  4.f,  5.f,  6.f,  7.f,  8.f,  9.f,  10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
-         16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
-    test_case.run();
-}
diff --git a/ngraph/test/backend/experimental_detectron_topk_rois.in.cpp b/ngraph/test/backend/experimental_detectron_topk_rois.in.cpp
deleted file mode 100644
index 57b6aef012d42e..00000000000000
--- a/ngraph/test/backend/experimental_detectron_topk_rois.in.cpp
+++ /dev/null
@@ -1,85 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/execute_tools.hpp"
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "ngraph/runtime/tensor.hpp"
-#include "runtime/backend.hpp"
-#include "util/all_close.hpp"
-#include "util/all_close_f.hpp"
-#include "util/ndarray.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-using ExperimentalTopK = op::v6::ExperimentalDetectronTopKROIs;
-
-NGRAPH_TEST(${BACKEND_NAME}, experimental_detectron_topk_rois_eval) {
-    size_t num_rois = 1;
-
-    const auto input_rois_shape = Shape{2, 4};
-    const auto input_probs_shape = Shape{2};
-    const auto output_shape = Shape{1, 4};
-
-    auto input_rois = std::make_shared<op::Parameter>(element::f32, input_rois_shape);
-    auto input_probs = std::make_shared<op::Parameter>(element::f32, input_probs_shape);
-    auto topk_rois = std::make_shared<ExperimentalTopK>(input_rois, input_probs, num_rois);
-    auto f = std::make_shared<Function>(topk_rois, ParameterVector{input_rois, input_probs});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-    auto topk_rois_output = backend->create_tensor(element::f32, output_shape);
-
-    std::vector<float> input_rois_data = {1.0f, 1.0f, 3.0f, 4.0f, 2.0f, 1.0f, 5.0f, 7.0f};
-    std::vector<float> input_probs_data = {0.5f, 0.3f};
-    std::vector<float> expected_result = {1.0, 1.0, 3.0, 4.0};
-
-    auto backend_input_rois_data = backend->create_tensor(element::f32, input_rois_shape);
-    copy_data(backend_input_rois_data, input_rois_data);
-    auto backend_input_probs_data = backend->create_tensor(element::f32, input_probs_shape);
-    copy_data(backend_input_probs_data, input_probs_data);
-
-    auto handle = backend->compile(f);
-    handle->call({topk_rois_output}, {backend_input_rois_data, backend_input_probs_data});
-
-    ASSERT_TRUE(test::all_close_f(read_vector<float>(topk_rois_output), expected_result));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, experimental_detectron_topk_rois_eval_2) {
-    size_t num_rois = 2;
-
-    const auto input_rois_shape = Shape{4, 4};
-    const auto input_probs_shape = Shape{4};
-    const auto output_shape = Shape{2, 4};
-
-    auto input_rois = std::make_shared<op::Parameter>(element::f32, input_rois_shape);
-    auto input_probs = std::make_shared<op::Parameter>(element::f32, input_probs_shape);
-    auto topk_rois = std::make_shared<ExperimentalTopK>(input_rois, input_probs, num_rois);
-    auto f = std::make_shared<Function>(topk_rois, ParameterVector{input_rois, input_probs});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-    auto topk_rois_output = backend->create_tensor(element::f32, output_shape);
-
-    std::vector<float> input_rois_data =
-        {1.0f, 1.0f, 4.0f, 5.0f, 3.0f, 2.0f, 7.0f, 9.0f, 10.0f, 15.0f, 13.0f, 17.0f, 13.0f, 10.0f, 18.0f, 15.0f};
-    std::vector<float> input_probs_data = {0.1f, 0.7f, 0.5f, 0.9f};
-    std::vector<float> expected_result = {13.0f, 10.0f, 18.0f, 15.0f, 3.0f, 2.0f, 7.0f, 9.0f};
-
-    auto backend_input_rois_data = backend->create_tensor(element::f32, input_rois_shape);
-    copy_data(backend_input_rois_data, input_rois_data);
-    auto backend_input_probs_data = backend->create_tensor(element::f32, input_probs_shape);
-    copy_data(backend_input_probs_data, input_probs_data);
-
-    auto handle = backend->compile(f);
-    handle->call({topk_rois_output}, {backend_input_rois_data, backend_input_probs_data});
-
-    ASSERT_TRUE(test::all_close_f(read_vector<float>(topk_rois_output), expected_result));
-}
diff --git a/ngraph/test/backend/gather.in.cpp b/ngraph/test/backend/gather.in.cpp
deleted file mode 100644
index 51f11d1822146a..00000000000000
--- a/ngraph/test/backend/gather.in.cpp
+++ /dev/null
@@ -1,1272 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <algorithm>
-#include <cinttypes>
-#include <cmath>
-#include <cstdlib>
-#include <random>
-#include <string>
-
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "util/test_control.hpp"
-
-NGRAPH_SUPPRESS_DEPRECATED_START
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_4d_indices_axis_0_uint8) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2, 3, 4};
-    Shape out_shape{2, 2, 3, 4, 2};
-    auto P = make_shared<op::Parameter>(element::u8, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint8_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2,
-                                  0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2});
-    test_case.add_expected_output<uint8_t>(
-        out_shape,
-        {10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
-         10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
-         10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
-         10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_4d_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2, 3, 4};
-    Shape out_shape{2, 2, 3, 4, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-
-    test_case.add_input<int32_t>({0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-
-
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2});
-    test_case.add_expected_output<float>(
-        out_shape,
-        { 1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f,
-
-          1.0f, 1.1f,
-          2.0f, 2.1f,
-          2.0f, 2.1f,
-          3.0f, 3.1f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_3d_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 3, 4};
-    Shape out_shape{2, 3, 4, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-    test_case.add_input<int32_t>(
-        {0, 1, 1, 2,
-         0, 1, 1, 2,
-         0, 1, 1, 2,
-
-         0, 1, 1, 2,
-         0, 1, 1, 2,
-         0, 1, 1, 2});
-    test_case.add_expected_output<float>(
-        out_shape, {1.0f, 1.1f,
-                    2.0f, 2.1f,
-                    2.0f, 2.1f,
-                    3.0f, 3.1f,
-
-                    1.0f, 1.1f,
-                    2.0f, 2.1f,
-                    2.0f, 2.1f,
-                    3.0f, 3.1f,
-
-                    1.0f, 1.1f,
-                    2.0f, 2.1f,
-                    2.0f, 2.1f,
-                    3.0f, 3.1f,
-
-
-                    1.0f, 1.1f,
-                    2.0f, 2.1f,
-                    2.0f, 2.1f,
-                    3.0f, 3.1f,
-
-                    1.0f, 1.1f,
-                    2.0f, 2.1f,
-                    2.0f, 2.1f,
-                    3.0f, 3.1f,
-
-                    1.0f, 1.1f,
-                    2.0f, 2.1f,
-                    2.0f, 2.1f,
-                    3.0f, 3.1f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_2d_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-    // clang-format on
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    // clang-format off
-    test_case.add_expected_output<float>(out_shape,
-                                         {1.0f, 1.1f,
-                                          2.0f, 2.1f,
-
-                                          2.0f, 2.1f,
-                                          3.0f, 3.1f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_2d_negative_and_positive_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-    // clang-format on
-
-    test_case.add_input<int32_t>({0, -2, 1, 2});
-
-    // clang-format off
-    test_case.add_expected_output<float>(out_shape,
-                                         {1.0f, 1.1f,
-                                          2.0f, 2.1f,
-
-                                          2.0f, 2.1f,
-                                          3.0f, 3.1f});
-    // clang-format on
-
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_1d_indices_axis_0_1d_input) {
-    Shape data_shape{3};
-    Shape indices_shape{2};
-    Shape out_shape{2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<float>({1.0f, 2.0f, 3.0f});
-    test_case.add_input<int32_t>({1, 0});
-    test_case.add_expected_output<float>(out_shape, {2.0f, 1.0f});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_scalar_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{};
-    Shape out_shape{2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<float>({1.0f, 1.1f, 2.0f, 2.1f, 3.0f, 3.1f});
-    test_case.add_input<int32_t>({1});
-    test_case.add_expected_output<float>(out_shape, {2.0f, 2.1f});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_2d_indices_axis_1_2d_input) {
-    Shape data_shape{3, 3};
-    Shape indices_shape{1, 2};
-    Shape out_shape{3, 1, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {1});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f, 1.2f,
-                                2.0f, 2.1f, 2.2f,
-                                3.0f, 3.1f, 3.2f});
-    // clang-format on
-    test_case.add_input<int32_t>({0, 2});
-
-    // clang-format off
-    test_case.add_expected_output<float>(out_shape, {1.0f, 1.2f,
-                                                     2.0f, 2.2f,
-                                                     3.0f, 3.2f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_1d_indices_axis_2_4d_input) {
-    Shape data_shape{2, 2, 3, 3};
-    Shape indices_shape{2};
-    Shape out_shape{2, 2, 2, 3};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {2});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    // clang-format off
-    test_case.add_input<float>({  1.0f,   1.1f,   1.2f,
-                                  2.0f,   2.1f,   2.2f,
-                                  3.0f,   3.1f,   3.2f,
-
-                                 11.0f,  11.1f,  11.2f,
-                                 12.0f,  12.1f,  12.2f,
-                                 13.0f,  13.1f,  13.2f,
-
-
-                                101.0f, 101.1f, 101.2f,
-                                102.0f, 102.1f, 102.2f,
-                                103.0f, 103.1f, 103.2f,
-
-                                111.0f, 111.1f, 111.2f,
-                                112.0f, 112.1f, 112.2f,
-                                113.0f, 113.1f, 113.2f});
-    // clang-format on
-    test_case.add_input<int32_t>({0, 2});
-    // clang-format off
-    test_case.add_expected_output<float>(
-        out_shape, {  1.0f,   1.1f,   1.2f,
-                      3.0f,   3.1f,   3.2f,
-
-                     11.0f,  11.1f,  11.2f,
-                     13.0f,  13.1f,  13.2f,
-
-
-                    101.0f, 101.1f, 101.2f,
-                    103.0f, 103.1f, 103.2f,
-
-                    111.0f, 111.1f, 111.2f,
-                    113.0f, 113.1f, 113.2f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_scalar_indices_axis_1_2d_input) {
-    Shape data_shape{3, 3};
-    Shape indices_shape{};
-    Shape out_shape{3};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {1});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<float>({1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f, 3.0f, 3.1f, 3.2f});
-    test_case.add_input<int32_t>({0});
-    test_case.add_expected_output<float>(out_shape, {1.0f, 2.0f, 3.0f});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_int8) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i8, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int8_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    test_case.add_expected_output<int8_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_int16) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i16, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int16_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<int16_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_int32) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-    // clang-format off
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({10, 11,
-                                  20, 21,
-                                  30, 31});
-    test_case.add_input<int32_t>({0, 1,
-                                  1, 2});
-    test_case.add_expected_output<int32_t>(out_shape, {10, 11,
-                                                       20, 21,
-
-                                                       20, 21,
-                                                       30, 31});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_int64) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i64, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int64_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<int64_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_uint8) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u8, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint8_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint8_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_uint16) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u16, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint16_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint16_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_uint32) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint32_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint32_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_axis_0_uint64) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u64, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v1::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint64_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint64_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_4d_indices_axis_0_uint8) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2, 3, 4};
-    Shape out_shape{2, 2, 3, 4, 2};
-    auto P = make_shared<op::Parameter>(element::u8, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint8_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2,
-                                  0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2, 0, 1, 1, 2});
-    test_case.add_expected_output<uint8_t>(
-        out_shape,
-        {10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
-         10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
-         10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31,
-         10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31, 10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_4d_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2, 3, 4};
-    Shape out_shape{2, 2, 3, 4, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-
-    test_case.add_input<int32_t>({0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-
-
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2,
-                                  0, 1, 1, 2});
-    test_case.add_expected_output<float>(
-            out_shape,
-            { 1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f,
-
-              1.0f, 1.1f,
-              2.0f, 2.1f,
-              2.0f, 2.1f,
-              3.0f, 3.1f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_3d_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 3, 4};
-    Shape out_shape{2, 3, 4, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-    test_case.add_input<int32_t>(
-            {0, 1, 1, 2,
-             0, 1, 1, 2,
-             0, 1, 1, 2,
-
-             0, 1, 1, 2,
-             0, 1, 1, 2,
-             0, 1, 1, 2});
-    test_case.add_expected_output<float>(
-            out_shape, {1.0f, 1.1f,
-                        2.0f, 2.1f,
-                        2.0f, 2.1f,
-                        3.0f, 3.1f,
-
-                        1.0f, 1.1f,
-                        2.0f, 2.1f,
-                        2.0f, 2.1f,
-                        3.0f, 3.1f,
-
-                        1.0f, 1.1f,
-                        2.0f, 2.1f,
-                        2.0f, 2.1f,
-                        3.0f, 3.1f,
-
-
-                        1.0f, 1.1f,
-                        2.0f, 2.1f,
-                        2.0f, 2.1f,
-                        3.0f, 3.1f,
-
-                        1.0f, 1.1f,
-                        2.0f, 2.1f,
-                        2.0f, 2.1f,
-                        3.0f, 3.1f,
-
-                        1.0f, 1.1f,
-                        2.0f, 2.1f,
-                        2.0f, 2.1f,
-                        3.0f, 3.1f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_1d_int32) {
-    Shape data_shape{3};
-    Shape indices_shape{2};
-    Shape out_shape{2};
-    auto P = make_shared<op::Parameter>(element::i32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    // clang-format off
-    test_case.add_input<int32_t>({1, 2, 3});
-    test_case.add_input<int32_t>({2, 0});
-    test_case.add_expected_output<int32_t>(out_shape, {3, 1});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_2d_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-    // clang-format on
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    // clang-format off
-    test_case.add_expected_output<float>(out_shape,
-                                         {1.0f, 1.1f,
-                                          2.0f, 2.1f,
-
-                                          2.0f, 2.1f,
-                                          3.0f, 3.1f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_2d_negative_and_positive_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f,
-                                2.0f, 2.1f,
-                                3.0f, 3.1f});
-    // clang-format on
-
-    test_case.add_input<int32_t>({0, -2, 1, 2});
-
-    // clang-format off
-    test_case.add_expected_output<float>(out_shape,
-                                         {1.0f, 1.1f,
-                                          2.0f, 2.1f,
-
-                                          2.0f, 2.1f,
-                                          3.0f, 3.1f});
-    // clang-format on
-
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_1d_indices_axis_0_1d_input) {
-    Shape data_shape{3};
-    Shape indices_shape{2};
-    Shape out_shape{2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<float>({1.0f, 2.0f, 3.0f});
-    test_case.add_input<int32_t>({1, 0});
-    test_case.add_expected_output<float>(out_shape, {2.0f, 1.0f});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_scalar_indices_axis_0_2d_input) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{};
-    Shape out_shape{2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<float>({1.0f, 1.1f, 2.0f, 2.1f, 3.0f, 3.1f});
-    test_case.add_input<int32_t>({1});
-    test_case.add_expected_output<float>(out_shape, {2.0f, 2.1f});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_2d_indices_axis_1_2d_input) {
-    Shape data_shape{3, 3};
-    Shape indices_shape{1, 2};
-    Shape out_shape{3, 1, 2};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {1});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-
-    // clang-format off
-    test_case.add_input<float>({1.0f, 1.1f, 1.2f,
-                                2.0f, 2.1f, 2.2f,
-                                3.0f, 3.1f, 3.2f});
-    // clang-format on
-    test_case.add_input<int32_t>({0, 2});
-
-    // clang-format off
-    test_case.add_expected_output<float>(out_shape, {1.0f, 1.2f,
-                                                     2.0f, 2.2f,
-                                                     3.0f, 3.2f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_1d_indices_axis_2_4d_input) {
-    Shape data_shape{2, 2, 3, 3};
-    Shape indices_shape{2};
-    Shape out_shape{2, 2, 2, 3};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {2});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    // clang-format off
-    test_case.add_input<float>({  1.0f,   1.1f,   1.2f,
-                                  2.0f,   2.1f,   2.2f,
-                                  3.0f,   3.1f,   3.2f,
-
-                                  11.0f,  11.1f,  11.2f,
-                                  12.0f,  12.1f,  12.2f,
-                                  13.0f,  13.1f,  13.2f,
-
-
-                                  101.0f, 101.1f, 101.2f,
-                                  102.0f, 102.1f, 102.2f,
-                                  103.0f, 103.1f, 103.2f,
-
-                                  111.0f, 111.1f, 111.2f,
-                                  112.0f, 112.1f, 112.2f,
-                                  113.0f, 113.1f, 113.2f});
-    // clang-format on
-    test_case.add_input<int32_t>({0, 2});
-    // clang-format off
-    test_case.add_expected_output<float>(
-            out_shape, {  1.0f,   1.1f,   1.2f,
-                          3.0f,   3.1f,   3.2f,
-
-                          11.0f,  11.1f,  11.2f,
-                          13.0f,  13.1f,  13.2f,
-
-
-                          101.0f, 101.1f, 101.2f,
-                          103.0f, 103.1f, 103.2f,
-
-                          111.0f, 111.1f, 111.2f,
-                          113.0f, 113.1f, 113.2f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_scalar_indices_axis_1_2d_input) {
-    Shape data_shape{3, 3};
-    Shape indices_shape{};
-    Shape out_shape{3};
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {1});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<float>({1.0f, 1.1f, 1.2f, 2.0f, 2.1f, 2.2f, 3.0f, 3.1f, 3.2f});
-    test_case.add_input<int32_t>({0});
-    test_case.add_expected_output<float>(out_shape, {1.0f, 2.0f, 3.0f});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_int8) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i8, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int8_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    test_case.add_expected_output<int8_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_int16) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i16, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int16_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<int16_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_int32) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-    // clang-format off
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({10, 11,
-                                  20, 21,
-                                  30, 31});
-    test_case.add_input<int32_t>({0, 1,
-                                  1, 2});
-    test_case.add_expected_output<int32_t>(out_shape, {10, 11,
-                                                       20, 21,
-
-                                                       20, 21,
-                                                       30, 31});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_int64) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::i64, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int64_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<int64_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_uint8) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u8, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint8_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint8_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_uint16) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u16, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint16_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint16_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_uint32) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint32_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int32_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint32_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_uint64) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::u64, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<uint64_t>({10, 11, 20, 21, 30, 31});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<uint64_t>(out_shape, {10, 11, 20, 21, 20, 21, 30, 31});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_axis_0_bool) {
-    Shape data_shape{3, 2};
-    Shape indices_shape{2, 2};
-    Shape out_shape{2, 2, 2};
-    auto P = make_shared<op::Parameter>(element::boolean, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {0});
-    auto G = make_shared<op::v7::Gather>(P, I, A);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<char>({1, 1, 1, 0, 0, 1});
-    test_case.add_input<int64_t>({0, 1, 1, 2});
-    test_case.add_expected_output<char>(out_shape, {1, 1, 1, 0, 1, 0, 0, 1});
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_data_int32_3d_indices_axis_1_batch_dims_1) {
-    Shape data_shape{2, 3};
-    Shape indices_shape{2, 2, 2};
-    Shape out_shape{2, 2, 2};
-    int64_t batch_dims = 1;
-    int64_t axis = 1;
-
-    auto P = make_shared<op::Parameter>(element::i32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {axis});
-    auto G = make_shared<op::v7::Gather>(P, I, A, batch_dims);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    // clang-format off
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({1, 2, 3,   // batch 0
-                                  4, 5, 6}); // batch 1
-
-    test_case.add_input<int64_t>({0, 1,      // batch 0
-                                  1, 2,
-
-                                  2, 0,      // batch 1
-                                  1, 2});
-    test_case.add_expected_output<int32_t>(out_shape, {1, 2,  // batch 1
-                                                       2, 3,
-
-                                                       6, 4,  // batch 1
-                                                       5, 6});
-    test_case.run();
-    // clang-format on
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_data_int32_2d_indices_axis_1_batch_dims_1) {
-    Shape data_shape{2, 5};
-    Shape indices_shape{2, 3};
-    Shape out_shape{2, 3};
-    int64_t batch_dims = 1;
-    int64_t axis = 1;
-
-    auto P = make_shared<op::Parameter>(element::i32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {axis});
-    auto G = make_shared<op::v7::Gather>(P, I, A, batch_dims);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    // clang-format off
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({1, 2, 3, 4, 5,    // batch 0
-                                  6, 7, 8, 9, 10}); // batch 1
-
-    test_case.add_input<int64_t>({0, 0, 4,    // batch 0
-                                  4, 0, 0});  // batch 1
-    test_case.add_expected_output<int32_t>(out_shape, {1, 1, 5,     // batch 0
-                                                       10, 6, 6});  // batch 1
-    test_case.run();
-    // clang-format on
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_data_int32_2d_indices_axis_1_negative_batch_dims) {
-    Shape data_shape{2, 5};
-    Shape indices_shape{2, 3};
-    Shape out_shape{2, 3};
-    int64_t batch_dims = -1;
-    int64_t axis = 1;
-
-    auto P = make_shared<op::Parameter>(element::i32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {axis});
-    auto G = make_shared<op::v7::Gather>(P, I, A, batch_dims);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    // clang-format off
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({1, 2, 3, 4, 5,    // batch 0
-                                  6, 7, 8, 9, 10}); // batch 1
-
-    test_case.add_input<int64_t>({0, 0, 4,    // batch 0
-                                  4, 0, 0});  // batch 1
-    test_case.add_expected_output<int32_t>(out_shape, {1, 1, 5,     // batch 0
-                                                       10, 6, 6});  // batch 1
-    test_case.run();
-    // clang-format on
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_4d_data_axis_2_batch_dims_1_int32) {
-    Shape data_shape{2, 1, 5, 4};
-    Shape indices_shape{2, 3};
-    Shape out_shape{2, 1, 3, 4};
-    int64_t batch_dims = 1;
-    int64_t axis = 2;
-
-    auto P = make_shared<op::Parameter>(element::i32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i64, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {axis});
-    auto G = make_shared<op::v7::Gather>(P, I, A, batch_dims);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    // clang-format off
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({
-           1,  2,  3,  4, // first batch
-           5,  6,  7,  8,
-           9, 10, 11, 12,
-          13, 14, 15, 16,
-          17, 18, 19, 20,
-
-          21, 22, 23, 24,  // second batch
-          25, 26, 27, 28,
-          29, 30, 31, 32,
-          33, 34, 35, 36,
-          37, 38, 39, 40
-    });
-
-    test_case.add_input<int64_t>({
-        1, 2, 4,  // first batch
-        4, 3, 2   // second batch
-    });
-    test_case.add_expected_output<int32_t>(out_shape, {
-         5,  6,  7,  8,
-         9, 10, 11, 12,
-        17, 18, 19, 20,
-
-        37, 38, 39, 40,
-        33, 34, 35, 36,
-        29, 30, 31, 32
-    });
-    test_case.run();
-    // clang-format on
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gather_v7_3d_indices_axis_1_batch_dims_1) {
-    Shape data_shape{2, 5, 2};
-    Shape indices_shape{2, 2, 3};
-    Shape out_shape{2, 2, 3, 2};
-    int64_t batch_dims = 1;
-    int64_t axis = 1;
-
-    auto P = make_shared<op::Parameter>(element::f32, data_shape);
-    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
-    auto A = op::Constant::create(element::i64, Shape{}, {axis});
-
-    auto G = make_shared<op::v7::Gather>(P, I, A, batch_dims);
-    auto f = make_shared<Function>(G, ParameterVector{P, I});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-
-    // clang-format off
-    test_case.add_input<float>({1.0f, 2.0f,
-                                3.0f, 4.0f,
-                                5.0f, 6.0f,
-                                7.0f, 8.0f,
-                                9.0f, 10.0f,
-
-                                11.0f, 12.0f,
-                                13.0f, 14.0f,
-                                15.0f, 16.0f,
-                                17.0f, 18.0f,
-                                19.0f, 20.0f});
-
-    test_case.add_input<int32_t>({0, 0, 4,
-                                  4, 0, 0,
-
-                                  1, 2, 4,
-                                  4, 3, 2});
-    test_case.add_expected_output<float>({1.0f, 2.0f,
-                                          1.0f, 2.0f,
-                                          9.0f, 10.0f,
-
-                                          9.0f, 10.0f,
-                                          1.0f, 2.0f,
-                                          1.0f, 2.0f,
-
-
-                                          13.0f, 14.0f,
-                                          15.0f, 16.0f,
-                                          19.0f, 20.0f,
-
-                                          19.0f, 20.0f,
-                                          17.0f, 18.0f,
-                                          15.0f, 16.0f});
-    // clang-format on
-    test_case.run(MIN_FLOAT_TOLERANCE_BITS);
-}
diff --git a/ngraph/test/backend/one_hot.in.cpp b/ngraph/test/backend/one_hot.in.cpp
deleted file mode 100644
index fd37ea852e3893..00000000000000
--- a/ngraph/test/backend/one_hot.in.cpp
+++ /dev/null
@@ -1,196 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <algorithm>
-#include <cinttypes>
-#include <cmath>
-#include <cstdlib>
-#include <random>
-#include <string>
-
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_scalar_2_in_3) {
-    Shape shape_a{};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    int axis = 0;
-    Shape shape_r{3};
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({2});
-    test_case.add_expected_output<int32_t>(shape_r, {0, 0, 1});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_scalar_1_in_3) {
-    Shape shape_a{};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    int axis = 0;
-    Shape shape_r{3};
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({1});
-    test_case.add_expected_output<int32_t>(shape_r, {0, 1, 0});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_scalar_0_in_3) {
-    Shape shape_a{};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    Shape shape_r{3};
-    int axis = 0;
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({0});
-    test_case.add_expected_output<int32_t>(shape_r, {1, 0, 0});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_0) {
-    Shape shape_a{8};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    Shape shape_r{3, 8};
-    int axis = 0;
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({2, 1, 0, 0, 2, 2, 1, 0});
-    test_case.add_expected_output<int32_t>(shape_r,
-                                           {0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_1) {
-    Shape shape_a{8};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    Shape shape_r{8, 3};
-    int axis = 1;
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({2, 1, 0, 0, 2, 2, 1, 0});
-    test_case.add_expected_output<int32_t>(shape_r,
-                                           {0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_1_barely_oob) {
-    Shape shape_a{8};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    Shape shape_r{8, 3};
-    int axis = 1;
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({2, 1, 0, 0, 3, 2, 1, 0});
-    // elements 12, 13, 14 are zeroed as out of bound
-    test_case.add_expected_output<int32_t>(shape_r,
-                                           {0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_matrix_0) {
-    Shape shape_a{3, 3};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    Shape shape_r{3, 3, 3};
-    int axis = 0;
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>({0, 1, 1, 2, 1, 0, 0, 2, 1});
-    test_case.add_expected_output<int32_t>(shape_r, {1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1,
-                                                     0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_many_categories) {
-    // Imagenet has roughly 20,000 categories
-    constexpr uint32_t category_count = 20000;
-    Shape shape_a{6};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    Shape shape_r{6, category_count};
-    int axis = 1;
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::i32, {}, {1});
-    auto off_value = op::Constant::create(element::i32, {}, {0});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    vector<int32_t> input{0, 11, 101, 1001, 10001, static_cast<int32_t>(category_count - 1)};
-    vector<int32_t> output(shape_size(shape_r), 0);
-    for (size_t i = 0; i < input.size(); ++i) {
-        output[i * category_count + input[i]] = 1;
-    }
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<int32_t>(input);
-    test_case.add_expected_output<int32_t>(shape_r, output);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, one_hot_on_off_float) {
-    Shape shape_a{3, 3};
-    auto A = make_shared<op::Parameter>(element::i32, shape_a);
-    Shape shape_r{3, 3, 3};
-    int axis = 0;
-    auto depth = op::Constant::create(element::i32, {}, {shape_r[axis]});
-    auto on_value = op::Constant::create(element::f32, {}, {2.5});
-    auto off_value = op::Constant::create(element::f32, {}, {0.5});
-    auto r = make_shared<op::v1::OneHot>(A, depth, on_value, off_value, axis);
-    auto f = make_shared<Function>(r, ParameterVector{A});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::i32, shape_a);
-    copy_data(a, vector<int32_t>{0, 1, 1, 2, 1, 0, 0, 2, 1});
-    auto result = backend->create_tensor(element::f32, shape_r);
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_EQ((vector<float>{2.5, 0.5, 0.5, 0.5, 0.5, 2.5, 2.5, 0.5, 0.5, 0.5, 2.5, 2.5, 0.5, 2.5,
-                             0.5, 0.5, 0.5, 2.5, 0.5, 0.5, 0.5, 2.5, 0.5, 0.5, 0.5, 2.5, 0.5}),
-              read_vector<float>(result));
-}
diff --git a/ngraph/test/backend/pad.in.cpp b/ngraph/test/backend/pad.in.cpp
deleted file mode 100644
index c506e843ab86b9..00000000000000
--- a/ngraph/test/backend/pad.in.cpp
+++ /dev/null
@@ -1,1306 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/execute_tools.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "ngraph/runtime/tensor.hpp"
-#include "runtime/backend.hpp"
-#include "util/all_close.hpp"
-#include "util/all_close_f.hpp"
-#include "util/ndarray.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-
-namespace {
-template <typename ValueType>
-struct Params {
-    using Data = ::ngraph::test::NDArrayBase<ValueType>;
-    using Pads = ::ngraph::test::NDArrayBase<int64_t>;
-
-    Params(Data input_data,
-           Pads pads_begin,
-           Pads pads_end,
-           Data expected_output,
-           op::PadMode pad_mode,
-           ValueType constant_value)
-        : input_data{std::move(input_data)},
-          pads_begin{std::move(pads_begin)},
-          pads_end{std::move(pads_end)},
-          expected_output{std::move(expected_output)},
-          pad_mode{pad_mode},
-          use_const_value{true},
-          constant_value{constant_value} {}
-
-    Params(Data input_data, Pads pads_begin, Pads pads_end, Data expected_output, op::PadMode pad_mode)
-        : input_data{std::move(input_data)},
-          pads_begin{std::move(pads_begin)},
-          pads_end{std::move(pads_end)},
-          expected_output{std::move(expected_output)},
-          pad_mode{pad_mode} {}
-
-    Data input_data;
-    Pads pads_begin;
-    Pads pads_end;
-    Data expected_output;
-    op::PadMode pad_mode;
-    bool use_const_value{false};
-    ValueType constant_value{};
-};
-
-class PadBackendTest : public ::testing::TestWithParam<Params<float>> {
-public:
-    static void execute_test(const Params<float>& params) {
-        const auto data = make_shared<op::Parameter>(element::f32, params.input_data.get_shape());
-
-        const auto pads_begin =
-            op::Constant::create(element::i64, params.pads_begin.get_shape(), params.pads_begin.get_vector());
-
-        const auto pads_end =
-            op::Constant::create(element::i64, params.pads_end.get_shape(), params.pads_end.get_vector());
-
-        auto f = [&] {
-            if (params.use_const_value) {
-                // pad_value should be used only in CONSTANT mode
-                const auto pad_val = op::Constant::create(element::f32, Shape{}, {params.constant_value});
-
-                return make_shared<Function>(
-                    make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, params.pad_mode),
-                    ParameterVector{data});
-            }
-
-            return make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, params.pad_mode),
-                                         ParameterVector{data});
-        }();
-
-        auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-        // Create some tensors for input/output
-        auto a = backend->create_tensor(element::f32, params.input_data.get_shape());
-        copy_data(a, params.input_data.get_vector());
-        auto result = backend->create_tensor(element::f32, params.expected_output.get_shape());
-
-        auto handle = backend->compile(f);
-        handle->call_with_validate({result}, {a});
-        EXPECT_TRUE(test::all_close_f(params.expected_output.get_vector(),
-                                      read_vector<float>(result),
-                                      MIN_FLOAT_TOLERANCE_BITS));
-    }
-};
-}  // namespace
-
-NGRAPH_TEST_P(${BACKEND_NAME}, PadBackendTest, PadBackendTestForSpec) {
-    execute_test(GetParam());
-}
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(
-    ${BACKEND_NAME},
-    pad_1d_constant_const_value_provided,
-    PadBackendTest,
-    testing::Values(
-        Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                      test::NDArray<int64_t, 1>({4}),
-                      test::NDArray<int64_t, 1>({5}),
-                      test::NDArray<float, 1>({2112, 2112, 2112, 2112, 1, 2, 3, 4, 5, 6, 2112, 2112, 2112, 2112, 2112}),
-                      op::PadMode::CONSTANT,
-                      2112},
-        Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                      test::NDArray<int64_t, 1>({4}),
-                      test::NDArray<int64_t, 1>({0}),
-                      test::NDArray<float, 1>({2112, 2112, 2112, 2112, 1, 2, 3, 4, 5, 6}),
-                      op::PadMode::CONSTANT,
-                      2112},
-        Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                      test::NDArray<int64_t, 1>({0}),
-                      test::NDArray<int64_t, 1>({3}),
-                      test::NDArray<float, 1>({1, 2, 3, 4, 5, 6, 2112, 2112, 2112}),
-                      op::PadMode::CONSTANT,
-                      2112}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(
-    ${BACKEND_NAME},
-    pad_1d_constant_use_default_const,
-    PadBackendTest,
-    testing::Values(Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({4}),
-                                  test::NDArray<int64_t, 1>({5}),
-                                  test::NDArray<float, 1>({0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0}),
-                                  op::PadMode::CONSTANT},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({4}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<float, 1>({0, 0, 0, 0, 1, 2, 3, 4, 5, 6}),
-                                  op::PadMode::CONSTANT},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<int64_t, 1>({3}),
-                                  test::NDArray<float, 1>({1, 2, 3, 4, 5, 6, 0, 0, 0}),
-                                  op::PadMode::CONSTANT}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(${BACKEND_NAME},
-                                pad_2d_constant_const_value_provided,
-                                PadBackendTest,
-                                testing::Values(Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({3, 4}),
-                                                              test::NDArray<float, 2>({
-                                                                  {2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112},
-                                                                  {2112, 2112, 1, 2, 2112, 2112, 2112, 2112},
-                                                                  {2112, 2112, 3, 4, 2112, 2112, 2112, 2112},
-                                                                  {2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112},
-                                                                  {2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112},
-                                                                  {2112, 2112, 2112, 2112, 2112, 2112, 2112, 2112},
-                                                              }),
-                                                              op::PadMode::CONSTANT,
-                                                              2112},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<float, 2>({
-                                                                  {2112, 2112, 2112, 2112},
-                                                                  {2112, 2112, 1, 2},
-                                                                  {2112, 2112, 3, 4},
-                                                              }),
-                                                              op::PadMode::CONSTANT,
-                                                              2112},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<float, 2>({
-                                                                  {1, 2, 2112, 2112},
-                                                                  {3, 4, 2112, 2112},
-                                                                  {2112, 2112, 2112, 2112},
-                                                              }),
-                                                              op::PadMode::CONSTANT,
-                                                              2112}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(${BACKEND_NAME},
-                                pad_2d_constant_use_default_const,
-                                PadBackendTest,
-                                testing::Values(Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({3, 4}),
-                                                              test::NDArray<float, 2>({
-                                                                  {0, 0, 0, 0, 0, 0, 0, 0},
-                                                                  {0, 0, 1, 2, 0, 0, 0, 0},
-                                                                  {0, 0, 3, 4, 0, 0, 0, 0},
-                                                                  {0, 0, 0, 0, 0, 0, 0, 0},
-                                                                  {0, 0, 0, 0, 0, 0, 0, 0},
-                                                                  {0, 0, 0, 0, 0, 0, 0, 0},
-                                                              }),
-                                                              op::PadMode::CONSTANT},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<float, 2>({
-                                                                  {0, 0, 0, 0},
-                                                                  {0, 0, 1, 2},
-                                                                  {0, 0, 3, 4},
-                                                              }),
-                                                              op::PadMode::CONSTANT},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<float, 2>({
-                                                                  {1, 2, 0, 0},
-                                                                  {3, 4, 0, 0},
-                                                                  {0, 0, 0, 0},
-                                                              }),
-                                                              op::PadMode::CONSTANT}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(
-    ${BACKEND_NAME},
-    pad_1d_edge,
-    PadBackendTest,
-    testing::Values(Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({2}),
-                                  test::NDArray<int64_t, 1>({3}),
-                                  test::NDArray<float, 1>({1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6}),
-                                  op::PadMode::EDGE},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({1}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<float, 1>({1, 1, 2, 3, 4, 5, 6}),
-                                  op::PadMode::EDGE},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<int64_t, 1>({2}),
-                                  test::NDArray<float, 1>({1, 2, 3, 4, 5, 6, 6, 6}),
-                                  op::PadMode::EDGE}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(${BACKEND_NAME},
-                                pad_2d_edge,
-                                PadBackendTest,
-                                testing::Values(Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({2, 1}),
-                                                              test::NDArray<float, 2>({
-                                                                  {1, 1, 1, 2, 2},
-                                                                  {1, 1, 1, 2, 2},
-                                                                  {3, 3, 3, 4, 4},
-                                                                  {3, 3, 3, 4, 4},
-                                                                  {3, 3, 3, 4, 4},
-                                                              }),
-                                                              op::PadMode::EDGE},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<float, 2>({
-                                                                  {1, 1, 1, 2},
-                                                                  {1, 1, 1, 2},
-                                                                  {3, 3, 3, 4},
-                                                              }),
-                                                              op::PadMode::EDGE},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2},
-                                                                  {3, 4},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<int64_t, 1>({2, 1}),
-                                                              test::NDArray<float, 2>({
-                                                                  {1, 2, 2},
-                                                                  {3, 4, 4},
-                                                                  {3, 4, 4},
-                                                                  {3, 4, 4},
-                                                              }),
-                                                              op::PadMode::EDGE}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(
-    ${BACKEND_NAME},
-    pad_1d_reflect,
-    PadBackendTest,
-    testing::Values(Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({2}),
-                                  test::NDArray<int64_t, 1>({3}),
-                                  test::NDArray<float, 1>({3, 2, 1, 2, 3, 4, 5, 6, 5, 4, 3}),
-                                  op::PadMode::REFLECT},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({1}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<float, 1>({2, 1, 2, 3, 4, 5, 6}),
-                                  op::PadMode::REFLECT},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<int64_t, 1>({2}),
-                                  test::NDArray<float, 1>({1, 2, 3, 4, 5, 6, 5, 4}),
-                                  op::PadMode::REFLECT}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(${BACKEND_NAME},
-                                pad_2d_reflect,
-                                PadBackendTest,
-                                testing::Values(Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2, 3},
-                                                                  {4, 5, 6},
-                                                                  {7, 8, 9},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({2, 1}),
-                                                              test::NDArray<float, 2>({
-                                                                  {6, 5, 4, 5, 6, 5},
-                                                                  {3, 2, 1, 2, 3, 2},
-                                                                  {6, 5, 4, 5, 6, 5},
-                                                                  {9, 8, 7, 8, 9, 8},
-                                                                  {6, 5, 4, 5, 6, 5},
-                                                                  {3, 2, 1, 2, 3, 2},
-                                                              }),
-                                                              op::PadMode::REFLECT},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2, 3},
-                                                                  {4, 5, 6},
-                                                                  {7, 8, 9},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<float, 2>({
-                                                                  {6, 5, 4, 5, 6},
-                                                                  {3, 2, 1, 2, 3},
-                                                                  {6, 5, 4, 5, 6},
-                                                                  {9, 8, 7, 8, 9},
-                                                              }),
-                                                              op::PadMode::REFLECT},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2, 3},
-                                                                  {4, 5, 6},
-                                                                  {7, 8, 9},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<int64_t, 1>({2, 1}),
-                                                              test::NDArray<float, 2>({
-                                                                  {1, 2, 3, 2},
-                                                                  {4, 5, 6, 5},
-                                                                  {7, 8, 9, 8},
-                                                                  {4, 5, 6, 5},
-                                                                  {1, 2, 3, 2},
-                                                              }),
-                                                              op::PadMode::REFLECT}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(
-    ${BACKEND_NAME},
-    pad_1d_symmetric,
-    PadBackendTest,
-    testing::Values(Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({2}),
-                                  test::NDArray<int64_t, 1>({3}),
-                                  test::NDArray<float, 1>({2, 1, 1, 2, 3, 4, 5, 6, 6, 5, 4}),
-                                  op::PadMode::SYMMETRIC},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({1}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<float, 1>({1, 1, 2, 3, 4, 5, 6}),
-                                  op::PadMode::SYMMETRIC},
-                    Params<float>{test::NDArray<float, 1>({1, 2, 3, 4, 5, 6}),
-                                  test::NDArray<int64_t, 1>({0}),
-                                  test::NDArray<int64_t, 1>({2}),
-                                  test::NDArray<float, 1>({1, 2, 3, 4, 5, 6, 6, 5}),
-                                  op::PadMode::SYMMETRIC}));
-
-NGRAPH_INSTANTIATE_TEST_SUITE_P(${BACKEND_NAME},
-                                pad_2d_symmetric,
-                                PadBackendTest,
-                                testing::Values(Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2, 3},
-                                                                  {4, 5, 6},
-                                                                  {7, 8, 9},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({2, 1}),
-                                                              test::NDArray<float, 2>({
-                                                                  {2, 1, 1, 2, 3, 3},
-                                                                  {2, 1, 1, 2, 3, 3},
-                                                                  {5, 4, 4, 5, 6, 6},
-                                                                  {8, 7, 7, 8, 9, 9},
-                                                                  {8, 7, 7, 8, 9, 9},
-                                                                  {5, 4, 4, 5, 6, 6},
-                                                              }),
-                                                              op::PadMode::SYMMETRIC},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2, 3},
-                                                                  {4, 5, 6},
-                                                                  {7, 8, 9},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({1, 2}),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<float, 2>({
-                                                                  {2, 1, 1, 2, 3},
-                                                                  {2, 1, 1, 2, 3},
-                                                                  {5, 4, 4, 5, 6},
-                                                                  {8, 7, 7, 8, 9},
-
-                                                              }),
-                                                              op::PadMode::SYMMETRIC},
-                                                Params<float>{test::NDArray<float, 2>({
-                                                                  {1, 2, 3},
-                                                                  {4, 5, 6},
-                                                                  {7, 8, 9},
-                                                              }),
-                                                              test::NDArray<int64_t, 1>({0, 0}),
-                                                              test::NDArray<int64_t, 1>({2, 1}),
-                                                              test::NDArray<float, 2>({
-                                                                  {1, 2, 3, 3},
-                                                                  {4, 5, 6, 6},
-                                                                  {7, 8, 9, 9},
-                                                                  {7, 8, 9, 9},
-                                                                  {4, 5, 6, 6},
-                                                              }),
-                                                              op::PadMode::SYMMETRIC}));
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_to_large_symmetric_padding) {
-    const auto params_to_large = Params<float>{test::NDArray<float, 2>({
-                                                   {1, 2},
-                                                   {4, 5},
-                                               }),
-                                               test::NDArray<int64_t, 1>({0, 3}),
-                                               test::NDArray<int64_t, 1>({0, 0}),
-                                               test::NDArray<float, 2>({
-                                                   {0, 0, 0, 0, 0},
-                                                   {0, 0, 0, 0, 0},
-                                               }),
-                                               op::PadMode::SYMMETRIC};
-
-    EXPECT_ANY_THROW(PadBackendTest::execute_test(params_to_large));
-
-    const auto params_ok = Params<float>{test::NDArray<float, 2>({
-                                             {1, 2},
-                                             {4, 5},
-                                         }),
-                                         test::NDArray<int64_t, 1>({0, 2}),
-                                         test::NDArray<int64_t, 1>({0, 0}),
-                                         test::NDArray<float, 2>({
-                                             {2, 1, 1, 2},
-                                             {5, 4, 4, 5},
-                                         }),
-                                         op::PadMode::SYMMETRIC};
-
-    EXPECT_NO_THROW(PadBackendTest::execute_test(params_ok));
-}
-NGRAPH_TEST(${BACKEND_NAME}, pad_to_large_reflect_padding) {
-    const auto params_to_large = Params<float>{test::NDArray<float, 2>({
-                                                   {1, 2},
-                                                   {4, 5},
-                                               }),
-                                               test::NDArray<int64_t, 1>({0, 2}),
-                                               test::NDArray<int64_t, 1>({0, 0}),
-                                               test::NDArray<float, 2>({
-                                                   {0, 0, 0, 0},
-                                                   {0, 0, 0, 0},
-                                               }),
-                                               op::PadMode::REFLECT};
-
-    EXPECT_ANY_THROW(PadBackendTest::execute_test(params_to_large));
-
-    const auto params_ok = Params<float>{test::NDArray<float, 2>({
-                                             {1, 2},
-                                             {4, 5},
-                                         }),
-                                         test::NDArray<int64_t, 1>({0, 1}),
-                                         test::NDArray<int64_t, 1>({0, 0}),
-                                         test::NDArray<float, 2>({
-                                             {2, 1, 2},
-                                             {5, 4, 5},
-                                         }),
-                                         op::PadMode::REFLECT};
-
-    EXPECT_NO_THROW(PadBackendTest::execute_test(params_ok));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_exterior_1d) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {4});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {5});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{15});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f({2112, 2112, 2112, 2112, 1, 2, 3, 4, 5, 6, 2112, 2112, 2112, 2112, 2112},
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_negative_exterior_1d) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {4});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {-2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{8});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(
-        test::all_close_f({2112, 2112, 2112, 2112, 1, 2, 3, 4}, read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_negative_exterior_1d_check_limits) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {4});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {-7});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{3});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f({2112, 2112, 2112}, read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_edge_1d) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::EDGE),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{11});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(
-        test::all_close_f({1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6}, read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_edge_1d_top_neg) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {-3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::EDGE),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{5});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f({1, 1, 1, 2, 3}, read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_edge_1d_top_neg_bigger_than_tensor) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {-7});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::EDGE),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{1});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f({1}, read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_edge_1d_bottom_neg) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {-2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::EDGE),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{7});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f({3, 4, 5, 6, 6, 6, 6}, read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_edge_1d_bottom_neg_bigger_than_tensor) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {-7});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::EDGE),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{2});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f({6, 6}, read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_edge_2d) {
-    const Shape data_shape{3, 4};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {2, 3});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {1, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::EDGE),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}));
-    auto result = backend->create_tensor(element::f32, Shape{6, 9});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{1, 1, 1, 1, 2, 3, 4, 4, 4},
-                                                           {1, 1, 1, 1, 2, 3, 4, 4, 4},
-                                                           {1, 1, 1, 1, 2, 3, 4, 4, 4},
-                                                           {5, 5, 5, 5, 6, 7, 8, 8, 8},
-                                                           {9, 9, 9, 9, 10, 11, 12, 12, 12},
-                                                           {9, 9, 9, 9, 10, 11, 12, 12, 12}})
-                                      .get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_edge_2d_with_neg) {
-    const Shape data_shape{3, 4};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {2, -1});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {1, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::EDGE),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}));
-    auto result = backend->create_tensor(element::f32, Shape{6, 5});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{2, 3, 4, 4, 4},
-                                                           {2, 3, 4, 4, 4},
-                                                           {2, 3, 4, 4, 4},
-                                                           {6, 7, 8, 8, 8},
-                                                           {10, 11, 12, 12, 12},
-                                                           {10, 11, 12, 12, 12}})
-                                      .get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_reflect_1d) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{11});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(std::vector<float>({3, 2, 1, 2, 3, 4, 5, 6, 5, 4, 3}),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_reflect_1d_top_neg) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {-3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{5});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(
-        test::all_close_f(std::vector<float>({3, 2, 1, 2, 3}), read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_reflect_1d_top_neg_bigger_than_tensor) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {-7});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{1});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(std::vector<float>({3}), read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_reflect_1d_bottom_neg) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {-2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{7});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(std::vector<float>({3, 4, 5, 6, 5, 4, 3}),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_reflect_1d_bottom_neg_bigger_than_tensor) {
-    const Shape data_shape{6};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {-7});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {3});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3, 4, 5, 6}));
-    auto result = backend->create_tensor(element::f32, Shape{2});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(std::vector<float>({4, 3}), read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, DISABLED_pad_reflect_1d_multi_reflect) {
-    const Shape data_shape{3};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{1}, {10});
-    const auto pads_end = op::Constant::create(element::i64, Shape{1}, {9});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, std::vector<float>({1, 2, 3}));
-    auto result = backend->create_tensor(element::f32, Shape{22});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(
-        test::all_close_f(std::vector<float>({3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3, 2}),
-                          read_vector<float>(result),
-                          MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_reflect_2d) {
-    const Shape data_shape{3, 4};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {2, 3});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {1, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, test::NDArray<float, 2>({{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}).get_vector());
-    auto result = backend->create_tensor(element::f32, Shape{6, 9});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{12, 11, 10, 9, 10, 11, 12, 11, 10},
-                                                           {8, 7, 6, 5, 6, 7, 8, 7, 6},
-                                                           {4, 3, 2, 1, 2, 3, 4, 3, 2},
-                                                           {8, 7, 6, 5, 6, 7, 8, 7, 6},
-                                                           {12, 11, 10, 9, 10, 11, 12, 11, 10},
-                                                           {8, 7, 6, 5, 6, 7, 8, 7, 6}})
-                                      .get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_reflect_2d_with_neg) {
-    const Shape data_shape{3, 4};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {2, -1});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {1, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::REFLECT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, test::NDArray<float, 2>({{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}).get_vector());
-    auto result = backend->create_tensor(element::f32, Shape{6, 5});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{10, 11, 12, 11, 10},
-                                                           {6, 7, 8, 7, 6},
-                                                           {2, 3, 4, 3, 2},
-                                                           {6, 7, 8, 7, 6},
-                                                           {10, 11, 12, 11, 10},
-                                                           {6, 7, 8, 7, 6}})
-                                      .get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_negative_exterior_2d) {
-    const Shape data_shape{2, 3};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {1, -1});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {2, 0});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {9});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, test::NDArray<float, 2>({{1, 2, 3}, {4, 5, 6}}).get_vector());
-    auto result = backend->create_tensor(element::f32, Shape{5, 2});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{9, 9}, {2, 3}, {5, 6}, {9, 9}, {9, 9}}).get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_negative_exterior_2d_all_negative) {
-    const Shape data_shape{3, 3};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {-1, -1});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {-1, -1});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {9});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, test::NDArray<float, 2>({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}).get_vector());
-    auto result = backend->create_tensor(element::f32, Shape{1, 1});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{5}}).get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_exterior_2d_0x0) {
-    const Shape data_shape{0, 0};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {2, 3});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {3, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    auto result = backend->create_tensor(element::f32, Shape{5, 5});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112}})
-                                      .get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_exterior_2d_0x3) {
-    const Shape data_shape{0, 3};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {2, 1});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {3, 1});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    auto result = backend->create_tensor(element::f32, Shape{5, 5});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112}})
-                                      .get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_exterior_2d_3x0) {
-    const Shape data_shape{3, 0};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {1, 3});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {1, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    auto result = backend->create_tensor(element::f32, Shape{5, 5});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 2>({{2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112},
-                                                           {2112, 2112, 2112, 2112, 2112}})
-                                      .get_vector(),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_exterior_4d_1x2x2x2) {
-    const Shape data_shape{1, 2, 2, 2};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{4}, {0, 0, 1, 1});
-    const auto pads_end = op::Constant::create(element::i64, Shape{4}, {0, 0, 1, 1});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {42});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    // clang-format off
-    copy_data(a, test::NDArray<float, 4>(
-        {
-            {
-                {
-                    {0.0f, 0.0f},
-                    {0.0f, 0.0f}
-                },
-                {
-                    {0.0f, 0.0f},
-                    {0.0f, 0.0f}
-                }
-            }
-        }).get_vector());
-    // clang-format on
-    auto result = backend->create_tensor(element::f32, Shape{1, 2, 4, 4});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    // clang-format off
-    EXPECT_TRUE(test::all_close_f((test::NDArray<float, 4>(
-        {
-            {
-                {
-                    {42.0f, 42.0f, 42.0f, 42.0f},
-                    {42.0f, 0.0f, 0.0f, 42.0f},
-                    {42.0f, 0.0f, 0.0f, 42.0f},
-                    {42.0f, 42.0f, 42.0f, 42.0f}
-                },
-                {
-                    {42.0f, 42.0f, 42.0f, 42.0f},
-                    {42.0f, 0.0f, 0.0f, 42.0f},
-                    {42.0f, 0.0f, 0.0f, 42.0f},
-                    {42.0f, 42.0f, 42.0f, 42.0f}
-                }
-            }
-        }).get_vector()),
-        read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-    // clang-format on
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_negative_exterior_4d) {
-    const Shape data_shape{1, 3, 2, 2};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{4}, {0, -1, 1, 1});
-    const auto pads_end = op::Constant::create(element::i64, Shape{4}, {0, -1, 1, 1});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {42});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    // clang-format off
-    copy_data(a, test::NDArray<float, 4>(
-        {
-            {
-                {
-                    {0.0f, 0.0f},
-                    {0.0f, 0.0f}
-                },
-                {
-                    {1.0f, 1.0f},
-                    {1.0f, 1.0f}
-                },
-                {
-                    {2.0f, 2.0f},
-                    {2.0f, 2.0f}
-                }
-            }
-        }).get_vector());
-    // clang-format on
-
-    auto result = backend->create_tensor(element::f32, Shape{1, 1, 4, 4});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-
-    // clang-format off
-    EXPECT_TRUE(test::all_close_f((test::NDArray<float, 4>(
-        {
-            {
-                {
-                    {42.0f, 42.0f, 42.0f, 42.0f},
-                    {42.0f, 1.0f, 1.0f, 42.0f},
-                    {42.0f, 1.0f, 1.0f, 42.0f},
-                    {42.0f, 42.0f, 42.0f, 42.0f}
-                }
-            }
-        }).get_vector()),
-        read_vector<float>(result), MIN_FLOAT_TOLERANCE_BITS));
-    // clang-format on
-}
-
-// This test covers the case with multiple image and with asymetric pad
-// bug has been found on nvGPU side now covered by this test
-NGRAPH_TEST(${BACKEND_NAME}, pad_2channel_2image_asym) {
-    const Shape data_shape{2, 2, 4, 4};
-    const auto window_movement_strides = Strides{2, 2};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{4}, {0, 0, 0, 0});
-    const auto pads_end = op::Constant::create(element::i64, Shape{4}, {0, 0, 2, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {42});
-
-    auto f = make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::CONSTANT),
-                                   ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a,
-              test::NDArray<float, 4>({{{{0, 1, 0, 2},  // img 0 chan 0
-                                         {0, 3, 2, 0},
-                                         {2, 0, 0, 0},
-                                         {0, 2, 1, 0}},
-
-                                        {{0, 0, 0, 2},  // img 0 chan 1
-                                         {0, 2, 3, 0},
-                                         {2, 0, 1, 0},
-                                         {2, 0, 0, 0}}},
-
-                                       {{{0, 2, 1, 1},  // img 1 chan 0
-                                         {0, 0, 2, 0},
-                                         {0, 0, 1, 2},
-                                         {0, 0, 0, 0}},
-
-                                        {{2, 1, 0, 0},  // img 1 chan 1
-                                         {0, 2, 0, 0},
-                                         {1, 1, 2, 0},
-                                         {1, 0, 0, 0}}}})
-                  .get_vector());
-
-    auto result = backend->create_tensor(element::f32, Shape{2, 2, 6, 6});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-
-    EXPECT_TRUE(test::all_close_f((test::NDArray<float, 4>({{{{0, 1, 0, 2, 42, 42},  // img 0 chan 0
-                                                              {0, 3, 2, 0, 42, 42},
-                                                              {2, 0, 0, 0, 42, 42},
-                                                              {0, 2, 1, 0, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42}},
-
-                                                             {{0, 0, 0, 2, 42, 42},  // img 1 chan 0
-                                                              {0, 2, 3, 0, 42, 42},
-                                                              {2, 0, 1, 0, 42, 42},
-                                                              {2, 0, 0, 0, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42}}},
-
-                                                            {{{0, 2, 1, 1, 42, 42},  // img 1 chan 0
-                                                              {0, 0, 2, 0, 42, 42},
-                                                              {0, 0, 1, 2, 42, 42},
-                                                              {0, 0, 0, 0, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42}},
-
-                                                             {{2, 1, 0, 0, 42, 42},  // img 1 chan 1
-                                                              {0, 2, 0, 0, 42, 42},
-                                                              {1, 1, 2, 0, 42, 42},
-                                                              {1, 0, 0, 0, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42},
-                                                              {42, 42, 42, 42, 42, 42}}}})
-                                       .get_vector()),
-                                  read_vector<float>(result),
-                                  MIN_FLOAT_TOLERANCE_BITS));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, pad_symmetric) {
-    const Shape data_shape{2, 3};
-    const auto data = make_shared<op::Parameter>(element::f32, data_shape);
-
-    const auto pads_begin = op::Constant::create(element::i64, Shape{2}, {1, 2});
-    const auto pads_end = op::Constant::create(element::i64, Shape{2}, {1, 2});
-    const auto pad_val = op::Constant::create(element::f32, Shape{}, {2112});
-
-    auto f =
-        make_shared<Function>(make_shared<op::v1::Pad>(data, pads_begin, pads_end, pad_val, op::PadMode::SYMMETRIC),
-                              ParameterVector{data});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, data_shape);
-    copy_data(a, test::NDArray<float, 2>({{1, 2, 3}, {4, 5, 6}}).get_vector());
-    auto result = backend->create_tensor(element::f32, Shape{4, 7});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a});
-    EXPECT_TRUE(test::all_close_f(
-        (test::NDArray<float, 2>(
-             {{2, 1, 1, 2, 3, 3, 2}, {2, 1, 1, 2, 3, 3, 2}, {5, 4, 4, 5, 6, 6, 5}, {5, 4, 4, 5, 6, 6, 5}})
-             .get_vector()),
-        read_vector<float>(result),
-        MIN_FLOAT_TOLERANCE_BITS));
-}
diff --git a/ngraph/test/backend/prior_box.in.cpp b/ngraph/test/backend/prior_box.in.cpp
deleted file mode 100644
index c95eec8c82987b..00000000000000
--- a/ngraph/test/backend/prior_box.in.cpp
+++ /dev/null
@@ -1,44 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "ngraph/op/prior_box.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, prior_box) {
-    op::PriorBoxAttrs attrs;
-    attrs.min_size = {2.0f};
-    attrs.aspect_ratio = {1.5f};
-    attrs.scale_all_sizes = false;
-
-    Shape layer_shape_shape{2};
-    Shape image_shape_shape{2};
-    vector<int64_t> layer_shape{2, 2};
-    vector<int64_t> image_shape{10, 10};
-
-    auto LS = op::Constant::create(element::i64, layer_shape_shape, layer_shape);
-    auto IS = op::Constant::create(element::i64, image_shape_shape, image_shape);
-    auto f = make_shared<Function>(make_shared<op::PriorBox>(LS, IS, attrs), ParameterVector{});
-    const auto exp_shape = Shape{2, 32};
-    vector<float> out{-0.75, -0.75, 1.25, 1.25, -0.974745, -0.566497,  1.47474, 1.0665,
-                      -0.25, -0.75, 1.75, 1.25, -0.474745, -0.566497,  1.97474, 1.0665,
-                      -0.75, -0.25, 1.25, 1.75, -0.974745, -0.0664966, 1.47474, 1.5665,
-                      -0.25, -0.25, 1.75, 1.75, -0.474745, -0.0664966, 1.97474, 1.5665,
-                      0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1,
-                      0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1,
-                      0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1,
-                      0.1,   0.1,   0.1,  0.1,  0.1,       0.1,        0.1,     0.1};
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_expected_output<float>(exp_shape, out);
-    test_case.run_with_tolerance_as_fp(1.0e-5f);
-}
diff --git a/ngraph/test/backend/prior_box_clustered.in.cpp b/ngraph/test/backend/prior_box_clustered.in.cpp
deleted file mode 100644
index 478723aa9f2c42..00000000000000
--- a/ngraph/test/backend/prior_box_clustered.in.cpp
+++ /dev/null
@@ -1,65 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "ngraph/op/prior_box_clustered.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, prior_box_clustered) {
-    op::PriorBoxClusteredAttrs attrs;
-    attrs.widths = {3.0f};
-    attrs.heights = {3.0f};
-    attrs.clip = true;
-
-    Shape layer_shape_shape{2};
-    Shape image_shape_shape{2};
-    vector<int64_t> layer_shape{2, 2};
-    vector<int64_t> image_shape{10, 10};
-
-    auto LS = op::Constant::create(element::i64, layer_shape_shape, layer_shape);
-    auto IS = op::Constant::create(element::i64, image_shape_shape, image_shape);
-    auto f = make_shared<Function>(make_shared<op::PriorBoxClustered>(LS, IS, attrs), ParameterVector{});
-    const auto exp_shape = Shape{2, 16};
-    vector<float> out{0,        0,        0.15f,    0.15f,    0.34999f, 0,    0.64999f, 0.15f, 0,    0.34999f, 0.15f,
-                      0.64999f, 0.34999f, 0.34999f, 0.64999f, 0.64999f, 0.1f, 0.1f,     0.1f,  0.1f, 0.1f,     0.1f,
-                      0.1f,     0.1f,     0.1f,     0.1f,     0.1f,     0.1f, 0.1f,     0.1f,  0.1f, 0.1f};
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_expected_output<float>(exp_shape, out);
-    test_case.run_with_tolerance_as_fp(1.0e-5f);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, prior_box_clustered_non_default_variances) {
-    op::PriorBoxClusteredAttrs attrs;
-    attrs.widths = {3.0f};
-    attrs.heights = {3.0f};
-    attrs.clip = true;
-    attrs.variances = {0.1f, 0.2f, 0.3f, 0.4f};
-
-    Shape layer_shape_shape{2};
-    Shape image_shape_shape{2};
-    vector<int64_t> layer_shape{2, 2};
-    vector<int64_t> image_shape{10, 10};
-
-    auto LS = op::Constant::create(element::i64, layer_shape_shape, layer_shape);
-    auto IS = op::Constant::create(element::i64, image_shape_shape, image_shape);
-    auto f = make_shared<Function>(make_shared<op::PriorBoxClustered>(LS, IS, attrs), ParameterVector{});
-    const auto exp_shape = Shape{2, 16};
-    vector<float> out{0,        0,        0.15f,    0.15f,    0.34999f, 0,    0.64999f, 0.15f, 0,    0.34999f, 0.15f,
-                      0.64999f, 0.34999f, 0.34999f, 0.64999f, 0.64999f, 0.1f, 0.2f,     0.3f,  0.4f, 0.1f,     0.2f,
-                      0.3f,     0.4f,     0.1f,     0.2f,     0.3f,     0.4f, 0.1f,     0.2f,  0.3f, 0.4f};
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_expected_output<float>(exp_shape, out);
-    test_case.run_with_tolerance_as_fp(1.0e-5f);
-}
diff --git a/ngraph/test/backend/recurrent_cells.in.cpp b/ngraph/test/backend/recurrent_cells.in.cpp
deleted file mode 100644
index bf9b55a84a2de5..00000000000000
--- a/ngraph/test/backend/recurrent_cells.in.cpp
+++ /dev/null
@@ -1,603 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-// clang-format off
-#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#endif
-// clang-format on
-
-#include "engines_util/execute_tools.hpp"
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/check.hpp"
-#include "ngraph/ngraph.hpp"
-#include "ngraph/opsets/opset4.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, lstm_cell_zero_bias_default_attrs) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    const size_t gates_count = 4;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto C_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size});
-
-    const auto lstm_cell =
-        make_shared<opset4::LSTMCell>(X,
-                                      H_t,
-                                      C_t,
-                                      op::util::convert_lstm_node_format(W, op::util::LSTMWeightsFormat::IOFC),
-                                      op::util::convert_lstm_node_format(R, op::util::LSTMWeightsFormat::IOFC),
-                                      op::util::convert_lstm_node_format(B, op::util::LSTMWeightsFormat::IOFC),
-                                      hidden_size);
-
-    auto ht_function = make_shared<Function>(OutputVector{lstm_cell->output(0)}, ParameterVector{X, H_t, C_t, W, R, B});
-    auto ht_test_case = test::TestCase<TestEngine>(ht_function);
-
-    // X
-    vector<float> in_X{0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f};
-    // W
-    vector<float> in_W{3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
-                       9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
-                       6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
-                       8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
-                       5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
-                       3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f};
-    // R
-    vector<float> in_R{0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
-                       0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
-                       0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
-                       0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
-                       0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
-                       0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f};
-    // Ht
-    vector<float> in_Ht{0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f};
-    // Ct
-    vector<float> in_Ct{0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f};
-    // B
-    vector<float> in_B(gates_count * hidden_size, 0.f);
-
-    ht_test_case.add_multiple_inputs(vector<vector<float>>{in_X, in_Ht, in_Ct, in_W, in_R, in_B});
-    ht_test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                            {0.81457126f, 0.61109227f, 0.769522f, 0.52239674f, 0.4324641f, 0.63183f});
-    ht_test_case.run();
-
-    auto ct_function = make_shared<Function>(OutputVector{lstm_cell->output(1)}, ParameterVector{X, H_t, C_t, W, R, B});
-    auto ct_test_case = test::TestCase<TestEngine>(ct_function);
-    ct_test_case.add_multiple_inputs(vector<vector<float>>{in_X, in_Ht, in_Ct, in_W, in_R, in_B});
-    ct_test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                            {1.4444952f, 0.9635685f, 1.2875274f, 0.8053419f, 0.7184521f, 0.95803297f});
-    ct_test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, lstm_cell_bias_default_attrs) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    const size_t gates_count = 4;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto C_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size});
-
-    const auto lstm_cell = make_shared<opset4::LSTMCell>(X, H_t, C_t, W, R, B, hidden_size);
-
-    auto ht_function = make_shared<Function>(OutputVector{lstm_cell->output(0)}, ParameterVector{X, H_t, C_t, W, R, B});
-    auto ht_test_case = test::TestCase<TestEngine>(ht_function);
-
-    // X
-    vector<float> in_X{0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f};
-    // W
-    vector<float> in_W{3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
-                       9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
-                       6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
-                       8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
-                       5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
-                       3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f};
-    // R
-    vector<float> in_R{0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
-                       0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
-                       0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
-                       0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
-                       0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
-                       0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f};
-    // Ht
-    vector<float> in_Ht{0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f};
-    // Ct
-    vector<float> in_Ct{0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f};
-    // B
-    vector<float> in_B{1.07393714f,
-                       1.15248052f,
-                       1.16671345f,
-                       0.21450312f,
-                       1.2380678f,
-                       1.51688835f,
-                       0.46718366f,
-                       0.91810346f,
-                       1.1274234f,
-                       0.51022074f,
-                       1.11389844f,
-                       0.74174305f};
-
-    ht_test_case.add_multiple_inputs(vector<vector<float>>{in_X, in_Ht, in_Ct, in_W, in_R, in_B});
-    ht_test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                            {0.81014400720596313,
-                                             0.76665538549423218,
-                                             0.82509011030197144,
-                                             0.6479143500328064,
-                                             0.66586339473724365,
-                                             0.74838578701019287});
-    ht_test_case.run();
-
-    auto ct_function = make_shared<Function>(OutputVector{lstm_cell->output(1)}, ParameterVector{X, H_t, C_t, W, R, B});
-    auto ct_test_case = test::TestCase<TestEngine>(ct_function);
-    ct_test_case.add_multiple_inputs(vector<vector<float>>{in_X, in_Ht, in_Ct, in_W, in_R, in_B});
-    ct_test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                            {1.6800162792205811,
-                                             1.1150213479995728,
-                                             1.4578367471694946,
-                                             1.0649888515472412,
-                                             0.93761754035949707,
-                                             1.3659683465957642});
-    ct_test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, lstm_cell_bias_clip) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    const size_t gates_count = 4;
-    const float clip_threshold = 3.5f;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto C_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size});
-
-    const auto lstm_cell = make_shared<opset4::LSTMCell>(X,
-                                                         H_t,
-                                                         C_t,
-                                                         W,
-                                                         R,
-                                                         B,
-                                                         hidden_size,
-                                                         vector<string>{"sigmoid", "tanh", "tanh"},
-                                                         vector<float>{},
-                                                         vector<float>{},
-                                                         clip_threshold);
-    auto ht_function = make_shared<Function>(OutputVector{lstm_cell->output(0)}, ParameterVector{X, H_t, C_t, W, R, B});
-    auto ht_test_case = test::TestCase<TestEngine>(ht_function);
-
-    // X
-    vector<float> in_X{0.81342685f, 0.84108883f, 0.8152282f, 0.46893653f, 0.0901856f, 0.37088776f};
-    // W
-    vector<float> in_W{3.3330739e-01f, 3.6229487e-04f, 4.6773660e-01f, 4.3046016e-01f, 7.3950343e-02f, 3.8063636e-01f,
-                       9.6921772e-01f, 9.6897459e-01f, 6.2964785e-01f, 3.1134409e-01f, 8.4709978e-01f, 9.4928098e-01f,
-                       6.1676943e-01f, 6.6020679e-01f, 1.9072217e-01f, 8.8032126e-02f, 4.0472135e-01f, 6.8342745e-01f,
-                       8.3432144e-01f, 4.4928190e-01f, 7.9524308e-01f, 5.3966165e-01f, 8.5936421e-01f, 8.3136767e-01f,
-                       5.5125546e-02f, 4.7791195e-01f, 3.5788772e-01f, 6.7507404e-01f, 2.1716513e-01f, 2.7473119e-01f,
-                       3.3999152e-02f, 9.6835363e-01f, 3.7581277e-01f, 2.4026000e-01f, 6.7418844e-01f, 3.4199652e-01f};
-    // R
-    vector<float> in_R{0.0987983f,  0.52032113f, 0.5848073f,  0.5356095f,  0.74497133f, 0.73260087f,
-                       0.1700787f,  0.45684233f, 0.1495722f,  0.42734373f, 0.4433832f,  0.25906256f,
-                       0.03854987f, 0.47480518f, 0.37215272f, 0.99890584f, 0.74019486f, 0.3518967f,
-                       0.6881257f,  0.8170279f,  0.54088944f, 0.81225616f, 0.14619833f, 0.42941234f,
-                       0.86843914f, 0.45967972f, 0.6237719f,  0.11074839f, 0.6029616f,  0.3149305f,
-                       0.46504205f, 0.5843412f,  0.8733427f,  0.7687243f,  0.07074859f, 0.39188156f};
-    // Ht
-    vector<float> in_Ht{0.77956f, 0.5331557f, 0.04297554f, 0.7962175f, 0.7635707f, 0.11989366f};
-    // Ct
-    vector<float> in_Ct{0.8488452f, 0.18851636f, 0.5020695f, 0.29716516f, 0.06740791f, 0.45384037f};
-    // B
-    vector<float> in_B{1.07393714f,
-                       1.15248052f,
-                       1.16671345f,
-                       0.21450312f,
-                       1.2380678f,
-                       1.51688835f,
-                       0.46718366f,
-                       0.91810346f,
-                       1.1274234f,
-                       0.51022074f,
-                       1.11389844f,
-                       0.74174305f};
-
-    ht_test_case.add_multiple_inputs(vector<vector<float>>{in_X, in_Ht, in_Ct, in_W, in_R, in_B});
-    ht_test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                            {0.81014400720596313,
-                                             0.76665538549423218,
-                                             0.82387429475784302,
-                                             0.6479143500328064,
-                                             0.66586339473724365,
-                                             0.74838578701019287});
-    ht_test_case.run();
-
-    auto ct_function = make_shared<Function>(OutputVector{lstm_cell->output(1)}, ParameterVector{X, H_t, C_t, W, R, B});
-    auto ct_test_case = test::TestCase<TestEngine>(ct_function);
-    ct_test_case.add_multiple_inputs(vector<vector<float>>{in_X, in_Ht, in_Ct, in_W, in_R, in_B});
-    ct_test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                            {1.6800162792205811,
-                                             1.1150213479995728,
-                                             1.4510968923568726,
-                                             1.0649888515472412,
-                                             0.93761754035949707,
-                                             1.3659683465957642});
-    ct_test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, rnn_cell_zero_bias_default_attrs) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{hidden_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{hidden_size});
-
-    const auto rnn_cell = make_shared<opset4::RNNCell>(X, H_t, W, R, B, hidden_size);
-    auto function = make_shared<Function>(rnn_cell, ParameterVector{X, H_t, W, R, B});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    // X
-    test_case.add_input<float>({0.3432185f, 0.612268f, 0.20272376f, 0.9513413f, 0.30585995f, 0.7265472f});
-    // Ht
-    test_case.add_input<float>({0.12444675f, 0.52055854f, 0.46489045f, 0.4983964f, 0.7730452f, 0.28439692f});
-    // W
-    test_case.add_input<float>({0.41930267f,
-                                0.7872176f,
-                                0.89940447f,
-                                0.23659843f,
-                                0.24676207f,
-                                0.17101714f,
-                                0.3147149f,
-                                0.6555601f,
-                                0.4559603f});
-    // R
-    test_case.add_input<float>({0.8374871f,
-                                0.86660194f,
-                                0.82114047f,
-                                0.71549815f,
-                                0.18775631f,
-                                0.3182116f,
-                                0.25392973f,
-                                0.38301638f,
-                                0.85531586f});
-    // B
-    test_case.add_input<float>({0.0f, 0.0f, 0.0f});
-
-    test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                         {0.9408395f, 0.53823817f, 0.84270686f, 0.98932856f, 0.768665f, 0.90461975f});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, rnn_cell_bias_clip) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    float clip = 2.88f;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{hidden_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{hidden_size});
-
-    const auto rnn_cell = make_shared<
-        opset4::RNNCell>(X, H_t, W, R, B, hidden_size, vector<string>{"tanh"}, vector<float>{}, vector<float>{}, clip);
-    auto function = make_shared<Function>(rnn_cell, ParameterVector{X, H_t, W, R, B});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    // X
-    test_case.add_input<float>({0.3432185f, 0.612268f, 0.20272376f, 0.9513413f, 0.30585995f, 0.7265472f});
-    // Ht
-    test_case.add_input<float>({0.12444675f, 0.52055854f, 0.46489045f, 0.4983964f, 0.7730452f, 0.28439692f});
-    // W
-    test_case.add_input<float>({0.41930267f,
-                                0.7872176f,
-                                0.89940447f,
-                                0.23659843f,
-                                0.24676207f,
-                                0.17101714f,
-                                0.3147149f,
-                                0.6555601f,
-                                0.4559603f});
-    // R
-    test_case.add_input<float>({0.8374871f,
-                                0.86660194f,
-                                0.82114047f,
-                                0.71549815f,
-                                0.18775631f,
-                                0.3182116f,
-                                0.25392973f,
-                                0.38301638f,
-                                0.85531586f});
-    // B
-    test_case.add_input<float>({1.0289404f, 1.6362579f, 0.4370661f});
-
-    test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                         {0.9922437f, 0.97749525f, 0.9312212f, 0.9937176f, 0.9901317f, 0.95906746f});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, rnn_cell_sigmoid_activation_function) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    float clip = 2.88f;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{hidden_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{hidden_size});
-
-    const auto rnn_cell = make_shared<opset4::RNNCell>(X,
-                                                       H_t,
-                                                       W,
-                                                       R,
-                                                       B,
-                                                       hidden_size,
-                                                       vector<string>{"sigmoid"},
-                                                       vector<float>{},
-                                                       vector<float>{},
-                                                       clip);
-    auto function = make_shared<Function>(rnn_cell, ParameterVector{X, H_t, W, R, B});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    // X
-    test_case.add_input<float>({0.3432185f, 0.612268f, 0.20272376f, 0.9513413f, 0.30585995f, 0.7265472f});
-    // Ht
-    test_case.add_input<float>({0.12444675f, 0.52055854f, 0.46489045f, 0.4983964f, 0.7730452f, 0.28439692f});
-    // W
-    test_case.add_input<float>({0.41930267f,
-                                0.7872176f,
-                                0.89940447f,
-                                0.23659843f,
-                                0.24676207f,
-                                0.17101714f,
-                                0.3147149f,
-                                0.6555601f,
-                                0.4559603f});
-    // R
-    test_case.add_input<float>({0.8374871f,
-                                0.86660194f,
-                                0.82114047f,
-                                0.71549815f,
-                                0.18775631f,
-                                0.3182116f,
-                                0.25392973f,
-                                0.38301638f,
-                                0.85531586f});
-    // B
-    test_case.add_input<float>({1.0289404f, 1.6362579f, 0.4370661f});
-
-    test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                         {0.94126844f, 0.9036043f, 0.841243f, 0.9468489f, 0.934215f, 0.873708f});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gru_cell_bias_clip) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    const size_t gates_count = 3;
-    float clip = 2.88f;
-    bool linear_before_reset = false;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size});
-
-    const auto gru_cell = make_shared<opset4::GRUCell>(X,
-                                                       H_t,
-                                                       W,
-                                                       R,
-                                                       B,
-                                                       hidden_size,
-                                                       vector<string>{"sigmoid", "tanh"},
-                                                       vector<float>{},
-                                                       vector<float>{},
-                                                       clip,
-                                                       linear_before_reset);
-    auto function = make_shared<Function>(gru_cell, ParameterVector{X, H_t, W, R, B});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    // X
-    test_case.add_input<float>({0.52421564f, 0.78845507f, 0.9372873f, 0.59783894f, 0.18278378f, 0.2084126f});
-
-    // Ht
-    test_case.add_input<float>({0.45738035f, 0.996877f, 0.82882977f, 0.47492632f, 0.88471466f, 0.57833236f});
-
-    // W
-    test_case.add_input<float>({0.5815369f, 0.16559383f, 0.08464007f, 0.843122f,   0.73968244f, 0.11359601f,
-                                0.8295078f, 0.9240567f,  0.10007995f, 0.20573162f, 0.09002485f, 0.2839569f,
-                                0.3096991f, 0.5638341f,  0.5787327f,  0.84552664f, 0.16263747f, 0.7243242f,
-                                0.8049057f, 0.43966424f, 0.46294412f, 0.9833361f,  0.31369713f, 0.1719934f,
-                                0.4937093f, 0.6353004f,  0.77982515f});
-
-    // R
-    test_case.add_input<float>({0.16510165f, 0.52435565f, 0.2788478f, 0.99427545f, 0.1623331f,  0.01389796f,
-                                0.99669236f, 0.53901845f, 0.8737506f, 0.9254788f,  0.21172932f, 0.11634306f,
-                                0.40111724f, 0.37497616f, 0.2903471f, 0.6796794f,  0.65131867f, 0.78163475f,
-                                0.12058706f, 0.45591718f, 0.791677f,  0.76497287f, 0.9895242f,  0.7845312f,
-                                0.51267904f, 0.49030215f, 0.08498167f});
-
-    // B (the sum of biases for W and R)
-    test_case.add_input<float>({
-        0.8286678f + 0.9175602f,
-        0.9153158f + 0.14958014f,
-        0.9581612f + 0.49230585f,
-        0.6639213f + 0.63162816f,
-        0.84239805f + 0.4161903f,
-        0.5282445f + 0.22148274f,
-        0.14153397f + 0.50496656f,
-        0.22404431f + 0.34798595f,
-        0.6549655f + 0.6699164f,
-    });
-
-    test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                         {0.48588726f, 0.99670005f, 0.83759373f, 0.5023099f, 0.89410484f, 0.60011315f});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, gru_cell_linear_before_reset) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    const size_t gates_count = 3;
-    float clip = 2.88f;
-    bool linear_before_reset = true;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{(gates_count + 1) * hidden_size});
-
-    const auto gru_cell = make_shared<opset4::GRUCell>(X,
-                                                       H_t,
-                                                       W,
-                                                       R,
-                                                       B,
-                                                       hidden_size,
-                                                       vector<string>{"sigmoid", "tanh"},
-                                                       vector<float>{},
-                                                       vector<float>{},
-                                                       clip,
-                                                       linear_before_reset);
-    auto function = make_shared<Function>(gru_cell, ParameterVector{X, H_t, W, R, B});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    // X
-    test_case.add_input<float>({0.12249453f, 0.6127907f, 0.5001741f, 0.5124603f, 0.04329684f, 0.023834f});
-    // Ht
-    test_case.add_input<float>({0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f});
-
-    // W
-    test_case.add_input<float>({0.72259396f, 0.11561195f, 0.9457856f,  0.19037509f, 0.6964006f,  0.33459795f,
-                                0.5468904f,  0.85646594f, 0.5101311f,  0.9712257f,  0.3687071f,  0.60280246f,
-                                0.56943774f, 0.7475505f,  0.2490578f,  0.86977345f, 0.85542053f, 0.29660386f,
-                                0.49717373f, 0.7473479f,  0.53454477f, 0.15974349f, 0.5804805f,  0.14303213f,
-                                0.07514781f, 0.5865731f,  0.76409274f});
-    // R
-    test_case.add_input<float>({0.91382647f, 0.41527033f, 0.28040004f, 0.23601337f, 0.04471736f, 0.03888785f,
-                                0.06308217f, 0.44844428f, 0.29384327f, 0.49037653f, 0.50421673f, 0.7366393f,
-                                0.63143945f, 0.00277612f, 0.37198433f, 0.06966069f, 0.4613444f,  0.10999731f,
-                                0.78273284f, 0.21453214f, 0.10751773f, 0.18332677f, 0.1326976f,  0.9998985f,
-                                0.19263928f, 0.10979804f, 0.52575564f});
-
-    // B (the sum of biases for W and R for z and r gates, and separately for W and R for h gate)
-    test_case.add_input<float>({0.61395123f,  // 0.09875853f + 0.5151927f,
-                                1.08667738f,  // 0.37801138f + 0.708666f,
-                                1.32600244f,  // 0.7729636f + 0.55303884f,
-                                0.81917698f,  // 0.78493553f + 0.03424145f,
-                                1.37736335f,  // 0.5662702f + 0.81109315f,
-                                0.42931147f,  // 0.12406381f + 0.30524766f,
-                                0.66729516f,
-                                0.7752771f,
-                                0.78819966f,
-                                0.6606634f,
-                                0.99040645f,
-                                0.21112025f});
-
-    test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                         {0.8709214f, 0.48411977f, 0.74495184f, 0.6074972f, 0.44572943f, 0.1467715f});
-
-    test_case.run();
-}
-
-// Hard Sigmoid activation function is unsupprted with v3::GRUCell
-NGRAPH_TEST(${BACKEND_NAME}, gru_cell_hardsigmoid_activation_function) {
-    const size_t batch_size = 2;
-    const size_t input_size = 3;
-    const size_t hidden_size = 3;
-    const size_t gates_count = 3;
-    float clip = 2.88f;
-    bool linear_before_reset = true;
-
-    const auto X = make_shared<op::Parameter>(element::f32, Shape{batch_size, input_size});
-    const auto W = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, input_size});
-    const auto R = make_shared<op::Parameter>(element::f32, Shape{gates_count * hidden_size, hidden_size});
-    const auto H_t = make_shared<op::Parameter>(element::f32, Shape{batch_size, hidden_size});
-    const auto B = make_shared<op::Parameter>(element::f32, Shape{(gates_count + 1) * hidden_size});
-
-    const auto gru_cell = make_shared<opset4::GRUCell>(X,
-                                                       H_t,
-                                                       W,
-                                                       R,
-                                                       B,
-                                                       hidden_size,
-                                                       vector<string>{"hardsigmoid", "hardsigmoid"},
-                                                       vector<float>{1.8345f, 1.8345f},
-                                                       vector<float>{3.05f, 3.05f},
-                                                       clip,
-                                                       linear_before_reset);
-    auto function = make_shared<Function>(gru_cell, ParameterVector{X, H_t, W, R, B});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    // X
-    test_case.add_input<float>({0.12249453f, 0.6127907f, 0.5001741f, 0.5124603f, 0.04329684f, 0.023834f});
-
-    // Ht
-    test_case.add_input<float>({0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f});
-
-    // W
-    test_case.add_input<float>({0.72259396f, 0.11561195f, 0.9457856f,  0.19037509f, 0.6964006f,  0.33459795f,
-                                0.5468904f,  0.85646594f, 0.5101311f,  0.9712257f,  0.3687071f,  0.60280246f,
-                                0.56943774f, 0.7475505f,  0.2490578f,  0.86977345f, 0.85542053f, 0.29660386f,
-                                0.49717373f, 0.7473479f,  0.53454477f, 0.15974349f, 0.5804805f,  0.14303213f,
-                                0.07514781f, 0.5865731f,  0.76409274f});
-    // R
-    test_case.add_input<float>({0.91382647f, 0.41527033f, 0.28040004f, 0.23601337f, 0.04471736f, 0.03888785f,
-                                0.06308217f, 0.44844428f, 0.29384327f, 0.49037653f, 0.50421673f, 0.7366393f,
-                                0.63143945f, 0.00277612f, 0.37198433f, 0.06966069f, 0.4613444f,  0.10999731f,
-                                0.78273284f, 0.21453214f, 0.10751773f, 0.18332677f, 0.1326976f,  0.9998985f,
-                                0.19263928f, 0.10979804f, 0.52575564f});
-
-    // B (the sum of biases for W and R for z and r gates, and separately for W and R for h gate)
-    test_case.add_input<float>({0.09875853f + 0.5151927f,
-                                0.37801138f + 0.708666f,
-                                0.7729636f + 0.55303884f,
-                                0.78493553f + 0.03424145f,
-                                0.5662702f + 0.81109315f,
-                                0.12406381f + 0.30524766f,
-                                0.66729516f,
-                                0.7752771f,
-                                0.78819966f,
-                                0.6606634f,
-                                0.99040645f,
-                                0.21112025f});
-
-    test_case.add_expected_output<float>(Shape{batch_size, hidden_size},
-                                         {0.8598948f, 0.41189128f, 0.72824323f, 0.53940123f, 0.31485787f, 0.04053852f});
-
-    test_case.run();
-}
diff --git a/ngraph/test/backend/reorg_yolo.in.cpp b/ngraph/test/backend/reorg_yolo.in.cpp
deleted file mode 100644
index 7131833fa749e7..00000000000000
--- a/ngraph/test/backend/reorg_yolo.in.cpp
+++ /dev/null
@@ -1,84 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <algorithm>
-#include <cinttypes>
-#include <cmath>
-#include <cstdlib>
-#include <random>
-#include <string>
-
-// clang-format off
-#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#endif
-
-#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#endif
-// clang-format on
-
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "engines_util/test_engines.hpp"
-#include "engines_util/test_case.hpp"
-#include "util/test_control.hpp"
-#include "util/type_prop.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, reorg_yolo_stride_2) {
-    // in_shape [N,C,H,W]
-    const auto in_shape = Shape{1, 8, 4, 4};
-    auto p = make_shared<op::Parameter>(element::f32, in_shape);
-    size_t stride = 2;
-    auto reorg_yolo = make_shared<op::v0::ReorgYolo>(p, Strides{stride});
-    auto fun = make_shared<Function>(OutputVector{reorg_yolo}, ParameterVector{p});
-
-    std::vector<float> inputs(128);
-    std::iota(inputs.begin(), inputs.end(), 0);
-    std::vector<float> expected_result{
-        0,   2,   4,   6,   16,  18,  20,  22,  32,  34,  36,  38,  48,  50,  52,  54,  64,  66,  68,  70,  80, 82,
-        84,  86,  96,  98,  100, 102, 112, 114, 116, 118, 1,   3,   5,   7,   17,  19,  21,  23,  33,  35,  37, 39,
-        49,  51,  53,  55,  65,  67,  69,  71,  81,  83,  85,  87,  97,  99,  101, 103, 113, 115, 117, 119, 8,  10,
-        12,  14,  24,  26,  28,  30,  40,  42,  44,  46,  56,  58,  60,  62,  72,  74,  76,  78,  88,  90,  92, 94,
-        104, 106, 108, 110, 120, 122, 124, 126, 9,   11,  13,  15,  25,  27,  29,  31,  41,  43,  45,  47,  57, 59,
-        61,  63,  73,  75,  77,  79,  89,  91,  93,  95,  105, 107, 109, 111, 121, 123, 125, 127};
-    // in_shape [N,C,H,W] -> out_shape [N, C*stride*stride, H/stride, W/stride]
-    Shape expected_shape =
-        Shape{in_shape[0], in_shape[1] * stride * stride, in_shape[2] / stride, in_shape[3] / stride};
-
-    auto test_case = test::TestCase<TestEngine>(fun);
-    test_case.add_input<float>(inputs);
-    test_case.add_expected_output<float>(expected_shape, expected_result);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, reorg_yolo_stride_3) {
-    // in_shape [N,C,H,W]
-    const auto in_shape = Shape{1, 9, 3, 3};
-    auto p = make_shared<op::Parameter>(element::f32, in_shape);
-    size_t stride = 3;
-    auto reorg_yolo = make_shared<op::v0::ReorgYolo>(p, Strides{stride});
-    auto fun = make_shared<Function>(OutputVector{reorg_yolo}, ParameterVector{p});
-
-    std::vector<float> inputs(81);
-    std::iota(inputs.begin(), inputs.end(), 0);
-    std::vector<float> expected_result{
-        0,  3,  6,  27, 30, 33, 54, 57, 60, 1,  4,  7,  28, 31, 34, 55, 58, 61, 2,  5,  8,  29, 32, 35, 56, 59, 62,
-        9,  12, 15, 36, 39, 42, 63, 66, 69, 10, 13, 16, 37, 40, 43, 64, 67, 70, 11, 14, 17, 38, 41, 44, 65, 68, 71,
-        18, 21, 24, 45, 48, 51, 72, 75, 78, 19, 22, 25, 46, 49, 52, 73, 76, 79, 20, 23, 26, 47, 50, 53, 74, 77, 80};
-    // in_shape [N,C,H,W] -> out_shape [N, C*stride*stride, H/stride, W/stride]
-    Shape expected_shape =
-        Shape{in_shape[0], in_shape[1] * stride * stride, in_shape[2] / stride, in_shape[3] / stride};
-
-    auto test_case = test::TestCase<TestEngine>(fun);
-    test_case.add_input<float>(inputs);
-    test_case.add_expected_output<float>(expected_shape, expected_result);
-    test_case.run();
-}
diff --git a/ngraph/test/backend/roll.in.cpp b/ngraph/test/backend/roll.in.cpp
deleted file mode 100644
index 1024e591c136e6..00000000000000
--- a/ngraph/test/backend/roll.in.cpp
+++ /dev/null
@@ -1,197 +0,0 @@
-//*****************************************************************************
-// Copyright 2017-2021 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-//*****************************************************************************
-
-#include "engines_util/execute_tools.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "ngraph/opsets/opset7.hpp"
-#include "ngraph/runtime/tensor.hpp"
-#include "ngraph/shape.hpp"
-#include "runtime/backend.hpp"
-#include "util/all_close.hpp"
-#include "util/all_close_f.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-
-NGRAPH_TEST(${BACKEND_NAME}, roll_2d_input) {
-    Shape shape{4, 3};
-    auto x = make_shared<opset7::Parameter>(element::f32, shape);
-    auto shift = make_shared<opset7::Constant>(element::i64, Shape{1}, vector<int64_t>{1});
-    auto axes = make_shared<opset7::Constant>(element::i64, Shape{1}, vector<int64_t>{0});
-    auto f = make_shared<Function>(make_shared<opset7::Roll>(x, shift, axes), ParameterVector{x});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto x_tensor = backend->create_tensor(element::f32, shape);
-    copy_data(x_tensor,
-              vector<float>{50.2907,
-                            70.8054,
-                            -68.3403,
-                            62.6444,
-                            4.9748,
-                            -18.5551,
-                            40.5383,
-                            -15.3859,
-                            -4.5881,
-                            -43.3479,
-                            94.1676,
-                            -95.7097});
-    auto result = backend->create_tensor(element::f32, shape);
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {x_tensor});
-    EXPECT_TRUE(test::all_close_f((vector<float>{-43.3479,
-                                                 94.1676,
-                                                 -95.7097,
-                                                 50.2907,
-                                                 70.8054,
-                                                 -68.3403,
-                                                 62.6444,
-                                                 4.9748,
-                                                 -18.5551,
-                                                 40.5383,
-                                                 -15.3859,
-                                                 -4.5881}),
-                                  read_vector<float>(result)));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, roll_2d_input_negative_shift) {
-    Shape shape{4, 3};
-    auto x = make_shared<opset7::Parameter>(element::f32, shape);
-    auto shift = make_shared<opset7::Constant>(element::i32, Shape{2}, vector<int32_t>{-1, 2});
-    auto axes = make_shared<opset7::Constant>(element::i32, Shape{2}, vector<int32_t>{0, 1});
-    auto f = make_shared<Function>(make_shared<opset7::Roll>(x, shift, axes), ParameterVector{x});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto x_tensor = backend->create_tensor(element::f32, shape);
-    copy_data(x_tensor,
-              vector<float>{50.2907,
-                            70.8054,
-                            -68.3403,
-                            62.6444,
-                            4.9748,
-                            -18.5551,
-                            40.5383,
-                            -15.3859,
-                            -4.5881,
-                            -43.3479,
-                            94.1676,
-                            -95.7097});
-    auto result = backend->create_tensor(element::f32, shape);
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {x_tensor});
-    EXPECT_TRUE(test::all_close_f((vector<float>{4.9748,
-                                                 -18.5551,
-                                                 62.6444,
-                                                 -15.3859,
-                                                 -4.5881,
-                                                 40.5383,
-                                                 94.1676,
-                                                 -95.7097,
-                                                 -43.3479,
-                                                 70.8054,
-                                                 -68.3403,
-                                                 50.2907}),
-                                  read_vector<float>(result)));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, roll_repeated_axes) {
-    Shape shape{4, 3};
-    auto x = make_shared<opset7::Parameter>(element::i64, shape);
-    auto shift = make_shared<opset7::Constant>(element::i64, Shape{3}, vector<int64_t>{1, 2, 1});
-    auto axes = make_shared<opset7::Constant>(element::i64, Shape{3}, vector<int64_t>{0, 1, 0});
-    auto f = make_shared<Function>(make_shared<opset7::Roll>(x, shift, axes), ParameterVector{x});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto x_tensor = backend->create_tensor(element::i64, shape);
-    copy_data(x_tensor, vector<int64_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
-    auto result = backend->create_tensor(element::i64, shape);
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {x_tensor});
-    EXPECT_TRUE(
-        test::all_close((vector<int64_t>{8, 9, 7, 11, 12, 10, 2, 3, 1, 5, 6, 4}), read_vector<int64_t>(result)));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, roll_3d_input) {
-    Shape shape{4, 2, 3};
-    auto x = make_shared<opset7::Parameter>(element::f32, shape);
-    auto shift = make_shared<opset7::Constant>(element::i64, Shape{3}, vector<int64_t>{2, 1, 3});
-    auto axes = make_shared<opset7::Constant>(element::i64, Shape{3}, vector<int64_t>{0, 1, 2});
-    auto f = make_shared<Function>(make_shared<opset7::Roll>(x, shift, axes), ParameterVector{x});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-    auto x_tensor = backend->create_tensor(element::f32, shape);
-    copy_data(x_tensor, vector<float>{94.0773,  33.0599, 58.1724,  -20.3640, 54.5372, -54.3023, 10.4662, 11.7532,
-                                      -11.7692, 56.4223, -95.3774, 8.8978,   1.9305,  13.8025,  12.0827, 81.4669,
-                                      19.5321,  -8.9553, -75.3226, 20.8033,  20.7660, 62.7361,  14.9372, -33.0825});
-    auto result = backend->create_tensor(element::f32, shape);
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {x_tensor});
-    EXPECT_TRUE(
-        test::all_close_f((vector<float>{81.4669,  19.5321,  -8.9553, 1.9305,   13.8025,  12.0827, 62.7361,  14.9372,
-                                         -33.0825, -75.3226, 20.8033, 20.7660,  -20.3640, 54.5372, -54.3023, 94.0773,
-                                         33.0599,  58.1724,  56.4223, -95.3774, 8.8978,   10.4662, 11.7532,  -11.7692}),
-                          read_vector<float>(result)));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, roll_3d_input_negative_shift) {
-    Shape shape{4, 2, 3};
-    auto x = make_shared<opset7::Parameter>(element::f32, shape);
-    auto shift = make_shared<opset7::Constant>(element::i32, Shape{3}, vector<int32_t>{-5, 1, 3});
-    auto axes = make_shared<opset7::Constant>(element::i64, Shape{3}, vector<int64_t>{0, 1, 1});
-    auto f = make_shared<Function>(make_shared<opset7::Roll>(x, shift, axes), ParameterVector{x});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-    auto x_tensor = backend->create_tensor(element::f32, shape);
-    copy_data(x_tensor, vector<float>{94.0773,  33.0599, 58.1724,  -20.3640, 54.5372, -54.3023, 10.4662, 11.7532,
-                                      -11.7692, 56.4223, -95.3774, 8.8978,   1.9305,  13.8025,  12.0827, 81.4669,
-                                      19.5321,  -8.9553, -75.3226, 20.8033,  20.7660, 62.7361,  14.9372, -33.0825});
-    auto result = backend->create_tensor(element::f32, shape);
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {x_tensor});
-    EXPECT_TRUE(
-        test::all_close_f((vector<float>{10.4662, 11.7532,  -11.7692, 56.4223, -95.3774, 8.8978,   1.9305,  13.8025,
-                                         12.0827, 81.4669,  19.5321,  -8.9553, -75.3226, 20.8033,  20.7660, 62.7361,
-                                         14.9372, -33.0825, 94.0773,  33.0599, 58.1724,  -20.3640, 54.5372, -54.3023}),
-                          read_vector<float>(result)));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, roll_negative_axes) {
-    Shape shape{4, 2, 3};
-    auto x = make_shared<opset7::Parameter>(element::i32, shape);
-    auto shift = make_shared<opset7::Constant>(element::i64, Shape{3}, vector<int64_t>{2, -1, -7});
-    auto axes = make_shared<opset7::Constant>(element::i32, Shape{3}, vector<int32_t>{-1, -1, -2});
-    auto f = make_shared<Function>(make_shared<opset7::Roll>(x, shift, axes), ParameterVector{x});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-    auto x_tensor = backend->create_tensor(element::i32, shape);
-    copy_data(x_tensor,
-              vector<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
-    auto result = backend->create_tensor(element::i32, shape);
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {x_tensor});
-    EXPECT_TRUE(test::all_close(
-        (vector<int32_t>{6, 4, 5, 3, 1, 2, 12, 10, 11, 9, 7, 8, 18, 16, 17, 15, 13, 14, 24, 22, 23, 21, 19, 20}),
-        read_vector<int32_t>(result)));
-}
diff --git a/ngraph/test/backend/shuffle_channels.in.cpp b/ngraph/test/backend/shuffle_channels.in.cpp
deleted file mode 100644
index 7b1296b3cefb14..00000000000000
--- a/ngraph/test/backend/shuffle_channels.in.cpp
+++ /dev/null
@@ -1,174 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/execute_tools.hpp"
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "runtime/backend.hpp"
-#include "util/all_close.hpp"
-#include "util/all_close_f.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, shuffle_channels_simple) {
-    const auto data = make_shared<op::Parameter>(element::i32, Shape{1, 15, 2, 2});
-    auto tested_op = make_shared<op::ShuffleChannels>(data, 1, 5);
-    auto function = make_shared<Function>(tested_op, ParameterVector{data});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    std::vector<int32_t> input_data(60);
-    std::iota(std::begin(input_data), std::end(input_data), 0);
-    test_case.add_input(input_data);
-
-    test_case.add_expected_output<int32_t>(
-        Shape{1, 15, 2, 2},
-        {0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
-         4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
-         8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, shuffle_channels_negative_axis) {
-    // In this test the output is the same as in shuffle_channels_simple but
-    // the axis value is negative and the C(channels) value is in a different dimension(0) of the
-    // shape
-    const auto data = make_shared<op::Parameter>(element::i32, Shape{15, 2, 1, 2});
-    auto tested_op = make_shared<op::ShuffleChannels>(data, -4, 5);
-    auto function = make_shared<Function>(tested_op, ParameterVector{data});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    std::vector<int32_t> input_data(60);
-    std::iota(std::begin(input_data), std::end(input_data), 0);
-    test_case.add_input(input_data);
-
-    test_case.add_expected_output<int32_t>(
-        Shape{15, 2, 1, 2},
-        {0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
-         4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
-         8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, shuffle_channels_float) {
-    const auto data = make_shared<op::Parameter>(element::f32, Shape{6, 1, 1, 1});
-    auto tested_op = make_shared<op::ShuffleChannels>(data, 0, 2);
-    auto function = make_shared<Function>(tested_op, ParameterVector{data});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    test_case.add_input<float>({0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f});
-    test_case.add_expected_output<float>(Shape{6, 1, 1, 1}, {0.0f, 3.0f, 1.0f, 4.0f, 2.0f, 5.0f});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, shuffle_channels_1d) {
-    Shape data_shape{15};
-    const auto data = make_shared<op::Parameter>(element::i32, data_shape);
-    auto tested_op = make_shared<op::ShuffleChannels>(data, 0, 5);
-    auto function = make_shared<Function>(tested_op, ParameterVector{data});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    std::vector<int32_t> input_data{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14};
-
-    test_case.add_input(input_data);
-    test_case.add_expected_output<int32_t>(data_shape, {0, 3, 6, 9, 12, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, shuffle_channels_2d) {
-    Shape data_shape{15, 4};
-    const auto data = make_shared<op::Parameter>(element::i32, data_shape);
-    auto tested_op = make_shared<op::ShuffleChannels>(data, 0, 5);
-    auto function = make_shared<Function>(tested_op, ParameterVector{data});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    std::vector<int32_t> input_data{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-                                    20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-                                    40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59};
-
-    test_case.add_input(input_data);
-    test_case.add_expected_output<int32_t>(data_shape, {0,  1,  2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38,
-                                                        39, 48, 49, 50, 51, 4,  5,  6,  7,  16, 17, 18, 19, 28, 29,
-                                                        30, 31, 40, 41, 42, 43, 52, 53, 54, 55, 8,  9,  10, 11, 20,
-                                                        21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, shuffle_channels_3d) {
-    Shape data_shape{15, 2, 2};
-    const auto data = make_shared<op::Parameter>(element::i32, data_shape);
-    auto tested_op = make_shared<op::ShuffleChannels>(data, 0, 5);
-    auto function = make_shared<Function>(tested_op, ParameterVector{data});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    std::vector<int32_t> input_data{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-                                    20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-                                    40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59};
-
-    test_case.add_input(input_data);
-    test_case.add_expected_output<int32_t>(data_shape, {0,  1,  2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38,
-                                                        39, 48, 49, 50, 51, 4,  5,  6,  7,  16, 17, 18, 19, 28, 29,
-                                                        30, 31, 40, 41, 42, 43, 52, 53, 54, 55, 8,  9,  10, 11, 20,
-                                                        21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59});
-
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, shuffle_channels_5d) {
-    Shape data_shape{2, 2, 15, 2, 2};
-    const auto data = make_shared<op::Parameter>(element::i32, data_shape);
-    auto tested_op = make_shared<op::ShuffleChannels>(data, 2, 5);
-    auto function = make_shared<Function>(tested_op, ParameterVector{data});
-    auto test_case = test::TestCase<TestEngine>(function);
-
-    std::vector<int32_t> input_data{0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-                                    20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-                                    40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-
-                                    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-                                    20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-                                    40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-
-                                    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-                                    20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-                                    40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-
-                                    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-                                    20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-                                    40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59};
-
-    test_case.add_input(input_data);
-    test_case.add_expected_output<int32_t>(
-        data_shape,
-        {0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
-         4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
-         8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59,
-
-         0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
-         4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
-         8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59,
-
-         0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
-         4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
-         8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59,
-
-         0, 1, 2,  3,  12, 13, 14, 15, 24, 25, 26, 27, 36, 37, 38, 39, 48, 49, 50, 51,
-         4, 5, 6,  7,  16, 17, 18, 19, 28, 29, 30, 31, 40, 41, 42, 43, 52, 53, 54, 55,
-         8, 9, 10, 11, 20, 21, 22, 23, 32, 33, 34, 35, 44, 45, 46, 47, 56, 57, 58, 59});
-
-    test_case.run();
-}
diff --git a/ngraph/test/backend/space_to_batch.in.cpp b/ngraph/test/backend/space_to_batch.in.cpp
deleted file mode 100644
index d168be4bfca8c4..00000000000000
--- a/ngraph/test/backend/space_to_batch.in.cpp
+++ /dev/null
@@ -1,119 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-static void SpaceToBatchTest(const std::vector<float>& inputs,
-                             const Shape inputs_shape,
-                             const std::vector<int64_t>& block_shapes,
-                             const Shape blocks_shape,
-                             const std::vector<int64_t>& pads_begins,
-                             const std::vector<int64_t>& pads_ends,
-                             const Shape pads_shape,
-                             const std::vector<float>& outputs,
-                             const Shape outputs_shape) {
-    auto inputs_param = make_shared<op::Parameter>(element::f32, inputs_shape);
-    auto block_shapes_param = make_shared<op::Constant>(element::i64, blocks_shape, block_shapes);
-    auto pads_begins_param = make_shared<op::Constant>(element::i64, pads_shape, pads_begins);
-    auto pads_ends_param = make_shared<op::Constant>(element::i64, pads_shape, pads_ends);
-
-    auto space_to_batch =
-        make_shared<op::v1::SpaceToBatch>(inputs_param, block_shapes_param, pads_begins_param, pads_ends_param);
-    auto f = make_shared<Function>(space_to_batch, ParameterVector{inputs_param});
-
-    auto test_case = test::TestCase<TestEngine>(f);
-    test_case.add_input<float>(inputs);
-    test_case.add_expected_output<float>(outputs_shape, outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_batch_4D) {
-    const Shape inputs_shape{1, 1, 2, 2};
-    const std::vector<float> inputs{1.0f, 1.0f, 1.0f, 1.0f};
-
-    const Shape blocks_shape{4};
-    const std::vector<int64_t> block_shapes{1, 1, 1, 1};
-
-    const Shape pads_shape{4};
-    const std::vector<int64_t> pads_begins{0, 0, 0, 0};
-    const std::vector<int64_t> pads_ends{0, 0, 0, 0};
-
-    const Shape outputs_shape{1, 1, 2, 2};
-    const std::vector<float> outputs{1.0f, 1.0f, 1.0f, 1.0f};
-
-    SpaceToBatchTest(inputs,
-                     inputs_shape,
-                     block_shapes,
-                     blocks_shape,
-                     pads_begins,
-                     pads_ends,
-                     pads_shape,
-                     outputs,
-                     outputs_shape);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_batch_5D) {
-    const Shape inputs_shape{1, 1, 3, 2, 1};
-    const std::vector<float> inputs{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f};
-
-    const Shape blocks_shape{5};
-    const std::vector<int64_t> block_shapes{1, 1, 3, 2, 2};
-
-    const Shape pads_shape{5};
-    const std::vector<int64_t> pads_begins{0, 0, 1, 0, 3};
-    const std::vector<int64_t> pads_ends{0, 0, 2, 0, 0};
-
-    const Shape outputs_shape{12, 1, 2, 1, 2};
-    const std::vector<float> outputs{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-                                     0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f,
-                                     0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-                                     0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f};
-
-    SpaceToBatchTest(inputs,
-                     inputs_shape,
-                     block_shapes,
-                     blocks_shape,
-                     pads_begins,
-                     pads_ends,
-                     pads_shape,
-                     outputs,
-                     outputs_shape);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_batch_4x4) {
-    const Shape inputs_shape{1, 1, 4, 4};
-    const std::vector<float>
-        inputs{1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f};
-
-    const Shape blocks_shape{4};
-    const std::vector<int64_t> block_shapes{1, 1, 1, 1};
-
-    const Shape pads_shape{4};
-    const std::vector<int64_t> pads_begins{0, 0, 1, 0};
-    const std::vector<int64_t> pads_ends{0, 0, 0, 0};
-
-    const Shape outputs_shape{1, 1, 5, 4};
-    const std::vector<float> outputs{0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f,
-                                     0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f};
-
-    SpaceToBatchTest(inputs,
-                     inputs_shape,
-                     block_shapes,
-                     blocks_shape,
-                     pads_begins,
-                     pads_ends,
-                     pads_shape,
-                     outputs,
-                     outputs_shape);
-}
diff --git a/ngraph/test/backend/space_to_depth.in.cpp b/ngraph/test/backend/space_to_depth.in.cpp
deleted file mode 100644
index d3467a5d9a351c..00000000000000
--- a/ngraph/test/backend/space_to_depth.in.cpp
+++ /dev/null
@@ -1,109 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "engines_util/test_case.hpp"
-#include "engines_util/test_engines.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/op/space_to_depth.hpp"
-#include "util/test_control.hpp"
-
-using namespace ngraph;
-
-static std::string s_manifest = "${MANIFEST}";
-
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_depth_block_first_K2_BS2) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 2, 4, 4});
-    const auto mode = op::SpaceToDepth::SpaceToDepthMode::BLOCKS_FIRST;
-    auto space_to_depth = std::make_shared<op::SpaceToDepth>(A, mode, 2);
-    auto function = std::make_shared<Function>(NodeVector{space_to_depth}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  1.f,  2.f,  3.f,  4.f,  5.f,  6.f,  7.f,  8.f,  9.f,  10.f,
-                                11.f, 12.f, 13.f, 14.f, 15.f, 16.f, 17.f, 18.f, 19.f, 20.f, 21.f,
-                                22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 8, 2, 2},
-        {0.f, 2.f, 8.f,  10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f,  11.f, 17.f, 19.f, 25.f, 27.f,
-         4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_depth_block_first_K2_BS3) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 2, 6, 3});
-    const auto mode = op::SpaceToDepth::SpaceToDepthMode::BLOCKS_FIRST;
-    auto space_to_depth = std::make_shared<op::SpaceToDepth>(A, mode, 3);
-    auto function = std::make_shared<Function>(NodeVector{space_to_depth}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  4.f,  8.f,  12.f, 16.f, 20.f, 24.f, 28.f, 32.f, 1.f,  5.f,  9.f,
-                                13.f, 17.f, 21.f, 25.f, 29.f, 33.f, 2.f,  6.f,  10.f, 14.f, 18.f, 22.f,
-                                26.f, 30.f, 34.f, 3.f,  7.f,  11.f, 15.f, 19.f, 23.f, 27.f, 31.f, 35.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 18, 2, 1},
-        {0.f,  1.f,  2.f,  3.f,  4.f,  5.f,  6.f,  7.f,  8.f,  9.f,  10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 16.f, 17.f,
-         18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 34.f, 35.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_depth_block_first_K1_BS3) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 2, 6});
-    const auto mode = op::SpaceToDepth::SpaceToDepthMode::BLOCKS_FIRST;
-    auto space_to_depth = std::make_shared<op::SpaceToDepth>(A, mode, 3);
-    auto function = std::make_shared<Function>(NodeVector{space_to_depth}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f, 4.f, 8.f, 1.f, 5.f, 9.f, 2.f, 6.f, 10.f, 3.f, 7.f, 11.f});
-    test_case.add_expected_output<float>(Shape{1, 6, 2},
-                                         {0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_depth_depth_first_K2_BS2) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 2, 4, 4});
-    const auto mode = op::SpaceToDepth::SpaceToDepthMode::DEPTH_FIRST;
-    auto space_to_depth = std::make_shared<op::SpaceToDepth>(A, mode, 2);
-    auto function = std::make_shared<Function>(NodeVector{space_to_depth}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  16.f, 2.f,  18.f, 1.f,  17.f, 3.f,  19.f, 8.f,  24.f, 10.f,
-                                26.f, 9.f,  25.f, 11.f, 27.f, 4.f,  20.f, 6.f,  22.f, 5.f,  21.f,
-                                7.f,  23.f, 12.f, 28.f, 14.f, 30.f, 13.f, 29.f, 15.f, 31.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 8, 2, 2},
-        {0.f, 2.f, 8.f,  10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f,  11.f, 17.f, 19.f, 25.f, 27.f,
-         4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_depth_depth_first_K2_BS3) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 2, 6, 3});
-    const auto mode = op::SpaceToDepth::SpaceToDepthMode::DEPTH_FIRST;
-    auto space_to_depth = std::make_shared<op::SpaceToDepth>(A, mode, 3);
-    auto function = std::make_shared<Function>(NodeVector{space_to_depth}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f,  2.f,  4.f,  6.f,  8.f,  10.f, 12.f, 14.f, 16.f, 1.f,  3.f,  5.f,
-                                7.f,  9.f,  11.f, 13.f, 15.f, 17.f, 18.f, 20.f, 22.f, 24.f, 26.f, 28.f,
-                                30.f, 32.f, 34.f, 19.f, 21.f, 23.f, 25.f, 27.f, 29.f, 31.f, 33.f, 35.f});
-    test_case.add_expected_output<float>(
-        Shape{1, 18, 2, 1},
-        {0.f,  1.f,  2.f,  3.f,  4.f,  5.f,  6.f,  7.f,  8.f,  9.f,  10.f, 11.f, 12.f, 13.f, 14.f, 15.f, 16.f, 17.f,
-         18.f, 19.f, 20.f, 21.f, 22.f, 23.f, 24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f, 32.f, 33.f, 34.f, 35.f});
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, space_to_depth_depth_first_K1_BS3) {
-    auto A = std::make_shared<op::Parameter>(element::f32, Shape{1, 2, 6});
-    const auto mode = op::SpaceToDepth::SpaceToDepthMode::DEPTH_FIRST;
-    auto space_to_depth = std::make_shared<op::SpaceToDepth>(A, mode, 3);
-    auto function = std::make_shared<Function>(NodeVector{space_to_depth}, ParameterVector{A});
-
-    auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_input<float>({0.f, 2.f, 4.f, 1.f, 3.f, 5.f, 6.f, 8.f, 10.f, 7.f, 9.f, 11.f});
-    test_case.add_expected_output<float>(Shape{1, 6, 2},
-                                         {0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f});
-    test_case.run();
-}
diff --git a/ngraph/test/backend/strided_slice.in.cpp b/ngraph/test/backend/strided_slice.in.cpp
deleted file mode 100644
index 7d0bbfe116a5eb..00000000000000
--- a/ngraph/test/backend/strided_slice.in.cpp
+++ /dev/null
@@ -1,192 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <numeric>
-
-#include "engines_util/execute_tools.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "ngraph/runtime/tensor.hpp"
-#include "runtime/backend.hpp"
-#include "util/all_close_f.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-
-template <typename T>
-void check_strided_slice_success(const element::Type& input_element_type,
-                                 const Shape& input_shape,
-                                 const std::vector<int64_t>& begin_values,
-                                 const std::vector<int64_t>& end_values,
-                                 const std::vector<int64_t>& strides_values,
-                                 const std::vector<int64_t>& begin_mask,
-                                 const std::vector<int64_t>& end_mask,
-                                 const std::vector<int64_t>& new_axis_mask,
-                                 const std::vector<int64_t>& shrink_axis_mask,
-                                 const std::vector<int64_t>& ellipsis_mask,
-                                 const Shape& expected_output_shape,
-                                 const std::vector<T>& expected_values) {
-    auto arg = std::make_shared<op::Parameter>(input_element_type, input_shape);
-    auto begin_op = make_shared<ngraph::op::Parameter>(element::i64, Shape{begin_values.size()});
-    auto end_op = make_shared<ngraph::op::Parameter>(element::i64, Shape{end_values.size()});
-    auto strides_op = make_shared<ngraph::op::Parameter>(element::i64, Shape{strides_values.size()});
-
-    std::vector<T> input_values(shape_size(input_shape));
-    std::iota(input_values.begin(), input_values.end(), static_cast<T>(0));
-
-    auto strided_slice = std::make_shared<op::v1::StridedSlice>(arg,
-                                                                begin_op,
-                                                                end_op,
-                                                                strides_op,
-                                                                begin_mask,
-                                                                end_mask,
-                                                                new_axis_mask,
-                                                                shrink_axis_mask,
-                                                                ellipsis_mask);
-
-    auto f = std::make_shared<Function>(NodeVector{strided_slice}, ParameterVector{arg, begin_op, end_op, strides_op});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
-    auto ex = backend->compile(f);
-
-    auto arg_tensor = backend->create_tensor(input_element_type, input_shape);
-    auto begin_tensor = backend->create_tensor(element::i64, Shape{begin_values.size()});
-    auto end_tensor = backend->create_tensor(element::i64, Shape{end_values.size()});
-    auto strides_tensor = backend->create_tensor(element::i64, Shape{strides_values.size()});
-    copy_data(arg_tensor, input_values);
-    copy_data(begin_tensor, begin_values);
-    copy_data(end_tensor, end_values);
-    copy_data(strides_tensor, strides_values);
-
-    auto output = backend->create_dynamic_tensor(input_element_type, PartialShape::dynamic());
-
-    ex->call_with_validate({output}, {arg_tensor, begin_tensor, end_tensor, strides_tensor});
-
-    EXPECT_EQ(output->get_element_type(), input_element_type);
-    EXPECT_EQ(output->get_shape(), expected_output_shape);
-
-    auto output_values = read_vector<T>(output);
-
-    EXPECT_EQ(output_values, expected_values);
-}
-
-template <typename T>
-void check_strided_slice_stride_optional_success(const element::Type& input_element_type,
-                                                 const Shape& input_shape,
-                                                 const std::vector<int64_t>& begin_values,
-                                                 const std::vector<int64_t>& end_values,
-                                                 const std::vector<int64_t>& begin_mask,
-                                                 const std::vector<int64_t>& end_mask,
-                                                 const std::vector<int64_t>& new_axis_mask,
-                                                 const std::vector<int64_t>& shrink_axis_mask,
-                                                 const std::vector<int64_t>& ellipsis_mask,
-                                                 const Shape& expected_output_shape,
-                                                 const std::vector<T>& expected_values) {
-    auto arg = std::make_shared<op::Parameter>(input_element_type, input_shape);
-    auto begin_op = make_shared<ngraph::op::Parameter>(element::i64, Shape{begin_values.size()});
-    auto end_op = make_shared<ngraph::op::Parameter>(element::i64, Shape{end_values.size()});
-
-    std::vector<T> input_values(shape_size(input_shape));
-    std::iota(input_values.begin(), input_values.end(), static_cast<T>(0));
-
-    auto strided_slice = std::make_shared<op::v1::StridedSlice>(arg,
-                                                                begin_op,
-                                                                end_op,
-                                                                begin_mask,
-                                                                end_mask,
-                                                                new_axis_mask,
-                                                                shrink_axis_mask,
-                                                                ellipsis_mask);
-
-    auto f = std::make_shared<Function>(NodeVector{strided_slice}, ParameterVector{arg, begin_op, end_op});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
-    auto ex = backend->compile(f);
-
-    auto arg_tensor = backend->create_tensor(input_element_type, input_shape);
-    auto begin_tensor = backend->create_tensor(element::i64, Shape{begin_values.size()});
-    auto end_tensor = backend->create_tensor(element::i64, Shape{end_values.size()});
-    copy_data(arg_tensor, input_values);
-    copy_data(begin_tensor, begin_values);
-    copy_data(end_tensor, end_values);
-
-    auto output = backend->create_dynamic_tensor(input_element_type, PartialShape::dynamic());
-
-    ex->call_with_validate({output}, {arg_tensor, begin_tensor, end_tensor});
-
-    EXPECT_EQ(output->get_element_type(), input_element_type);
-    EXPECT_EQ(output->get_shape(), expected_output_shape);
-
-    auto output_values = read_vector<T>(output);
-
-    EXPECT_EQ(output_values, expected_values);
-}
-
-// slices are: [1,newaxis]
-// dtype is: uint32
-// input shape is: Shape{2,3,4}
-// expected output shape is Shape{1,3,4}
-NGRAPH_TEST(${BACKEND_NAME}, strided_slice_0) {
-    check_strided_slice_success<uint32_t>(element::u32,
-                                          Shape{2, 3, 4},
-                                          std::vector<int64_t>{1, 0},
-                                          std::vector<int64_t>{0, 0},
-                                          std::vector<int64_t>{1, 1},
-                                          std::vector<int64_t>{0, 0, 0},
-                                          std::vector<int64_t>{0, 0, 0},
-                                          std::vector<int64_t>{0, 1, 0},
-                                          std::vector<int64_t>{1, 0, 0},
-                                          std::vector<int64_t>{0, 0, 0},
-                                          Shape{1, 3, 4},
-                                          std::vector<uint32_t>{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23});
-}
-
-// slices are: [0:,:4,2:6:2,7:3:-2,newaxis,...,1]
-// dtype is: uint32
-// input shape is: Shape{2,4,6,8,2,2,2}
-// expected output shape is Shape{2,4,2,2,1,2,2}
-NGRAPH_TEST(${BACKEND_NAME}, strided_slice_1) {
-    check_strided_slice_success<uint32_t>(
-        element::u32,
-        Shape{2, 4, 6, 8, 2, 2, 2},
-        std::vector<int64_t>{0, 0, 2, 7, 0, 0, 1},
-        std::vector<int64_t>{0, 4, 6, 3, 0, 0, 0},
-        std::vector<int64_t>{1, 1, 2, -2, 1, 1, 1},
-        std::vector<int64_t>{0, 1, 0, 0, 0, 0, 0},
-        std::vector<int64_t>{1, 0, 0, 0, 0, 0, 0},
-        std::vector<int64_t>{0, 0, 0, 0, 1, 0, 0},
-        std::vector<int64_t>{0, 0, 0, 0, 0, 0, 1},
-        std::vector<int64_t>{0, 0, 0, 0, 0, 1, 0},
-        Shape{2, 4, 2, 2, 1, 2, 2},
-        std::vector<uint32_t>{185,  187,  189,  191,  169,  171,  173,  175,  313,  315,  317,  319,  297,  299,  301,
-                              303,  569,  571,  573,  575,  553,  555,  557,  559,  697,  699,  701,  703,  681,  683,
-                              685,  687,  953,  955,  957,  959,  937,  939,  941,  943,  1081, 1083, 1085, 1087, 1065,
-                              1067, 1069, 1071, 1337, 1339, 1341, 1343, 1321, 1323, 1325, 1327, 1465, 1467, 1469, 1471,
-                              1449, 1451, 1453, 1455, 1721, 1723, 1725, 1727, 1705, 1707, 1709, 1711, 1849, 1851, 1853,
-                              1855, 1833, 1835, 1837, 1839, 2105, 2107, 2109, 2111, 2089, 2091, 2093, 2095, 2233, 2235,
-                              2237, 2239, 2217, 2219, 2221, 2223, 2489, 2491, 2493, 2495, 2473, 2475, 2477, 2479, 2617,
-                              2619, 2621, 2623, 2601, 2603, 2605, 2607, 2873, 2875, 2877, 2879, 2857, 2859, 2861, 2863,
-                              3001, 3003, 3005, 3007, 2985, 2987, 2989, 2991});
-}
-
-// slices are: [-1,-1,newaxis]
-// dtype is: uint32
-// input shape is: Shape{2,3,4}
-// expected output shape is Shape{1,4}
-NGRAPH_TEST(${BACKEND_NAME}, strided_slice_stride_optional) {
-    check_strided_slice_stride_optional_success<uint32_t>(element::u32,
-                                                          Shape{2, 3, 4},
-                                                          std::vector<int64_t>{-1, -1, 0},
-                                                          std::vector<int64_t>{0, 0, 0},
-                                                          std::vector<int64_t>{0, 0, 0},
-                                                          std::vector<int64_t>{0, 0, 0},
-                                                          std::vector<int64_t>{0, 0, 1},
-                                                          std::vector<int64_t>{1, 1, 0},
-                                                          std::vector<int64_t>{0, 0, 0},
-                                                          Shape{1, 4},
-                                                          std::vector<uint32_t>{20, 21, 22, 23});
-}
diff --git a/ngraph/test/engines_util/CMakeLists.txt b/ngraph/test/engines_util/CMakeLists.txt
index 49639ae1662319..4f39565c210d23 100644
--- a/ngraph/test/engines_util/CMakeLists.txt
+++ b/ngraph/test/engines_util/CMakeLists.txt
@@ -11,7 +11,7 @@ if(COMMAND ie_faster_build)
 endif()
 
 target_include_directories(engines_test_util PRIVATE $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
-target_link_libraries(engines_test_util PUBLIC ngraph ngraph_backend gtest gmock ngraph_test_util)
+target_link_libraries(engines_test_util PUBLIC ngraph ngraph_backend inference_engine gtest gmock ngraph_test_util)
 target_include_directories(engines_test_util PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 
 add_clang_format_target(engines_test_util_clang FOR_SOURCES ${ENGINES_UTIL_SRC})
diff --git a/ngraph/test/extension.cpp b/ngraph/test/extension.cpp
index 2fcc2be9be27ce..4dccb828a840f9 100644
--- a/ngraph/test/extension.cpp
+++ b/ngraph/test/extension.cpp
@@ -11,40 +11,8 @@
 #include "openvino/util/file_util.hpp"
 #include "so_extension.hpp"
 
-#ifdef _WIN32
-#    ifndef NOMINMAX
-#        define NOMINMAX
-#    endif
-#    include <windows.h>
-#    if defined(WINAPI_FAMILY) && !WINAPI_PARTITION_DESKTOP
-#        error "Only WINAPI_PARTITION_DESKTOP is supported, because of LoadLibrary[A|W]"
-#    endif
-#elif defined(__linux) || defined(__APPLE__)
-#    include <dlfcn.h>
-#endif
-
-static std::string find_my_pathname() {
-#ifdef _WIN32
-    HMODULE hModule = GetModuleHandleW(SHARED_LIB_PREFIX L"ngraph" SHARED_LIB_SUFFIX);
-    WCHAR wpath[MAX_PATH];
-    GetModuleFileNameW(hModule, wpath, MAX_PATH);
-    std::wstring ws(wpath);
-    std::string path(ws.begin(), ws.end());
-    replace(path.begin(), path.end(), '\\', '/');
-    path = ov::util::get_directory(path);
-    path += "/";
-    return path;
-#elif defined(__linux) || defined(__APPLE__)
-    Dl_info dl_info;
-    dladdr(reinterpret_cast<void*>(ov::replace_output_update_name), &dl_info);
-    return ov::util::get_directory(dl_info.dli_fname);
-#else
-#    error "Unsupported OS"
-#endif
-}
-std::string get_extension_path() {
-    return ov::util::make_plugin_library_name<char>(find_my_pathname(),
-                                                    std::string("template_ov_extension") + IE_BUILD_POSTFIX);
+inline std::string get_extension_path() {
+    return ov::util::make_plugin_library_name<char>({}, std::string("template_ov_extension") + IE_BUILD_POSTFIX);
 }
 
 TEST(extension, load_extension) {
@@ -52,9 +20,8 @@ TEST(extension, load_extension) {
 }
 
 TEST(extension, load_extension_and_cast) {
-    std::vector<ov::Extension::Ptr> so_extensions;
-    EXPECT_NO_THROW(so_extensions = ov::detail::load_extensions(get_extension_path()));
-    EXPECT_EQ(1, so_extensions.size());
+    std::vector<ov::Extension::Ptr> so_extensions = ov::detail::load_extensions(get_extension_path());
+    ASSERT_EQ(1, so_extensions.size());
     std::vector<ov::Extension::Ptr> extensions;
     std::vector<std::shared_ptr<void>> so;
     for (const auto& ext : so_extensions) {
diff --git a/ngraph/test/frontend/CMakeLists.txt b/ngraph/test/frontend/CMakeLists.txt
index f6362d551bc30d..9abc95fba35f9e 100644
--- a/ngraph/test/frontend/CMakeLists.txt
+++ b/ngraph/test/frontend/CMakeLists.txt
@@ -27,6 +27,12 @@ add_dependencies(unit-test mock1_ngraph_frontend)
 
 add_clang_format_target(mock1_ngraph_frontend_clang FOR_TARGETS mock1_ngraph_frontend)
 
-install(TARGETS mock1_ngraph_frontend
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+if(BUILD_SHARED_LIBS OR NOT WIN32)
+    install(TARGETS mock1_ngraph_frontend
+            RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+else()
+    install(TARGETS mock1_ngraph_frontend
+            RUNTIME DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+endif()
diff --git a/ngraph/test/frontend/frontend_manager.cpp b/ngraph/test/frontend/frontend_manager.cpp
index 5be485bd0c6e1c..92393b324f0593 100644
--- a/ngraph/test/frontend/frontend_manager.cpp
+++ b/ngraph/test/frontend/frontend_manager.cpp
@@ -18,7 +18,7 @@ const char FrontEndPathSeparator[] = ":";
 #endif  // _WIN32
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 static int set_test_env(const char* name, const char* value) {
 #ifdef _WIN32
@@ -156,7 +156,7 @@ TEST(FrontEndExceptionTest, frontend_general_error_no_throw_info) {
 }
 
 TEST(FrontEndExceptionTest, frontend_general_error_throw_no_info) {
-    EXPECT_THROW(FRONT_END_GENERAL_CHECK(false), ngraph::frontend::GeneralFailure);
+    EXPECT_THROW(FRONT_END_GENERAL_CHECK(false), ov::frontend::GeneralFailure);
 }
 
 TEST(FrontEndExceptionTest, frontend_initialization_error_no_throw) {
@@ -168,7 +168,7 @@ TEST(FrontEndExceptionTest, frontend_initialization_error_no_throw_info) {
 }
 
 TEST(FrontEndExceptionTest, frontend_initialization_error_throw_no_info) {
-    EXPECT_THROW(FRONT_END_INITIALIZATION_CHECK(false), ngraph::frontend::InitializationFailure);
+    EXPECT_THROW(FRONT_END_INITIALIZATION_CHECK(false), ov::frontend::InitializationFailure);
 }
 
 TEST(FrontEndExceptionTest, frontend_op_conversion_error_no_throw) {
@@ -180,14 +180,14 @@ TEST(FrontEndExceptionTest, frontend_op_conversion_error_no_throw_info) {
 }
 
 TEST(FrontEndExceptionTest, frontend_op_conversion_error_throw_no_info) {
-    EXPECT_THROW(FRONT_END_OP_CONVERSION_CHECK(false), ngraph::frontend::OpConversionFailure);
+    EXPECT_THROW(FRONT_END_OP_CONVERSION_CHECK(false), ov::frontend::OpConversionFailure);
 }
 
 TEST(FrontEndExceptionTest, frontend_assert_throw_check_info) {
     std::string msg("msg example");
     try {
         FRONT_END_THROW(msg);
-    } catch (const ngraph::frontend::GeneralFailure& ex) {
+    } catch (const ov::frontend::GeneralFailure& ex) {
         std::string caught_msg(ex.what());
         EXPECT_NE(caught_msg.find(msg), std::string::npos);
         return;
@@ -201,7 +201,7 @@ TEST(FrontEndExceptionTest, frontend_not_implemented_throw_check_info) {
     struct TestClass {};
     try {
         FRONT_END_NOT_IMPLEMENTED(TestClass);
-    } catch (const ngraph::frontend::NotImplementedFailure& ex) {
+    } catch (const ov::frontend::NotImplementedFailure& ex) {
         std::string caught_msg(ex.what());
         EXPECT_NE(caught_msg.find("TestClass"), std::string::npos);
         return;
@@ -215,7 +215,7 @@ TEST(FrontEndExceptionTest, frontend_general_error_throw_info) {
     std::string msg("msg example");
     try {
         FRONT_END_GENERAL_CHECK(false, msg);
-    } catch (const ngraph::frontend::GeneralFailure& ex) {
+    } catch (const ov::frontend::GeneralFailure& ex) {
         std::string caught_msg(ex.what());
         EXPECT_NE(caught_msg.find(msg), std::string::npos);
         return;
@@ -229,7 +229,7 @@ TEST(FrontEndExceptionTest, frontend_op_conversion_error_throw_info) {
     std::string msg("msg example");
     try {
         FRONT_END_OP_CONVERSION_CHECK(false, msg);
-    } catch (const ngraph::frontend::OpConversionFailure& ex) {
+    } catch (const ov::frontend::OpConversionFailure& ex) {
         std::string caught_msg(ex.what());
         EXPECT_NE(caught_msg.find(msg), std::string::npos);
         return;
@@ -243,7 +243,7 @@ TEST(FrontEndExceptionTest, frontend_initialization_error_throw_info) {
     std::string msg("msg example");
     try {
         FRONT_END_INITIALIZATION_CHECK(false, msg);
-    } catch (const ngraph::frontend::InitializationFailure& ex) {
+    } catch (const ov::frontend::InitializationFailure& ex) {
         std::string caught_msg(ex.what());
         EXPECT_NE(caught_msg.find(msg), std::string::npos);
         return;
diff --git a/ngraph/test/frontend/mock_frontend.cpp b/ngraph/test/frontend/mock_frontend.cpp
index ecedf21058d987..36ff6f9d91db81 100644
--- a/ngraph/test/frontend/mock_frontend.cpp
+++ b/ngraph/test/frontend/mock_frontend.cpp
@@ -14,7 +14,7 @@
 #endif  // mock1_ngraph_frontend_EXPORTS
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 class FrontEndMock : public FrontEnd {
 public:
@@ -34,4 +34,4 @@ extern "C" MOCK_API void* GetFrontEndData() {
         return std::make_shared<FrontEndMock>();
     };
     return res;
-}
\ No newline at end of file
+}
diff --git a/ngraph/test/frontend/onnx/load_from.cpp b/ngraph/test/frontend/onnx/load_from.cpp
index 4d98644bc722de..749606b6d0a648 100644
--- a/ngraph/test/frontend/onnx/load_from.cpp
+++ b/ngraph/test/frontend/onnx/load_from.cpp
@@ -12,7 +12,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using ONNXLoadTest = FrontEndLoadFromTest;
 
diff --git a/ngraph/test/frontend/paddlepaddle/basic_api.cpp b/ngraph/test/frontend/paddlepaddle/basic_api.cpp
index a188f3a7d11ee9..f01ff120c10291 100644
--- a/ngraph/test/frontend/paddlepaddle/basic_api.cpp
+++ b/ngraph/test/frontend/paddlepaddle/basic_api.cpp
@@ -7,7 +7,7 @@
 #include "paddle_utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using PDPDBasicTest = FrontEndBasicTest;
 
diff --git a/ngraph/test/frontend/paddlepaddle/convert_model.cpp b/ngraph/test/frontend/paddlepaddle/convert_model.cpp
index d3a68116e39e37..3dbb8e0ecf8f8e 100644
--- a/ngraph/test/frontend/paddlepaddle/convert_model.cpp
+++ b/ngraph/test/frontend/paddlepaddle/convert_model.cpp
@@ -6,8 +6,8 @@
 
 #include "paddle_utils.hpp"
 
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov;
+using namespace ov::frontend;
 
 using PDPDConvertModelTest = FrontEndConvertModelTest;
 
diff --git a/ngraph/test/frontend/paddlepaddle/convert_unsupported.cpp b/ngraph/test/frontend/paddlepaddle/convert_unsupported.cpp
index 66a9557ef2af9c..2737ebbb637e17 100644
--- a/ngraph/test/frontend/paddlepaddle/convert_unsupported.cpp
+++ b/ngraph/test/frontend/paddlepaddle/convert_unsupported.cpp
@@ -10,7 +10,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 TEST(FrontEndConvertModelTest, test_unsupported_op) {
     FrontEndManager fem;
diff --git a/ngraph/test/frontend/paddlepaddle/cut_specific_model.cpp b/ngraph/test/frontend/paddlepaddle/cut_specific_model.cpp
index 192e93b316956c..cacb420770abcc 100644
--- a/ngraph/test/frontend/paddlepaddle/cut_specific_model.cpp
+++ b/ngraph/test/frontend/paddlepaddle/cut_specific_model.cpp
@@ -7,7 +7,7 @@
 #include "paddle_utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using PDPDCutTest = FrontEndCutModelTest;
 
@@ -29,4 +29,4 @@ static CutModelParam getTestData_2in_2out() {
 INSTANTIATE_TEST_SUITE_P(PDPDCutTest,
                          FrontEndCutModelTest,
                          ::testing::Values(getTestData_2in_2out()),
-                         FrontEndCutModelTest::getTestCaseName);
\ No newline at end of file
+                         FrontEndCutModelTest::getTestCaseName);
diff --git a/ngraph/test/frontend/paddlepaddle/incorrect_cut_model.cpp b/ngraph/test/frontend/paddlepaddle/incorrect_cut_model.cpp
index 95b2465b88e1e4..baef4d38696664 100644
--- a/ngraph/test/frontend/paddlepaddle/incorrect_cut_model.cpp
+++ b/ngraph/test/frontend/paddlepaddle/incorrect_cut_model.cpp
@@ -11,7 +11,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 TEST(FrontEndIncorrectCutModelTest, test_incorrect_cut) {
     FrontEndManager fem;
@@ -30,4 +30,4 @@ TEST(FrontEndIncorrectCutModelTest, test_incorrect_cut) {
     std::shared_ptr<ngraph::Function> function;
     ASSERT_THROW(function = frontEnd->convert(inputModel), GeneralFailure);
     ASSERT_EQ(function, nullptr);
-}
\ No newline at end of file
+}
diff --git a/ngraph/test/frontend/paddlepaddle/load_from.cpp b/ngraph/test/frontend/paddlepaddle/load_from.cpp
index acb13ed606543b..ac3dfd51ba51b7 100644
--- a/ngraph/test/frontend/paddlepaddle/load_from.cpp
+++ b/ngraph/test/frontend/paddlepaddle/load_from.cpp
@@ -7,7 +7,7 @@
 #include "paddle_utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using PDPDCutTest = FrontEndLoadFromTest;
 
@@ -25,4 +25,4 @@ static LoadFromFEParam getTestData() {
 INSTANTIATE_TEST_SUITE_P(PDPDCutTest,
                          FrontEndLoadFromTest,
                          ::testing::Values(getTestData()),
-                         FrontEndLoadFromTest::getTestCaseName);
\ No newline at end of file
+                         FrontEndLoadFromTest::getTestCaseName);
diff --git a/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp b/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp
index d895829b272e36..0a01edccbc1c9b 100644
--- a/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp
+++ b/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp
@@ -16,7 +16,7 @@
 using namespace ngraph;
 using namespace InferenceEngine;
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 using TestEngine = test::IE_CPU_Engine;
 
 using PDPDFuzzyOpTest = FrontEndFuzzyOpTest;
@@ -158,7 +158,6 @@ static const std::vector<std::string> models{std::string("argmax"),
                                              std::string("maxPool_test7"),
                                              std::string("maxPool_test8"),
                                              std::string("maxPool_test9"),
-                                             std::string("mul_fp32"),
                                              std::string("nearest_downsample_false_0"),
                                              std::string("nearest_downsample_false_1"),
                                              std::string("nearest_upsample_false_0"),
@@ -203,6 +202,9 @@ static const std::vector<std::string> models{std::string("argmax"),
                                              std::string("sigmoid"),
                                              std::string("slice"),
                                              std::string("slice_1d"),
+                                             std::string("slice_decrease_axis/slice_decrease_axis.pdmodel"),
+                                             std::string("slice_decrease_axis_all/slice_decrease_axis_all.pdmodel"),
+                                             std::string("slice_reshape/slice_reshape.pdmodel"),
                                              std::string("softmax"),
                                              std::string("softmax_minus"),
                                              std::string("split_test1"),
diff --git a/ngraph/test/frontend/paddlepaddle/partial_shape.cpp b/ngraph/test/frontend/paddlepaddle/partial_shape.cpp
index 2aff8f8bf81531..525c468481344c 100644
--- a/ngraph/test/frontend/paddlepaddle/partial_shape.cpp
+++ b/ngraph/test/frontend/paddlepaddle/partial_shape.cpp
@@ -7,7 +7,7 @@
 #include "paddle_utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using PDPDPartialShapeTest = FrontEndPartialShapeTest;
 
@@ -65,4 +65,4 @@ INSTANTIATE_TEST_SUITE_P(
                                                                   getTestShape_conv2d(),
                                                                   getTestShape_conv2d_setDynamicBatch(),
                                                                   getTestShape_2in_2out_dynbatch()})),
-    FrontEndPartialShapeTest::getTestCaseName);
\ No newline at end of file
+    FrontEndPartialShapeTest::getTestCaseName);
diff --git a/ngraph/test/frontend/paddlepaddle/places.cpp b/ngraph/test/frontend/paddlepaddle/places.cpp
index b83e0a8c1710d7..411af90d65c6ec 100644
--- a/ngraph/test/frontend/paddlepaddle/places.cpp
+++ b/ngraph/test/frontend/paddlepaddle/places.cpp
@@ -8,7 +8,7 @@
 #include "gtest/gtest.h"
 #include "paddle_utils.hpp"
 
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 const std::string model_file = std::string(TEST_PADDLE_MODELS_DIRNAME) + "place_test_model/place_test_model.pdmodel";
 
diff --git a/ngraph/test/frontend/paddlepaddle/set_element_type.cpp b/ngraph/test/frontend/paddlepaddle/set_element_type.cpp
index ef437ceb6b041b..f9decce7316a07 100644
--- a/ngraph/test/frontend/paddlepaddle/set_element_type.cpp
+++ b/ngraph/test/frontend/paddlepaddle/set_element_type.cpp
@@ -7,7 +7,7 @@
 #include "paddle_utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using PDPDCutTest = FrontEndElementTypeTest;
 
@@ -22,4 +22,4 @@ static SetTypeFEParam getTestData_relu() {
 INSTANTIATE_TEST_SUITE_P(PDPDCutTest,
                          FrontEndElementTypeTest,
                          ::testing::Values(getTestData_relu()),
-                         FrontEndElementTypeTest::getTestCaseName);
\ No newline at end of file
+                         FrontEndElementTypeTest::getTestCaseName);
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_mul.py b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_mul.py
index 1489fd80909b44..02ce1d6d9ee383 100644
--- a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_mul.py
+++ b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_mul.py
@@ -2,30 +2,6 @@
 from save_model import saveModel
 import sys
 
-
-def pdpd_mul(name, x1, x2):
-    import paddle as pdpd
-
-    pdpd.enable_static()
-    with pdpd.static.program_guard(pdpd.static.Program(), pdpd.static.Program()):
-        node_x1 = pdpd.static.data(name='x1', shape=x1.shape, dtype=x1.dtype)
-        node_x2 = pdpd.static.data(name='x2', shape=x2.shape, dtype=x2.dtype)
-        bmm_node = pdpd.fluid.layers.mul(node_x1, node_x2)
-        result = pdpd.static.nn.batch_norm(bmm_node, use_global_stats=True)
-
-        cpu = pdpd.static.cpu_places(1)
-        exe = pdpd.static.Executor(cpu[0])
-        # startup program will call initializer to initialize the parameters.
-        exe.run(pdpd.static.default_startup_program())
-
-        outs = exe.run(
-            feed={'x1': x1, 'x2': x2},
-            fetch_list=[result])
-        saveModel(name, exe, feedkeys=['x1', 'x2'], fetchlist=[result], inputs=[x1, x2], outputs=[outs[0]], target_dir=sys.argv[1])
-
-    return outs[0]
-
-
 def pdpd_matmul(name, x1, x2, x_transpose=False, y_transpose=False):
     import paddle as pdpd
 
@@ -68,8 +44,6 @@ def pdpd_matmul(name, x1, x2, x_transpose=False, y_transpose=False):
     input_2x3 = np.array([[1, 2, 3],
                           [4, 5, 6]]).astype(np.float32)
 
-    pdpd_result = pdpd_mul("mul_fp32", input_2x5, input_5x3)
-
     pdpd_matmul("matmul_xt", input_2x5, input_2x3, x_transpose=True, y_transpose=False)
     pdpd_matmul("matmul_yt", input_2x3, input_5x3, x_transpose=False, y_transpose=True)
     pdpd_matmul("matmul_xt_yt", input_2x5, input_5x2, x_transpose=True, y_transpose=True)
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
index bcfabdd28c428d..8c6b5fd1eeb28e 100644
--- a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
+++ b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_slice.py
@@ -1,10 +1,14 @@
 #
 # slice paddle model generator
 #
+import sys
+import os
+
 import numpy as np
-from save_model import saveModel
 import paddle as pdpd
-import sys
+
+from save_model import exportModel
+from save_model import saveModel
 
 data_type = 'float32'
 
@@ -28,6 +32,59 @@ def slice(name : str, x, axes : list, start : list, end : list):
 
     return outs[0]
 
+
+def slice_dyn(test_shape=[2,8,10,10]):
+    pdpd.disable_static()
+
+    data = pdpd.rand(shape=test_shape, dtype='float32')
+
+    '''
+    slice w/ decrease_axis
+    '''
+    @pdpd.jit.to_static
+    def test_slice_decrease_axis(x):
+        return x[0, 1:3, :, 5]
+    exportModel('slice_decrease_axis', test_slice_decrease_axis, [data], target_dir=sys.argv[1]) # output shape (2, 10)
+
+    '''
+    slice w/o decrease_axis
+    '''
+    @pdpd.jit.to_static
+    def test_slice(x):
+        return pdpd.slice(x, axes=[0,1,3], starts=[0,1,5], ends=[1,3,6])
+    # exportModel('slice_dyn', test_slice, [data], target_dir=sys.argv[1]) # output shape (1, 2, 10, 1)  # disable it by default as this kind of test model already there. It's for comparsion only.
+
+    '''
+    slice w/ decrease_axis of all dims
+    '''
+    @pdpd.jit.to_static
+    def test_slice_decrease_axis_all(x):
+        return x[0, 0, 0, 0]
+    exportModel('slice_decrease_axis_all', test_slice_decrease_axis_all, [data], target_dir=sys.argv[1]) # output shape (1,)
+
+    '''
+    slice w/o decrease_axis of all dims
+    '''
+    @pdpd.jit.to_static
+    def test_slice_alldim(x):
+        return pdpd.slice(x, axes=[0,1,2,3], starts=[0,0,0,0], ends=[1,1,1,1])
+    # exportModel('slice_alldim', test_slice_alldim, [data], target_dir=sys.argv[1]) # output shape (1, 1, 1, 1) # disable it by default as this kind of test model already there. It's for comparsion only.
+
+'''
+a test case simulating the last reshape2 of ocrnet which accepts slice (with decrease_axes in all dims) as its parents.
+'''
+def slice_reshape(B=1, C=256, H=16, W=32):
+    pdpd.disable_static()
+
+    data = pdpd.rand(shape=[B, C, H*W], dtype='float32')
+
+    @pdpd.jit.to_static
+    def test_model(x):
+        x2 = pdpd.assign([-1, -1, 16, 32]).astype('int32')
+        node_reshape = pdpd.reshape(x, [0, 256, x2[2], x2[3]])
+        return node_reshape
+    exportModel('slice_reshape', test_model, [data], target_dir=sys.argv[1])
+
 def main():
     x = np.linspace(1, 60, num = 60, dtype=np.int32).reshape(4, 3, 5).astype(data_type)
     slice("slice", x, axes=[1, 2], start=(0, 1), end=(-1, 3))
@@ -36,4 +93,6 @@ def main():
     slice("slice_1d", x, axes=[0], start=[0], end=[1])
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
+    slice_dyn()
+    slice_reshape()
\ No newline at end of file
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/save_model.py b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/save_model.py
index 95727ac9632797..3512fb2fd14fe6 100644
--- a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/save_model.py
+++ b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/save_model.py
@@ -1,4 +1,5 @@
 import os
+import sys
 import numpy as np
 import paddle as pdpd
 
@@ -55,6 +56,42 @@ def saveModel(name, exe, feedkeys:list, fetchlist:list, inputs:list, outputs:lis
     pdpd.fluid.io.save_inference_model(model_dir, feedkeys, fetchlist, exe, model_filename=name+".pdmodel", params_filename=name+".pdiparams")   
 
 
+'''
+export dyn model, along with input and output for reference.
+input_data: list of all inputs
+'''
+def exportModel(name, dyn_func, input_data:list, target_dir:str):
+    model_dir = os.path.join(target_dir, name)
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    save_path = '{}/{}'.format(model_dir, name)
+
+    input_specs = []
+    for idx, data in enumerate(input_data):
+        input_name = 'input{}'.format(idx)
+        input_specs.append(
+            pdpd.static.InputSpec(shape=data.shape, dtype=data.dtype, name=input_name)
+        )
+
+        # dump input
+        np.save(os.path.join(model_dir, "input{}".format(idx)), data)        
+
+    pdpd.jit.save(dyn_func, save_path, input_specs)
+    print('saved exported model to {}'.format(save_path))
+
+    # infer
+    model = pdpd.jit.load(save_path)
+
+    result = model(*[input[:] for input in input_data])
+   
+    # dump output for reference
+    if isinstance(result, (tuple, list)):
+        for idx, out in enumerate(result):
+            np.save(os.path.join(model_dir, "output{}".format(idx)), out.numpy())
+    else:       
+        np.save(os.path.join(model_dir, "output{}".format(0)), result.numpy())
+
+
 if __name__ == "__main__":
     np.set_printoptions(precision=2)
     np.set_printoptions(suppress=True)  
diff --git a/ngraph/test/frontend/paddlepaddle/throw_in_conversion.cpp b/ngraph/test/frontend/paddlepaddle/throw_in_conversion.cpp
index 3032c2722022a3..5ce63027222508 100644
--- a/ngraph/test/frontend/paddlepaddle/throw_in_conversion.cpp
+++ b/ngraph/test/frontend/paddlepaddle/throw_in_conversion.cpp
@@ -10,7 +10,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 TEST(FrontEndConvertModelTest, throw_in_conversion) {
     FrontEndManager fem;
diff --git a/ngraph/test/frontend/shared/CMakeLists.txt b/ngraph/test/frontend/shared/CMakeLists.txt
index c048c3bfb7c85c..1c7f2aa6fedef3 100644
--- a/ngraph/test/frontend/shared/CMakeLists.txt
+++ b/ngraph/test/frontend/shared/CMakeLists.txt
@@ -12,6 +12,6 @@ add_library(${TARGET_NAME} STATIC ${LIBRARY_SRC} ${LIBRARY_HEADERS})
 target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
 target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/../..)
 target_link_libraries(${TARGET_NAME} PUBLIC frontend_manager
-        ngraph::ngraph cnpy ie_backend ngraph_test_util engines_test_util commonTestUtils openvino::util)
+        ngraph cnpy commonTestUtils ie_backend ngraph_test_util engines_test_util openvino::util)
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
diff --git a/ngraph/test/frontend/shared/include/basic_api.hpp b/ngraph/test/frontend/shared/include/basic_api.hpp
index f25548be3b8058..aad200b6458860 100644
--- a/ngraph/test/frontend/shared/include/basic_api.hpp
+++ b/ngraph/test/frontend/shared/include/basic_api.hpp
@@ -17,9 +17,9 @@ class FrontEndBasicTest : public ::testing::TestWithParam<BasicTestParam> {
     std::string m_feName;
     std::string m_pathToModels;
     std::string m_modelFile;
-    ngraph::frontend::FrontEndManager m_fem;
-    ngraph::frontend::FrontEnd::Ptr m_frontEnd;
-    ngraph::frontend::InputModel::Ptr m_inputModel;
+    ov::frontend::FrontEndManager m_fem;
+    ov::frontend::FrontEnd::Ptr m_frontEnd;
+    ov::frontend::InputModel::Ptr m_inputModel;
 
     static std::string getTestCaseName(const testing::TestParamInfo<BasicTestParam>& obj);
 
diff --git a/ngraph/test/frontend/shared/include/convert_model.hpp b/ngraph/test/frontend/shared/include/convert_model.hpp
index fd0fcbc8c050d2..a60434fee76885 100644
--- a/ngraph/test/frontend/shared/include/convert_model.hpp
+++ b/ngraph/test/frontend/shared/include/convert_model.hpp
@@ -17,9 +17,9 @@ class FrontEndConvertModelTest : public ::testing::TestWithParam<ConvertParam> {
     std::string m_feName;
     std::string m_pathToModels;
     std::string m_modelFile;
-    ngraph::frontend::FrontEndManager m_fem;
-    ngraph::frontend::FrontEnd::Ptr m_frontEnd;
-    ngraph::frontend::InputModel::Ptr m_inputModel;
+    ov::frontend::FrontEndManager m_fem;
+    ov::frontend::FrontEnd::Ptr m_frontEnd;
+    ov::frontend::InputModel::Ptr m_inputModel;
 
     static std::string getTestCaseName(const testing::TestParamInfo<ConvertParam>& obj);
 
diff --git a/ngraph/test/frontend/shared/include/cut_specific_model.hpp b/ngraph/test/frontend/shared/include/cut_specific_model.hpp
index a311abc340e9f4..34836872a4138a 100644
--- a/ngraph/test/frontend/shared/include/cut_specific_model.hpp
+++ b/ngraph/test/frontend/shared/include/cut_specific_model.hpp
@@ -24,9 +24,9 @@ struct CutModelParam {
 class FrontEndCutModelTest : public ::testing::TestWithParam<CutModelParam> {
 public:
     CutModelParam m_param;
-    ngraph::frontend::FrontEndManager m_fem;
-    ngraph::frontend::FrontEnd::Ptr m_frontEnd;
-    ngraph::frontend::InputModel::Ptr m_inputModel;
+    ov::frontend::FrontEndManager m_fem;
+    ov::frontend::FrontEnd::Ptr m_frontEnd;
+    ov::frontend::InputModel::Ptr m_inputModel;
 
     static std::string getTestCaseName(const testing::TestParamInfo<CutModelParam>& obj);
 
@@ -37,7 +37,7 @@ class FrontEndCutModelTest : public ::testing::TestWithParam<CutModelParam> {
 
     void doLoadFromFile();
 
-    std::vector<ngraph::frontend::Place::Ptr> constructNewInputs() const;
+    std::vector<ov::frontend::Place::Ptr> constructNewInputs() const;
 
-    std::vector<ngraph::frontend::Place::Ptr> constructNewOutputs() const;
+    std::vector<ov::frontend::Place::Ptr> constructNewOutputs() const;
 };
diff --git a/ngraph/test/frontend/shared/include/load_from.hpp b/ngraph/test/frontend/shared/include/load_from.hpp
index 0c19f63cc052ae..52896fd2fb1db9 100644
--- a/ngraph/test/frontend/shared/include/load_from.hpp
+++ b/ngraph/test/frontend/shared/include/load_from.hpp
@@ -20,9 +20,9 @@ struct LoadFromFEParam {
 class FrontEndLoadFromTest : public ::testing::TestWithParam<LoadFromFEParam> {
 public:
     LoadFromFEParam m_param;
-    ngraph::frontend::FrontEndManager m_fem;
-    ngraph::frontend::FrontEnd::Ptr m_frontEnd;
-    ngraph::frontend::InputModel::Ptr m_inputModel;
+    ov::frontend::FrontEndManager m_fem;
+    ov::frontend::FrontEnd::Ptr m_frontEnd;
+    ov::frontend::InputModel::Ptr m_inputModel;
 
     static std::string getTestCaseName(const testing::TestParamInfo<LoadFromFEParam>& obj);
 
diff --git a/ngraph/test/frontend/shared/include/op_fuzzy.hpp b/ngraph/test/frontend/shared/include/op_fuzzy.hpp
index cc8abacc4f1a03..23bfd5ec8a8f71 100644
--- a/ngraph/test/frontend/shared/include/op_fuzzy.hpp
+++ b/ngraph/test/frontend/shared/include/op_fuzzy.hpp
@@ -17,9 +17,9 @@ class FrontEndFuzzyOpTest : public ::testing::TestWithParam<FuzzyOpTestParam> {
     std::string m_feName;
     std::string m_pathToModels;
     std::string m_modelFile;
-    ngraph::frontend::FrontEndManager m_fem;
-    ngraph::frontend::FrontEnd::Ptr m_frontEnd;
-    ngraph::frontend::InputModel::Ptr m_inputModel;
+    ov::frontend::FrontEndManager m_fem;
+    ov::frontend::FrontEnd::Ptr m_frontEnd;
+    ov::frontend::InputModel::Ptr m_inputModel;
 
     static std::string getTestCaseName(const testing::TestParamInfo<FuzzyOpTestParam>& obj);
 
diff --git a/ngraph/test/frontend/shared/include/partial_shape.hpp b/ngraph/test/frontend/shared/include/partial_shape.hpp
index ff9585cef08fdb..0f57056b52e962 100644
--- a/ngraph/test/frontend/shared/include/partial_shape.hpp
+++ b/ngraph/test/frontend/shared/include/partial_shape.hpp
@@ -30,9 +30,9 @@ class FrontEndPartialShapeTest : public ::testing::TestWithParam<PartialShapePar
 public:
     BaseFEParam m_baseParam;
     PartShape m_partShape;
-    ngraph::frontend::FrontEndManager m_fem;
-    ngraph::frontend::FrontEnd::Ptr m_frontEnd;
-    ngraph::frontend::InputModel::Ptr m_inputModel;
+    ov::frontend::FrontEndManager m_fem;
+    ov::frontend::FrontEnd::Ptr m_frontEnd;
+    ov::frontend::InputModel::Ptr m_inputModel;
 
     static std::string getTestCaseName(const testing::TestParamInfo<PartialShapeParam>& obj);
 
diff --git a/ngraph/test/frontend/shared/include/set_element_type.hpp b/ngraph/test/frontend/shared/include/set_element_type.hpp
index 544daf2f510061..83447ef22b40b1 100644
--- a/ngraph/test/frontend/shared/include/set_element_type.hpp
+++ b/ngraph/test/frontend/shared/include/set_element_type.hpp
@@ -17,9 +17,9 @@ struct SetTypeFEParam {
 class FrontEndElementTypeTest : public ::testing::TestWithParam<SetTypeFEParam> {
 public:
     SetTypeFEParam m_param;
-    ngraph::frontend::FrontEndManager m_fem;
-    ngraph::frontend::FrontEnd::Ptr m_frontEnd;
-    ngraph::frontend::InputModel::Ptr m_inputModel;
+    ov::frontend::FrontEndManager m_fem;
+    ov::frontend::FrontEnd::Ptr m_frontEnd;
+    ov::frontend::InputModel::Ptr m_inputModel;
 
     static std::string getTestCaseName(const testing::TestParamInfo<SetTypeFEParam>& obj);
 
diff --git a/ngraph/test/frontend/shared/include/utils.hpp b/ngraph/test/frontend/shared/include/utils.hpp
index b07137d64fab47..5ea2f0d162be2d 100644
--- a/ngraph/test/frontend/shared/include/utils.hpp
+++ b/ngraph/test/frontend/shared/include/utils.hpp
@@ -19,13 +19,11 @@ int run_tests(int argc, char** argv);
 
 std::string get_current_executable_path();
 
-inline std::tuple<ngraph::frontend::FrontEnd::Ptr, ngraph::frontend::InputModel::Ptr> load_from_file(
-    ngraph::frontend::FrontEndManager& fem,
-    const std::string& frontend_name,
-    const std::string& model_file) {
+inline std::tuple<ov::frontend::FrontEnd::Ptr, ov::frontend::InputModel::Ptr>
+load_from_file(ov::frontend::FrontEndManager& fem, const std::string& frontend_name, const std::string& model_file) {
     auto frontend = fem.load_by_framework(frontend_name);
     auto inputModel = frontend->load(model_file);
-    return std::tuple<ngraph::frontend::FrontEnd::Ptr, ngraph::frontend::InputModel::Ptr>{frontend, inputModel};
+    return std::tuple<ov::frontend::FrontEnd::Ptr, ov::frontend::InputModel::Ptr>{frontend, inputModel};
 }
 
 inline std::string fileToTestName(const std::string& fileName) {
diff --git a/ngraph/test/frontend/shared/src/basic_api.cpp b/ngraph/test/frontend/shared/src/basic_api.cpp
index a6e16be4e03bf1..b2aa118844e71f 100644
--- a/ngraph/test/frontend/shared/src/basic_api.cpp
+++ b/ngraph/test/frontend/shared/src/basic_api.cpp
@@ -7,7 +7,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 std::string FrontEndBasicTest::getTestCaseName(const testing::TestParamInfo<BasicTestParam>& obj) {
     std::string fe, path, fileName;
diff --git a/ngraph/test/frontend/shared/src/convert_model.cpp b/ngraph/test/frontend/shared/src/convert_model.cpp
index c73b963be3a05d..d83e3d64a5cdc7 100644
--- a/ngraph/test/frontend/shared/src/convert_model.cpp
+++ b/ngraph/test/frontend/shared/src/convert_model.cpp
@@ -8,7 +8,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 std::string FrontEndConvertModelTest::getTestCaseName(const testing::TestParamInfo<ConvertParam>& obj) {
     std::string fe, path, fileName;
diff --git a/ngraph/test/frontend/shared/src/cut_specific_model.cpp b/ngraph/test/frontend/shared/src/cut_specific_model.cpp
index 69f29984ea0f00..6a82a86005b34a 100644
--- a/ngraph/test/frontend/shared/src/cut_specific_model.cpp
+++ b/ngraph/test/frontend/shared/src/cut_specific_model.cpp
@@ -8,7 +8,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 static std::string joinStrings(const std::vector<std::string>& strings) {
     std::ostringstream res;
@@ -39,7 +39,7 @@ void FrontEndCutModelTest::doLoadFromFile() {
         FrontEndTestUtils::load_from_file(m_fem, m_param.m_frontEndName, m_param.m_modelName);
 }
 
-std::vector<ngraph::frontend::Place::Ptr> FrontEndCutModelTest::constructNewInputs() const {
+std::vector<ov::frontend::Place::Ptr> FrontEndCutModelTest::constructNewInputs() const {
     std::vector<Place::Ptr> newInputs;
     for (const auto& name : m_param.m_newInputs) {
         newInputs.push_back(m_inputModel->get_place_by_tensor_name(name));
@@ -47,7 +47,7 @@ std::vector<ngraph::frontend::Place::Ptr> FrontEndCutModelTest::constructNewInpu
     return newInputs;
 }
 
-std::vector<ngraph::frontend::Place::Ptr> FrontEndCutModelTest::constructNewOutputs() const {
+std::vector<ov::frontend::Place::Ptr> FrontEndCutModelTest::constructNewOutputs() const {
     std::vector<Place::Ptr> newOutputs;
     for (const auto& name : m_param.m_newOutputs) {
         newOutputs.push_back(m_inputModel->get_place_by_tensor_name(name));
diff --git a/ngraph/test/frontend/shared/src/load_from.cpp b/ngraph/test/frontend/shared/src/load_from.cpp
index 0c93f71e197bc8..bbd371f55920e5 100644
--- a/ngraph/test/frontend/shared/src/load_from.cpp
+++ b/ngraph/test/frontend/shared/src/load_from.cpp
@@ -10,7 +10,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 std::string FrontEndLoadFromTest::getTestCaseName(const testing::TestParamInfo<LoadFromFEParam>& obj) {
     std::string res = obj.param.m_frontEndName;
diff --git a/ngraph/test/frontend/shared/src/op_fuzzy.cpp b/ngraph/test/frontend/shared/src/op_fuzzy.cpp
index 6db34a4d358879..3128e5a6e5ac74 100644
--- a/ngraph/test/frontend/shared/src/op_fuzzy.cpp
+++ b/ngraph/test/frontend/shared/src/op_fuzzy.cpp
@@ -15,7 +15,7 @@ using namespace ngraph;
 using namespace InferenceEngine;
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 using TestEngine = test::IE_CPU_Engine;
 
 std::string FrontEndFuzzyOpTest::getTestCaseName(const testing::TestParamInfo<FuzzyOpTestParam>& obj) {
diff --git a/ngraph/test/frontend/shared/src/partial_shape.cpp b/ngraph/test/frontend/shared/src/partial_shape.cpp
index 91774a41e27673..46a2a26b4af13f 100644
--- a/ngraph/test/frontend/shared/src/partial_shape.cpp
+++ b/ngraph/test/frontend/shared/src/partial_shape.cpp
@@ -7,7 +7,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 std::string FrontEndPartialShapeTest::getTestCaseName(const testing::TestParamInfo<PartialShapeParam>& obj) {
     BaseFEParam base;
diff --git a/ngraph/test/frontend/shared/src/set_element_type.cpp b/ngraph/test/frontend/shared/src/set_element_type.cpp
index 9de46aa7dcce28..ad3ff2f90bcf02 100644
--- a/ngraph/test/frontend/shared/src/set_element_type.cpp
+++ b/ngraph/test/frontend/shared/src/set_element_type.cpp
@@ -7,7 +7,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 std::string FrontEndElementTypeTest::getTestCaseName(const testing::TestParamInfo<SetTypeFEParam>& obj) {
     std::string res = obj.param.m_frontEndName + "_" + obj.param.m_modelName;
diff --git a/ngraph/test/frontend/tensorflow/CMakeLists.txt b/ngraph/test/frontend/tensorflow/CMakeLists.txt
index 8533cafd58c625..18cc63b460766b 100644
--- a/ngraph/test/frontend/tensorflow/CMakeLists.txt
+++ b/ngraph/test/frontend/tensorflow/CMakeLists.txt
@@ -63,7 +63,6 @@ else()
 endif()
 
 add_dependencies(${TARGET_NAME} tensorflow_test_models)
-add_dependencies(${TARGET_NAME} tensorflow_ngraph_frontend)
 
 get_target_property(TENSORFLOW_FRONTEND_SRC_DIR tensorflow_ngraph_frontend SOURCE_DIR)
 target_include_directories(${TARGET_NAME} PRIVATE ${TENSORFLOW_FRONTEND_SRC_DIR}/src/pass/)
diff --git a/ngraph/test/frontend/tensorflow/basic_api.cpp b/ngraph/test/frontend/tensorflow/basic_api.cpp
index 5aab060b8d6ec1..081fd51613f513 100644
--- a/ngraph/test/frontend/tensorflow/basic_api.cpp
+++ b/ngraph/test/frontend/tensorflow/basic_api.cpp
@@ -7,7 +7,7 @@
 #include "tf_utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using TFBasicTest = FrontEndBasicTest;
 
diff --git a/ngraph/test/frontend/tensorflow/convert_model.cpp b/ngraph/test/frontend/tensorflow/convert_model.cpp
index 046063113fb5e3..5f224051dd4306 100644
--- a/ngraph/test/frontend/tensorflow/convert_model.cpp
+++ b/ngraph/test/frontend/tensorflow/convert_model.cpp
@@ -7,7 +7,7 @@
 #include "tf_utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 using TFConvertModelTest = FrontEndConvertModelTest;
 
diff --git a/ngraph/test/frontend/tensorflow/convert_unsupported.cpp b/ngraph/test/frontend/tensorflow/convert_unsupported.cpp
index 37ab156f428d03..fad6df2287223e 100644
--- a/ngraph/test/frontend/tensorflow/convert_unsupported.cpp
+++ b/ngraph/test/frontend/tensorflow/convert_unsupported.cpp
@@ -10,7 +10,7 @@
 #include "utils.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 TEST(FrontEndConvertModelTest, test_unsupported_op) {
     FrontEndManager fem;
diff --git a/ngraph/test/models/ir/if_body_without_parameters.bin b/ngraph/test/models/ir/if_body_without_parameters.bin
new file mode 100644
index 00000000000000..7a623333dcd697
Binary files /dev/null and b/ngraph/test/models/ir/if_body_without_parameters.bin differ
diff --git a/ngraph/test/models/ir/if_body_without_parameters.xml b/ngraph/test/models/ir/if_body_without_parameters.xml
new file mode 100644
index 00000000000000..7cab07da9d023a
--- /dev/null
+++ b/ngraph/test/models/ir/if_body_without_parameters.xml
@@ -0,0 +1,184 @@
+<?xml version="1.0" ?>
+<net name="saved_model" version="11">
+	<layers>
+		<layer id="0" name="x" type="Parameter" version="opset1">
+			<data element_type="f32" shape="1,0"/>
+			<output>
+				<port id="0" names="Func/PartitionedCall/input/_0:0,x:0" precision="FP32">
+					<dim>1</dim>
+					<dim>0</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="y" type="Parameter" version="opset1">
+			<data element_type="f32" shape="1,0"/>
+			<output>
+				<port id="0" names="Func/PartitionedCall/input/_1:0,y:0" precision="FP32">
+					<dim>1</dim>
+					<dim>0</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="PartitionedCall/model/if/Less" type="Less" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0">
+					<dim>1</dim>
+					<dim>0</dim>
+				</port>
+				<port id="1">
+					<dim>1</dim>
+					<dim>0</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="PartitionedCall/model/if/Less:0" precision="BOOL">
+					<dim>1</dim>
+					<dim>0</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="PartitionedCall/model/if/cond/Squeeze/Dims" type="Const" version="opset1">
+			<data element_type="i64" offset="0" shape="1" size="8"/>
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="PartitionedCall/model/if/cond/Squeeze" type="Squeeze" version="opset1">
+			<input>
+				<port id="0">
+					<dim>1</dim>
+					<dim>0</dim>
+				</port>
+				<port id="1">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="PartitionedCall/model/if/cond/Squeeze:0" precision="BOOL">
+					<dim>0</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="PartitionedCall/model/if/cond" type="If" version="opset8">
+			<input>
+				<port id="0">
+					<dim>0</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="PartitionedCall/model/if/cond/Identity:0,PartitionedCall/model/if/cond:0" precision="FP32"/>
+			</output>
+			<then_port_map>
+				<output external_port_id="0" internal_layer_id="1"/>
+			</then_port_map>
+			<else_port_map>
+				<output external_port_id="0" internal_layer_id="1"/>
+			</else_port_map>
+			<then_body>
+				<layers>
+					<layer id="0" name="model/if/cond/Identity" type="Const" version="opset1">
+						<data element_type="f32" offset="8" shape="" size="4"/>
+						<output>
+							<port id="0" precision="FP32"/>
+						</output>
+					</layer>
+					<layer id="1" name="model/if/cond/Identity/sink_port_0" type="Result" version="opset1">
+						<input>
+							<port id="0"/>
+						</input>
+					</layer>
+				</layers>
+				<edges>
+					<edge from-layer="0" from-port="0" to-layer="1" to-port="0"/>
+				</edges>
+			</then_body>
+			<else_body>
+				<layers>
+					<layer id="0" name="model/if/cond/Identity" type="Const" version="opset1">
+						<data element_type="f32" offset="12" shape="" size="4"/>
+						<output>
+							<port id="0" precision="FP32"/>
+						</output>
+					</layer>
+					<layer id="1" name="model/if/cond/Identity/sink_port_0" type="Result" version="opset1">
+						<input>
+							<port id="0"/>
+						</input>
+					</layer>
+				</layers>
+				<edges>
+					<edge from-layer="0" from-port="0" to-layer="1" to-port="0"/>
+				</edges>
+			</else_body>
+		</layer>
+		<layer id="6" name="PartitionedCall/model/if/Relu" type="ReLU" version="opset1">
+			<input>
+				<port id="0"/>
+			</input>
+			<output>
+				<port id="1" names="Func/PartitionedCall/output/_2:0,Identity:0,PartitionedCall/Identity:0,PartitionedCall/model/if/Relu:0" precision="FP32"/>
+			</output>
+		</layer>
+		<layer id="7" name="Func/PartitionedCall/output/_2:0" type="Result" version="opset1">
+			<input>
+				<port id="0"/>
+			</input>
+		</layer>
+	</layers>
+	<edges>
+		<edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
+		<edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
+		<edge from-layer="2" from-port="2" to-layer="4" to-port="0"/>
+		<edge from-layer="3" from-port="0" to-layer="4" to-port="1"/>
+		<edge from-layer="4" from-port="2" to-layer="5" to-port="0"/>
+		<edge from-layer="5" from-port="1" to-layer="6" to-port="0"/>
+		<edge from-layer="6" from-port="1" to-layer="7" to-port="0"/>
+	</edges>
+	<meta_data>
+		<MO_version value="custom_mo/q_dq_linear_resolver_f23175abffa68339339ddc916cce0fc98cfa8727"/>
+		<cli_parameters>
+			<batch value="1"/>
+			<caffe_parser_path value="DIR"/>
+			<data_type value="float"/>
+			<disable_nhwc_to_nchw value="False"/>
+			<disable_omitting_optional value="False"/>
+			<disable_resnet_optimization value="False"/>
+			<disable_weights_compression value="False"/>
+			<enable_concat_optimization value="False"/>
+			<enable_flattening_nested_params value="False"/>
+			<enable_ssd_gluoncv value="False"/>
+			<extensions value="DIR"/>
+			<framework value="tf"/>
+			<freeze_placeholder_with_value value="{}"/>
+			<generate_deprecated_IR_V7 value="False"/>
+			<input_model_is_text value="False"/>
+			<k value="DIR\CustomLayersMapping.xml"/>
+			<keep_shape_ops value="True"/>
+			<legacy_ir_generation value="False"/>
+			<legacy_mxnet_model value="False"/>
+			<log_level value="ERROR"/>
+			<mean_scale_values value="{}"/>
+			<mean_values value="()"/>
+			<model_name value="saved_model"/>
+			<output_dir value="DIR"/>
+			<placeholder_data_types value="{}"/>
+			<progress value="False"/>
+			<remove_memory value="False"/>
+			<remove_output_softmax value="False"/>
+			<reverse_input_channels value="False"/>
+			<save_params_from_nd value="False"/>
+			<saved_model_dir value="DIR"/>
+			<scale_values value="()"/>
+			<silent value="False"/>
+			<static_shape value="False"/>
+			<stream_output value="False"/>
+			<transform value=""/>
+			<use_legacy_frontend value="False"/>
+			<use_new_frontend value="False"/>
+			<unset unset_cli_parameters="counts, disable_fusing, disable_gfusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_model, input_proto, input_shape, input_symbol, mean_file, mean_file_offsets, move_to_preprocess, nd_prefix_name, output, placeholder_shapes, pretrained_model_name, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
+		</cli_parameters>
+	</meta_data>
+</net>
diff --git a/ngraph/test/models/onnx/clip_no_min_no_max.prototxt b/ngraph/test/models/onnx/clip_no_min_no_max.prototxt
new file mode 100644
index 00000000000000..082bdf84123d0a
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_no_min_no_max.prototxt
@@ -0,0 +1,48 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
+  node {
+    input: "X"
+    input: ""
+    input: ""
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  input {
+    name: "X"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "Y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  domain: ""
+  version: 12
+}
diff --git a/ngraph/test/models/onnx/clip_no_min_no_max_int64.prototxt b/ngraph/test/models/onnx/clip_no_min_no_max_int64.prototxt
new file mode 100644
index 00000000000000..03300976a8ff9d
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_no_min_no_max_int64.prototxt
@@ -0,0 +1,48 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
+  node {
+    input: "X"
+    input: ""
+    input: ""
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  input {
+    name: "X"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "Y"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  domain: ""
+  version: 12
+}
diff --git a/ngraph/test/models/onnx/clip_no_min_set_max.prototxt b/ngraph/test/models/onnx/clip_no_min_set_max.prototxt
new file mode 100644
index 00000000000000..e5e027600e581f
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_no_min_set_max.prototxt
@@ -0,0 +1,58 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
+  node {
+    input: "X"
+    input: ""
+    input: "MAX"
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  input {
+    name: "X"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "MAX"
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+}
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+  version: 12
+}
diff --git a/ngraph/test/models/onnx/clip_no_min_set_max_int64.prototxt b/ngraph/test/models/onnx/clip_no_min_set_max_int64.prototxt
new file mode 100644
index 00000000000000..08c4e2e709b6f2
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_no_min_set_max_int64.prototxt
@@ -0,0 +1,58 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
+  node {
+    input: "X"
+    input: ""
+    input: "MAX"
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  input {
+    name: "X"
+    type {
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+        shape {
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+}
+opset_import {
+  domain: ""
+  version: 12
+}
diff --git a/ngraph/test/models/onnx/clip_set_min_no_max.prototxt b/ngraph/test/models/onnx/clip_set_min_no_max.prototxt
new file mode 100644
index 00000000000000..10fcc2c46691ed
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_set_min_no_max.prototxt
@@ -0,0 +1,58 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
+  node {
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+    input: "MIN"
+    input: ""
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  input {
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+    type {
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+opset_import {
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+  version: 12
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diff --git a/ngraph/test/models/onnx/clip_set_min_no_max_initializers.prototxt b/ngraph/test/models/onnx/clip_set_min_no_max_initializers.prototxt
new file mode 100644
index 00000000000000..0901b86fb7af73
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_set_min_no_max_initializers.prototxt
@@ -0,0 +1,53 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
+  node {
+    input: "X"
+    input: "MIN"
+    input: ""
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  initializer {
+    data_type: 1
+    float_data: -1.590000033378601
+    name: "MIN"
+  }
+  input {
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+  output {
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+opset_import {
+  domain: ""
+  version: 12
+}
diff --git a/ngraph/test/models/onnx/clip_set_min_set_max.prototxt b/ngraph/test/models/onnx/clip_set_min_set_max.prototxt
new file mode 100644
index 00000000000000..a5ad023d81dfc6
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_set_min_set_max.prototxt
@@ -0,0 +1,68 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
+  node {
+    input: "X"
+    input: "MIN"
+    input: "MAX"
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  input {
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+  output {
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+opset_import {
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+}
diff --git a/ngraph/test/models/onnx/clip_set_min_set_max_initializers.prototxt b/ngraph/test/models/onnx/clip_set_min_set_max_initializers.prototxt
new file mode 100644
index 00000000000000..c09b604cee7e2a
--- /dev/null
+++ b/ngraph/test/models/onnx/clip_set_min_set_max_initializers.prototxt
@@ -0,0 +1,58 @@
+ir_version: 8
+producer_name: "onnx-importer-test"
+graph {
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+    input: "MAX"
+    output: "Y"
+    op_type: "Clip"
+  }
+  name: "test-model-clip"
+  initializer {
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+  initializer {
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diff --git a/ngraph/test/models/onnx/com.microsoft/attention.prototxt b/ngraph/test/models/onnx/com.microsoft/attention.prototxt
new file mode 100644
index 00000000000000..53ac350573b055
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention.prototxt
@@ -0,0 +1,123 @@
+ir_version: 6
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+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/com.microsoft/attention_dynamic_shapes.prototxt b/ngraph/test/models/onnx/com.microsoft/attention_dynamic_shapes.prototxt
new file mode 100644
index 00000000000000..97a4f3f1f9134a
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention_dynamic_shapes.prototxt
@@ -0,0 +1,90 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
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+    input: "past"
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+opset_import {
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+}
+opset_import {
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diff --git a/ngraph/test/models/onnx/com.microsoft/attention_extra_add.prototxt b/ngraph/test/models/onnx/com.microsoft/attention_extra_add.prototxt
new file mode 100644
index 00000000000000..f8664f4507f459
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention_extra_add.prototxt
@@ -0,0 +1,190 @@
+ir_version: 6
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+    input: "extra_add"
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+}
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+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/com.microsoft/attention_mask_index_1.prototxt b/ngraph/test/models/onnx/com.microsoft/attention_mask_index_1.prototxt
new file mode 100644
index 00000000000000..56d4e1d1142a4e
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention_mask_index_1.prototxt
@@ -0,0 +1,163 @@
+ir_version: 6
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diff --git a/ngraph/test/models/onnx/com.microsoft/attention_mask_index_2.prototxt b/ngraph/test/models/onnx/com.microsoft/attention_mask_index_2.prototxt
new file mode 100644
index 00000000000000..481d9ea86f5488
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention_mask_index_2.prototxt
@@ -0,0 +1,168 @@
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diff --git a/ngraph/test/models/onnx/com.microsoft/attention_mask_index_3.prototxt b/ngraph/test/models/onnx/com.microsoft/attention_mask_index_3.prototxt
new file mode 100644
index 00000000000000..67558f33599282
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention_mask_index_3.prototxt
@@ -0,0 +1,166 @@
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new file mode 100644
index 00000000000000..9b9387991a9c64
--- /dev/null
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new file mode 100644
index 00000000000000..7625195fa044c8
--- /dev/null
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+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/com.microsoft/attention_qkv_hidden_sizes.prototxt b/ngraph/test/models/onnx/com.microsoft/attention_qkv_hidden_sizes.prototxt
new file mode 100644
index 00000000000000..5ee43aa5c3624a
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention_qkv_hidden_sizes.prototxt
@@ -0,0 +1,130 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
+  node {
+    input: "input"
+    input: "weights"
+    input: "bias"
+    output: "output"
+    name: "Attention_1"
+    op_type: "Attention"
+    attribute {
+      name: "num_heads"
+      i: 2
+      type: INT
+    }
+    attribute {
+      name: "qkv_hidden_sizes"
+      ints: 2
+      ints: 2
+      ints: 8
+      type: INTS
+    }
+    domain: "com.microsoft"
+  }
+  name: "attention-model"
+  initializer {
+    name: "weights"
+    dims: 3
+    dims: 12
+    data_type: 1
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+    data_type: 1
+    float_data: -0.2587452232837677
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+    float_data: 0.14739133417606354
+    float_data: -0.19812127947807312
+    float_data: 0.50444173812866211
+  }
+  input {
+    name: "input"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
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+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
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+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/com.microsoft/attention_unidirectional.prototxt b/ngraph/test/models/onnx/com.microsoft/attention_unidirectional.prototxt
new file mode 100644
index 00000000000000..31a65b299d60ee
--- /dev/null
+++ b/ngraph/test/models/onnx/com.microsoft/attention_unidirectional.prototxt
@@ -0,0 +1,154 @@
+ir_version: 6
+producer_name: "nGraph"
+graph {
+  node {
+    input: "input"
+    input: "weights"
+    input: "bias"
+    output: "output"
+    output: "present"
+    name: "Attention_1"
+    op_type: "Attention"
+    attribute {
+      name: "num_heads"
+      i: 2
+      type: INT
+    }
+    attribute {
+      name: "unidirectional"
+      i: 1
+      type: INT
+    }
+    domain: "com.microsoft"
+  }
+  name: "attention-model"
+  initializer {
+    name: "weights"
+    dims: 3
+    dims: 12
+    data_type: 1
+    float_data: 0.01326417364180088
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+    float_data: 0.01037109550088644
+    float_data: -0.011854520998895168
+    float_data: 0.035907052457332611
+  }
+  initializer {
+    name: "bias"
+    dims: 12
+    data_type: 1
+    float_data: -0.2587452232837677
+    float_data: -0.095395378768444061
+    float_data: 0.12785771489143372
+    float_data: 0.16469171643257141
+    float_data: -0.58997648954391479
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+    float_data: 0.077637940645217896
+    float_data: -0.03203071653842926
+    float_data: 0.075582884252071381
+    float_data: 0.14739133417606354
+    float_data: -0.19812127947807312
+    float_data: 0.50444173812866211
+  }
+  input {
+    name: "input"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "present"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}
+opset_import {
+  domain: "com.microsoft"
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/bias_gelu.prototxt b/ngraph/test/models/onnx/com.microsoft/bias_gelu.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/bias_gelu.prototxt
rename to ngraph/test/models/onnx/com.microsoft/bias_gelu.prototxt
diff --git a/ngraph/test/models/onnx/embed_layer_normalization.prototxt b/ngraph/test/models/onnx/com.microsoft/embed_layer_normalization.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/embed_layer_normalization.prototxt
rename to ngraph/test/models/onnx/com.microsoft/embed_layer_normalization.prototxt
diff --git a/ngraph/test/models/onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.prototxt b/ngraph/test/models/onnx/com.microsoft/embed_layer_normalization_dynamic_shapes.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.prototxt
rename to ngraph/test/models/onnx/com.microsoft/embed_layer_normalization_dynamic_shapes.prototxt
diff --git a/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding.prototxt b/ngraph/test/models/onnx/com.microsoft/embed_layer_normalization_with_segment_embedding.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding.prototxt
rename to ngraph/test/models/onnx/com.microsoft/embed_layer_normalization_with_segment_embedding.prototxt
diff --git a/ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding_and_mask.prototxt b/ngraph/test/models/onnx/com.microsoft/embed_layer_normalization_with_segment_embedding_and_mask.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/embed_layer_normalization_with_segment_embedding_and_mask.prototxt
rename to ngraph/test/models/onnx/com.microsoft/embed_layer_normalization_with_segment_embedding_and_mask.prototxt
diff --git a/ngraph/test/models/onnx/dynamic_shapes/skip_layer_normalization.prototxt b/ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_dynamic_shapes.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/dynamic_shapes/skip_layer_normalization.prototxt
rename to ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_dynamic_shapes.prototxt
diff --git a/ngraph/test/models/onnx/skip_layer_normalization_with_gamma.prototxt b/ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_with_gamma.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/skip_layer_normalization_with_gamma.prototxt
rename to ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_with_gamma.prototxt
diff --git a/ngraph/test/models/onnx/skip_layer_normalization_with_gamma_beta.prototxt b/ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_with_gamma_beta.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/skip_layer_normalization_with_gamma_beta.prototxt
rename to ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_with_gamma_beta.prototxt
diff --git a/ngraph/test/models/onnx/skip_layer_normalization_with_gamma_beta_bias.prototxt b/ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_with_gamma_beta_bias.prototxt
similarity index 100%
rename from ngraph/test/models/onnx/skip_layer_normalization_with_gamma_beta_bias.prototxt
rename to ngraph/test/models/onnx/com.microsoft/skip_layer_normalization_with_gamma_beta_bias.prototxt
diff --git a/ngraph/test/models/onnx/max_pool_3d.prototxt b/ngraph/test/models/onnx/max_pool_3d.prototxt
new file mode 100644
index 00000000000000..d84830a05d30ec
--- /dev/null
+++ b/ngraph/test/models/onnx/max_pool_3d.prototxt
@@ -0,0 +1,76 @@
+ir_version: 3
+producer_name: "backend-test"
+graph {
+  node {
+    input: "x"
+    output: "y"
+    output: "z"
+    op_type: "MaxPool"
+    attribute {
+      name: "kernel_shape"
+      ints: 2
+      type: INTS
+    }
+  }
+  name: "maxpool_test"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "z"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/ngraph/test/models/onnx/max_pool_4d_ceil_mode.prototxt b/ngraph/test/models/onnx/max_pool_4d_ceil_mode.prototxt
new file mode 100644
index 00000000000000..456aa6df3c4773
--- /dev/null
+++ b/ngraph/test/models/onnx/max_pool_4d_ceil_mode.prototxt
@@ -0,0 +1,91 @@
+ir_version: 3
+producer_name: "backend-test"
+graph {
+  node {
+    input: "x"
+    output: "y"
+    output: "z"
+    op_type: "MaxPool"
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "ceil_mode"
+      i: 1
+      type: INT
+    }
+  }
+  name: "maxpool_test"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "z"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/ngraph/test/models/onnx/max_pool_4d_ceil_strides.prototxt b/ngraph/test/models/onnx/max_pool_4d_ceil_strides.prototxt
new file mode 100644
index 00000000000000..dab7cfdb0908f3
--- /dev/null
+++ b/ngraph/test/models/onnx/max_pool_4d_ceil_strides.prototxt
@@ -0,0 +1,97 @@
+ir_version: 4
+producer_name: "backend-test"
+graph {
+  node {
+    input: "x"
+    output: "y"
+    output: "z"
+    op_type: "MaxPool"
+    attribute {
+      name: "ceil_mode"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 2
+      ints: 2
+      type: INTS
+    }
+  }
+  name: "test_maxpool_2d_ceil"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "z"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 10
+}
diff --git a/ngraph/test/models/onnx/max_pool_4d_dilations.prototxt b/ngraph/test/models/onnx/max_pool_4d_dilations.prototxt
new file mode 100644
index 00000000000000..12cfdbe162bb2f
--- /dev/null
+++ b/ngraph/test/models/onnx/max_pool_4d_dilations.prototxt
@@ -0,0 +1,92 @@
+ir_version: 3
+producer_name: "backend-test"
+graph {
+  node {
+    input: "x"
+    output: "y"
+    output: "z"
+    op_type: "MaxPool"
+    attribute {
+      name: "kernel_shape"
+      ints: 2
+      ints: 2
+      type: INTS
+    }
+    attribute {
+      name: "dilations"
+      ints: 2
+      ints: 2
+      type: INTS
+    }
+  }
+  name: "maxpool_test"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 4
+          }
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "z"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/ngraph/test/models/onnx/max_pool_4d_strides.prototxt b/ngraph/test/models/onnx/max_pool_4d_strides.prototxt
new file mode 100644
index 00000000000000..2c207a1a9c0cc7
--- /dev/null
+++ b/ngraph/test/models/onnx/max_pool_4d_strides.prototxt
@@ -0,0 +1,100 @@
+ir_version: 3
+producer_name: "backend-test"
+graph {
+  node {
+    input: "x"
+    output: "y"
+    output: "z"
+    op_type: "MaxPool"
+    attribute {
+      name: "kernel_shape"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "strides"
+      ints: 3
+      ints: 3
+      type: INTS
+    }
+    attribute {
+      name: "pads"
+      ints: 2
+      ints: 2
+      ints: 2
+      ints: 2
+      type: INTS
+    }
+  }
+  name: "maxpool_test"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 3
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 3
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "z"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 13
+}
diff --git a/ngraph/test/models/onnx/random_normal.prototxt b/ngraph/test/models/onnx/random_normal.prototxt
new file mode 100644
index 00000000000000..bebf03d10d8163
--- /dev/null
+++ b/ngraph/test/models/onnx/random_normal.prototxt
@@ -0,0 +1,49 @@
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    output: "y"
+    op_type: "RandomNormal"
+    attribute {
+      name: "shape"
+      ints: 2
+      ints: 2
+      type: INTS
+    }
+    attribute {
+      name: "mean"
+      f: 50
+      type: FLOAT
+    }
+    attribute {
+      name: "scale"
+      f: 40
+      type: FLOAT
+    }
+    attribute {
+      name: "seed"
+      i: 100
+      type: INT
+    }
+  }
+  name: "test_model"
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/random_normal_like.prototxt b/ngraph/test/models/onnx/random_normal_like.prototxt
new file mode 100644
index 00000000000000..eb901eb33a3607
--- /dev/null
+++ b/ngraph/test/models/onnx/random_normal_like.prototxt
@@ -0,0 +1,60 @@
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "x"
+    output: "y"
+    op_type: "RandomNormalLike"
+    attribute {
+      name: "mean"
+      f: 50
+      type: FLOAT
+    }
+    attribute {
+      name: "scale"
+      f: 40
+      type: FLOAT
+    }
+    attribute {
+      name: "seed"
+      i: 100
+      type: INT
+    }
+  }
+  name: "test_model"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/random_uniform.prototxt b/ngraph/test/models/onnx/random_uniform.prototxt
index 00b7d8ee3a5ae9..ce8e57b867e22c 100644
--- a/ngraph/test/models/onnx/random_uniform.prototxt
+++ b/ngraph/test/models/onnx/random_uniform.prototxt
@@ -22,8 +22,8 @@ graph {
     }
     attribute {
       name: "seed"
-      i: 100
-      type: INT
+      f: 100
+      type: FLOAT
     }
   }
   name: "test_model"
diff --git a/ngraph/test/models/onnx/random_uniform_like.prototxt b/ngraph/test/models/onnx/random_uniform_like.prototxt
index c90a94c7eca2fc..92b6904700e4eb 100644
--- a/ngraph/test/models/onnx/random_uniform_like.prototxt
+++ b/ngraph/test/models/onnx/random_uniform_like.prototxt
@@ -17,8 +17,8 @@ graph {
     }
     attribute {
       name: "seed"
-      i: 100
-      type: INT
+      f: 100
+      type: FLOAT
     }
   }
   name: "test_model"
diff --git a/ngraph/test/models/onnx/tensor_names.prototxt b/ngraph/test/models/onnx/tensor_names.prototxt
index 784beb19916e04..6b5b521220e375 100644
--- a/ngraph/test/models/onnx/tensor_names.prototxt
+++ b/ngraph/test/models/onnx/tensor_names.prototxt
@@ -3,6 +3,12 @@ producer_name: "test_model"
 graph {
   node {
     input: "input"
+    output: "identity_on_input"
+    name: "identity_node_on_input"
+    op_type: "Identity"
+  }
+  node {
+    input: "identity_on_input"
     output: "relu_t"
     op_type: "Relu"
     name: "relu"
diff --git a/ngraph/test/models/onnx/transformations/reference/softmax_crossentropy_consumed_expanded.prototxt b/ngraph/test/models/onnx/transformations/reference/softmax_crossentropy_consumed_expanded.prototxt
index 307c80d8d5b511..70214b9957b5fe 100644
--- a/ngraph/test/models/onnx/transformations/reference/softmax_crossentropy_consumed_expanded.prototxt
+++ b/ngraph/test/models/onnx/transformations/reference/softmax_crossentropy_consumed_expanded.prototxt
@@ -2,58 +2,80 @@ ir_version: 7
 producer_name: "nGraph ONNX Importer"
 graph {
   node {
-    output: "Func_SoftmaxCrossEntropyLoss0x557617acabe0axes"
+    output: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640Shape3D"
     op_type: "Constant"
     attribute {
       name: "value"
       t {
-        dims: 1
+        dims: 3
         data_type: 7
-        int64_data: 1
+        int64_data: 0
+        int64_data: 0
+        int64_data: -1
       }
       type: TENSOR
     }
+    domain: ""
   }
   node {
     input: "x"
-    output: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Max"
-    op_type: "ReduceMax"
+    input: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640Shape3D"
+    output: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_NCD"
+    op_type: "Reshape"
+    domain: ""
+  }
+  node {
+    input: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_NCD"
+    output: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_NDC"
+    op_type: "Transpose"
     attribute {
-      name: "axes"
+      name: "perm"
+      ints: 0
+      ints: 2
       ints: 1
       type: INTS
     }
+    domain: ""
   }
   node {
-    input: "x"
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Max"
-    output: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Sub"
-    op_type: "Sub"
-  }
-  node {
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Sub"
-    output: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Exp"
-    op_type: "Exp"
+    input: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_NDC"
+    output: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_LogSM"
+    op_type: "LogSoftmax"
+    attribute {
+      name: "axis"
+      i: 2
+      type: INT
+    }
+    domain: ""
   }
   node {
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Exp"
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0axes"
-    output: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_RS"
-    op_type: "ReduceSum"
+    input: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_LogSM"
+    output: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_LogSM_NCD"
+    op_type: "Transpose"
+    attribute {
+      name: "perm"
+      ints: 0
+      ints: 2
+      ints: 1
+      type: INTS
+    }
+    domain: ""
   }
   node {
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Exp"
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_RS"
-    output: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Div"
-    op_type: "Div"
+    input: "x"
+    output: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_shape"
+    op_type: "Shape"
+    domain: ""
   }
   node {
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Div"
-    output: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Log"
-    op_type: "Log"
+    input: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_LogSM_NCD"
+    input: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_shape"
+    output: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_Log"
+    op_type: "Reshape"
+    domain: ""
   }
   node {
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0const_zero"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640const_zero"
     op_type: "Constant"
     attribute {
       name: "value"
@@ -64,9 +86,10 @@ graph {
       }
       type: TENSOR
     }
+    domain: ""
   }
   node {
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0const_one"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640const_one"
     op_type: "Constant"
     attribute {
       name: "value"
@@ -77,9 +100,10 @@ graph {
       }
       type: TENSOR
     }
+    domain: ""
   }
   node {
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0axes"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640axes"
     op_type: "Constant"
     attribute {
       name: "value"
@@ -90,45 +114,51 @@ graph {
       }
       type: TENSOR
     }
+    domain: ""
   }
   node {
     input: "y"
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0axes"
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0expanded_target"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640axes"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640expanded_target"
     op_type: "Unsqueeze"
+    domain: ""
   }
   node {
-    input: "Func_SoftmaxCrossEntropyLoss0x557617acabe0X_Log"
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0expanded_target"
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0input_gather_element"
+    input: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_Log"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640expanded_target"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640input_gather_element"
     op_type: "GatherElements"
     attribute {
       name: "axis"
       i: 1
       type: INT
     }
+    domain: ""
   }
   node {
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0input_gather_element"
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0loss_NCdd"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640input_gather_element"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640loss_NCdd"
     op_type: "Neg"
+    domain: ""
   }
   node {
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0loss_NCdd"
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0const_zero"
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0const_one"
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0const_one"
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0loss_N1dd"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640loss_NCdd"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640const_zero"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640const_one"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640const_one"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640loss_N1dd"
     op_type: "Slice"
+    domain: ""
   }
   node {
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0loss_N1dd"
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0axes"
-    output: "Func_NegativeLogLikelihoodLoss0x557617d1bba0loss_Ndd"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640loss_N1dd"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640axes"
+    output: "Func_NegativeLogLikelihoodLoss0x7ffe150df640loss_Ndd"
     op_type: "Squeeze"
+    domain: ""
   }
   node {
-    input: "Func_NegativeLogLikelihoodLoss0x557617d1bba0loss_Ndd"
+    input: "Func_NegativeLogLikelihoodLoss0x7ffe150df640loss_Ndd"
     output: "z"
     op_type: "ReduceMean"
     attribute {
@@ -136,6 +166,7 @@ graph {
       i: 0
       type: INT
     }
+    domain: ""
   }
   node {
     input: "z"
@@ -186,6 +217,126 @@ graph {
       }
     }
   }
+  value_info {
+    name: "z"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+        }
+      }
+    }
+  }
+  value_info {
+    name: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640Shape3D"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  value_info {
+    name: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_NCD"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  value_info {
+    name: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_NDC"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  value_info {
+    name: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_LogSM"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 5
+          }
+        }
+      }
+    }
+  }
+  value_info {
+    name: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_LogSM_NCD"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  value_info {
+    name: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_shape"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  value_info {
+    name: "Func_SoftmaxCrossEntropyLoss0x7ffe150df640X_Log"
+    type {
+      tensor_type {
+        elem_type: 1
+      }
+    }
+  }
 }
 opset_import {
   version: 13
diff --git a/ngraph/test/onnx/onnx_editor.cpp b/ngraph/test/onnx/onnx_editor.cpp
index 12ac45826c4870..69521c64a79b68 100644
--- a/ngraph/test/onnx/onnx_editor.cpp
+++ b/ngraph/test/onnx/onnx_editor.cpp
@@ -19,8 +19,8 @@
 
 NGRAPH_SUPPRESS_DEPRECATED_START
 
-using namespace ngraph;
-using namespace onnx_editor;
+using namespace ov;
+using namespace ov::onnx_editor;
 using namespace ngraph::test;
 
 static std::string s_manifest = "${MANIFEST}";
@@ -35,7 +35,7 @@ InputTypePred element_type_is(const element::Type et) {
     };
 }
 
-std::shared_ptr<op::Parameter> find_input(const ParameterVector& inputs, const std::string& name) {
+std::shared_ptr<op::v0::Parameter> find_input(const ParameterVector& inputs, const std::string& name) {
     const auto input_pos =
         std::find_if(std::begin(inputs), std::end(inputs), [&name](const ParameterVector::value_type i) {
             return i->get_friendly_name() == name;
@@ -47,7 +47,7 @@ std::shared_ptr<op::Parameter> find_input(const ParameterVector& inputs, const s
 
 NGRAPH_TEST(onnx_editor, types__single_input_type_substitution) {
     // the original model contains 2 inputs with i64 data type and one f32 input
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
 
     editor.set_input_types({{"A", element::i64}});
 
@@ -68,7 +68,7 @@ NGRAPH_TEST(onnx_editor, types__single_input_type_substitution) {
 
 NGRAPH_TEST(onnx_editor, types__all_inputs_type_substitution) {
     // the original model contains 2 inputs with i64 data type and one f32 input
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
 
     editor.set_input_types({{"A", element::i8}, {"B", element::i8}, {"C", element::i8}});
 
@@ -87,34 +87,37 @@ NGRAPH_TEST(onnx_editor, types__all_inputs_type_substitution) {
 }
 
 NGRAPH_TEST(onnx_editor, types__missing_type_in_input_descriptor) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/invalid_input_no_type.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/invalid_input_no_type.onnx")};
 
     // input A doesn't have the "type" field in the model and so the data type cannot be modified
-    EXPECT_THROW(editor.set_input_types({{"A", element::f32}}), ngraph_error);
+    EXPECT_THROW(editor.set_input_types({{"A", element::f32}}), ngraph::ngraph_error);
 }
 
 NGRAPH_TEST(onnx_editor, types__missing_tensor_type_in_input_descriptor) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/invalid_input_no_tensor_type.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/invalid_input_no_tensor_type.onnx")};
 
     // input A doesn't have the "tensor_type" field in the model
-    EXPECT_THROW(editor.set_input_types({{"A", element::f32}}), ngraph_error);
+    EXPECT_THROW(editor.set_input_types({{"A", element::f32}}), ngraph::ngraph_error);
 }
 
 NGRAPH_TEST(onnx_editor, types__unsupported_data_type_passed) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
 
-    EXPECT_THROW(editor.set_input_types({{"A", element::dynamic}}), ngraph_error);
+    EXPECT_THROW(editor.set_input_types({{"A", element::dynamic}}), ngraph::ngraph_error);
 }
 
 NGRAPH_TEST(onnx_editor, types__incorrect_input_name_passed) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_abc.onnx")};
 
-    EXPECT_THROW(editor.set_input_types({{"ShiaLaBeouf", element::i64}}), ngraph_error);
+    EXPECT_THROW(editor.set_input_types({{"ShiaLaBeouf", element::i64}}), ngraph::ngraph_error);
 }
 
 NGRAPH_TEST(onnx_editor, types__elem_type_missing_in_input) {
     // the original model contains 2 inputs with i64 data type and one f32 input
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/elem_type_missing_in_input.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/elem_type_missing_in_input.onnx")};
 
     // the "elem_type" is missing in the model but it should be possible to set the type anyway
     EXPECT_NO_THROW(editor.set_input_types({{"A", element::i64}}));
@@ -133,7 +136,8 @@ NGRAPH_TEST(onnx_editor, types__elem_type_missing_in_input) {
 }
 
 NGRAPH_TEST(onnx_editor, shapes__modify_single_input) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
 
     const auto new_shape = PartialShape{1};
 
@@ -146,7 +150,8 @@ NGRAPH_TEST(onnx_editor, shapes__modify_single_input) {
 }
 
 NGRAPH_TEST(onnx_editor, shapes__modify_all_inputs) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
 
     const auto new_shape = PartialShape{1, 2, 3, 5, 8, 13};
 
@@ -162,7 +167,7 @@ NGRAPH_TEST(onnx_editor, shapes__modify_all_inputs) {
 
 NGRAPH_TEST(onnx_editor, shapes__dynamic_rank_in_model) {
     ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__dynamic_rank_in_model.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__dynamic_rank_in_model.onnx")};
 
     // input A in the model doesn't have the "shape" field meaning it has dynamic rank
     // it should still be possible to set such input's shape to some custom value
@@ -176,7 +181,8 @@ NGRAPH_TEST(onnx_editor, shapes__dynamic_rank_in_model) {
 }
 
 NGRAPH_TEST(onnx_editor, shapes__set_dynamic_dimension) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
 
     const auto new_shape = PartialShape{Dimension::dynamic()};
 
@@ -189,7 +195,8 @@ NGRAPH_TEST(onnx_editor, shapes__set_dynamic_dimension) {
 }
 
 NGRAPH_TEST(onnx_editor, shapes__set_mixed_dimensions) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
 
     const auto new_shape_A = PartialShape{21, Dimension::dynamic()};
     const auto new_shape_B = PartialShape{Dimension::dynamic(), 37};
@@ -207,7 +214,8 @@ NGRAPH_TEST(onnx_editor, shapes__set_mixed_dimensions) {
 }
 
 NGRAPH_TEST(onnx_editor, shapes__set_scalar_inputs) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
 
     const auto new_shape = PartialShape{};
 
@@ -224,7 +232,8 @@ NGRAPH_TEST(onnx_editor, shapes__set_scalar_inputs) {
 }
 
 NGRAPH_TEST(onnx_editor, shapes__static_to_dynamic_rank_substitution) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/shapes__add_two_inputs.onnx")};
 
     const auto new_shape = PartialShape::dynamic();
 
@@ -239,12 +248,14 @@ NGRAPH_TEST(onnx_editor, shapes__static_to_dynamic_rank_substitution) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__linear_model_head_cut) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     editor.cut_graph_fragment({{InputEdge(1, 0)}}, {});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__linear_model_head_cut.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__linear_model_head_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -252,13 +263,15 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_head_cut) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__linear_model_head_cut_ins_and_outs) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     editor.cut_graph_fragment({{InputEdge(1, 0)}}, {{OutputEdge(2, 0)}});
 
     // expected to behave the same way as subgraph__linear_model_head_cut
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__linear_model_head_cut.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__linear_model_head_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -266,12 +279,14 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_head_cut_ins_and_outs) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__linear_model_deeper_head_cut) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     editor.cut_graph_fragment({{InputEdge(2, 0)}}, {});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__linear_model_deeper_head_cut.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__linear_model_deeper_head_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -279,12 +294,14 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_deeper_head_cut) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__linear_model_tail_cut) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     editor.cut_graph_fragment({}, {{OutputEdge{1, 0}}});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__linear_model_tail_cut.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__linear_model_tail_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -292,13 +309,15 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_tail_cut) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__linear_model_tail_cut_ins_and_outs) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{0, 0}}}, {{OutputEdge{1, 0}}});
 
     // expected to behave the same way as subgraph__linear_model_tail_cut
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__linear_model_tail_cut.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__linear_model_tail_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -307,13 +326,14 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_tail_cut_ins_and_outs) {
 
 NGRAPH_TEST(onnx_editor, subgraph__linear_model_with_initializer_tail_cut) {
     ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head_with_initializer.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/subgraph__inception_head_with_initializer.onnx")};
 
     editor.cut_graph_fragment({}, {{OutputEdge{1, 0}}});
 
-    const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO,
-                             "onnx/model_editor/reference/subgraph__linear_model_with_initializer_tail_cut.onnx");
+    const auto ref_model = ngraph::file_util::path_join(
+        SERIALIZED_ZOO,
+        "onnx/model_editor/reference/subgraph__linear_model_with_initializer_tail_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -322,13 +342,14 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_with_initializer_tail_cut) {
 
 NGRAPH_TEST(onnx_editor, subgraph__initializer_without_matching_input_tail_cut) {
     ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__initializer_without_matching_input.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/subgraph__initializer_without_matching_input.onnx")};
 
     editor.cut_graph_fragment({}, {{OutputEdge{1, 0}}});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__initializer_without_matching_input_tail_cut.onnx");
+    const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                                        "onnx/model_editor/reference/"
+                                                        "subgraph__initializer_without_matching_input_tail_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -336,12 +357,14 @@ NGRAPH_TEST(onnx_editor, subgraph__initializer_without_matching_input_tail_cut)
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__linear_model_deeper_tail_cut) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     editor.cut_graph_fragment({}, {{OutputEdge{0, 0}}});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__linear_model_deeper_tail_cut.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__linear_model_deeper_tail_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -349,7 +372,8 @@ NGRAPH_TEST(onnx_editor, subgraph__linear_model_deeper_tail_cut) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__no_input_params) {
-    const auto model_path = file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
+    const auto model_path =
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
 
     ONNXModelEditor editor{model_path};
 
@@ -362,13 +386,14 @@ NGRAPH_TEST(onnx_editor, subgraph__no_input_params) {
 
 NGRAPH_TEST(onnx_editor, subgraph__initializer_to_input_replacement) {
     ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head_with_initializer.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/subgraph__inception_head_with_initializer.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{0, 2}}}, {{OutputEdge{0, 0}}});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO,
-                             "onnx/model_editor/reference/subgraph__initializer_to_input_replacement.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__initializer_to_input_replacement.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -377,13 +402,14 @@ NGRAPH_TEST(onnx_editor, subgraph__initializer_to_input_replacement) {
 
 NGRAPH_TEST(onnx_editor, subgraph__initializer_to_input_replacement_2) {
     ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__initializer_without_matching_input.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/subgraph__initializer_without_matching_input.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{0, 2}}}, {{OutputEdge{0, 0}}});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO,
-                             "onnx/model_editor/reference/subgraph__initializer_to_input_replacement.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__initializer_to_input_replacement.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -391,12 +417,14 @@ NGRAPH_TEST(onnx_editor, subgraph__initializer_to_input_replacement_2) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__multiout_op_output_edge) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({}, {{OutputEdge{5, 1}}});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__multiout_op_output_edge.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__multiout_op_output_edge.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -404,13 +432,14 @@ NGRAPH_TEST(onnx_editor, subgraph__multiout_op_output_edge) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__existing_inputs_and_outputs_based_extraction) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{1, 1}, InputEdge{2, 0}}}, {{OutputEdge{4, 0}}});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__existing_inputs_and_outputs_based_extraction.onnx");
+    const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                                        "onnx/model_editor/reference/"
+                                                        "subgraph__existing_inputs_and_outputs_based_extraction.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -418,13 +447,14 @@ NGRAPH_TEST(onnx_editor, subgraph__existing_inputs_and_outputs_based_extraction)
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__twice_input_edge_from_tensor_with_single_consumer) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
 
     editor.cut_graph_fragment({InputEdge{1, 1}}, {});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__twice_input_edge_from_tensor_with_single_consumer.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__twice_input_edge_from_tensor_with_single_consumer.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -432,13 +462,15 @@ NGRAPH_TEST(onnx_editor, subgraph__twice_input_edge_from_tensor_with_single_cons
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumers) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{1, 0}, InputEdge{6, 0}}}, {{OutputEdge{6, 0}, OutputEdge{4, 0}}});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__input_edge_from_tensor_with_multiple_consumers.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__input_edge_from_tensor_with_multiple_consumers.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -446,13 +478,15 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumers_2) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{3, 0}, InputEdge{3, 1}}}, {{OutputEdge{3, 0}, OutputEdge{4, 0}}});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__input_edge_from_tensor_with_multiple_consumers_2.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__input_edge_from_tensor_with_multiple_consumers_2.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -460,13 +494,15 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumers_3) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{3, 0}, InputEdge{6, 0}}}, {{OutputEdge{6, 0}, OutputEdge{5, 1}}});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__input_edge_from_tensor_with_multiple_consumers_3.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__input_edge_from_tensor_with_multiple_consumers_3.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -474,13 +510,15 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumers_4) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{1, 0}, InputEdge{3, 0}}}, {});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__input_edge_from_tensor_with_multiple_consumers_4.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__input_edge_from_tensor_with_multiple_consumers_4.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -488,14 +526,16 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumers_5) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({InputEdge{3, 0}}, {{OutputEdge{6, 0}, OutputEdge{5, 1}}});
 
     // expected to behave the same way as the test above
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__input_edge_from_tensor_with_multiple_consumers_5.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__input_edge_from_tensor_with_multiple_consumers_5.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -503,15 +543,16 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumers_custom_names) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{1, 0, "new_name_1"}, InputEdge{6, 0, "new_name_2"}}},
                               {{OutputEdge{6, 0}, OutputEdge{4, 0}}});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO,
-                             "onnx/model_editor/reference/"
-                             "subgraph__input_edge_from_tensor_with_multiple_consumers_custom_names.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__input_edge_from_tensor_with_multiple_consumers_custom_names.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -519,13 +560,14 @@ NGRAPH_TEST(onnx_editor, subgraph__input_edge_from_tensor_with_multiple_consumer
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_input_relu2) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{4, 0}}}, {});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__multiple_consumers_of_graph_input_relu2.onnx");
+    const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                                        "onnx/model_editor/reference/"
+                                                        "subgraph__multiple_consumers_of_graph_input_relu2.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -533,13 +575,14 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_input_relu2) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{2, 0}}}, {});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__multiple_consumers_of_graph_initializer.onnx");
+    const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                                        "onnx/model_editor/reference/"
+                                                        "subgraph__multiple_consumers_of_graph_initializer.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -547,14 +590,15 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer_2) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{2, 0}, InputEdge{3, 0}}}, {});
 
     // same as above
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__multiple_consumers_of_graph_initializer.onnx");
+    const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                                        "onnx/model_editor/reference/"
+                                                        "subgraph__multiple_consumers_of_graph_initializer.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -562,14 +606,15 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer_2) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer_relu2_and_init) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{5, 0}, InputEdge{3, 0}}}, {});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO,
-                             "onnx/model_editor/reference/"
-                             "subgraph__multiple_consumers_of_graph_initializer_relu2_and_init.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__multiple_consumers_of_graph_initializer_relu2_and_init.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -577,7 +622,8 @@ NGRAPH_TEST(onnx_editor, subgraph__multiple_consumers_of_graph_initializer_relu2
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__invalid_edge_idx) {
-    const auto model_path = file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
+    const auto model_path =
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
 
     ONNXModelEditor editor{model_path};
     try {
@@ -590,7 +636,8 @@ NGRAPH_TEST(onnx_editor, subgraph__invalid_edge_idx) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__invalid_port_idx) {
-    const auto model_path = file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
+    const auto model_path =
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
 
     ONNXModelEditor editor{model_path};
     try {
@@ -602,7 +649,8 @@ NGRAPH_TEST(onnx_editor, subgraph__invalid_port_idx) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__inputs_getter) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     EXPECT_EQ(editor.model_inputs(), (std::vector<std::string>{"data_0", "conv1/7x7_s2_w_0", "conv1/7x7_s2_b_0"}));
 
@@ -612,7 +660,8 @@ NGRAPH_TEST(onnx_editor, subgraph__inputs_getter) {
 }
 
 NGRAPH_TEST(onnx_editor, subgraph__custom_input_name_already_exist) {
-    const auto model_path = file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
+    const auto model_path =
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx");
 
     ONNXModelEditor editor{model_path};
     try {
@@ -627,7 +676,8 @@ NGRAPH_TEST(onnx_editor, subgraph__custom_input_name_already_exist) {
 // HIGHT LEVEL API TESTS
 // INPUT EDGES TEST
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_output_name_and_input_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     const InputEdge edge =
         editor.find_input_edge(EditorNode{EditorOutput{"conv1/7x7_s2_2"}}, EditorInput{"conv1/7x7_s2_1"});
@@ -640,7 +690,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_output_name_and_input_n
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_output_name_and_input_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     const InputEdge edge = editor.find_input_edge(EditorNode{EditorOutput{"conv1/7x7_s2_2"}}, EditorInput{0});
     EXPECT_EQ(edge.m_node_idx, 1);
@@ -656,7 +707,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_output_name_and_input_i
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_name_and_input_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     const InputEdge edge = editor.find_input_edge(EditorNode{"relu1"}, EditorInput{"conv1/7x7_s2_1"});
     EXPECT_EQ(edge.m_node_idx, 1);
@@ -668,7 +720,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_name_and_input_nam
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_name_and_input_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const InputEdge edge = editor.find_input_edge(EditorNode{"relu1_name"}, EditorInput{0});
     EXPECT_EQ(edge.m_node_idx, 0);
@@ -680,7 +733,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_name_and_input_ind
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_name_and_input_index_custom_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const InputEdge edge = editor.find_input_edge(EditorNode{"relu1_name"}, EditorInput{0, "custom_input_name_1"});
     EXPECT_EQ(edge.m_node_idx, 0);
@@ -694,7 +748,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_name_and_input_ind
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const InputEdge edge = editor.find_input_edge(EditorNode{0}, EditorInput{0, "custom_input_name_1"});
     EXPECT_EQ(edge.m_node_idx, 0);
@@ -714,7 +769,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_node_index) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_empty_node_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     try {
         editor.find_input_edge(EditorNode{""}, EditorInput{"conv1/7x7_s2_1"});
@@ -727,7 +783,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_empty_node_name) {
 
 // OUTPUT EDGES TEST
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_output_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const OutputEdge edge = editor.find_output_edge(EditorNode{EditorOutput{"mul2"}}, EditorOutput{"mul2"});
     EXPECT_EQ(edge.m_node_idx, 4);
@@ -748,7 +805,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_output_name) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_output_name_and_output_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const OutputEdge edge = editor.find_output_edge(EditorNode{EditorOutput{"add2"}}, EditorOutput{0});
     EXPECT_EQ(edge.m_node_idx, 3);
@@ -764,7 +822,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_output_name_and_output
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_node_name_and_output_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const OutputEdge edge = editor.find_output_edge(EditorNode{"relu1_name"}, EditorOutput{"relu1"});
     EXPECT_EQ(edge.m_node_idx, 0);
@@ -776,7 +835,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_node_name_and_output_n
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_node_name_and_output_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const OutputEdge edge = editor.find_output_edge(EditorNode{"relu1_name"}, EditorOutput{0});
     EXPECT_EQ(edge.m_node_idx, 0);
@@ -788,7 +848,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_node_name_and_output_i
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_node_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const OutputEdge edge = editor.find_output_edge(EditorNode{5}, EditorOutput{1});
     EXPECT_EQ(edge.m_node_idx, 5);
@@ -803,7 +864,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_node_index) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_edge_const_network) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
     const InputEdge edge = editor.find_input_edge(EditorNode{EditorOutput{"relu4"}}, EditorInput{0});
     EXPECT_EQ(edge.m_node_idx, 3);
@@ -819,7 +881,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_edge_const_network) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_edge_error_handling) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
     // node with given output name not found
     try {
@@ -874,7 +937,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_edge_error_handling) {
 
 // Nodes with ambiguous node names tests
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_ambiguous_node_name_but_matched_input) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     InputEdge edge = editor.find_input_edge(EditorNode{"add_ambiguous_name"}, EditorInput{"in2"});
     EXPECT_EQ(edge.m_node_idx, 1);
@@ -886,7 +950,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_ambiguous_node_name_but
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_ambiguous_node_name_and_not_matched_input) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     try {
         editor.find_input_edge(EditorNode{"add_ambiguous_name"}, EditorInput{"in3"});
@@ -908,7 +973,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_ambiguous_node_name_and
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_ambiguous_node_name_and_input_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     try {
         editor.find_input_edge(EditorNode{"add_ambiguous_name"}, EditorInput{0});
@@ -921,7 +987,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_input_edge_by_ambiguous_node_name_and
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_ambiguous_node_name_but_matched_output) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const OutputEdge edge = editor.find_output_edge(EditorNode{"add_ambiguous_name"}, EditorOutput{"add1"});
     EXPECT_EQ(edge.m_node_idx, 1);
@@ -933,7 +1000,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_ambiguous_node_name_bu
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_the_same_node_name_and_output_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests_2.onnx")};
 
     const OutputEdge edge = editor.find_output_edge(EditorNode{"add1"}, EditorOutput{0});
     EXPECT_EQ(edge.m_node_idx, 0);
@@ -945,7 +1013,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_the_same_node_name_and
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_ambiguous_node_name_and_not_matched_output) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     try {
         editor.find_output_edge(EditorNode{"add_ambiguous_name"}, EditorOutput{"split2"});
@@ -957,7 +1026,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_ambiguous_node_name_an
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_ambiguous_node_name_and_output_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     try {
         editor.find_output_edge(EditorNode{"add_ambiguous_name"}, EditorOutput{0});
@@ -971,7 +1041,8 @@ NGRAPH_TEST(onnx_editor, editor_api_select_output_edge_by_ambiguous_node_name_an
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     // InputEdge{1, "in2"}
     const auto input_edge_1 = editor.find_input_edge(EditorNode(EditorOutput("add1")), EditorInput(1));
@@ -982,9 +1053,9 @@ NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter) {
     // OutputEdge{4, "mul2"}
     editor.cut_graph_fragment({input_edge_1, input_edge_2}, {output_edge});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__existing_inputs_and_outputs_based_extraction.onnx");
+    const auto ref_model = ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                                        "onnx/model_editor/reference/"
+                                                        "subgraph__existing_inputs_and_outputs_based_extraction.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -1005,7 +1076,8 @@ NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter_custom_names) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const auto input_edge_1 = editor.find_input_edge(EditorNode{EditorOutput{"mul2"}}, EditorInput{1, "new_name_1"});
     const auto input_edge_2 =
@@ -1013,9 +1085,10 @@ NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter_custom_nam
 
     editor.cut_graph_fragment({input_edge_1, input_edge_2}, {});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__use_edge_mapper_with_graph_cutter_custom_names.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__use_edge_mapper_with_graph_cutter_custom_names.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -1023,7 +1096,8 @@ NGRAPH_TEST(onnx_editor, editor_api_use_edge_mapper_with_graph_cutter_custom_nam
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_find_output_consumers) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     std::vector<InputEdge> output_consumers = editor.find_output_consumers("relu1");
     EXPECT_EQ(output_consumers.size(), 3);
@@ -1048,14 +1122,15 @@ NGRAPH_TEST(onnx_editor, editor_api_find_output_consumers) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_find_output_consumers_empty_result) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const std::vector<InputEdge> output_consumers = editor.find_output_consumers("not_existed");
     EXPECT_EQ(output_consumers.size(), 0);
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_inputs_with_the_same_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
 
     std::vector<InputEdge> output_consumers = editor.find_output_consumers("X");
     EXPECT_EQ(output_consumers[0].m_node_idx, 1);
@@ -1065,7 +1140,8 @@ NGRAPH_TEST(onnx_editor, editor_api_inputs_with_the_same_name) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_is_correct_and_unambiguous_node) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     bool is_correct_node = editor.is_correct_and_unambiguous_node(EditorNode{EditorOutput{"relu1"}});
     EXPECT_EQ(is_correct_node, true);
@@ -1096,7 +1172,8 @@ NGRAPH_TEST(onnx_editor, editor_api_is_correct_and_unambiguous_node) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_get_node_index) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_EQ(editor.get_node_index(EditorNode{2}), 2);
     EXPECT_EQ(editor.get_node_index(EditorNode{EditorOutput{"relu1"}}), 0);
@@ -1122,14 +1199,15 @@ NGRAPH_TEST(onnx_editor, editor_api_get_node_index) {
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_input_edge_from_tensor_with_single_consumer) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
 
     const auto edge = editor.find_input_edge(EditorNode{EditorOutput{"Y"}}, EditorInput{1});
     editor.cut_graph_fragment({edge}, {});
 
-    const auto ref_model = file_util::path_join(SERIALIZED_ZOO,
-                                                "onnx/model_editor/reference/"
-                                                "subgraph__twice_input_edge_from_tensor_with_single_consumer.onnx");
+    const auto ref_model =
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/"
+                                     "subgraph__twice_input_edge_from_tensor_with_single_consumer.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -1137,7 +1215,7 @@ NGRAPH_TEST(onnx_editor, editor_api_input_edge_from_tensor_with_single_consumer)
 }
 
 NGRAPH_TEST(onnx_editor, editor_api_input_edge_from_tensor_with_single_consumer_ambiguous) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_ab.onnx")};
 
     try {
         editor.find_input_edge(EditorNode{EditorOutput{"Y"}}, EditorInput{"X"});
@@ -1147,116 +1225,117 @@ NGRAPH_TEST(onnx_editor, editor_api_input_edge_from_tensor_with_single_consumer_
     }
 }
 
-using TestEngine = test::INTERPRETER_Engine;
+using TestEngine = ngraph::test::INTERPRETER_Engine;
 
 NGRAPH_TEST(onnx_editor, values__append_one_initializer) {
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D.onnx")};
+    onnx_editor::ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D.onnx")};
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
-    in_vals.emplace("A", op::Constant::create(element::i64, Shape{2}, {1, 2}));
+    in_vals.emplace("A", ngraph::op::Constant::create(element::i64, Shape{2}, {1, 2}));
     editor.set_input_values(in_vals);
 
     const auto function = editor.get_function();
-    auto test_case = test::TestCase<TestEngine>(function);
+    auto test_case = ngraph::test::TestCase<TestEngine>(function);
     test_case.add_input<int64_t>(Shape{2}, {5, 6});
     test_case.add_expected_output<int64_t>(Shape{2}, {6, 8});
     test_case.run();
 }
 
 NGRAPH_TEST(onnx_editor, values__append_two_initializers_to_invalid) {
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_invalid.onnx")};
+    onnx_editor::ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_invalid.onnx")};
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
-    in_vals.emplace("A", op::Constant::create(element::i64, Shape{2}, {4, 2}));
-    in_vals.emplace("B", op::Constant::create(element::i64, Shape{2}, {1, 3}));
+    in_vals.emplace("A", ngraph::op::Constant::create(element::i64, Shape{2}, {4, 2}));
+    in_vals.emplace("B", ngraph::op::Constant::create(element::i64, Shape{2}, {1, 3}));
     editor.set_input_values(in_vals);
 
     const auto function = editor.get_function();
-    auto test_case = test::TestCase<TestEngine>(function);
+    auto test_case = ngraph::test::TestCase<TestEngine>(function);
     test_case.add_expected_output<int64_t>(Shape{2}, {5, 5});
     test_case.run();
 }
 
 NGRAPH_TEST(onnx_editor, values__modify_one_initializer) {
     onnx_editor::ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_with_initializers.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_with_initializers.onnx")};
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
-    in_vals.emplace("B", op::Constant::create(element::i64, Shape{2}, {3, 4}));
+    in_vals.emplace("B", ngraph::op::Constant::create(element::i64, Shape{2}, {3, 4}));
     editor.set_input_values(in_vals);
 
     const auto function = editor.get_function();
-    auto test_case = test::TestCase<TestEngine>(function);
+    auto test_case = ngraph::test::TestCase<TestEngine>(function);
     test_case.add_expected_output<int64_t>(Shape{2}, {4, 6});
     test_case.run();
 }
 
 NGRAPH_TEST(onnx_editor, values__modify_two_initializers) {
     onnx_editor::ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_with_initializers.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_with_initializers.onnx")};
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
-    in_vals.emplace("A", op::Constant::create(element::i64, Shape{2}, {3, 6}));
-    in_vals.emplace("B", op::Constant::create(element::i64, Shape{2}, {2, 1}));
+    in_vals.emplace("A", ngraph::op::Constant::create(element::i64, Shape{2}, {3, 6}));
+    in_vals.emplace("B", ngraph::op::Constant::create(element::i64, Shape{2}, {2, 1}));
     editor.set_input_values(in_vals);
 
     const auto function = editor.get_function();
-    auto test_case = test::TestCase<TestEngine>(function);
+    auto test_case = ngraph::test::TestCase<TestEngine>(function);
     test_case.add_expected_output<int64_t>(Shape{2}, {5, 7});
     test_case.run();
 }
 
 NGRAPH_TEST(onnx_editor, values__no_inputs_modify_two_initializers) {
     onnx_editor::ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_with_initializers_only.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D_with_initializers_only.onnx")};
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
-    in_vals.emplace("A", op::Constant::create(element::i64, Shape{2}, {1, 2}));
-    in_vals.emplace("B", op::Constant::create(element::i64, Shape{2}, {11, 22}));
+    in_vals.emplace("A", ngraph::op::Constant::create(element::i64, Shape{2}, {1, 2}));
+    in_vals.emplace("B", ngraph::op::Constant::create(element::i64, Shape{2}, {11, 22}));
     editor.set_input_values(in_vals);
 
     const auto function = editor.get_function();
-    auto test_case = test::TestCase<TestEngine>(function);
+    auto test_case = ngraph::test::TestCase<TestEngine>(function);
     test_case.add_expected_output<int64_t>(Shape{2}, {12, 24});
     test_case.run();
 }
 
 NGRAPH_TEST(onnx_editor, values__append_two_initializers_change_shape_type) {
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D.onnx")};
+    onnx_editor::ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/add_1D.onnx")};
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
-    in_vals.emplace("A", op::Constant::create(element::i8, Shape{2, 1}, {-1, 1}));
-    in_vals.emplace("B", op::Constant::create(element::i8, Shape{2, 1}, {-2, 2}));
+    in_vals.emplace("A", ngraph::op::Constant::create(element::i8, Shape{2, 1}, {-1, 1}));
+    in_vals.emplace("B", ngraph::op::Constant::create(element::i8, Shape{2, 1}, {-2, 2}));
     editor.set_input_values(in_vals);
 
     const auto function = editor.get_function();
-    auto test_case = test::TestCase<TestEngine>(function);
+    auto test_case = ngraph::test::TestCase<TestEngine>(function);
     test_case.add_expected_output<int8_t>(Shape{2, 1}, {-3, 3});
     test_case.run();
 }
 
 NGRAPH_TEST(onnx_editor, values__append_two_initializers_mixed_types) {
     onnx_editor::ONNXModelEditor editor{
-        file_util::path_join(SERIALIZED_ZOO, "onnx/gather_elements_float_3D_axis_2.onnx")};
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/gather_elements_float_3D_axis_2.onnx")};
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
-    in_vals.emplace("data", op::Constant::create(element::i16, Shape{2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}));
-    in_vals.emplace("indices", op::Constant::create(element::i32, Shape{2, 2, 1}, {0, 1, 0, 1}));
+    in_vals.emplace("data", ngraph::op::Constant::create(element::i16, Shape{2, 2, 2}, {1, 2, 3, 4, 5, 6, 7, 8}));
+    in_vals.emplace("indices", ngraph::op::Constant::create(element::i32, Shape{2, 2, 1}, {0, 1, 0, 1}));
     editor.set_input_values(in_vals);
 
     const auto function = editor.get_function();
-    auto test_case = test::TestCase<TestEngine>(function);
+    auto test_case = ngraph::test::TestCase<TestEngine>(function);
     test_case.add_expected_output<int16_t>(Shape{2, 2, 1}, {1, 4, 5, 8});
     test_case.run();
 }
 
 NGRAPH_TEST(onnx_editor, read_model_from_stream) {
-    std::string path = file_util::path_join(SERIALIZED_ZOO, "onnx/external_data/external_data.onnx");
+    std::string path = ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/external_data/external_data.onnx");
     std::ifstream stream{path, std::ios::in | std::ios::binary};
     ASSERT_TRUE(stream.is_open());
     ONNXModelEditor editor{stream, path};
 
-    auto test_case = test::TestCase<TestEngine>(editor.get_function());
+    auto test_case = ngraph::test::TestCase<TestEngine>(editor.get_function());
     test_case.add_input<float>({1.f, 2.f, 3.f, 4.f});
     test_case.add_expected_output<float>(Shape{2, 2}, {3.f, 6.f, 9.f, 12.f});
 
@@ -1266,14 +1345,16 @@ NGRAPH_TEST(onnx_editor, read_model_from_stream) {
 }
 
 NGRAPH_TEST(onnx_editor, combined__cut_and_replace_shape) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph__inception_head.onnx")};
 
     const auto new_shape = PartialShape({1, 64, 112, 112});
     editor.cut_graph_fragment({{InputEdge(1, 0)}}, {});
     editor.set_input_shapes({{"conv1/7x7_s2_1", new_shape}});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/subgraph__linear_model_head_cut.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO,
+                                     "onnx/model_editor/reference/subgraph__linear_model_head_cut.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -1284,12 +1365,13 @@ NGRAPH_TEST(onnx_editor, combined__cut_and_replace_shape) {
 }
 
 NGRAPH_TEST(onnx_editor, cut_operator_with_no_schema) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/unknown_input_value_info.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/unknown_input_value_info.onnx")};
 
     editor.cut_graph_fragment({{InputEdge{1, 0}}}, {});
 
     const auto ref_model =
-        file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/unknown_input_value_info.onnx");
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/reference/unknown_input_value_info.onnx");
 
     const auto result = compare_onnx_models(editor.model_string(), ref_model);
 
@@ -1297,7 +1379,8 @@ NGRAPH_TEST(onnx_editor, cut_operator_with_no_schema) {
 }
 
 NGRAPH_TEST(onnx_editor, get_source_tensor_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_EQ(editor.get_source_tensor_name(InputEdge{0, 0}), "in1");
     EXPECT_EQ(editor.get_source_tensor_name(InputEdge{1, 0}), "relu1");
@@ -1310,7 +1393,8 @@ NGRAPH_TEST(onnx_editor, get_source_tensor_name) {
 }
 
 NGRAPH_TEST(onnx_editor, is_model_input) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_TRUE(editor.is_input(InputEdge{0, 0}));
     const auto edge1 = editor.find_input_edge(EditorOutput{"add1"}, 1);
@@ -1327,7 +1411,8 @@ NGRAPH_TEST(onnx_editor, is_model_input) {
 }
 
 NGRAPH_TEST(onnx_editor, get_target_tensor_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_EQ(editor.get_target_tensor_name(OutputEdge{0, 0}), "relu1");
     EXPECT_EQ(editor.get_target_tensor_name(OutputEdge{1, 0}), "add1");
@@ -1338,7 +1423,8 @@ NGRAPH_TEST(onnx_editor, get_target_tensor_name) {
 }
 
 NGRAPH_TEST(onnx_editor, is_model_output) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_TRUE(editor.is_output(OutputEdge{4, 0}));
     EXPECT_TRUE(editor.is_output(OutputEdge{5, 1}));
@@ -1353,28 +1439,31 @@ NGRAPH_TEST(onnx_editor, is_model_output) {
 }
 
 NGRAPH_TEST(onnx_editor, model_inputs) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const auto inputs = editor.model_inputs();
     EXPECT_TRUE(inputs == (std::vector<std::string>{"in1", "in2", "in3"}));  // in4 is initializer
 }
 
 NGRAPH_TEST(onnx_editor, model_inputs_with_non_input_initializers) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/instance_norm_dynamic.onnx")};
+    ONNXModelEditor editor{ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/instance_norm_dynamic.onnx")};
 
     const auto inputs = editor.model_inputs();
     EXPECT_TRUE(inputs == (std::vector<std::string>{"input"}));
 }
 
 NGRAPH_TEST(onnx_editor, model_output) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const auto outputs = editor.model_outputs();
     EXPECT_TRUE(outputs == (std::vector<std::string>{"mul1", "split2", "mul2"}));
 }
 
 NGRAPH_TEST(onnx_editor, get_tensor_shape) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_EQ(editor.get_tensor_shape("mul2"), (PartialShape{1, 1, 2, 2}));
     EXPECT_EQ(editor.get_tensor_shape("in1"), (PartialShape{2, 2}));
@@ -1391,7 +1480,8 @@ NGRAPH_TEST(onnx_editor, get_tensor_shape) {
 }
 
 NGRAPH_TEST(onnx_editor, get_tensor_shape_after_modification) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_EQ(editor.get_tensor_shape("in3"), (PartialShape{1, 1, 2, 2}));
     EXPECT_EQ(editor.get_tensor_shape("conv1"), (PartialShape{1, 1, 2, 2}));
@@ -1402,7 +1492,8 @@ NGRAPH_TEST(onnx_editor, get_tensor_shape_after_modification) {
 }
 
 NGRAPH_TEST(onnx_editor, is_correct_tensor_name) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     EXPECT_TRUE(editor.is_correct_tensor_name("in1"));
     EXPECT_TRUE(editor.is_correct_tensor_name("relu1"));
@@ -1416,7 +1507,8 @@ NGRAPH_TEST(onnx_editor, is_correct_tensor_name) {
 }
 
 NGRAPH_TEST(onnx_editor, get_input_ports) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const auto ports_1 = editor.get_input_ports(EditorNode{"relu1_name"});
     EXPECT_EQ(ports_1.size(), 1);
@@ -1447,7 +1539,8 @@ NGRAPH_TEST(onnx_editor, get_input_ports) {
     }
 }
 NGRAPH_TEST(onnx_editor, get_output_ports) {
-    ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
+    ONNXModelEditor editor{
+        ngraph::file_util::path_join(SERIALIZED_ZOO, "onnx/model_editor/subgraph_extraction_tests.onnx")};
 
     const auto ports_1 = editor.get_output_ports(EditorNode{"relu1_name"});
     EXPECT_EQ(ports_1.size(), 1);
diff --git a/ngraph/test/onnx/onnx_import.in.cpp b/ngraph/test/onnx/onnx_import.in.cpp
index af4a7f883d6412..8775d91e54df55 100644
--- a/ngraph/test/onnx/onnx_import.in.cpp
+++ b/ngraph/test/onnx/onnx_import.in.cpp
@@ -22,14 +22,16 @@
 #endif
 // clang-format on
 
-#include "onnx_import/core/null_node.hpp"
-#include "gtest/gtest.h"
-#include "onnx_import/onnx.hpp"
-#include "onnx_import/onnx_utils.hpp"
+#include <cpp/ie_cnn_network.h>
+
 #include "default_opset.hpp"
+#include "gtest/gtest.h"
 #include "ngraph/ngraph.hpp"
-#include "ngraph/pass/manager.hpp"
 #include "ngraph/pass/constant_folding.hpp"
+#include "ngraph/pass/manager.hpp"
+#include "onnx_import/core/null_node.hpp"
+#include "onnx_import/onnx.hpp"
+#include "onnx_import/onnx_utils.hpp"
 #include "util/all_close.hpp"
 #include "util/all_close_f.hpp"
 #include "util/ndarray.hpp"
@@ -38,7 +40,6 @@
 #include "engines_util/test_engines.hpp"
 #include "util/test_tools.hpp"
 #include "util/type_prop.hpp"
-#include <cpp/ie_cnn_network.h>
 
 NGRAPH_SUPPRESS_DEPRECATED_START
 
@@ -3674,6 +3675,155 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_inbounds) {
     test_case.run();
 }
 
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_no_min_no_max) {
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/clip_no_min_no_max.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<float> data{-1.6, -0.1, 10., 0., -10., 1.99, 2.015, 3.};
+
+    test_case.add_input<float>(data);
+
+    test_case.add_expected_output<float>(Shape{2, 4}, data);
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_no_min_no_max_inf) {
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/clip_no_min_no_max.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<float> data{std::numeric_limits<float>::infinity(),
+                                  -std::numeric_limits<float>::infinity(),
+                                  static_cast<float>(std::numeric_limits<double>::max()),
+                                  std::numeric_limits<float>::min(),
+                                  std::numeric_limits<float>::max(),
+                                  std::numeric_limits<float>::lowest(),
+                                  0,
+                                  -1};
+
+    const std::vector<float> expected_output{std::numeric_limits<float>::max(),
+                                             std::numeric_limits<float>::lowest(),
+                                             std::numeric_limits<float>::max(),
+                                             std::numeric_limits<float>::min(),
+                                             std::numeric_limits<float>::max(),
+                                             std::numeric_limits<float>::lowest(),
+                                             0,
+                                             -1};
+
+    test_case.add_input<float>(data);
+
+    test_case.add_expected_output<float>(Shape{2, 4}, expected_output);
+    test_case.run_with_tolerance_as_fp(0);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_no_min_set_max) {
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/clip_no_min_set_max.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<float> data{-1.6, -0.1, 10., 0., -10., 1.99, 2.015, 3.};
+    const std::vector<float> max_val{2.01};
+    const std::vector<float> output{-1.6, -0.1, 2.01, 0., -10., 1.99, 2.01, 2.01};
+
+    test_case.add_input<float>(data);
+    test_case.add_input<float>(max_val);
+
+    test_case.add_expected_output<float>(Shape{2, 4}, output);
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_set_min_no_max) {
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/clip_set_min_no_max.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<float> data{-1.6, -0.1, 10., 0., -10., 1.99, 2.015, 3.};
+    const std::vector<float> min_val{-1.59};
+    const std::vector<float> output{-1.59, -0.1, 10., 0., -1.59, 1.99, 2.015, 3.};
+
+    test_case.add_input<float>(data);
+    test_case.add_input<float>(min_val);
+
+    test_case.add_expected_output<float>(Shape{2, 4}, output);
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_no_min_no_max_int64) {
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/clip_no_min_no_max_int64.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<int64_t> data{INT64_MAX, INT64_MIN, INT32_MAX, INT32_MIN, 0, -1, 1, 0};
+
+    test_case.add_input<int64_t>(data);
+
+    test_case.add_expected_output<int64_t>(Shape{2, 4}, data);
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_no_min_set_max_int64) {
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/clip_no_min_set_max_int64.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<int64_t> data{INT64_MAX, INT64_MIN, INT32_MAX, INT32_MIN, 0, -1, 1, 0};
+    const std::vector<int64_t> max_val{INT32_MAX};
+    const std::vector<int64_t> output{INT32_MAX, INT64_MIN, INT32_MAX, INT32_MIN, 0, -1, 1, 0};
+
+    test_case.add_input<int64_t>(data);
+    test_case.add_input<int64_t>(max_val);
+
+    test_case.add_expected_output<int64_t>(Shape{2, 4}, output);
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_set_min_no_max_initializers) {
+    auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/clip_set_min_no_max_initializers.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<float> data{-1.6, -0.1, 10., 0., -10., 1.99, 2.015, 3.};
+    const std::vector<float> output{-1.59, -0.1, 10., 0., -1.59, 1.99, 2.015, 3.};
+
+    test_case.add_input<float>(data);
+
+    test_case.add_expected_output<float>(Shape{2, 4}, output);
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_set_min_set_max) {
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/clip_set_min_set_max.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<float> data{-1.6, -0.1, 10., 0., -10., 1.99, 2.015, 3.};
+    const std::vector<float> min_val{-1.59};
+    const std::vector<float> max_val{2.01};
+    const std::vector<float> output{-1.59, -0.1, 2.01, 0., -1.59, 1.99, 2.01, 2.01};
+
+    test_case.add_input<float>(data);
+    test_case.add_input<float>(min_val);
+    test_case.add_input<float>(max_val);
+
+    test_case.add_expected_output<float>(Shape{2, 4}, output);
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_clip_set_min_set_max_initializers) {
+    auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/clip_set_min_set_max_initializers.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    const std::vector<float> data{-1.6, -0.1, 10., 0., -10., 1.99, 2.015, 3.};
+    const std::vector<float> output{-1.59, -0.1, 2.01, 0., -1.59, 1.99, 2.01, 2.01};
+
+    test_case.add_input<float>(data);
+
+    test_case.add_expected_output<float>(Shape{2, 4}, output);
+    test_case.run();
+}
+
 NGRAPH_TEST(${BACKEND_NAME}, onnx_mvn_v6) {
     auto function = onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/mvn_v6.onnx"));
 
@@ -4165,14 +4315,79 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_float16_tensor_as_int32) {
     test_case.run();
 }
 
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_max_pool_3d) {
+    const auto function = onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_3d.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<int32_t>(Shape{1, 3, 3}, {-1, 0, 1, 20, -20, 10, 0, 2, 1});
+    test_case.add_expected_output<int32_t>(Shape{1, 3, 2}, {0, 1, 20, 10, 2, 2});
+    test_case.add_expected_output<int64_t>(Shape{1, 3, 2}, {1, 2, 3, 5, 7, 7});
+
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_max_pool_4d_ceil_mode) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_4d_ceil_mode.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<int32_t>(Shape{1, 1, 4, 4}, gen_range<int32_t>(16, 1));
+    test_case.add_expected_output<int32_t>(Shape{1, 1, 2, 2}, {11, 12, 15, 16});
+    test_case.add_expected_output<int64_t>(Shape{1, 1, 2, 2}, {10, 11, 14, 15});
+
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_max_pool_4d_dilations) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_4d_dilations.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<int32_t>(Shape{1, 1, 4, 4}, {9, 10, 11, 12, 1, 2, 3, 4, 16, 14, 15, 13, 5, 6, 8, 7});
+    test_case.add_expected_output<int32_t>(Shape{1, 1, 2, 2}, {16, 14, 8, 7});
+    test_case.add_expected_output<int64_t>(Shape{1, 1, 2, 2}, {8, 9, 14, 15});
+
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_max_pool_4d_strides) {
+    // kernel: 3x3
+    // strides: 3, 3
+    // explicit pads: 2, 2, 2, 2
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_4d_strides.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<int8_t>(Shape{1, 1, 5, 5}, gen_range<int8_t>(25, 1));
+    test_case.add_expected_output<int8_t>(Shape{1, 1, 3, 3}, {1, 4, 5, 16, 19, 20, 21, 24, 25});
+    test_case.add_expected_output<int64_t>(Shape{1, 1, 3, 3}, {0, 3, 4, 15, 18, 19, 20, 23, 24});
+
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_max_pool_4d_ceil_strides) {
+    // kernel: 3x3
+    // strides: 2, 2
+    // ceil_mode: 1
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_4d_ceil_strides.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(
+        Shape{1, 1, 4, 4},
+        {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f});
+    test_case.add_expected_output<float>(Shape{1, 1, 2, 2}, {11.0f, 12.0f, 15.0f, 16.0f});
+    test_case.add_expected_output<int64_t>(Shape{1, 1, 2, 2}, {10, 11, 14, 15});
+
+    test_case.run();
+}
+
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_random_uniform) {
     const auto function =
         onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/random_uniform.onnx"));
 
     auto test_case = test::TestCase<TestEngine>(function);
-    // These output values are unknown at this time as we don't have a reference implementation of random number
-    // generator
-    test_case.add_expected_output<ngraph::float16>(Shape{2, 2}, {41, 42, 43, 44});
+    test_case.add_expected_output<float>(Shape{2, 2}, {43.45518, 48.67585, 42.227386, 40.86294});
     test_case.run();
 }
 
@@ -4181,10 +4396,26 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_random_uniform_like) {
         onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/random_uniform_like.onnx"));
 
     auto test_case = test::TestCase<TestEngine>(function);
-    test_case.add_expected_output<ngraph::float16>(Shape{2, 2}, {0, 0, 0, 0});
+    test_case.add_input<float>(Shape{2, 2}, {41, 42, 43, 44});
+    test_case.add_expected_output<float>(Shape{2, 2}, {43.45518, 48.67585, 42.227386, 40.86294});
+    test_case.run();
+}
 
-    // These output values are unknown at this time as we don't have a reference implementation of random number
-    // generator
-    test_case.add_input<ngraph::float16>(Shape{2, 2}, {41, 42, 43, 44});
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_random_normal) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/random_normal.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_expected_output<float>(Shape{2, 2}, {13.459274, 41.75028, -19.311913, 131.79282});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_random_normal_like) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/random_normal_like.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{2, 2}, {0, 0, 0, 0});
+    test_case.add_expected_output<float>(Shape{2, 2}, {13.459274, 41.75028, -19.311913, 131.79282});
     test_case.run();
 }
diff --git a/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp b/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp
index 63611843a27dfd..5fb5999cf6c244 100644
--- a/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp
+++ b/ngraph/test/onnx/onnx_import_com_microsoft.in.cpp
@@ -25,13 +25,11 @@ using namespace ngraph;
 
 static std::string s_manifest = "${MANIFEST}";
 
-using Inputs = std::vector<std::vector<float>>;
-using Outputs = std::vector<std::vector<float>>;
-
 using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_bias_gelu) {
-    const auto function = onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/bias_gelu.onnx"));
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/bias_gelu.onnx"));
 
     auto test_case = test::TestCase<TestEngine>(function);
     test_case.add_input<float>({0.5488135,
@@ -52,7 +50,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_bias_gelu) {
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma_beta_bias) {
     const auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/skip_layer_normalization_with_gamma_beta_bias.onnx"));
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/skip_layer_normalization_with_gamma_beta_bias.onnx"));
 
     std::vector<float> input = {
         0.54881352, 0.71518934, 0.60276335, 0.54488319, 0.42365479, 0.64589411, 0.43758720, 0.89177299,
@@ -78,7 +76,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma_beta
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma_beta) {
     const auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/skip_layer_normalization_with_gamma_beta.onnx"));
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/skip_layer_normalization_with_gamma_beta.onnx"));
 
     std::vector<float> input = {
         0.54881352, 0.71518934, 0.60276335, 0.54488319, 0.42365479, 0.64589411, 0.43758720, 0.89177299,
@@ -104,7 +102,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma_beta
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma) {
     const auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/skip_layer_normalization_with_gamma.onnx"));
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/skip_layer_normalization_with_gamma.onnx"));
 
     std::vector<float> input = {
         0.54881352, 0.71518934, 0.60276335, 0.54488319, 0.42365479, 0.64589411, 0.43758720, 0.89177299,
@@ -130,7 +128,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_with_gamma) {
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_dynamic_shapes) {
     const auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/dynamic_shapes/skip_layer_normalization.onnx"));
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/skip_layer_normalization_dynamic_shapes.onnx"));
 
     std::vector<float> input = {
         0.54881352, 0.71518934, 0.60276335, 0.54488319, 0.42365479, 0.64589411, 0.43758720, 0.89177299,
@@ -177,8 +175,8 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_skip_layer_normalization_dynamic_shapes)
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization) {
-    const auto function =
-        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/embed_layer_normalization.onnx"));
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/embed_layer_normalization.onnx"));
 
     std::vector<int> input_ids = {
         8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
@@ -209,7 +207,8 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization) {
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_with_segment_embedding) {
     const auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/embed_layer_normalization_with_segment_embedding.onnx"));
+        file_util::path_join(SERIALIZED_ZOO,
+                             "onnx/com.microsoft/embed_layer_normalization_with_segment_embedding.onnx"));
 
     std::vector<int> input_ids = {
         8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
@@ -251,7 +250,8 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_with_segment_e
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_with_segment_embedding_and_mask) {
     const auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/embed_layer_normalization_with_segment_embedding_and_mask.onnx"));
+        file_util::path_join(SERIALIZED_ZOO,
+                             "onnx/com.microsoft/embed_layer_normalization_with_segment_embedding_and_mask.onnx"));
 
     std::vector<int> input_ids = {
         8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
@@ -296,7 +296,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_with_segment_e
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_dynamic_shapes) {
     const auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/dynamic_shapes/embed_layer_normalization_dynamic_shapes.onnx"));
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/embed_layer_normalization_dynamic_shapes.onnx"));
 
     std::vector<int> input_ids = {
         8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3, 8, 1, 3, 3, 3, 7, 0, 1, 9, 9,
@@ -389,3 +389,470 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_embed_layer_normalization_dynamic_shapes
     test_case.add_expected_output<int>(Shape{3}, expected_mask_index);
     test_case.run_with_tolerance_as_fp(1e-6);
 }
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.91475844, 0.91523546, 0.82536930, 0.37491974, 0.22384071, 0.05941105, 0.01902100, 0.70131350,
+        0.09603709, 0.44200060, 0.53106076, 0.79464376, 0.35469049, 0.25225943, 0.25179818, 0.29592562,
+        0.24836586, 0.65088797, 0.93126643, 0.67980725, 0.85708112, 0.59808528, 0.46321425, 0.19301885,
+    };
+    std::vector<float> output = {
+        0.07966283, 0.10783536, -0.19424979, 0.54514766, 0.07965867, 0.10783093, -0.19424866, 0.54510003,
+        0.07965846, 0.10783067, -0.19424550, 0.54509139, 0.07966217, 0.10783640, -0.19424903, 0.54512268,
+        0.06940663, 0.10962760, -0.19698445, 0.53492010, 0.06940675, 0.10962828, -0.19698484, 0.53492326,
+        0.06940714, 0.10963022, -0.19698712, 0.53494006, 0.06940673, 0.10962812, -0.19698519, 0.53492481,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_expected_output<float>(output);
+    test_case.run_with_tolerance_as_fp(1e-7);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_qkv_hidden_sizes) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_qkv_hidden_sizes.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.56477863, 0.60309958, 0.35158035, 0.03123519, 0.81918180, 0.76905495, 0.47219241, 0.72016627,
+        0.59377003, 0.91380632, 0.56797302, 0.34846428, 0.83839595, 0.16394103, 0.34676281, 0.09161621,
+        0.45562279, 0.23317528, 0.37197968, 0.06727808, 0.08500192, 0.84915495, 0.68266946, 0.00227691,
+    };
+    std::vector<float> output = {
+        -0.59370947, -0.30300471, 0.12048547, -0.09029539, 0.08041390, 0.10250041, -0.19381392, 0.55126983,
+        -0.59370828, -0.30301332, 0.12049319, -0.09029691, 0.08041921, 0.10250521, -0.19381438, 0.55127531,
+        -0.59370869, -0.30301058, 0.12049074, -0.09029643, 0.08041564, 0.10250199, -0.19381410, 0.55127168,
+        -0.59370929, -0.30300608, 0.12048667, -0.09029562, 0.08041184, 0.10249855, -0.19381374, 0.55126774,
+        -0.59681994, -0.26327702, 0.07638434, -0.06311120, 0.06671587, 0.10916986, -0.19412412, 0.51977092,
+        -0.59682053, -0.26328400, 0.07638102, -0.06311222, 0.06671817, 0.10917170, -0.19412397, 0.51977223,
+        -0.59682077, -0.26328647, 0.07637984, -0.06311259, 0.06671739, 0.10917108, -0.19412403, 0.51977175,
+        -0.59682101, -0.26328778, 0.07637922, -0.06311278, 0.06671065, 0.10916568, -0.19412443, 0.51976782,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_expected_output<float>(output);
+    test_case.run_with_tolerance_as_fp(1e-6);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_unidirectional) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_unidirectional.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.89578921, 0.42421508, 0.35630688, 0.77461642, 0.65753633, 0.09723099, 0.62597734, 0.72117692,
+        0.57636845, 0.17104276, 0.13245547, 0.59879875, 0.15624641, 0.44903454, 0.50483286, 0.92975074,
+        0.36934483, 0.29919949, 0.57185954, 0.83036488, 0.08384345, 0.20378476, 0.74684393, 0.46716982,
+    };
+    std::vector<float> output = {
+        0.05604819, 0.09000472, -0.19437021, 0.52487367, 0.06211422, 0.08740954, -0.19139624, 0.52762908,
+        0.06708897, 0.08992603, -0.19214047, 0.53631783, 0.06896879, 0.10248676, -0.19485690, 0.53477794,
+        0.08577005, 0.12807365, -0.19762954, 0.54432857, 0.06929274, 0.10893210, -0.19599904, 0.53184807,
+        0.07348281, 0.10215081, -0.19280069, 0.53552240, 0.07861833, 0.10517240, -0.19285706, 0.54126489,
+    };
+    std::vector<float> present = {
+        -0.60427380, -0.25958878, -0.59609234, -0.24055196, -0.59613681, -0.30088067, -0.59633607, -0.33270463,
+        0.06899665,  -0.09284544, 0.08059876,  -0.06146053, 0.11841078,  -0.10019838, 0.10605468,  -0.09273906,
+        -0.59036821, -0.32410735, -0.60532302, -0.25127757, -0.58926487, -0.25271094, -0.58640373, -0.31730092,
+        0.12509561,  -0.07968873, 0.06005794,  -0.08937149, 0.10523240,  -0.05083811, 0.14162725,  -0.07438751,
+        0.05604819,  0.09000472,  0.06819826,  0.08480665,  0.07700446,  0.09494394,  0.07459175,  0.14003153,
+        -0.19437021, 0.52487367,  -0.18843602, 0.53037173,  -0.19362189, 0.55360907,  -0.20299932, 0.53020388,
+        0.08577005,  0.12807365,  0.05276009,  0.08972625,  0.08190014,  0.08852972,  0.09400313,  0.11423884,
+        -0.19762954, 0.54432857,  -0.19435294, 0.51924801,  -0.18643703, 0.54280555,  -0.19302703, 0.55837619,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_expected_output<float>(output);
+    test_case.add_expected_output<float>(present);
+    test_case.run_with_tolerance_as_fp(1e-7);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_mask_index_1) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_mask_index_1.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.02841483, 0.47845092, 0.14633700, 0.54597300, 0.40160629, 0.55281311, 0.14931096, 0.64483738,
+        0.96559167, 0.05262021, 0.12391864, 0.20093553, 0.74290562, 0.19367455, 0.19253619, 0.41593507,
+        0.91188699, 0.61606920, 0.72673517, 0.86981291, 0.19963337, 0.22747350, 0.34308898, 0.57267183,
+    };
+    std::vector<int> mask_index = {
+        0,
+        1,
+    };
+    std::vector<float> output = {
+        0.08298690, 0.12711772, -0.19757506, 0.54029012, 0.08298548, 0.12711433, -0.19757731, 0.54031140,
+        0.08298430, 0.12711799, -0.19757695, 0.54031777, 0.08298548, 0.12711433, -0.19757444, 0.54028159,
+        0.05380550, 0.10459180, -0.19593412, 0.50907606, 0.05380550, 0.10459180, -0.19593412, 0.50907606,
+        0.05380550, 0.10459180, -0.19593412, 0.50907606, 0.05380550, 0.10459180, -0.19593412, 0.50907606,
+    };
+    std::vector<float> present = {
+        -0.58437425, -0.29483819, -0.59927911, -0.30336475, -0.59104657, -0.37327260, -0.59078789, -0.29863101,
+        0.11751597,  -0.04114649, 0.09933343,  -0.09884726, 0.16250694,  -0.12028439, 0.09319257,  -0.05129660,
+        -0.60341775, -0.25221461, -0.58933026, -0.31912822, -0.59271193, -0.25470981, -0.59399152, -0.32643768,
+        0.05398282,  -0.07468132, 0.14743008,  -0.09407346, 0.10399222,  -0.06682440, 0.11632499,  -0.08986320,
+        0.09104910,  0.12973849,  0.06917210,  0.11059431,  0.09356256,  0.12594685,  0.07814129,  0.14221822,
+        -0.19329809, 0.53526556,  -0.19787431, 0.53673857,  -0.20045389, 0.57165766,  -0.19869246, 0.51749766,
+        0.05380550,  0.10459180,  0.09169570,  0.09892380,  0.07746917,  0.08042616,  0.07953370,  0.12909687,
+        -0.19593412, 0.50907606,  -0.19202785, 0.56904894,  -0.18689045, 0.54643762,  -0.19969353, 0.53976399,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_input<int>(mask_index);
+    test_case.add_expected_output<float>(output);
+    test_case.add_expected_output<float>(present);
+    test_case.run_with_tolerance_as_fp();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_mask_index_2) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_mask_index_2.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.75259578, 0.81492645, 0.46713001, 0.29483622, 0.06768602, 0.95105755, 0.32065326, 0.52417183,
+        0.73136383, 0.77176476, 0.60997742, 0.64625764, 0.16311000, 0.89680773, 0.01331447, 0.42468646,
+        0.58711547, 0.00345124, 0.13053808, 0.46278623, 0.13786320, 0.65182054, 0.74864876, 0.81506181,
+    };
+    std::vector<int> mask_index = {
+        3,
+        3,
+        1,
+        1,
+    };
+    std::vector<float> output = {
+        0.07524174, 0.11320241, -0.19909523, 0.54785377, 0.06825337, 0.13981669, -0.20774621, 0.53718704,
+        0.07531278, 0.12957911, -0.20330518, 0.54547405, 0.07531209, 0.12958010, -0.20330583, 0.54547292,
+        0.08900890, 0.11150353, -0.18931937, 0.53757656, 0.07915881, 0.10416336, -0.18914750, 0.52921104,
+        0.08285815, 0.11462159, -0.19115375, 0.53077918, 0.08285838, 0.11462225, -0.19115454, 0.53077984,
+    };
+    std::vector<float> present = {
+        -0.59630549, -0.28110915, -0.60274345, -0.36154836, -0.59437746, -0.33717164, -0.60134649, -0.29849592,
+        0.11169122,  -0.09345293, 0.11103803,  -0.13096604, 0.13131849,  -0.10597084, 0.10463209,  -0.11332577,
+        -0.57949269, -0.27235535, -0.58941406, -0.25372508, -0.58658379, -0.28718373, -0.59821802, -0.32433146,
+        0.13244939,  -0.02865628, 0.09308393,  -0.04083736, 0.10948701,  -0.04423397, 0.13060363,  -0.12316251,
+        0.07509718,  0.08392500,  0.06825337,  0.13981669,  0.08239168,  0.11931328,  0.06770951,  0.09240761,
+        -0.19074154, 0.55260652,  -0.20774621, 0.53718704,  -0.19888818, 0.55371630,  -0.19559640, 0.54754448,
+        0.09983939,  0.10603377,  0.07915881,  0.10416336,  0.08655046,  0.12505992,  0.07738422,  0.09509270,
+        -0.18571433, 0.55095005,  -0.18914750, 0.52921104,  -0.19315663, 0.53234470,  -0.19601485, 0.56322992,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_input<int>(mask_index);
+    test_case.add_expected_output<float>(output);
+    test_case.add_expected_output<float>(present);
+    test_case.run_with_tolerance_as_fp();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_mask_index_3) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_mask_index_3.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.33093750, 0.39181390, 0.14586255, 0.39709702, 0.98086524, 0.03891133, 0.72234219, 0.21966648,
+        0.79986620, 0.97251678, 0.04131543, 0.43971965, 0.50185394, 0.11452501, 0.88111717, 0.76076663,
+        0.31870860, 0.54107893, 0.91756296, 0.58112669, 0.99117357, 0.00256292, 0.58885485, 0.93481058,
+    };
+    std::vector<int> mask = {
+        1,
+        1,
+        1,
+        0,
+        0,
+        0,
+        0,
+        1,
+    };
+    std::vector<float> output = {
+        0.07551830, 0.10666487, -0.19357042, 0.53683108, 0.07551410, 0.10666656, -0.19356072, 0.53684169,
+        0.07552745, 0.10666100, -0.19358172, 0.53682435, 0.07552218, 0.10666317, -0.19358677, 0.53681952,
+        0.09727416, 0.13513327, -0.20121223, 0.57003713, 0.09727416, 0.13513327, -0.20121223, 0.57003713,
+        0.09727416, 0.13513327, -0.20121223, 0.57003713, 0.09727416, 0.13513327, -0.20121223, 0.57003713,
+    };
+    std::vector<float> present = {
+        -0.59174627, -0.27471560, -0.58307797, -0.25967693, -0.60766846, -0.31754097, -0.61241394, -0.26291698,
+        0.09206123,  -0.05307099, 0.12491645,  -0.03853742, 0.08732655,  -0.13050151, 0.04073093,  -0.10792807,
+        -0.60556883, -0.34055573, -0.60474855, -0.28785610, -0.60757709, -0.32514900, -0.58872569, -0.37967020,
+        0.09779400,  -0.13136166, 0.07915612,  -0.10649752, 0.11043755,  -0.15124020, 0.16626491,  -0.11274654,
+        0.07639833,  0.11762549,  0.09370039,  0.09133558,  0.05661478,  0.11096847,  0.04019671,  0.10117501,
+        -0.19371650, 0.52530587,  -0.18429738, 0.55240726,  -0.20283231, 0.53265429,  -0.20036045, 0.50568837,
+        0.06171235,  0.12687264,  0.05802051,  0.10266830,  0.06172965,  0.08967118,  0.09727416,  0.13513327,
+        -0.20576829, 0.53365225,  -0.19832623, 0.52809310,  -0.19971462, 0.55584043,  -0.20121223, 0.57003713,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_input<int>(mask);
+    test_case.add_expected_output<float>(output);
+    test_case.add_expected_output<float>(present);
+    test_case.run_with_tolerance_as_fp(1e-7);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_mask_index_4) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_mask_index_4.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.23565151, 0.58627969, 0.75137484, 0.68586946, 0.62750375, 0.13284931, 0.13347220, 0.36357051,
+        0.56910241, 0.48275986, 0.49440190, 0.45483324, 0.63547862, 0.97893149, 0.40630588, 0.38783622,
+        0.07172249, 0.46385381, 0.99764502, 0.22219376, 0.67735291, 0.40799847, 0.74337566, 0.87263006,
+    };
+    std::vector<int> mask = {
+        1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
+    };
+    std::vector<float> output = {
+        0.07771622, 0.10724538, -0.19453585, 0.54342043, 0.07459468, 0.10934003, -0.19561143, 0.53936625,
+        0.07927690, 0.10619678, -0.19399606, 0.54543519, 0.07459468, 0.10934003, -0.19561143, 0.53936625,
+        0.05485561, 0.11278091, -0.20117569, 0.52096349, 0.06629646, 0.10195158, -0.19900991, 0.54654449,
+        0.06491723, 0.10292297, -0.19678673, 0.53451663, 0.06549793, 0.11126325, -0.19989857, 0.53717279,
+    };
+    std::vector<float> present = {
+        -0.59188855, -0.34495637, -0.59508181, -0.25013468, -0.59176934, -0.33229247, -0.59576762, -0.29731843,
+        0.14217430,  -0.10403840, 0.08584045,  -0.06193545, 0.12358667,  -0.08588549, 0.10515238,  -0.08629489,
+        -0.59092808, -0.28260738, -0.60047609, -0.30411413, -0.61210287, -0.28645760, -0.59391296, -0.34649473,
+        0.12789863,  -0.08159252, 0.08122411,  -0.08866425, 0.06395009,  -0.12896645, 0.14855847,  -0.11978809,
+        0.08783118,  0.12152332,  0.07067389,  0.09078297,  0.08385989,  0.13306075,  0.07459468,  0.10934003,
+        -0.19849420, 0.55928540,  -0.18948570, 0.53154731,  -0.19960676, 0.54237455,  -0.19561143, 0.53936625,
+        0.08509844,  0.08314656,  0.06388859,  0.12990499,  0.04582624,  0.09566365,  0.08674107,  0.10823163,
+        -0.18808734, 0.56137776,  -0.20168513, 0.51830697,  -0.20066255, 0.52363914,  -0.19737384, 0.56921995,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_input<int>(mask);
+    test_case.add_expected_output<float>(output);
+    test_case.add_expected_output<float>(present);
+    test_case.run_with_tolerance_as_fp(1e-7);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_past) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_past.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.82966000, 0.77751911, 0.08977074, 0.06076468, 0.40659550, 0.19995944, 0.55544919, 0.83971608,
+        0.86254036, 0.30894691, 0.80156928, 0.83092463, 0.14506543, 0.32196075, 0.42209163, 0.24465553,
+        0.93944097, 0.73528159, 0.23347616, 0.60544974, 0.93329269, 0.67604774, 0.56349903, 0.26199624,
+    };
+    std::vector<int> mask = {
+        1,
+        1,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        1,
+        0,
+        1,
+        0,
+        1,
+        0,
+        1,
+        1,
+        1,
+        1,
+    };
+    std::vector<float> past = {
+        0.92467678, 0.79873562, 0.00939191, 0.34891853, 0.35521412, 0.21872006, 0.89974332, 0.74132687, 0.73566031,
+        0.75168055, 0.06773245, 0.85702997, 0.76256698, 0.51739877, 0.91567177, 0.66617578, 0.88056499, 0.08436447,
+        0.54744655, 0.25466520, 0.08500137, 0.19271941, 0.86525357, 0.21717627, 0.97158766, 0.42288730, 0.09890039,
+        0.01148765, 0.97024685, 0.19697112, 0.67671591, 0.67960924, 0.46656516, 0.30850092, 0.73536104, 0.73938161,
+        0.91650903, 0.57628596, 0.51164514, 0.11695814, 0.79792547, 0.97192264, 0.29246020, 0.41030061, 0.19014873,
+        0.90233624, 0.84986305, 0.26141909, 0.84528726, 0.81416380, 0.00429944, 0.31476986, 0.00440918, 0.77413058,
+        0.13409913, 0.20965169, 0.61764991, 0.55266041, 0.56107825, 0.42051074, 0.16804738, 0.80362344, 0.52392679,
+        0.27550557, 0.66738850, 0.39348483, 0.31801429, 0.30325863, 0.37068403, 0.92767614, 0.60799408, 0.01458820,
+        0.24194679, 0.59596598, 0.81762302, 0.38094005, 0.16618672, 0.92488551, 0.84298438, 0.21752745,
+    };
+    std::vector<float> output = {
+        0.26186451, 0.45950246, -0.04001215, 0.47680017, 0.26333901, 0.46158865, -0.04006424, 0.47588652,
+        0.26875457, 0.47031689, -0.03951600, 0.47674999, 0.26851410, 0.46987134, -0.03919901, 0.47629333,
+        0.18083976, 0.16579385, -0.05161894, 0.63075018, 0.18228555, 0.16642828, -0.04873618, 0.63316816,
+        0.18362364, 0.16702136, -0.05045432, 0.63178891, 0.18000112, 0.16541445, -0.05139139, 0.63105792,
+    };
+    std::vector<float> present = {
+        0.92467678,  0.79873562,  0.00939191,  0.34891853,  0.35521412,  0.21872006,  0.89974332,  0.74132687,
+        0.73566031,  0.75168055,  -0.59527576, -0.23625080, -0.58657664, -0.29827437, -0.59528387, -0.33578828,
+        -0.59068960, -0.34870598, 0.06773245,  0.85702997,  0.76256698,  0.51739877,  0.91567177,  0.66617578,
+        0.88056499,  0.08436447,  0.54744655,  0.25466520,  0.08536442,  -0.06134639, 0.11295843,  -0.04818217,
+        0.14562836,  -0.12305059, 0.15695867,  -0.11161390, 0.08500137,  0.19271941,  0.86525357,  0.21717627,
+        0.97158766,  0.42288730,  0.09890039,  0.01148765,  0.97024685,  0.19697112,  -0.59141791, -0.31600696,
+        -0.58647990, -0.34302223, -0.59306550, -0.36427227, -0.59695083, -0.26431620, 0.67671591,  0.67960924,
+        0.46656516,  0.30850092,  0.73536104,  0.73938161,  0.91650903,  0.57628596,  0.51164514,  0.11695814,
+        0.11255538,  -0.07302766, 0.16620418,  -0.09871224, 0.15272795,  -0.12076923, 0.08827571,  -0.07442430,
+        0.79792547,  0.97192264,  0.29246020,  0.41030061,  0.19014873,  0.90233624,  0.84986305,  0.26141909,
+        0.84528726,  0.81416380,  0.07014155,  0.07749540,  0.08745074,  0.13131952,  0.08430066,  0.09709007,
+        0.09247591,  0.11065811,  0.00429944,  0.31476986,  0.00440918,  0.77413058,  0.13409913,  0.20965169,
+        0.61764991,  0.55266041,  0.56107825,  0.42051074,  -0.18658412, 0.53568852,  -0.19482780, 0.53271860,
+        -0.19558203, 0.57155901,  -0.19633618, 0.57260245,  0.16804738,  0.80362344,  0.52392679,  0.27550557,
+        0.66738850,  0.39348483,  0.31801429,  0.30325863,  0.37068403,  0.92767614,  0.08172131,  0.13249113,
+        0.09947956,  0.10781212,  0.08890627,  0.12280971,  0.06911418,  0.09499176,  0.60799408,  0.01458820,
+        0.24194679,  0.59596598,  0.81762302,  0.38094005,  0.16618672,  0.92488551,  0.84298438,  0.21752745,
+        -0.19839945, 0.53462923,  -0.19349247, 0.57778782,  -0.20039621, 0.56689924,  -0.19190890, 0.53286803,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_input<int>(mask);
+    test_case.add_input<float>(past);
+    test_case.add_expected_output<float>(output);
+    test_case.add_expected_output<float>(present);
+    test_case.run_with_tolerance_as_fp(1e-6);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_extra_add) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_extra_add.onnx"));
+    auto test_case = test::TestCase<TestEngine>(function);
+
+    std::vector<float> input = {
+        0.14930259, 0.11199699, 0.81292826, 0.08368169, 0.05704883, 0.41276145, 0.38760167, 0.00146112,
+        0.14275745, 0.54254925, 0.07962929, 0.31023681, 0.09597706, 0.60583973, 0.90233743, 0.33360451,
+        0.18193199, 0.19159532, 0.07869831, 0.86026299, 0.20683478, 0.40150928, 0.93124926, 0.31805834,
+    };
+    std::vector<int> mask = {
+        0,
+        0,
+        1,
+        0,
+        1,
+        1,
+        1,
+        0,
+    };
+    std::vector<float> extra_add = {
+        0.73230380, 0.61824518, 0.19738488, 0.57034588, 0.22331032, 0.53262889, 0.60098642, 0.72943515,
+        0.09009175, 0.81116527, 0.47240964, 0.49679127, 0.41110733, 0.29418564, 0.93818313, 0.64175284,
+        0.06807775, 0.66733366, 0.78848422, 0.48788327, 0.38806340, 0.14002480, 0.72263688, 0.22772972,
+        0.24000823, 0.75820386, 0.64254439, 0.19385594, 0.95595860, 0.59840417, 0.93769604, 0.62474734,
+        0.36690548, 0.76047903, 0.62352085, 0.58574778, 0.64251810, 0.78072041, 0.43344691, 0.75383639,
+        0.73950553, 0.92625278, 0.05066428, 0.08448382, 0.25980917, 0.50312829, 0.97800279, 0.05422170,
+        0.05171391, 0.82828254, 0.42234898, 0.95752198, 0.96325767, 0.97909677, 0.35578200, 0.48091716,
+        0.03637243, 0.91552693, 0.43403026, 0.94275808, 0.51182085, 0.86773109, 0.38459453, 0.87822068,
+    };
+    std::vector<float> output = {
+        0.06090815, 0.12919067, -0.19883196, 0.50295448, 0.06090815, 0.12919067, -0.19883196, 0.50295448,
+        0.06090815, 0.12919067, -0.19883196, 0.50295448, 0.06090815, 0.12919067, -0.19883196, 0.50295448,
+        0.08714182, 0.12259886, -0.19516067, 0.54010558, 0.08671370, 0.12369543, -0.19658084, 0.54502594,
+        0.08458151, 0.12488046, -0.19519810, 0.53906947, 0.09063499, 0.12088943, -0.19583938, 0.54266596,
+    };
+    std::vector<float> present = {
+        -0.59800303, -0.35666457, -0.59420627, -0.31881350, -0.59887993, -0.27025288, -0.60216135, -0.27772796,
+        0.11659990,  -0.11224300, 0.09693416,  -0.07304113, 0.06023501,  -0.05941332, 0.06434284,  -0.07978789,
+        -0.59005713, -0.37009716, -0.59542215, -0.27914333, -0.57998544, -0.29826957, -0.58625919, -0.28872511,
+        0.15994480,  -0.11288825, 0.07906821,  -0.05991337, 0.14479136,  -0.04415035, 0.13493451,  -0.06541853,
+        0.07513385,  0.14411135,  0.07505661,  0.14532046,  0.06090815,  0.12919067,  0.05788904,  0.12018456,
+        -0.20586906, 0.53715372,  -0.20203318, 0.52092510,  -0.19883196, 0.50295448,  -0.19937295, 0.51055026,
+        0.09417956,  0.12943678,  0.06923291,  0.12574309,  0.10221909,  0.11366953,  0.09235901,  0.09584601,
+        -0.20036517, 0.56818324,  -0.19709785, 0.51547027,  -0.18871340, 0.55736589,  -0.18826833, 0.55965197,
+    };
+
+    test_case.add_input<float>(input);
+    test_case.add_input<int>(mask);
+    test_case.add_input<float>(extra_add);
+    test_case.add_expected_output<float>(output);
+    test_case.add_expected_output<float>(present);
+    test_case.run_with_tolerance_as_fp(1e-7);
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_model_attention_dynamic_shapes) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/com.microsoft/attention_dynamic_shapes.onnx"));
+    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(function);
+
+    std::vector<float> input = {
+        0.42226878, 0.50984067, 0.80440795, 0.68040705, 0.93614250, 0.45104721, 0.71767306, 0.48596525,
+        0.70076728, 0.04500086, 0.28930107, 0.77435863, 0.19392140, 0.90290719, 0.91955870, 0.58811885,
+        0.76795286, 0.62884814, 0.23377730, 0.49212688, 0.87256873, 0.11944817, 0.57715887, 0.91886938,
+    };
+    std::vector<float> weights = {
+        0.99377930, 0.22733542, 0.43217131, 0.60717988, 0.97224706, 0.70020503, 0.92439449, 0.41512674, 0.47728160,
+        0.40306625, 0.72619593, 0.37954643, 0.36950976, 0.84305370, 0.61671126, 0.22251014, 0.73839295, 0.73471880,
+        0.37428924, 0.80240524, 0.23120961, 0.06072779, 0.92840081, 0.71558088, 0.08719950, 0.51666921, 0.53768843,
+        0.48113129, 0.46389169, 0.01036468, 0.37341005, 0.67195475, 0.53599644, 0.41795707, 0.58081782, 0.97939289,
+    };
+    std::vector<float> bias = {
+        0.77122736,
+        0.75600564,
+        0.86177206,
+        0.69982684,
+        0.74719858,
+        0.78054035,
+        0.80007398,
+        0.74902135,
+        0.81258053,
+        0.01575289,
+        0.08463049,
+        0.39671996,
+    };
+    std::vector<int> mask = {
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        1,
+        1,
+        0,
+        0,
+        1,
+        1,
+        0,
+        0,
+        0,
+        0,
+    };
+    std::vector<float> past = {
+        0.27759778, 0.18458818, 0.63114458, 0.09953160, 0.59739488, 0.63917851, 0.18828323, 0.65625650, 0.84574437,
+        0.91846281, 0.55102497, 0.27506110, 0.06816208, 0.82616585, 0.85912132, 0.88682729, 0.14730524, 0.61618829,
+        0.89891797, 0.27753425, 0.57438278, 0.33753166, 0.88768929, 0.35533753, 0.30193496, 0.81678063, 0.26569194,
+        0.62769043, 0.61990744, 0.59077013, 0.11058200, 0.97370809, 0.81339806, 0.57207322, 0.80417949, 0.54185718,
+        0.80831683, 0.29390740, 0.29051417, 0.51964313, 0.04341308, 0.05925354, 0.82397246, 0.55753845, 0.61247689,
+        0.98571628, 0.07566493, 0.37537411, 0.42080343, 0.21715857, 0.57869565, 0.55962265, 0.82500041, 0.60776925,
+        0.19367239, 0.88382334, 0.20328504, 0.58192456, 0.94542676, 0.98562658, 0.64355153, 0.69856495, 0.30377558,
+        0.02857198, 0.96969068, 0.48450547, 0.98341352, 0.03546083, 0.84963584, 0.94460547, 0.90907097, 0.22525074,
+        0.12530145, 0.52223104, 0.09549426, 0.93127102, 0.93429947, 0.01428344, 0.74249738, 0.22606593,
+    };
+    std::vector<float> output = {
+        1.47439122, 0.50951630, 1.17974961, 1.58501005, 1.49403512, 0.51560062, 1.18972027, 1.59668207,
+        1.48384988, 0.51248586, 1.18596375, 1.59219086, 1.44181466, 0.50219649, 1.15537691, 1.55348074,
+        0.83429223, 0.59521818, 0.87688094, 0.13611843, 0.82936716, 0.61004817, 0.87633312, 0.13887596,
+        0.83155584, 0.59382534, 0.87496555, 0.14041223, 0.83309680, 0.58982348, 0.87517864, 0.13930768,
+    };
+    std::vector<float> present = {
+        0.27759778, 0.18458818, 0.63114458, 0.09953160, 0.59739488, 0.63917851, 0.18828323, 0.65625650, 0.84574437,
+        0.91846281, 1.90736914, 1.45914197, 2.30920029, 1.94944119, 2.12886763, 1.64736962, 1.36378694, 1.03263116,
+        0.55102497, 0.27506110, 0.06816208, 0.82616585, 0.85912132, 0.88682729, 0.14730524, 0.61618829, 0.89891797,
+        0.27753425, 1.68161881, 1.87394094, 1.94785213, 2.08572555, 1.90705216, 1.90777159, 1.23910809, 1.52017307,
+        0.57438278, 0.33753166, 0.88768929, 0.35533753, 0.30193496, 0.81678063, 0.26569194, 0.62769043, 0.61990744,
+        0.59077013, 2.02901411, 1.58923888, 2.17776394, 1.76309133, 1.74264824, 1.31485105, 1.71575761, 1.29775190,
+        0.11058200, 0.97370809, 0.81339806, 0.57207322, 0.80417949, 0.54185718, 0.80831683, 0.29390740, 0.29051417,
+        0.51964313, 1.66065478, 2.17192268, 1.86598253, 2.03193212, 1.52620018, 1.82728052, 1.46963060, 1.87916136,
+        0.04341308, 0.05925354, 0.82397246, 0.55753845, 0.61247689, 0.98571628, 0.07566493, 0.37537411, 0.42080343,
+        0.21715857, 1.56316149, 0.55312467, 1.59553123, 0.53537023, 1.64308119, 0.62742490, 1.31600118, 0.37510848,
+        0.57869565, 0.55962265, 0.82500041, 0.60776925, 0.19367239, 0.88382334, 0.20328504, 0.58192456, 0.94542676,
+        0.98562658, 1.33183134, 1.70965421, 1.70983100, 1.76660407, 1.46399045, 1.70318413, 0.83565855, 1.37921953,
+        0.64355153, 0.69856495, 0.30377558, 0.02857198, 0.96969068, 0.48450547, 0.98341352, 0.03546083, 0.84963584,
+        0.94460547, 1.60677671, 0.53308368, 1.60789728, 0.56227136, 1.50563633, 0.50456268, 1.49554634, 0.48299593,
+        0.90907097, 0.22525074, 0.12530145, 0.52223104, 0.09549426, 0.93127102, 0.93429947, 0.01428344, 0.74249738,
+        0.22606593, 1.59781134, 2.01703453, 1.58993423, 1.78536010, 1.21809304, 1.69219351, 1.24090374, 1.75499403,
+    };
+
+    test_case.add_input<float>(Shape{2, 4, 3}, input);
+    test_case.add_input<float>(Shape{3, 12}, weights);
+    test_case.add_input<float>(Shape{12}, bias);
+    test_case.add_input<int>(Shape{2, 9}, mask);
+    test_case.add_input<float>(Shape{2, 2, 2, 5, 2}, past);
+    test_case.add_expected_output<float>(Shape{2, 4, 4}, output);
+    test_case.add_expected_output<float>(Shape{2, 2, 2, 9, 2}, present);
+    test_case.run_with_tolerance_as_fp(1e-6);
+}
diff --git a/ngraph/test/onnx/onnx_import_dyn_shapes.in.cpp b/ngraph/test/onnx/onnx_import_dyn_shapes.in.cpp
index df3184753be966..fdc4eab69bdb60 100644
--- a/ngraph/test/onnx/onnx_import_dyn_shapes.in.cpp
+++ b/ngraph/test/onnx/onnx_import_dyn_shapes.in.cpp
@@ -15,9 +15,9 @@
 #include <iterator>
 #include <numeric>
 
+#include "default_opset.hpp"
 #include "gtest/gtest.h"
 #include "ngraph/file_util.hpp"
-#include "default_opset.hpp"
 #include "onnx_import/onnx.hpp"
 #include "engines_util/test_engines.hpp"
 #include "engines_util/test_case.hpp"
@@ -333,10 +333,9 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_dyn_shapes_max_pool_with_indices_output) {
                                        25.f, 25.f, 23.f, 24.f, 25.f, 25.f, 25.f, 23.f, 24.f, 25.f, 25.f, 25.f};
     test_case.add_expected_output<float>(Shape{1, 1, 5, 5}, expected_values);
 
-    // indices output is not supported and is ingored in current implementation
-    // std::vector<int64_t> expected_indices{12, 13, 14, 14, 14, 17, 18, 19, 19, 19, 22, 23, 24, 24,
-    // 24, 22, 23, 24, 24, 24, 22, 23, 24, 24, 24};
-    // test_case.add_expected_output<float>(Shape{1, 1, 5, 5}, expected_indices);
+    std::vector<int64_t> expected_indices{12, 13, 14, 14, 14, 17, 18, 19, 19, 19, 22, 23, 24,
+                                          24, 24, 22, 23, 24, 24, 24, 22, 23, 24, 24, 24};
+    test_case.add_expected_output<int64_t>(Shape{1, 1, 5, 5}, expected_indices);
     test_case.run();
 }
 
diff --git a/ngraph/test/onnx/onnx_import_library.cpp b/ngraph/test/onnx/onnx_import_library.cpp
index 61df4cf84e3018..0968e59295798f 100644
--- a/ngraph/test/onnx/onnx_import_library.cpp
+++ b/ngraph/test/onnx/onnx_import_library.cpp
@@ -21,7 +21,7 @@ NGRAPH_TEST(onnx, check_ir_version_support) {
     //
     // The last step is to also update the details::onnx::contains_onnx_model_keys() function
     // in the same file to make sure that prototxt format validation also covers the changes in ONNX
-    EXPECT_EQ(ONNX_NAMESPACE::Version::IR_VERSION, 7)
+    EXPECT_EQ(ONNX_NAMESPACE::Version::IR_VERSION, 8)
         << "The IR_VERSION defined in ONNX does not match the version that OpenVINO supports. "
            "Please check the source code of this test for details and explanation how to proceed.";
 }
diff --git a/ngraph/test/onnx/onnx_import_with_editor.in.cpp b/ngraph/test/onnx/onnx_import_with_editor.in.cpp
index 88aef0c548a7d4..8f62f3228e1bfe 100644
--- a/ngraph/test/onnx/onnx_import_with_editor.in.cpp
+++ b/ngraph/test/onnx/onnx_import_with_editor.in.cpp
@@ -26,7 +26,7 @@ using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
 
 // ############################################################################ CORE TESTS
 NGRAPH_TEST(${BACKEND_NAME}, onnx_compress_axis_0) {
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_0.onnx")};
+    ov::onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_0.onnx")};
 
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
@@ -42,7 +42,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_compress_axis_0) {
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_compress_axis_1) {
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_1.onnx")};
+    ov::onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_1.onnx")};
 
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
@@ -58,7 +58,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_compress_axis_1) {
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_compress_default_axis) {
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_default_axis.onnx")};
+    ov::onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_default_axis.onnx")};
 
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
@@ -74,7 +74,7 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_compress_default_axis) {
 }
 
 NGRAPH_TEST(${BACKEND_NAME}, onnx_compress_negative_axis) {
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_negative_axis.onnx")};
+    ov::onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/compress_negative_axis.onnx")};
 
     std::map<std::string, std::shared_ptr<ngraph::op::Constant>> in_vals;
 
diff --git a/ngraph/test/onnx/onnx_test_utils.in.cpp b/ngraph/test/onnx/onnx_test_utils.in.cpp
index 40ce14c595636c..7ab1cb22882ad8 100644
--- a/ngraph/test/onnx/onnx_test_utils.in.cpp
+++ b/ngraph/test/onnx/onnx_test_utils.in.cpp
@@ -31,7 +31,7 @@ TYPED_TEST_P(ElemTypesTests, onnx_test_add_abc_set_precission) {
     using DataType = TypeParam;
     const element::Type ng_type = element::from<DataType>();
 
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/add_abc_3d.onnx")};
+    ov::onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/add_abc_3d.onnx")};
 
     editor.set_input_types({{"A", ng_type}, {"B", ng_type}, {"C", ng_type}});
 
@@ -48,7 +48,8 @@ TYPED_TEST_P(ElemTypesTests, onnx_test_split_multioutput_set_precission) {
     using DataType = TypeParam;
     const element::Type ng_type = element::from<DataType>();
 
-    onnx_editor::ONNXModelEditor editor{file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_default.onnx")};
+    ov::onnx_editor::ONNXModelEditor editor{
+        file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_default.onnx")};
 
     editor.set_input_types({{"input", ng_type}});
 
diff --git a/ngraph/test/onnx/onnx_transformations.cpp b/ngraph/test/onnx/onnx_transformations.cpp
index b9c400a498d060..7cd92624de136b 100644
--- a/ngraph/test/onnx/onnx_transformations.cpp
+++ b/ngraph/test/onnx/onnx_transformations.cpp
@@ -13,7 +13,7 @@ static std::string s_manifest = "${MANIFEST}";
 NGRAPH_SUPPRESS_DEPRECATED_START
 
 using namespace ngraph;
-using namespace onnx_editor;
+using namespace ov::onnx_editor;
 using namespace ngraph::test;
 
 namespace {
diff --git a/ngraph/test/opset.cpp b/ngraph/test/opset.cpp
index 90a106b1cbcf49..a4b16ac49aef87 100644
--- a/ngraph/test/opset.cpp
+++ b/ngraph/test/opset.cpp
@@ -196,17 +196,18 @@ TEST(opset, custom_opset) {
     ov::OpSet opset;
 #ifndef OPENVINO_STATIC_LIBRARY
     opset.insert<MyOpOld>();
-    opset.insert<MyOpNewFromOld>();
-    opset.insert<MyOpIncorrect>();
 #endif
+    opset.insert<MyOpIncorrect>();
+    opset.insert<MyOpNewFromOld>();
     opset.insert<MyOpNew>();
 #ifdef OPENVINO_STATIC_LIBRARY
-    ASSERT_EQ(opset.get_types_info().size(), 1);
+    EXPECT_EQ(opset.get_types_info().size(), 2);
 #else
-    ASSERT_EQ(opset.get_types_info().size(), 3);
+    EXPECT_EQ(opset.get_types_info().size(), 3);
+    EXPECT_TRUE(opset.contains_type("MyOpOld"));
+    // TODO: why is it not registered?
+    EXPECT_TRUE(opset.contains_type("MyOpNewFromOld"));
 #endif
-    ASSERT_TRUE(opset.contains_type("MyOpNew"));
-    ASSERT_TRUE(opset.contains_type("MyOpOld"));
-    ASSERT_TRUE(opset.contains_type("MyOpNewFromOld"));
-    ASSERT_FALSE(opset.contains_type("MyOpIncorrect"));
+    EXPECT_TRUE(opset.contains_type("MyOpNew"));
+    EXPECT_FALSE(opset.contains_type("MyOpIncorrect"));
 }
diff --git a/ngraph/test/partial_shape.cpp b/ngraph/test/partial_shape.cpp
index 75329b632afa7f..756f793ddcb6e9 100644
--- a/ngraph/test/partial_shape.cpp
+++ b/ngraph/test/partial_shape.cpp
@@ -9,6 +9,44 @@
 
 using namespace ngraph;
 
+TEST(partial_shape, interators) {
+    const PartialShape ps({1, 2, 3});
+    ASSERT_TRUE(ps.is_static());
+    {
+        auto p = ps;
+        for (auto& d : p) {
+            d = Dimension::dynamic();
+        }
+        ASSERT_TRUE(p.is_dynamic());
+    }
+    {
+        auto p = ps;
+        auto it = p.begin();
+        *it = Dimension::dynamic();
+        ASSERT_TRUE(p.is_dynamic());
+    }
+    {
+        auto p = ps;
+        auto it = p.rbegin();
+        *it = Dimension::dynamic();
+        ASSERT_TRUE(p.is_dynamic());
+    }
+    {
+        auto p = ps;
+        auto it = p.end();
+        --it;
+        *it = Dimension::dynamic();
+        ASSERT_TRUE(p.is_dynamic());
+    }
+    {
+        auto p = ps;
+        auto it = p.rend();
+        --it;
+        *it = Dimension::dynamic();
+        ASSERT_TRUE(p.is_dynamic());
+    }
+}
+
 TEST(partial_shape, ps_construction_empty) {
     auto ps = PartialShape{};
     ASSERT_TRUE(ps.rank().is_static());
diff --git a/ngraph/test/pass/serialization/read_ir.hpp b/ngraph/test/pass/serialization/read_ir.hpp
index fcc70e1a1e702a..c3d60935bf31e8 100644
--- a/ngraph/test/pass/serialization/read_ir.hpp
+++ b/ngraph/test/pass/serialization/read_ir.hpp
@@ -12,9 +12,9 @@ namespace ov {
 namespace test {
 
 inline std::shared_ptr<ov::Function> readModel(const std::string& model_path, const std::string& weights_path) {
-    static ngraph::frontend::FrontEndManager manager;
-    ngraph::frontend::FrontEnd::Ptr FE;
-    ngraph::frontend::InputModel::Ptr inputModel;
+    static ov::frontend::FrontEndManager manager;
+    ov::frontend::FrontEnd::Ptr FE;
+    ov::frontend::InputModel::Ptr inputModel;
 
     ov::VariantVector params{ov::make_variant(model_path)};
     if (!weights_path.empty())
@@ -31,9 +31,9 @@ inline std::shared_ptr<ov::Function> readModel(const std::string& model_path, co
 }
 
 inline std::shared_ptr<ov::Function> readModel(const std::string& model) {
-    static ngraph::frontend::FrontEndManager manager;
-    ngraph::frontend::FrontEnd::Ptr FE;
-    ngraph::frontend::InputModel::Ptr inputModel;
+    static ov::frontend::FrontEndManager manager;
+    ov::frontend::FrontEnd::Ptr FE;
+    ov::frontend::InputModel::Ptr inputModel;
     std::istringstream modelStringStream(model);
     std::istream& modelStream = modelStringStream;
 
diff --git a/ngraph/test/pass/serialization/serialize.cpp b/ngraph/test/pass/serialization/serialize.cpp
index 7b556a8f1cf38b..cac86facd02489 100644
--- a/ngraph/test/pass/serialization/serialize.cpp
+++ b/ngraph/test/pass/serialization/serialize.cpp
@@ -74,7 +74,8 @@ INSTANTIATE_TEST_SUITE_P(
                     std::make_tuple("conv_with_rt_info.xml", ""),
                     std::make_tuple("loop_2d_add.xml", "loop_2d_add.bin"),
                     std::make_tuple("nms5_dynamism.xml", "nms5_dynamism.bin"),
-                    std::make_tuple("if_diff_case.xml", "if_diff_case.bin")));
+                    std::make_tuple("if_diff_case.xml", "if_diff_case.bin"),
+                    std::make_tuple("if_body_without_parameters.xml", "if_body_without_parameters.bin")));
 
 #ifdef NGRAPH_ONNX_FRONTEND_ENABLE
 
diff --git a/ngraph/test/preprocess.cpp b/ngraph/test/preprocess.cpp
index 868a47723feb8a..aa532cd816476e 100644
--- a/ngraph/test/preprocess.cpp
+++ b/ngraph/test/preprocess.cpp
@@ -46,27 +46,35 @@ static std::shared_ptr<Function> create_n_inputs(element::Type type, const Parti
 
 TEST(pre_post_process, simple_mean_scale) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-    f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().mean(1.f).scale(2.f))).build(f);
+    f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().mean(1.f).scale(2.f))).build();
+    EXPECT_EQ(f->get_output_element_type(0), element::f32);
+}
+
+TEST(pre_post_process, simple_mean_scale_getters) {
+    auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
+    auto p = PrePostProcessor(f);
+    p.input("tensor_input1").preprocess().mean(1).scale(2);
+    f = p.build();
     EXPECT_EQ(f->get_output_element_type(0), element::f32);
 }
 
 TEST(pre_post_process, convert_element_type_and_scale) {
     auto f = create_simple_function(element::i8, Shape{1, 3, 2, 2});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_element_type(element::i16))
                        .preprocess(PreProcessSteps()
                                        .convert_element_type(element::f32)
                                        .scale(2.f)
                                        .convert_element_type(element::i8)))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::i16);
     EXPECT_EQ(f->get_output_element_type(0), element::i8);
 }
 
 TEST(pre_post_process, convert_element_type_implicit) {
     auto f = create_simple_function(element::i32, Shape{1, 3, 224, 224});
-    f = PrePostProcessor().input(InputInfo().tensor(InputTensorInfo().set_element_type(element::f32))).build(f);
+    f = PrePostProcessor(f).input(InputInfo().tensor(InputTensorInfo().set_element_type(element::f32))).build();
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::f32);
     EXPECT_EQ(f->get_results().front()->get_element_type(), element::i32);
 }
@@ -74,11 +82,11 @@ TEST(pre_post_process, convert_element_type_implicit) {
 TEST(pre_post_process, convert_element_type_same) {
     auto f = create_simple_function(element::i32, Shape{1, 3, 224, 224});
     auto old_size = f->get_ops().size();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo("tensor_input1")
                        .tensor(InputTensorInfo().set_element_type(element::i32))
                        .preprocess(PreProcessSteps().convert_element_type(element::i32)))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::i32);
     EXPECT_EQ(old_size, f->get_ops().size());
 }
@@ -87,7 +95,7 @@ TEST(pre_post_process, convert_element_type_default) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
     auto type_custom1 = element::Type();
     auto type_custom2 = element::Type();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_element_type(element::i32))
                        .preprocess(PreProcessSteps()
@@ -100,7 +108,7 @@ TEST(pre_post_process, convert_element_type_default) {
                                            type_custom2 = node.get_element_type();
                                            return node;
                                        })))
-            .build(f);
+            .build();
     EXPECT_EQ(type_custom1, element::i32);
     EXPECT_EQ(type_custom2, element::f32);
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::i32);
@@ -109,7 +117,7 @@ TEST(pre_post_process, convert_element_type_default) {
 
 TEST(pre_post_process, empty_preprocess) {
     auto f = create_simple_function(element::i8, Shape{1, 3, 2, 2});
-    f = PrePostProcessor().input(InputInfo().tensor(InputTensorInfo().set_element_type(element::i8))).build(f);
+    f = PrePostProcessor(f).input(InputInfo().tensor(InputTensorInfo().set_element_type(element::i8))).build();
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::i8);
     EXPECT_EQ(f->get_output_element_type(0), element::i8);
 }
@@ -117,45 +125,45 @@ TEST(pre_post_process, empty_preprocess) {
 TEST(pre_post_process, preprocess_assert_input_without_index) {
     auto f = create_n_inputs<2>(element::f32, Shape{1, 3, 2, 2});
     auto inp = InputInfo();
-    EXPECT_ANY_THROW(f = PrePostProcessor().input(std::move(inp)).build(f));
+    EXPECT_ANY_THROW(f = PrePostProcessor(f).input(std::move(inp)).build());
     inp = InputInfo("some_non_existing_name");
-    EXPECT_ANY_THROW(f = PrePostProcessor().input(std::move(inp)).build(f));
+    EXPECT_ANY_THROW(f = PrePostProcessor(f).input(std::move(inp)).build());
 }
 
 TEST(pre_post_process, convert_element_type_from_unknown) {
     auto f = create_simple_function(element::i32, Shape{1, 3, 224, 224});
     ASSERT_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo().preprocess(
                     PreProcessSteps().convert_element_type(element::dynamic).convert_element_type(element::i32)))
-                .build(f),
+                .build(),
         ov::AssertFailure);
 }
 
 TEST(pre_post_process, scale_not_float) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
     ASSERT_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo().preprocess(PreProcessSteps().convert_element_type(element::i32).scale(2.0f)))
-                .build(f),
+                .build(),
         ov::AssertFailure);
 }
 
 TEST(pre_post_process, mean_not_float) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
-    ASSERT_THROW(f = PrePostProcessor()
+    ASSERT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo().preprocess(PreProcessSteps().convert_element_type(element::i32).mean(2.0f)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, tensor_element_type_and_scale) {
     auto f = create_simple_function(element::i8, Shape{1, 3, 1, 1});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_element_type(element::f32))
                        .preprocess(PreProcessSteps().scale(2.0f).convert_element_type(element::i8)))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::f32);
     EXPECT_EQ(f->get_output_element_type(0), element::i8);
@@ -166,14 +174,14 @@ TEST(pre_post_process, convert_color_nv12_rgb_single) {
     auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 2, 2, 3});
     auto name = f->get_parameters()[0]->get_friendly_name();
     auto tensor_names = f->get_parameters().front()->get_output_tensor(0).get_names();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(
                 InputInfo()
                     .tensor(InputTensorInfo()
                                 .set_element_type(element::u8)
                                 .set_color_format(ColorFormat::NV12_SINGLE_PLANE))
                     .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB).convert_element_type(element::f32)))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->get_parameters().size(), 1);
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::u8);
@@ -187,11 +195,11 @@ TEST(pre_post_process, convert_color_nv12_bgr_single) {
     auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 2, 2, 3});
     auto name = f->get_parameters()[0]->get_friendly_name();
     auto tensor_names = f->get_parameters().front()->get_output_tensor(0).get_names();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_SINGLE_PLANE))
                        .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR)))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->get_parameters().size(), 1);
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::f32);
@@ -203,11 +211,11 @@ TEST(pre_post_process, convert_color_nv12_bgr_single) {
 
 TEST(pre_post_process, convert_color_nv12_bgr_2_planes) {
     auto f = create_simple_function(element::f32, Shape{5, 2, 2, 3});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES, {"TestY", "TestUV"}))
                        .preprocess(PreProcessSteps().convert_color(ColorFormat::BGR)))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->get_parameters().size(), 2);
     EXPECT_EQ(f->get_parameters()[0]->get_friendly_name(), "input1/TestY");
@@ -223,11 +231,11 @@ TEST(pre_post_process, convert_color_nv12_bgr_2_planes) {
 
 TEST(pre_post_process, convert_color_nv12_rgb_2_planes) {
     auto f = create_simple_function(element::f32, Shape{5, 2, 2, 3});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES))
                        .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB)))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->get_parameters().size(), 2);
     EXPECT_EQ(f->get_parameters()[0]->get_element_type(), element::f32);
@@ -242,9 +250,9 @@ TEST(pre_post_process, convert_color_nv12_bgr_2_planes_u8_lvalue) {
     input_tensor_info.set_color_format(ColorFormat::NV12_TWO_PLANES);
     auto steps = PreProcessSteps();
     steps.convert_color(ColorFormat::BGR);
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo().tensor(std::move(input_tensor_info)).preprocess(std::move(steps)))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->get_parameters().size(), 2);
     EXPECT_EQ(f->get_parameters()[0]->get_element_type(), element::u8);
@@ -256,14 +264,14 @@ TEST(pre_post_process, convert_color_nv12_bgr_2_planes_u8_lvalue) {
 TEST(pre_post_process, convert_color_nv12_bgr_2_planes_el_type) {
     auto f = create_simple_function(element::u8, Shape{1, 2, 2, 3});
     EXPECT_NO_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                            .tensor(InputTensorInfo()
                                        .set_element_type(element::f32)
                                        .set_color_format(ColorFormat::NV12_TWO_PLANES))
                            .preprocess(
                                PreProcessSteps().convert_element_type(element::u8).convert_color(ColorFormat::BGR)))
-                .build(f));
+                .build());
 
     ASSERT_EQ(f->get_parameters().size(), 2);
     EXPECT_EQ(f->get_parameters()[0]->get_element_type(), element::f32);
@@ -272,11 +280,11 @@ TEST(pre_post_process, convert_color_nv12_bgr_2_planes_el_type) {
 
 TEST(pre_post_process, convert_color_same_type) {
     auto f = create_simple_function(element::u8, Shape{1, 2, 2, 3});
-    EXPECT_NO_THROW(f = PrePostProcessor()
+    EXPECT_NO_THROW(f = PrePostProcessor(f)
                             .input(InputInfo()
                                        .tensor(InputTensorInfo().set_color_format(ColorFormat::RGB))
                                        .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB)))
-                            .build(f));
+                            .build());
 
     EXPECT_EQ(f->get_parameters().size(), 1);
     EXPECT_EQ(f->get_parameters()[0]->get_partial_shape(), (PartialShape{1, 2, 2, 3}));
@@ -285,34 +293,34 @@ TEST(pre_post_process, convert_color_same_type) {
 TEST(pre_post_process, convert_color_unsupported) {
     // Feel free to update this test when more color conversions are supported in future
     auto f = create_simple_function(element::f32, PartialShape{1, 4, 4, 3});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_SINGLE_PLANE))
                                     .preprocess(PreProcessSteps().convert_color(ColorFormat::UNDEFINED)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES))
                                     .preprocess(PreProcessSteps().convert_color(ColorFormat::UNDEFINED)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
     auto colors = {ColorFormat::NV12_TWO_PLANES, ColorFormat::NV12_SINGLE_PLANE, ColorFormat::RGB, ColorFormat::BGR};
     for (const auto& color : colors) {
-        EXPECT_THROW(f = PrePostProcessor()
+        EXPECT_THROW(f = PrePostProcessor(f)
                              .input(InputInfo()
                                         .tensor(InputTensorInfo().set_color_format(ColorFormat::UNDEFINED))
                                         .preprocess(PreProcessSteps().convert_color(color)))
-                             .build(f),
+                             .build(),
                      ov::AssertFailure);
 
-        EXPECT_THROW(f = PrePostProcessor()
+        EXPECT_THROW(f = PrePostProcessor(f)
                              .input(InputInfo()
                                         .tensor(InputTensorInfo().set_color_format(color))
                                         .preprocess(PreProcessSteps().convert_color(ColorFormat::UNDEFINED)))
-                             .build(f),
+                             .build(),
                      ov::AssertFailure);
     }
 }
@@ -320,23 +328,23 @@ TEST(pre_post_process, convert_color_unsupported) {
 TEST(pre_post_process, convert_color_incorrect_subnames) {
     auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 2, 2, 3});
     EXPECT_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                            .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_SINGLE_PLANE, {"Test"}))
                            .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB)))
-                .build(f),
+                .build(),
         ov::AssertFailure);
 
     EXPECT_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo().tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES, {"Test"})))
-                .build(f),
+                .build(),
         ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo().tensor(
                              InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES, {"1", "2", "3"})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
@@ -344,12 +352,12 @@ TEST(pre_post_process, convert_color_duplicate_subnames) {
     auto f = create_n_inputs<2>(element::f32, PartialShape{1, 2, 2, 3});
     f->get_parameters()[0]->get_output_tensor(0).set_names({"tensor_input1"});
     f->get_parameters()[1]->get_output_tensor(0).set_names({"tensor_input1/CustomUV"});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_SINGLE_PLANE,
                                                                                {"CustomY", "CustomUV"}))
                                     .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
@@ -357,22 +365,22 @@ TEST(pre_post_process, convert_color_duplicate_internal_subnames_mean) {
     auto f = create_simple_function(element::f32, PartialShape{1, 2, 2, 3});
     for (int i = 0; i < 10; i++) {
         // Create preprocessing step several times (try to duplicate internal node names this way)
-        EXPECT_NO_THROW(f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().mean(0.1f))).build(f));
-        EXPECT_NO_THROW(f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().scale(1.1f))).build(f));
+        EXPECT_NO_THROW(f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().mean(0.1f))).build());
+        EXPECT_NO_THROW(f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().scale(1.1f))).build());
         EXPECT_NO_THROW(
-            f = PrePostProcessor()
+            f = PrePostProcessor(f)
                     .input(InputInfo().preprocess(
                         PreProcessSteps().convert_element_type(element::u8).convert_element_type(element::f32)))
-                    .build(f));
+                    .build());
     }
     f = create_simple_function(element::f32, PartialShape{1, 2, 2, 3});
     for (int i = 0; i < 10; i++) {
-        (f = PrePostProcessor()
+        (f = PrePostProcessor(f)
                  .input(InputInfo()
                             .tensor(InputTensorInfo().set_layout("NHWC"))
                             .preprocess(PreProcessSteps().convert_layout("NCHW"))
                             .network(InputNetworkInfo().set_layout("NHWC")))
-                 .build(f));
+                 .build());
     }
     f = create_simple_function(element::f32, PartialShape{1, 2, 2, 3});
     auto p = PreProcessSteps();
@@ -380,56 +388,56 @@ TEST(pre_post_process, convert_color_duplicate_internal_subnames_mean) {
         p.resize(ResizeAlgorithm::RESIZE_LINEAR, i, i);
     }
     p.resize(ResizeAlgorithm::RESIZE_LINEAR);
-    EXPECT_NO_THROW(f = PrePostProcessor()
+    EXPECT_NO_THROW(f = PrePostProcessor(f)
                             .input(InputInfo()
                                        .tensor(InputTensorInfo().set_spatial_static_shape(480, 640))
                                        .preprocess(std::move(p))
                                        .network(InputNetworkInfo().set_layout("NHWC")))
-                            .build(f));
+                            .build());
 }
 
 TEST(pre_post_process, unsupported_network_color_format) {
     auto f = create_simple_function(element::f32, PartialShape{1, 4, 4, 3});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo().tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_SINGLE_PLANE)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo().tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
     EXPECT_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                            .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES))
                            .preprocess(PreProcessSteps().convert_layout("NCHW").convert_color(ColorFormat::RGB)))
-                .build(f),
+                .build(),
         ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES))
                                     .preprocess(PreProcessSteps().mean(0.1f).convert_color(ColorFormat::RGB)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES))
                                     .preprocess(PreProcessSteps().scale(2.1f).convert_color(ColorFormat::RGB)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, custom_preprocessing) {
     auto f = create_simple_function(element::i32, Shape{1, 3, 1, 1});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo().preprocess(PreProcessSteps().custom([](const Output<Node>& node) {
                 return std::make_shared<op::v0::Abs>(node);
             })))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_output_element_type(0), element::i32);
 }
 
@@ -437,7 +445,7 @@ TEST(pre_post_process, test_lvalue) {
     auto f = create_simple_function(element::i8, Shape{1, 3, 1, 1});
     auto name = f->get_parameters()[0]->get_friendly_name();
     auto tensor_names = f->get_parameters().front()->get_output_tensor(0).get_names();
-    auto p = PrePostProcessor();
+    auto p = PrePostProcessor(f);
     auto p1 = std::move(p);
     p = std::move(p1);
     auto inputInfo = InputInfo();
@@ -466,7 +474,7 @@ TEST(pre_post_process, test_lvalue) {
         inputInfo.preprocess(std::move(same));
     }
     p.input(std::move(inputInfo));
-    f = p.build(f);
+    f = p.build();
     EXPECT_EQ(f->get_parameters().front()->get_element_type(), element::f32);
     EXPECT_EQ(f->get_parameters().front()->get_friendly_name(), name);
     EXPECT_EQ(f->get_parameters().front()->get_layout(), "?CHW");
@@ -476,7 +484,7 @@ TEST(pre_post_process, test_lvalue) {
 
 TEST(pre_post_process, test_2_inputs_basic) {
     auto f = create_n_inputs<2>(element::f32, Shape{1, 3, 1, 1});
-    { f = PrePostProcessor().input(InputInfo(1).preprocess(PreProcessSteps().mean(1.f).scale(2.0f))).build(f); }
+    { f = PrePostProcessor(f).input(InputInfo(1).preprocess(PreProcessSteps().mean(1.f).scale(2.0f))).build(); }
     EXPECT_EQ(f->get_output_element_type(0), element::f32);
     EXPECT_EQ(f->get_output_element_type(1), element::f32);
 }
@@ -484,23 +492,23 @@ TEST(pre_post_process, test_2_inputs_basic) {
 TEST(pre_post_process, reuse_network_layout_no_tensor_info) {
     auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 2, 1});
     f->get_parameters().front()->set_layout("NC??");
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo().preprocess(PreProcessSteps().mean({1.f, 2.f, 3.f}).scale({2.f, 3.f, 4.f})))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters().front()->get_layout(), "NC??");
 }
 
 TEST(pre_post_process, reuse_network_layout_tensor_info) {
     auto f = create_simple_function(element::u8, PartialShape{Dimension::dynamic(), 3, 2, 1});
     f->get_parameters().front()->set_layout("NC??");
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_element_type(element::f32))
                        .preprocess(PreProcessSteps()
                                        .mean({1.f, 2.f, 3.f})
                                        .scale({2.f, 3.f, 4.f})
                                        .convert_element_type(element::u8)))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters().front()->get_layout(), "NC??");
 }
 
@@ -508,11 +516,11 @@ TEST(pre_post_process, mean_scale_vector_tensor_layout) {
     auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 2, 1});
     auto name = f->get_parameters().front()->get_friendly_name();
     auto tensor_names = f->get_parameters().front()->get_output_tensor(0).get_names();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_layout("NC??"))
                        .preprocess(PreProcessSteps().mean({1.f, 2.f, 3.f}).scale({2.f, 3.f, 4.f})))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters().front()->get_friendly_name(), name);
     EXPECT_EQ(f->get_parameters().front()->get_layout(), "NC??");
     EXPECT_EQ(f->get_parameters().front()->get_output_tensor(0).get_names(), tensor_names);
@@ -524,11 +532,11 @@ TEST(pre_post_process, mean_scale_dynamic_layout) {
                                     PartialShape{Dimension::dynamic(), Dimension::dynamic(), Dimension::dynamic(), 3});
     auto name = f->get_parameters().front()->get_friendly_name();
     auto tensor_names = f->get_parameters().front()->get_output_tensor(0).get_names();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_layout("N...C"))
                        .preprocess(PreProcessSteps().mean({1.f, 2.f, 3.f}).scale({2.f, 3.f, 4.f})))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->get_parameters().front()->get_friendly_name(), name);
     EXPECT_EQ(f->get_parameters().front()->get_layout(), "N...C");
@@ -539,33 +547,33 @@ TEST(pre_post_process, mean_scale_dynamic_layout) {
 TEST(pre_post_process, scale_vector_no_channels_layout) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
     EXPECT_EQ(f->get_output_element_type(0), element::f32);
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("N?HW"))
                                     .preprocess(PreProcessSteps().scale({0.1f, 0.2f, 0.3f})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, scale_vector_dim_mismatch) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
     EXPECT_EQ(f->get_output_element_type(0), element::f32);
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("NCHW"))
                                     .preprocess(PreProcessSteps().scale({0.1f, 0.2f, 0.3f, 0.4f})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, scale_vector_channels_out_of_range) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
     ASSERT_EQ(f->get_output_element_type(0), element::f32);
-    ASSERT_THROW(f = PrePostProcessor()
+    ASSERT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("0123C"))
                                     .preprocess(PreProcessSteps().scale({0.1f, 0.2f, 0.3f})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
@@ -573,7 +581,7 @@ TEST(pre_post_process, mean_vector_no_layout) {
     auto f = create_simple_function(element::f32, PartialShape{Dimension::dynamic(), 3, 224, 224});
     ASSERT_EQ(f->get_output_element_type(0), element::f32);
     ASSERT_THROW(
-        f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().mean({0.1f, 0.2f, 0.3f}))).build(f),
+        f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().mean({0.1f, 0.2f, 0.3f}))).build(),
         ov::AssertFailure);
 }
 
@@ -582,71 +590,61 @@ TEST(pre_post_process, mean_vector_dynamic_channels_shape) {
         element::f32,
         PartialShape{Dimension::dynamic(), Dimension::dynamic(), Dimension::dynamic(), Dimension::dynamic()});
     EXPECT_EQ(f->get_output_element_type(0), element::f32);
-    EXPECT_NO_THROW(f = PrePostProcessor()
+    EXPECT_NO_THROW(f = PrePostProcessor(f)
                             .input(InputInfo()
                                        .tensor(InputTensorInfo().set_layout("NCHW"))
                                        .preprocess(PreProcessSteps().mean({0.1f, 0.2f, 0.3f})))
-                            .build(f));
+                            .build());
     EXPECT_EQ(f->get_output_element_type(0), element::f32);
 }
 
 // Error cases for 'resize'
 TEST(pre_post_process, resize_no_network_layout) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("NHWC"))
                                     .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_CUBIC)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, tensor_spatial_shape_no_layout_dims) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("NC?W").set_spatial_static_shape(480, 640))
                                     .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_CUBIC)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("NCH?").set_spatial_static_shape(480, 640))
                                     .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_CUBIC)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, resize_no_tensor_height) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("N?WC"))
                                     .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                     .network(InputNetworkInfo().set_layout("NHWC")))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, resize_no_tensor_width) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("NH?C"))
                                     .preprocess(PreProcessSteps().resize(ResizeAlgorithm::RESIZE_LINEAR))
                                     .network(InputNetworkInfo().set_layout("NHWC")))
-                         .build(f),
-                 ov::AssertFailure);
-}
-
-TEST(pre_post_process, double_input_info) {
-    auto f = create_simple_function(element::f32, Shape{1, 3, 224, 224});
-    // Parameter is replaced during first pre-processing, parameter for second step will not be resolved properly
-    EXPECT_THROW(f = PrePostProcessor()
-                         .input(InputInfo().tensor(InputTensorInfo().set_element_type(element::u8)))
-                         .input(InputInfo().tensor(InputTensorInfo().set_element_type(element::u8)))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
@@ -656,10 +654,10 @@ TEST(pre_post_process, preprocess_convert_layout_implicit) {
     auto name_last_op = f->get_results().front()->get_input_source_output(0).get_node_shared_ptr()->get_friendly_name();
     auto tensor_names = f->output().get_tensor().get_names();
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(
                 InputInfo().tensor(InputTensorInfo().set_layout("NHWC")).network(InputNetworkInfo().set_layout("NCHW")))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters()[0]->get_layout(), "NHWC");
     EXPECT_EQ(f->get_parameters()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
     EXPECT_EQ(name, f->get_results().front()->get_friendly_name());
@@ -671,12 +669,12 @@ TEST(pre_post_process, preprocess_convert_layout_implicit) {
 TEST(pre_post_process, preprocess_convert_layout_default) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_layout("NHWC"))
                        .preprocess(PreProcessSteps().convert_layout())
                        .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters()[0]->get_layout(), "NHWC");
     EXPECT_EQ(f->get_parameters()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
 }
@@ -685,12 +683,12 @@ TEST(pre_post_process, preprocess_convert_layout_same) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     auto size_old = f->get_ordered_ops().size();
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo()
                        .tensor(InputTensorInfo().set_layout("NCHW"))
                        .preprocess(PreProcessSteps().convert_layout("NCHW"))
                        .network(InputNetworkInfo().set_layout("NCHW")))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_parameters()[0]->get_layout(), "NCHW");
     EXPECT_EQ(f->get_parameters()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 3, 2, 2}));
     // Verify that redundant ops were not added
@@ -700,7 +698,7 @@ TEST(pre_post_process, preprocess_convert_layout_same) {
 TEST(pre_post_process, preprocess_convert_layout_dims) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 480, 640});
 
-    f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 1, 2}))).build(f);
+    f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 1, 2}))).build();
 
     EXPECT_EQ(f->input().get_partial_shape(), (PartialShape{1, 480, 640, 3}));
 }
@@ -708,9 +706,9 @@ TEST(pre_post_process, preprocess_convert_layout_dims) {
 TEST(pre_post_process, preprocess_convert_layout_dims_empty) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 480, 640});
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo().preprocess(PreProcessSteps().convert_layout(std::vector<uint64_t>{})))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->input().get_partial_shape(), (PartialShape{1, 3, 480, 640}));
 }
@@ -718,7 +716,7 @@ TEST(pre_post_process, preprocess_convert_layout_dims_empty) {
 TEST(pre_post_process, preprocess_convert_layout_dims_dyn_shape) {
     auto f = create_simple_function(element::f32, PartialShape::dynamic());
 
-    f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 1, 2}))).build(f);
+    f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 1, 2}))).build();
 
     EXPECT_EQ(f->input().get_partial_shape(), (PartialShape::dynamic()));
 }
@@ -726,47 +724,47 @@ TEST(pre_post_process, preprocess_convert_layout_dims_dyn_shape) {
 TEST(pre_post_process, preprocess_convert_layout_invalid_dims) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     EXPECT_THROW(
-        f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 2, 2}))).build(f),
+        f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 2, 2}))).build(),
         ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo().preprocess(
                              PreProcessSteps().convert_layout({0, 3, 1, std::numeric_limits<uint64_t>::max()})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, preprocess_convert_layout_invalid_dims_dyn_shape) {
     auto f = create_simple_function(element::f32, PartialShape::dynamic());
     EXPECT_THROW(
-        f = PrePostProcessor().input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 2, 2}))).build(f),
+        f = PrePostProcessor(f).input(InputInfo().preprocess(PreProcessSteps().convert_layout({0, 3, 2, 2}))).build(),
         ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo().preprocess(
                              PreProcessSteps().convert_layout({0, 3, 1, std::numeric_limits<uint64_t>::max()})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, preprocess_reverse_channels_multiple_planes) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     EXPECT_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .input(InputInfo()
                            .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES, {"Y", "UV"}))
                            .preprocess(PreProcessSteps().reverse_channels()))
-                .build(f),
+                .build(),
         ov::AssertFailure);
 }
 
 TEST(pre_post_process, preprocess_reverse_channels_no_c_dim) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo()
                                     .tensor(InputTensorInfo().set_layout("N?HW"))
                                     .preprocess(PreProcessSteps().reverse_channels()))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
@@ -777,9 +775,9 @@ TEST(pre_post_process, postprocess_convert_element_type_explicit) {
     auto name = f->output().get_node_shared_ptr()->get_friendly_name();
     auto name_last_op = f->get_results().front()->get_input_source_output(0).get_node_shared_ptr()->get_friendly_name();
     auto old_names = f->output().get_tensor().get_names();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo().postprocess(PostProcessSteps().convert_element_type(element::u8)))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_results().size(), 1);
     EXPECT_EQ(f->get_results()[0]->get_element_type(), element::u8);
     EXPECT_EQ(f->output().get_tensor().get_names(), old_names);
@@ -803,11 +801,11 @@ TEST(pre_post_process, postprocess_convert_element_type_default) {
     auto name = f->output(1).get_node_shared_ptr()->get_friendly_name();
     auto name_last_op = f->get_results().front()->get_input_source_output(0).get_node_shared_ptr()->get_friendly_name();
     auto tensor_names = f->output(1).get_tensor().get_names();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo(1)
                         .postprocess(PostProcessSteps().convert_element_type())
                         .tensor(OutputTensorInfo().set_element_type(element::u8)))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_results()[0]->get_element_type(), element::f32);
     EXPECT_EQ(f->get_results()[1]->get_element_type(), element::u8);
     EXPECT_EQ(name, f->output(1).get_node_shared_ptr()->get_friendly_name());
@@ -819,11 +817,11 @@ TEST(pre_post_process, postprocess_convert_element_type_default) {
 TEST(pre_post_process, postprocess_convert_element_type_same) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     auto size_old = f->get_ordered_ops().size();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo("tensor_output1")
                         .postprocess(PostProcessSteps().convert_element_type(element::f32))
                         .tensor(OutputTensorInfo().set_element_type(element::f32)))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_results()[0]->get_element_type(), element::f32);
 
     // Verify that redundant ops were not added
@@ -833,19 +831,19 @@ TEST(pre_post_process, postprocess_convert_element_type_same) {
 TEST(pre_post_process, postprocess_convert_element_type_default_error) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     EXPECT_THROW(
-        f = PrePostProcessor().output(OutputInfo().postprocess(PostProcessSteps().convert_element_type())).build(f),
+        f = PrePostProcessor(f).output(OutputInfo().postprocess(PostProcessSteps().convert_element_type())).build(),
         ov::AssertFailure);
 }
 
 TEST(pre_post_process, postprocess_convert_element_type_implicit) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-    f = PrePostProcessor().output(OutputInfo().tensor(OutputTensorInfo().set_element_type(element::u8))).build(f);
+    f = PrePostProcessor(f).output(OutputInfo().tensor(OutputTensorInfo().set_element_type(element::u8))).build();
     EXPECT_EQ(f->get_results()[0]->get_element_type(), element::u8);
 }
 
 TEST(pre_post_process, preprocess_keep_params_order) {
     auto f = create_n_inputs<3>(element::f32, Shape{1, 2, 2, 3});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .input(InputInfo(1)
                        .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES, {"Y", "UV"}))
                        .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB)))
@@ -853,7 +851,7 @@ TEST(pre_post_process, preprocess_keep_params_order) {
             .input(InputInfo(2)
                        .tensor(InputTensorInfo().set_color_format(ColorFormat::NV12_TWO_PLANES, {"Y", "UV"}))
                        .preprocess(PreProcessSteps().convert_color(ColorFormat::RGB)))
-            .build(f);
+            .build();
     ASSERT_EQ(f->get_parameters().size(), 5);
     EXPECT_EQ(f->get_parameters()[0]->get_layout(), "NCHW");
     EXPECT_EQ(f->get_parameters()[1]->get_layout(), "NHWC");
@@ -877,29 +875,29 @@ TEST(pre_post_process, preprocess_keep_params_order) {
 // --- PostProcess - set/convert layout ---
 TEST(pre_post_process, postprocess_set_layout_network) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-    f = PrePostProcessor().output(OutputInfo().network(OutputNetworkInfo().set_layout("NCHW"))).build(f);
+    f = PrePostProcessor(f).output(OutputInfo().network(OutputNetworkInfo().set_layout("NCHW"))).build();
     EXPECT_EQ(f->get_results()[0]->get_layout(), "NCHW");
 }
 
 TEST(pre_post_process, postprocess_convert_layout_implicit) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo()
                         .network(OutputNetworkInfo().set_layout("NCHW"))
                         .tensor(OutputTensorInfo().set_layout("NHWC")))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_results()[0]->get_layout(), "NHWC");
     EXPECT_EQ(f->get_results()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
 }
 
 TEST(pre_post_process, postprocess_convert_layout_explicit_no_target) {
     auto f = create_n_inputs<2>(element::f32, Shape{1, 3, 2, 2});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo(1)
                         .network(OutputNetworkInfo().set_layout("NCHW"))
                         .postprocess(PostProcessSteps().convert_layout("NHWC")))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_results()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 3, 2, 2}));
     EXPECT_EQ(f->get_results()[1]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
 }
@@ -907,12 +905,25 @@ TEST(pre_post_process, postprocess_convert_layout_explicit_no_target) {
 TEST(pre_post_process, postprocess_convert_layout_default) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo()
                         .network(OutputNetworkInfo().set_layout("NCHW"))
                         .postprocess(PostProcessSteps().convert_layout())
                         .tensor(OutputTensorInfo().set_layout("NHWC")))
-            .build(f);
+            .build();
+    EXPECT_EQ(f->get_results()[0]->get_layout(), "NHWC");
+    EXPECT_EQ(f->get_results()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
+}
+
+TEST(pre_post_process, postprocess_convert_layout_default_getters) {
+    auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
+
+    auto p = PrePostProcessor(f);
+    auto& out = p.output();
+    out.network().set_layout("NCHW");
+    out.postprocess().convert_layout();
+    out.tensor().set_layout("NHWC");
+    f = p.build();
     EXPECT_EQ(f->get_results()[0]->get_layout(), "NHWC");
     EXPECT_EQ(f->get_results()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
 }
@@ -921,12 +932,12 @@ TEST(pre_post_process, postprocess_convert_layout_same) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     auto size_old = f->get_ordered_ops().size();
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo()
                         .network(OutputNetworkInfo().set_layout("NCHW"))
                         .postprocess(PostProcessSteps().convert_layout("NCHW"))
                         .tensor(OutputTensorInfo().set_layout("NCHW")))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_results()[0]->get_layout(), "NCHW");
     EXPECT_EQ(f->get_results()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 3, 2, 2}));
     // Verify that redundant ops were not added
@@ -936,7 +947,7 @@ TEST(pre_post_process, postprocess_convert_layout_same) {
 TEST(pre_post_process, postprocess_convert_layout_dims) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 480, 640});
 
-    f = PrePostProcessor().output(OutputInfo().postprocess(PostProcessSteps().convert_layout({0, 2, 3, 1}))).build(f);
+    f = PrePostProcessor(f).output(OutputInfo().postprocess(PostProcessSteps().convert_layout({0, 2, 3, 1}))).build();
 
     EXPECT_EQ(f->output().get_partial_shape(), (PartialShape{1, 480, 640, 3}));
 }
@@ -944,9 +955,9 @@ TEST(pre_post_process, postprocess_convert_layout_dims) {
 TEST(pre_post_process, postprocess_convert_layout_dims_empty) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 480, 640});
 
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo().postprocess(PostProcessSteps().convert_layout(std::vector<uint64_t>{})))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->output().get_partial_shape(), (PartialShape{1, 3, 480, 640}));
 }
@@ -956,9 +967,9 @@ TEST(pre_post_process, postprocess_convert_layout_has_layout) {
 
     auto p = PostProcessSteps();
     p.convert_layout({0, 2, 3, 1});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo().network(OutputNetworkInfo().set_layout("NC??")).postprocess(std::move(p)))
-            .build(f);
+            .build();
 
     EXPECT_EQ(f->output().get_partial_shape(), (PartialShape{1, 480, 640, 3}));
     EXPECT_EQ(f->get_results()[0]->get_layout(), "N??C");
@@ -966,29 +977,29 @@ TEST(pre_post_process, postprocess_convert_layout_has_layout) {
 
 TEST(pre_post_process, postprocess_convert_layout_invalid_dims) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .output(OutputInfo().postprocess(PostProcessSteps().convert_layout({0, 3, 2, 2})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .output(OutputInfo().postprocess(
                              PostProcessSteps().convert_layout({0, 3, 1, std::numeric_limits<uint64_t>::max()})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
 TEST(pre_post_process, postprocess_convert_layout_invalid_dims_dyn_shape) {
     auto f = create_simple_function(element::f32, PartialShape::dynamic());
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .output(OutputInfo().postprocess(PostProcessSteps().convert_layout({0, 3, 2, 2})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .output(OutputInfo().postprocess(
                              PostProcessSteps().convert_layout({0, 3, 1, std::numeric_limits<uint64_t>::max()})))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 }
 
@@ -997,13 +1008,13 @@ TEST(pre_post_process, postprocess_convert_layout_invalid_dims_dyn_shape) {
 TEST(pre_post_process, postprocess_custom_step) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
     bool hit = false;
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo().postprocess(PostProcessSteps().custom([&hit](const ov::Output<Node>& node) {
                 auto abs = std::make_shared<op::v0::Abs>(node);
                 hit = true;
                 return abs;
             })))
-            .build(f);
+            .build();
     EXPECT_TRUE(hit);
 
     EXPECT_EQ(std::string(f->get_results()[0]->get_input_source_output(0).get_node()->get_type_name()),
@@ -1012,11 +1023,11 @@ TEST(pre_post_process, postprocess_custom_step) {
 
 TEST(pre_post_process, postprocess_implicit_convert_element_type_and_layout) {
     auto f = create_simple_function(element::f32, Shape{1, 3, 2, 2});
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo()
                         .network(OutputNetworkInfo().set_layout("NCHW"))
                         .tensor(OutputTensorInfo().set_layout("NHWC").set_element_type(element::u8)))
-            .build(f);
+            .build();
     EXPECT_EQ(f->get_results()[0]->get_element_type(), element::u8);
     EXPECT_EQ(f->get_results()[0]->get_layout(), "NHWC");
     EXPECT_EQ(f->get_results()[0]->get_output_tensor(0).get_partial_shape(), (PartialShape{1, 2, 2, 3}));
@@ -1025,9 +1036,9 @@ TEST(pre_post_process, postprocess_implicit_convert_element_type_and_layout) {
 TEST(pre_post_process, postprocess_assert_output_without_index) {
     auto f = create_n_inputs<2>(element::f32, Shape{1, 3, 2, 2});
     auto out = OutputInfo();
-    EXPECT_ANY_THROW(f = PrePostProcessor().output(std::move(out)).build(f));
+    EXPECT_ANY_THROW(f = PrePostProcessor(f).output(std::move(out)).build());
     out = OutputInfo("some_non_existing_name");
-    EXPECT_ANY_THROW(f = PrePostProcessor().output(std::move(out)).build(f));
+    EXPECT_ANY_THROW(f = PrePostProcessor(f).output(std::move(out)).build());
 }
 
 TEST(pre_post_process, postprocess_keep_results_order) {
@@ -1035,12 +1046,12 @@ TEST(pre_post_process, postprocess_keep_results_order) {
     auto names0 = f->output(0).get_tensor().get_names();
     auto names1 = f->output(1).get_tensor().get_names();
     auto names2 = f->output(2).get_tensor().get_names();
-    f = PrePostProcessor()
+    f = PrePostProcessor(f)
             .output(OutputInfo(0).network(OutputNetworkInfo().set_layout("NCHW")))
             .output(OutputInfo(1)
                         .network(OutputNetworkInfo().set_layout("NCHW"))
                         .tensor(OutputTensorInfo().set_layout("NHWC").set_element_type(element::u8)))
-            .build(f);
+            .build();
     ASSERT_EQ(f->get_results().size(), 3);
     EXPECT_EQ(f->output(0).get_element_type(), element::f32);
     EXPECT_EQ(f->output(1).get_element_type(), element::u8);
@@ -1083,10 +1094,10 @@ TEST(pre_post_process, postprocess_lvalues_1) {
     outputInfo.postprocess(std::move(steps));
     outputInfo.tensor(std::move(tensorInfo));
 
-    auto p = PrePostProcessor();
+    auto p = PrePostProcessor(f);
     p.output(std::move(outputInfo));
 
-    f = p.build(f);
+    f = p.build();
     EXPECT_EQ(f->get_results().size(), 1);
     EXPECT_EQ(f->output().get_tensor().get_names().count("tensor_output1"), 1);
     EXPECT_EQ(f->output().get_node_shared_ptr()->get_friendly_name(), "Result1");
@@ -1106,7 +1117,7 @@ TEST(pre_post_process, exception_safety) {
     auto out_tensor_names0 = f->output(0).get_tensor().get_names();
     auto out_name1 = f->output(1).get_node_shared_ptr()->get_friendly_name();
     auto out_tensor_names1 = f->output(1).get_tensor().get_names();
-    EXPECT_THROW(f = PrePostProcessor()
+    EXPECT_THROW(f = PrePostProcessor(f)
                          .input(InputInfo(0)  // this one is correct
                                     .tensor(InputTensorInfo().set_element_type(element::u8))
                                     .preprocess(PreProcessSteps().convert_element_type(element::f32)))
@@ -1115,18 +1126,18 @@ TEST(pre_post_process, exception_safety) {
                                     .preprocess(PreProcessSteps().custom([](const Output<Node>& node) -> Output<Node> {
                                         throw ngraph::ngraph_error("test error");
                                     })))
-                         .build(f),
+                         .build(),
                  ov::AssertFailure);
 
     EXPECT_THROW(
-        f = PrePostProcessor()
+        f = PrePostProcessor(f)
                 .output(OutputInfo(0)  // this one is correct
                             .tensor(OutputTensorInfo().set_element_type(element::u8)))
                 .output(OutputInfo(1)  // This one is not
                             .postprocess(PostProcessSteps().custom([](const Output<Node>& node) -> Output<Node> {
                                 throw ngraph::ngraph_error("test error");
                             })))
-                .build(f),
+                .build(),
         ngraph::ngraph_error);
     EXPECT_EQ(f->get_parameters().size(), 2);
 
diff --git a/ngraph/test/runtime/CMakeLists.txt b/ngraph/test/runtime/CMakeLists.txt
index 3b562478e706bc..3d26cd6b42535f 100644
--- a/ngraph/test/runtime/CMakeLists.txt
+++ b/ngraph/test/runtime/CMakeLists.txt
@@ -38,17 +38,19 @@ target_compile_definitions(ngraph_backend
         SHARED_LIB_PREFIX="${CMAKE_SHARED_LIBRARY_PREFIX}"
         SHARED_LIB_SUFFIX="${IE_BUILD_POSTFIX}${CMAKE_SHARED_LIBRARY_SUFFIX}"
 )
-target_link_libraries(ngraph_backend PUBLIC ngraph
-                                            ngraph::builder
-                                            ngraph::reference)
-if (NOT WIN32)
-    target_link_libraries(ngraph_backend PRIVATE ${CMAKE_DL_LIBS})
-endif()
-target_include_directories(ngraph_backend PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
+target_link_libraries(ngraph_backend PUBLIC ngraph ngraph::builder ngraph::reference
+                                     PRIVATE ${CMAKE_DL_LIBS} openvino::util)
+
+target_include_directories(ngraph_backend PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}>)
 
 install(TARGETS ngraph_backend
         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
         LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+if(NOT BUILD_SHARED_LIBS)
+    install(TARGETS ngraph_backend
+            RUNTIME DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+endif()
 
 add_subdirectory(interpreter)
 add_subdirectory(ie)
diff --git a/ngraph/test/runtime/backend.cpp b/ngraph/test/runtime/backend.cpp
index 8bc4d3cfe92b86..e74687915c7997 100644
--- a/ngraph/test/runtime/backend.cpp
+++ b/ngraph/test/runtime/backend.cpp
@@ -21,6 +21,7 @@
 #include "dynamic/dynamic_backend.hpp"
 #include "ngraph/file_util.hpp"
 #include "ngraph/util.hpp"
+#include "openvino/util/file_util.hpp"
 
 using namespace std;
 using namespace ngraph;
@@ -45,7 +46,7 @@ static string find_my_pathname() {
 #elif defined(__linux) || defined(__APPLE__)
     Dl_info dl_info;
     dladdr(reinterpret_cast<void*>(ngraph::to_lower), &dl_info);
-    return dl_info.dli_fname;
+    return ov::util::get_absolute_file_path(dl_info.dli_fname);
 #else
 #    error "Unsupported OS"
 #endif
diff --git a/ngraph/test/runtime/ie/CMakeLists.txt b/ngraph/test/runtime/ie/CMakeLists.txt
index d89b723ee24981..fe0e79eab83c01 100644
--- a/ngraph/test/runtime/ie/CMakeLists.txt
+++ b/ngraph/test/runtime/ie/CMakeLists.txt
@@ -31,3 +31,8 @@ target_link_libraries(ie_backend PUBLIC ngraph_backend inference_engine)
 install(TARGETS ie_backend
         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
         LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+if(NOT BUILD_SHARED_LIBS)
+    install(TARGETS ie_backend
+            RUNTIME DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+endif()
diff --git a/ngraph/test/runtime/ie/unit_test.manifest b/ngraph/test/runtime/ie/unit_test.manifest
index b7e86e39791de0..6042755ad67ad7 100644
--- a/ngraph/test/runtime/ie/unit_test.manifest
+++ b/ngraph/test/runtime/ie/unit_test.manifest
@@ -24,9 +24,6 @@ IE_CPU.onnx_model_dequantize_linear_1d_zero_scale_int8
 # C++ exception with description "Input data precision not supported. Expected float.
 IE_CPU.onnx_model_dequantize_linear_1d_zero_scale_int8_4d
 
-# No support yet for RandomUniform
-onnx_model_random_uniform
-onnx_model_random_uniform_like
 
 # Result mismatch
 onnx_model_shape
@@ -113,6 +110,10 @@ onnx_model_scatterND_param_i64_indices
 IE_CPU.onnx_constant_sparse_tensor_int64_3x4
 IE_CPU.onnx_constant_sparse_tensor_uint64_3x4
 
+# I64 ONNX Clip Failing on ubuntu18/20_release CI
+IE_CPU.onnx_clip_no_min_no_max_int64 # -2147483648 is not close to 2147483647 at index 2
+IE_CPU.onnx_clip_no_min_set_max_int64 # -1 is not close to 2147483647 at index 0
+
 # TopK Incorrect input data/index values precision
 onnx_model_argmax_int32
 onnx_model_argmin_int32
@@ -374,14 +375,12 @@ tile_3d_few_repeats
 # Result mismatch
 sum_large_1d_to_scalar
 sum_stable_acc
-max_pool_3d
 avg_pool_2d_2channel_2image_padded_only_above_include_in_computation
 avg_pool_3d_uneven_strided_padded
 multiple_result
 lrn_across_all_dims
 elu
 elu_negative_alpha
-max_pool_2d_1channel_1image_overpadded
 grn_2d_with_bias
 erf
 divide_adjoint_stability
@@ -389,8 +388,6 @@ notequal
 less
 sum_3d_to_scalar_int32
 sum_2d_to_scalar_int8
-max_pool_uint8
-max_pool_int8
 avg_pool_uint8
 avg_pool_int8
 max_to_scalar_int8
@@ -435,6 +432,12 @@ onnx_constant_integer_array
 adaptive_max_pool_1d
 adaptive_max_pool_2d
 adaptive_max_pool_3d
+onnx_dyn_shapes_max_pool_with_indices_output
+onnx_model_max_pool_3d
+onnx_model_max_pool_4d_ceil_mode
+onnx_model_max_pool_4d_dilations
+onnx_model_max_pool_4d_strides
+onnx_model_max_pool_4d_ceil_strides
 
 # Unsupported primitive of type: SigmoidBackprop
 sigmoid_bprop_n1c1h4
@@ -557,9 +560,6 @@ product_to_scalar_int8
 min_to_scalar_int8
 
 # Pooling layer. Unsupported mode. Only 4D and 5D blobs are supported as input.
-max_pool_1d_1channel_1image
-max_pool_1d_1channel_2image
-max_pool_1d_2channel_2image
 avg_pool_1d_1channel_1image
 avg_pool_1d_1channel_2image
 avg_pool_1d_2channel_2image
@@ -1568,3 +1568,5 @@ IE_CPU.onnx_model_gather_float_2D_neg_indices
 onnx_model_skip_layer_normalization_dynamic_shapes
 # Doesn't support op with dynamic shapes
 onnx_model_embed_layer_normalization_dynamic_shapes
+# CPU plug-in doesn't support operation with dynamic rank
+onnx_model_attention_dynamic_shapes
diff --git a/ngraph/test/runtime/interpreter/CMakeLists.txt b/ngraph/test/runtime/interpreter/CMakeLists.txt
index 2b99d191106094..dc39db0fc28ff0 100644
--- a/ngraph/test/runtime/interpreter/CMakeLists.txt
+++ b/ngraph/test/runtime/interpreter/CMakeLists.txt
@@ -26,3 +26,8 @@ target_link_libraries(interpreter_backend PUBLIC ngraph_backend PRIVATE ngraph_r
 install(TARGETS interpreter_backend
         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
         LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
+if(NOT BUILD_SHARED_LIBS)
+    install(TARGETS interpreter_backend
+            RUNTIME DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL
+            LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
+endif()
diff --git a/ngraph/test/runtime/interpreter/evaluates_map.cpp b/ngraph/test/runtime/interpreter/evaluates_map.cpp
index 3cbb4e238403eb..c6d6a1e499f1fc 100644
--- a/ngraph/test/runtime/interpreter/evaluates_map.cpp
+++ b/ngraph/test/runtime/interpreter/evaluates_map.cpp
@@ -27,6 +27,7 @@
 #include <ngraph/runtime/reference/embedding_bag_offsets_sum.hpp>
 #include <ngraph/runtime/reference/embedding_bag_packed_sum.hpp>
 #include <ngraph/runtime/reference/embedding_segments_sum.hpp>
+#include <ngraph/runtime/reference/exp.hpp>
 #include <ngraph/runtime/reference/experimental_detectron_detection_output.hpp>
 #include <ngraph/runtime/reference/experimental_detectron_prior_grid_generator.hpp>
 #include <ngraph/runtime/reference/experimental_detectron_proposal_single_image.hpp>
@@ -45,6 +46,7 @@
 #include <ngraph/runtime/reference/gru_cell.hpp>
 #include <ngraph/runtime/reference/hard_sigmoid.hpp>
 #include <ngraph/runtime/reference/if.hpp>
+#include <ngraph/runtime/reference/log.hpp>
 #include <ngraph/runtime/reference/log_softmax.hpp>
 #include <ngraph/runtime/reference/lrn.hpp>
 #include <ngraph/runtime/reference/lstm_cell.hpp>
@@ -67,9 +69,11 @@
 #include <ngraph/runtime/reference/scatter_nd_update.hpp>
 #include <ngraph/runtime/reference/selu.hpp>
 #include <ngraph/runtime/reference/sequences.hpp>
+#include <ngraph/runtime/reference/sigmoid.hpp>
 #include <ngraph/runtime/reference/sign.hpp>
 #include <ngraph/runtime/reference/slice.hpp>
 #include <ngraph/runtime/reference/squared_difference.hpp>
+#include <ngraph/runtime/reference/tanh.hpp>
 #include <ngraph/runtime/reference/tensor_iterator.hpp>
 #include <ngraph/runtime/reference/utils/nms_common.hpp>
 
@@ -1684,6 +1688,42 @@ bool evaluate(const shared_ptr<op::v0::Abs>& op, const HostTensorVector& outputs
     return true;
 }
 
+template <element::Type_t ET>
+bool evaluate(const shared_ptr<op::v0::Sigmoid>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
+    using T = typename element_type_traits<ET>::value_type;
+    runtime::reference::sigmoid<T>(inputs[0]->get_data_ptr<T>(),
+                                   outputs[0]->get_data_ptr<T>(),
+                                   shape_size(inputs[0]->get_shape()));
+    return true;
+}
+
+template <element::Type_t ET>
+bool evaluate(const shared_ptr<op::v0::Exp>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
+    using T = typename element_type_traits<ET>::value_type;
+    runtime::reference::exp<T>(inputs[0]->get_data_ptr<T>(),
+                               outputs[0]->get_data_ptr<T>(),
+                               shape_size(inputs[0]->get_shape()));
+    return true;
+}
+
+template <element::Type_t ET>
+bool evaluate(const shared_ptr<op::v0::Tanh>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
+    using T = typename element_type_traits<ET>::value_type;
+    runtime::reference::tanh<T>(inputs[0]->get_data_ptr<T>(),
+                                outputs[0]->get_data_ptr<T>(),
+                                shape_size(inputs[0]->get_shape()));
+    return true;
+}
+
+template <element::Type_t ET>
+bool evaluate(const shared_ptr<op::v0::Log>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
+    using T = typename element_type_traits<ET>::value_type;
+    runtime::reference::log<T>(inputs[0]->get_data_ptr<T>(),
+                               outputs[0]->get_data_ptr<T>(),
+                               shape_size(inputs[0]->get_shape()));
+    return true;
+}
+
 namespace ctc_loss_v4 {
 template <element::Type_t t1, element::Type_t t2>
 inline void evaluate(const shared_ptr<op::v4::CTCLoss>& op,
@@ -2476,17 +2516,42 @@ bool evaluate(const shared_ptr<op::v5::GatherND>& op, const HostTensorVector& ou
         runtime::reference::gather_nd<T, int64_t>(inputs[0]->get_data_ptr<T>(),
                                                   inputs[1]->get_data_ptr<int64_t>(),
                                                   outputs[0]->get_data_ptr<T>(),
-                                                  op->get_input_shape(0),
-                                                  op->get_input_shape(1),
-                                                  op->get_output_shape(0),
+                                                  inputs[0]->get_shape(),
+                                                  inputs[1]->get_shape(),
+                                                  outputs[0]->get_shape(),
+                                                  op->get_batch_dims());
+    } else if (op->get_input_element_type(1) == element::i32) {
+        runtime::reference::gather_nd<T, int32_t>(inputs[0]->get_data_ptr<T>(),
+                                                  inputs[1]->get_data_ptr<int32_t>(),
+                                                  outputs[0]->get_data_ptr<T>(),
+                                                  inputs[0]->get_shape(),
+                                                  inputs[1]->get_shape(),
+                                                  outputs[0]->get_shape(),
+                                                  op->get_batch_dims());
+    } else {
+        throw ngraph_error("Unexpected indices type for GatherND operation");
+    }
+    return true;
+}
+
+template <element::Type_t ET>
+bool evaluate(const shared_ptr<op::v8::GatherND>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
+    using T = typename element_type_traits<ET>::value_type;
+    if (op->get_input_element_type(1) == element::i64) {
+        runtime::reference::gather_nd<T, int64_t>(inputs[0]->get_data_ptr<T>(),
+                                                  inputs[1]->get_data_ptr<int64_t>(),
+                                                  outputs[0]->get_data_ptr<T>(),
+                                                  inputs[0]->get_shape(),
+                                                  inputs[1]->get_shape(),
+                                                  outputs[0]->get_shape(),
                                                   op->get_batch_dims());
     } else if (op->get_input_element_type(1) == element::i32) {
         runtime::reference::gather_nd<T, int32_t>(inputs[0]->get_data_ptr<T>(),
                                                   inputs[1]->get_data_ptr<int32_t>(),
                                                   outputs[0]->get_data_ptr<T>(),
-                                                  op->get_input_shape(0),
-                                                  op->get_input_shape(1),
-                                                  op->get_output_shape(0),
+                                                  inputs[0]->get_shape(),
+                                                  inputs[1]->get_shape(),
+                                                  outputs[0]->get_shape(),
                                                   op->get_batch_dims());
     } else {
         throw ngraph_error("Unexpected indices type for GatherND operation");
diff --git a/ngraph/test/runtime/interpreter/opset_int_tbl.hpp b/ngraph/test/runtime/interpreter/opset_int_tbl.hpp
index b5e6584febe822..706c606c83a703 100644
--- a/ngraph/test/runtime/interpreter/opset_int_tbl.hpp
+++ b/ngraph/test/runtime/interpreter/opset_int_tbl.hpp
@@ -106,3 +106,9 @@ NGRAPH_OP(MulticlassNms, op::v8)
 NGRAPH_OP(Slice, op::v8)
 NGRAPH_OP(DeformableConvolution, ngraph::op::v8)
 NGRAPH_OP(If, ngraph::op::v8)
+NGRAPH_OP(GatherND, op::v8)
+
+NGRAPH_OP(Sigmoid, op::v0)
+NGRAPH_OP(Tanh, op::v0)
+NGRAPH_OP(Exp, op::v0)
+NGRAPH_OP(Log, op::v0)
diff --git a/ngraph/test/runtime/interpreter/unit_test.manifest b/ngraph/test/runtime/interpreter/unit_test.manifest
index eeb1970a214bd7..9ace71fff4a21d 100644
--- a/ngraph/test/runtime/interpreter/unit_test.manifest
+++ b/ngraph/test/runtime/interpreter/unit_test.manifest
@@ -125,7 +125,3 @@ onnx_model_deformable_conv_2d
 
 # No support for unsigned types
 INTERPRETER.zero_sized_negative
-
-# No support yet for RandomUniform
-INTERPRETER.onnx_model_random_uniform
-INTERPRETER.onnx_model_random_uniform_like
diff --git a/ngraph/test/threading.cpp b/ngraph/test/threading.cpp
index 1cfb462c654086..5e9cd3e3ee247a 100644
--- a/ngraph/test/threading.cpp
+++ b/ngraph/test/threading.cpp
@@ -2,36 +2,95 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include <gtest/gtest.h>
+
 #include <chrono>
 #include <mutex>
+#include <queue>
 #include <thread>
 #include <vector>
 
 #include "atomic_guard.hpp"
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
+#include "ngraph_ops/type_relaxed.hpp"
+#include "openvino/core/function.hpp"
+#include "openvino/core/node.hpp"
+#include "openvino/core/node_vector.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 using namespace ngraph;
 using namespace std;
 
+std::shared_ptr<ov::Function> create_complex_function(size_t wide = 50) {
+    const auto& split_subgraph = [](const ov::Output<ov::Node>& input) -> ov::OutputVector {
+        auto relu = std::make_shared<ov::opset8::Relu>(input);
+        auto type_relaxed =
+            std::make_shared<ngraph::op::TypeRelaxed<ov::opset8::Asin>>(std::vector<element::Type>{element::f32},
+                                                                        std::vector<element::Type>{element::f32},
+                                                                        relu);
+        auto axis_node = ov::opset8::Constant::create(ov::element::i64, Shape{}, {1});
+        auto split = std::make_shared<ov::opset8::Split>(type_relaxed, axis_node, 2);
+        return split->outputs();
+    };
+    const auto& concat_subgraph = [](const ov::OutputVector& inputs) -> ov::Output<ov::Node> {
+        auto concat = std::make_shared<ov::opset8::Concat>(inputs, 1);
+        auto type_relaxed =
+            std::make_shared<ngraph::op::TypeRelaxed<ov::opset8::Asin>>(std::vector<element::Type>{element::f32},
+                                                                        std::vector<element::Type>{element::f32},
+                                                                        concat);
+        auto relu = std::make_shared<ov::opset8::Relu>(concat);
+        return relu->output(0);
+    };
+
+    auto parameter = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape::dynamic(4));
+    std::queue<ov::Output<ov::Node>> nodes;
+    {
+        auto outputs = split_subgraph(parameter->output(0));
+        for (const auto& out : outputs) {
+            nodes.push(out);
+        }
+    }
+
+    while (nodes.size() < wide) {
+        auto first = nodes.front();
+        nodes.pop();
+        auto outputs = split_subgraph(first);
+
+        for (const auto& out : outputs) {
+            nodes.push(out);
+        }
+    }
+
+    while (nodes.size() > 1) {
+        auto first = nodes.front();
+        nodes.pop();
+        auto second = nodes.front();
+        nodes.pop();
+        auto out = concat_subgraph(ov::OutputVector{first, second});
+
+        nodes.push(out);
+    }
+    auto result = std::make_shared<ov::opset8::Result>(nodes.front());
+    return std::make_shared<Function>(ov::ResultVector{result}, ov::ParameterVector{parameter});
+}
+
 TEST(threading, get_friendly_name) {
     const size_t number = 20;
     Shape shape{};
-    auto a = make_shared<op::Parameter>(element::i32, shape);
-    auto iconst0 = op::Constant::create(element::i32, Shape{}, {0});
-    auto add_a1 = make_shared<op::v1::Add>(a, iconst0);
-    auto add_a2 = make_shared<op::v1::Add>(add_a1, iconst0);
-    auto add_a3 = make_shared<op::v1::Add>(add_a2, iconst0);
-    auto abs_add_a3 = std::make_shared<op::Abs>(add_a3);
+    auto a = make_shared<ov::opset8::Parameter>(element::i32, shape);
+    auto iconst0 = ov::opset8::Constant::create(element::i32, Shape{}, {0});
+    auto add_a1 = make_shared<ov::opset8::Add>(a, iconst0);
+    auto add_a2 = make_shared<ov::opset8::Add>(add_a1, iconst0);
+    auto add_a3 = make_shared<ov::opset8::Add>(add_a2, iconst0);
+    auto abs_add_a3 = std::make_shared<ov::opset8::Abs>(add_a3);
 
     auto b = make_shared<op::Parameter>(element::i32, shape);
-    auto add_b1 = make_shared<op::v1::Add>(b, iconst0);
-    auto add_b2 = make_shared<op::v1::Add>(add_b1, iconst0);
-    auto abs_add_b2 = std::make_shared<op::Abs>(add_b2);
+    auto add_b1 = make_shared<ov::opset8::Add>(b, iconst0);
+    auto add_b2 = make_shared<ov::opset8::Add>(add_b1, iconst0);
+    auto abs_add_b2 = std::make_shared<ov::opset8::Abs>(add_b2);
 
-    auto graph = make_shared<op::v1::Multiply>(abs_add_a3, abs_add_b2);
+    auto graph = make_shared<ov::opset8::Multiply>(abs_add_a3, abs_add_b2);
 
-    auto f = std::make_shared<Function>(ngraph::NodeVector{graph}, ParameterVector{a, b});
+    auto f = std::make_shared<Function>(ov::NodeVector{graph}, ParameterVector{a, b});
 
     const auto compare_names = [](const std::vector<std::string>& names) {
         static std::unordered_set<std::string> ref_names;
@@ -87,3 +146,39 @@ TEST(threading, check_atomic_guard) {
     }
     ASSERT_EQ(result, 15);
 }
+
+TEST(threading, clone_with_new_inputs) {
+    auto function = create_complex_function(100);
+    const auto cloneNodes = [&](const std::shared_ptr<const ngraph::Function>& f) {
+        auto orderedOps = function->get_ordered_ops();
+        std::vector<std::shared_ptr<ov::Node>> nodes;
+        for (const auto& op : orderedOps) {
+            ngraph::OutputVector inputsForShapeInfer;
+            std::shared_ptr<ngraph::Node> opToShapeInfer;
+
+            const auto inSize = op->get_input_size();
+            for (size_t i = 0; i < inSize; i++) {
+                if (dynamic_cast<ov::opset8::Constant*>(op->get_input_node_ptr(i))) {
+                    inputsForShapeInfer.push_back(op->get_input_node_ptr(i)->clone_with_new_inputs(ov::OutputVector{}));
+                } else {
+                    inputsForShapeInfer.push_back(
+                        std::make_shared<ov::opset8::Parameter>(op->get_input_element_type(i),
+                                                                op->get_input_partial_shape(i)));
+                }
+            }
+
+            opToShapeInfer = op->clone_with_new_inputs(inputsForShapeInfer);
+            nodes.push_back(opToShapeInfer);
+        }
+    };
+
+    const size_t numThreads = 6;
+    std::vector<std::thread> threads(numThreads);
+
+    for (auto&& thread : threads)
+        thread = std::thread(cloneNodes, function);
+
+    for (auto&& th : threads) {
+        th.join();
+    }
+}
diff --git a/ngraph/test/type_info.cpp b/ngraph/test/type_info.cpp
index 45b2ae47d89762..ddba89fc5d33c0 100644
--- a/ngraph/test/type_info.cpp
+++ b/ngraph/test/type_info.cpp
@@ -5,6 +5,8 @@
 #include <gtest/gtest.h>
 
 #include "openvino/core/type.hpp"
+#include "openvino/opsets/opset.hpp"
+#include "openvino/util/common_util.hpp"
 
 TEST(type_info, compare_old_type) {
     ov::DiscreteTypeInfo type1("type1", 0);
@@ -47,3 +49,75 @@ TEST(type_info, compare_new_with_old_type) {
     ov::DiscreteTypeInfo type1_o("type1", 0);
     ASSERT_TRUE(type1 == type1_o);
 }
+
+TEST(type_info, check_hash_value) {
+    const auto& hash_val = [](const char* name, const char* version_id, uint64_t version) -> size_t {
+        size_t name_hash = name ? std::hash<std::string>()(std::string(name)) : 0;
+        size_t version_hash = std::hash<decltype(version)>()(version);
+        size_t version_id_hash = version_id ? std::hash<std::string>()(std::string(version_id)) : 0;
+        // don't use parent for hash calculation, it is not a part of type (yet)
+        return ov::util::hash_combine(std::vector<size_t>{name_hash, version_hash, version_id_hash});
+    };
+    ov::DiscreteTypeInfo type("type1", 0, "version1");
+    ov::DiscreteTypeInfo type_old("type1", 1);
+    ov::DiscreteTypeInfo type_with_version("type1", 1, "version1");
+    ov::DiscreteTypeInfo type_empty_name("", 0);
+    ov::DiscreteTypeInfo type_empty_ver("type", 0, "");
+    EXPECT_EQ(hash_val(type.name, type.version_id, type.version), type.hash());
+    EXPECT_EQ(hash_val(type_old.name, type_old.version_id, type_old.version), type_old.hash());
+    EXPECT_EQ(hash_val(type_with_version.name, type_with_version.version_id, type_with_version.version),
+              type_with_version.hash());
+    EXPECT_EQ(hash_val(type_empty_name.name, type_empty_name.version_id, type_empty_name.version),
+              type_empty_name.hash());
+    EXPECT_EQ(hash_val(type_empty_ver.name, type_empty_ver.version_id, type_empty_ver.version), type_empty_ver.hash());
+}
+
+TEST(type_info, find_in_map) {
+    std::vector<std::string> vector_names;
+    ov::DiscreteTypeInfo a("Mod", 1, "opset1");
+    ov::DiscreteTypeInfo b("Prelu", 0, "opset1");
+    ov::DiscreteTypeInfo c("Vector", 0);
+    ov::DiscreteTypeInfo d("Mod", 1, "opset3");
+    ov::DiscreteTypeInfo f("Mod", 2);
+
+    std::map<ov::DiscreteTypeInfo, int> test_map;
+    test_map[a] = 1;
+    test_map[b] = 1;
+    test_map[c] = 1;
+
+    const auto& opset = ov::get_opset8();
+    // Reserve memory to avoid reallocation and copy of strings
+    // because type info uses pointers from the original memory
+    vector_names.reserve(opset.size() * 3);
+    for (const auto& type : opset.get_types_info()) {
+        test_map[type] = 2;
+        std::string name = type.name;
+        vector_names.emplace_back(name);
+        ov::DiscreteTypeInfo t(vector_names.rbegin()->c_str(), 1000);
+        ov::DiscreteTypeInfo t2(vector_names.rbegin()->c_str(), 0);
+        test_map[t] = 3;
+        test_map[t2] = 4;
+        std::string name1 = "a" + name;
+        vector_names.emplace_back(name1);
+        ov::DiscreteTypeInfo t3(vector_names.rbegin()->c_str(), 1000);
+        ov::DiscreteTypeInfo t4(vector_names.rbegin()->c_str(), 0);
+        test_map[t3] = 5;
+        test_map[t4] = 6;
+        std::string name2 = name + "z";
+        vector_names.emplace_back(name2);
+        ov::DiscreteTypeInfo t5(vector_names.rbegin()->c_str(), 1000);
+        ov::DiscreteTypeInfo t6(vector_names.rbegin()->c_str(), 0);
+        test_map[t5] = 7;
+        test_map[t6] = 8;
+    }
+
+    for (const auto& it : test_map) {
+        ASSERT_NE(test_map.end(), test_map.find(it.first));
+    }
+
+    ASSERT_NE(test_map.end(), test_map.find(a));
+    ASSERT_NE(test_map.end(), test_map.find(b));
+    ASSERT_NE(test_map.end(), test_map.find(c));
+    ASSERT_EQ(test_map.end(), test_map.find(d));
+    ASSERT_EQ(test_map.end(), test_map.find(f));
+}
diff --git a/ngraph/test/type_prop/gather_nd.cpp b/ngraph/test/type_prop/gather_nd.cpp
index e05c23a52b3dc8..ab3ab01d2b8c25 100644
--- a/ngraph/test/type_prop/gather_nd.cpp
+++ b/ngraph/test/type_prop/gather_nd.cpp
@@ -316,3 +316,135 @@ TEST(type_prop, gather_nd_fail_indices_element_type) {
         FAIL() << "Deduced type check failed for unexpected reason";
     }
 }
+
+// ------------------------------ V8 ------------------------------
+
+TEST(type_prop, gather_nd_8_slices_from_4d_batch_dims0) {
+    Shape params_shape{2, 3, 11, 12};
+    Shape indices_shape{2, 3, 2};
+    Shape out_shape{2, 3, 11, 12};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+    auto G5 = make_shared<op::v8::GatherND>(P, I, 0);
+    ASSERT_EQ(G5->get_element_type(), element::f32);
+    ASSERT_EQ(G5->get_shape(), out_shape);
+}
+
+TEST(type_prop, gather_nd_8_scalars_from_4d_batch_dims2) {
+    Shape params_shape{2, 3, 11, 12};
+    Shape indices_shape{2, 3, 2};
+    Shape out_shape{2, 3};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+    auto G5 = make_shared<op::v8::GatherND>(P, I, 2);
+    ASSERT_EQ(G5->get_element_type(), element::f32);
+    ASSERT_EQ(G5->get_shape(), out_shape);
+}
+
+TEST(type_prop, gather_nd_8_slices_from_5d_batch_dims2) {
+    Shape params_shape{7, 5, 11, 12, 32};
+    Shape indices_shape{7, 5, 3, 1};
+    Shape out_shape{7, 5, 3, 12, 32};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+    auto G5 = make_shared<op::v8::GatherND>(P, I, 2);
+    ASSERT_EQ(G5->get_element_type(), element::f32);
+    ASSERT_EQ(G5->get_shape(), out_shape);
+}
+
+TEST(type_prop, gather_nd_8_batch_dim2_with_dyn_dim) {
+    PartialShape params_shape{7, Dimension::dynamic(), 11, 12, 32};
+    Shape indices_shape{7, 5, 3, 1};
+    Shape out_shape{7, 5, 3, 12, 32};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+    auto G5 = make_shared<op::v8::GatherND>(P, I, 2);
+    ASSERT_EQ(G5->get_element_type(), element::f32);
+    ASSERT_EQ(G5->get_shape(), out_shape);
+}
+
+TEST(type_prop, gather_nd_8_batch_dim2_with_dyn_dim2) {
+    PartialShape params_shape{7, Dimension::dynamic(), Dimension::dynamic(), 12, 32};
+    Shape indices_shape{7, 5, 3, 1};
+    Shape out_shape{7, 5, 3, 12, 32};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+    auto G5 = make_shared<op::v8::GatherND>(P, I, 2);
+    ASSERT_EQ(G5->get_element_type(), element::f32);
+    ASSERT_EQ(G5->get_shape(), out_shape);
+}
+
+TEST(type_prop, gather_nd_8_batch_dim2_with_dyn_dim3) {
+    PartialShape params_shape{7, Dimension::dynamic(), Dimension::dynamic(), 12, Dimension::dynamic()};
+    Shape indices_shape{7, 5, 3, 1};
+    PartialShape out_shape{7, 5, 3, 12, Dimension::dynamic()};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+    auto G5 = make_shared<op::v8::GatherND>(P, I, 2);
+    ASSERT_EQ(G5->get_element_type(), element::f32);
+    ASSERT_TRUE(G5->get_output_partial_shape(0).same_scheme(out_shape));
+}
+
+TEST(type_prop, gather_nd_8_batch_dim0_with_dyn_ind_dim) {
+    PartialShape params_shape{7, Dimension::dynamic(), Dimension::dynamic(), 12, Dimension::dynamic()};
+    PartialShape indices_shape{7, 5, 3, Dimension::dynamic()};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+    auto G5 = make_shared<op::v8::GatherND>(P, I, 0);
+    ASSERT_EQ(G5->get_element_type(), element::f32);
+    ASSERT_TRUE(G5->get_output_partial_shape(0).same_scheme(PartialShape::dynamic()));
+}
+
+TEST(type_prop, gather_nd_8_fail_batch_dims_greater_indices_rank) {
+    Shape params_shape{2, 3, 4, 5};
+    Shape indices_shape{2, 1};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+
+    try {
+        auto G5 = make_shared<op::v8::GatherND>(P, I, 3);
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Incorrect indices rank";
+    } catch (const NodeValidationFailure& error) {
+        EXPECT_HAS_SUBSTRING(error.what(),
+                             std::string("Number of batch dimensions must not exceed a rank of indices."));
+    } catch (...) {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, gather_nd_8_fail_unequal_batch_dims) {
+    Shape params_shape{2, 3, 4, 5};
+    Shape indices_shape{2, 1, 4};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+
+    try {
+        auto G5 = make_shared<op::v8::GatherND>(P, I, 2);
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Incorrect indices rank";
+    } catch (const NodeValidationFailure& error) {
+        EXPECT_HAS_SUBSTRING(error.what(), std::string("Batch dimensions of data and indices must be the same."));
+    } catch (...) {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
+TEST(type_prop, gather_nd_8_fail_indices_tuple_greater_data_rank_batch_dims2) {
+    Shape params_shape{2, 1, 4, 5};
+    Shape indices_shape{2, 1, 5, 3};
+    auto P = make_shared<op::Parameter>(element::f32, params_shape);
+    auto I = make_shared<op::Parameter>(element::i32, indices_shape);
+
+    try {
+        auto G5 = make_shared<op::v8::GatherND>(P, I, 2);
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Incorrect indices rank";
+    } catch (const NodeValidationFailure& error) {
+        EXPECT_HAS_SUBSTRING(error.what(),
+                             std::string("Length of a tuple with indices must not exceed a rank of "
+                                         "data tensor excluding batch dimensions."));
+    } catch (...) {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
diff --git a/ngraph/test/type_prop/lstm_sequence.cpp b/ngraph/test/type_prop/lstm_sequence.cpp
index 320268864a8c69..c815f7cdfd1815 100644
--- a/ngraph/test/type_prop/lstm_sequence.cpp
+++ b/ngraph/test/type_prop/lstm_sequence.cpp
@@ -324,3 +324,22 @@ TEST(type_prop, lstm_sequence_invalid_input_dynamic_rank) {
         EXPECT_EQ(check_dynamic_lstm(lstm_sequence), true);
     }
 }
+
+TEST(type_prop, lstm_sequence_invalid_input_direction) {
+    recurrent_sequence_parameters param;
+
+    param.batch_size = 24;
+    param.num_directions = 3;
+    param.seq_length = 12;
+    param.input_size = 8;
+    param.hidden_size = 256;
+    param.et = element::f32;
+
+    auto lstm_sequence = lstm_seq_tensor_initialization(param);
+    try {
+        lstm_sequence->validate_and_infer_types();
+    } catch (const NodeValidationFailure& error) {
+        EXPECT_HAS_SUBSTRING(error.what(),
+                             std::string("Parameter direction must be Forward or Reverse or Bidirectional"));
+    }
+}
diff --git a/ngraph/test/util/test_tools.hpp b/ngraph/test/util/test_tools.hpp
index 1a3dc91ad0d959..f559fe3c48f75b 100644
--- a/ngraph/test/util/test_tools.hpp
+++ b/ngraph/test/util/test_tools.hpp
@@ -55,3 +55,12 @@ std::vector<T> read_binary_file(const std::string& path) {
     inputs_fs.read(reinterpret_cast<char*>(file_content.data()), size);
     return file_content;
 }
+
+template <typename T = int32_t>
+std::vector<T> gen_range(const size_t elements, const T start = T{0}) {
+    std::vector<T> range;
+    range.resize(elements);
+    std::iota(range.begin(), range.end(), start);
+
+    return range;
+}
diff --git a/ngraph/test/visitors/op/ctc_greedy_decoder.cpp b/ngraph/test/visitors/op/ctc_greedy_decoder.cpp
new file mode 100644
index 00000000000000..4c1bc7fd9750ad
--- /dev/null
+++ b/ngraph/test/visitors/op/ctc_greedy_decoder.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/opsets/opset1.hpp"
+#include "util/visitor.hpp"
+
+using namespace std;
+using namespace ngraph;
+using ngraph::test::NodeBuilder;
+using ngraph::test::ValueMap;
+
+TEST(attributes, ctc_greedy_decoder_op) {
+    NodeBuilder::get_ops().register_factory<opset1::CTCGreedyDecoder>();
+    bool m_ctc_merge_repeated = false;
+    auto data = make_shared<op::Parameter>(element::f32, Shape{3, 1, 3});
+    auto masks = make_shared<op::Parameter>(element::i32, Shape{3, 1});
+    auto decoder = make_shared<op::v0::CTCGreedyDecoder>(data, masks, m_ctc_merge_repeated);
+
+    NodeBuilder builder(decoder);
+    auto g_decoder = ov::as_type_ptr<opset1::CTCGreedyDecoder>(builder.create());
+
+    EXPECT_EQ(g_decoder->get_ctc_merge_repeated(), decoder->get_ctc_merge_repeated());
+}
diff --git a/ngraph/test/visitors/op/ctc_greedy_decoder_seq_len.cpp b/ngraph/test/visitors/op/ctc_greedy_decoder_seq_len.cpp
new file mode 100644
index 00000000000000..b4536a93fd4115
--- /dev/null
+++ b/ngraph/test/visitors/op/ctc_greedy_decoder_seq_len.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/opsets/opset1.hpp"
+#include "ngraph/opsets/opset6.hpp"
+#include "util/visitor.hpp"
+
+using namespace std;
+using namespace ngraph;
+using ngraph::test::NodeBuilder;
+using ngraph::test::ValueMap;
+
+TEST(attributes, ctc_greedy_decoder_seq_len_op) {
+    NodeBuilder::get_ops().register_factory<opset6::CTCGreedyDecoderSeqLen>();
+    bool merge_repeated = false;
+    auto data = make_shared<op::Parameter>(element::f32, Shape{1, 3, 3});
+    auto seq_len = make_shared<op::Parameter>(element::i32, Shape{1});
+    auto blank_index = op::Constant::create<int32_t>(element::i32, Shape{}, {2});
+    auto decoder = make_shared<op::v6::CTCGreedyDecoderSeqLen>(data, seq_len, blank_index, merge_repeated);
+
+    NodeBuilder builder(decoder);
+    auto g_decoder = ov::as_type_ptr<opset6::CTCGreedyDecoderSeqLen>(builder.create());
+
+    EXPECT_EQ(g_decoder->get_merge_repeated(), decoder->get_merge_repeated());
+}
diff --git a/ngraph/test/visitors/op/embedding_segments_sum.cpp b/ngraph/test/visitors/op/embedding_segments_sum.cpp
new file mode 100644
index 00000000000000..df1a0ed704664f
--- /dev/null
+++ b/ngraph/test/visitors/op/embedding_segments_sum.cpp
@@ -0,0 +1,36 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/op/util/attr_types.hpp"
+#include "ngraph/opsets/opset3.hpp"
+#include "util/visitor.hpp"
+
+using namespace std;
+using namespace ngraph;
+using ngraph::test::NodeBuilder;
+using ngraph::test::ValueMap;
+
+TEST(visitor_without_attribute, embedding_segments_sum_op) {
+    NodeBuilder::get_ops().register_factory<opset3::EmbeddingSegmentsSum>();
+    auto emb_table = make_shared<op::Parameter>(element::f32, Shape{5, 2, 3});
+
+    auto indices = make_shared<op::Parameter>(element::i64, Shape{4});
+    auto segment_ids = make_shared<op::Parameter>(element::i64, Shape{4});
+    auto num_segments = make_shared<op::Parameter>(element::i64, Shape{});
+    auto per_sample_weights = make_shared<op::Parameter>(element::f32, Shape{4});
+    auto default_index = make_shared<op::Parameter>(element::i64, Shape{});
+
+    auto ess = make_shared<opset3::EmbeddingSegmentsSum>(emb_table,
+                                                         indices,
+                                                         segment_ids,
+                                                         num_segments,
+                                                         default_index,
+                                                         per_sample_weights);
+    NodeBuilder builder(ess);
+
+    const auto expected_attr_count = 0;
+    EXPECT_EQ(builder.get_value_map_size(), expected_attr_count);
+}
diff --git a/ngraph/test/visitors/op/embeddingbag_offsetssum.cpp b/ngraph/test/visitors/op/embeddingbag_offsetssum.cpp
new file mode 100644
index 00000000000000..f2b60a51b09701
--- /dev/null
+++ b/ngraph/test/visitors/op/embeddingbag_offsetssum.cpp
@@ -0,0 +1,31 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/op/util/attr_types.hpp"
+#include "ngraph/opsets/opset3.hpp"
+#include "util/visitor.hpp"
+
+using namespace std;
+using namespace ngraph;
+using ngraph::test::NodeBuilder;
+using ngraph::test::ValueMap;
+
+TEST(visitor_without_attribute, embedding_bag_offsets_sum_op) {
+    NodeBuilder::get_ops().register_factory<opset3::EmbeddingBagOffsetsSum>();
+    auto emb_table = make_shared<op::Parameter>(element::f32, Shape{5, 2, 3});
+
+    auto indices = make_shared<op::Parameter>(element::i64, Shape{4});
+    auto offsets = make_shared<op::Parameter>(element::i64, Shape{4});
+    auto default_index = make_shared<op::Parameter>(element::i64, Shape{});
+    auto per_sample_weights = make_shared<op::Parameter>(element::f32, Shape{4});
+
+    auto ebos =
+        make_shared<opset3::EmbeddingBagOffsetsSum>(emb_table, indices, offsets, default_index, per_sample_weights);
+    NodeBuilder builder(ebos);
+
+    const auto expected_attr_count = 0;
+    EXPECT_EQ(builder.get_value_map_size(), expected_attr_count);
+}
diff --git a/ngraph/test/visitors/op/embeddingbag_packedsum.cpp b/ngraph/test/visitors/op/embeddingbag_packedsum.cpp
new file mode 100644
index 00000000000000..b74da5949a20b6
--- /dev/null
+++ b/ngraph/test/visitors/op/embeddingbag_packedsum.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/op/util/attr_types.hpp"
+#include "ngraph/opsets/opset3.hpp"
+#include "util/visitor.hpp"
+
+using namespace std;
+using namespace ngraph;
+using ngraph::test::NodeBuilder;
+using ngraph::test::ValueMap;
+
+TEST(visitor_without_attribute, embedding_bag_packed_sum_op) {
+    NodeBuilder::get_ops().register_factory<opset3::EmbeddingBagOffsetsSum>();
+    auto emb_table = make_shared<op::Parameter>(element::f32, Shape{5, 2});
+    auto indices = make_shared<op::Parameter>(element::i64, Shape{3, 4});
+    auto per_sample_weights = make_shared<op::Parameter>(element::f32, Shape{3, 4});
+
+    auto ebos = make_shared<opset3::EmbeddingBagPackedSum>(emb_table, indices, per_sample_weights);
+    NodeBuilder builder(ebos);
+
+    const auto expected_attr_count = 0;
+    EXPECT_EQ(builder.get_value_map_size(), expected_attr_count);
+}
diff --git a/ngraph/test/visitors/op/experimental_detectron_topkrois.cpp b/ngraph/test/visitors/op/experimental_detectron_topkrois.cpp
new file mode 100644
index 00000000000000..8f77ffed7416cf
--- /dev/null
+++ b/ngraph/test/visitors/op/experimental_detectron_topkrois.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/opsets/opset1.hpp"
+#include "ngraph/opsets/opset6.hpp"
+#include "util/visitor.hpp"
+
+using namespace std;
+using namespace ngraph;
+using ngraph::test::NodeBuilder;
+using ngraph::test::ValueMap;
+
+TEST(attributes, experimental_detectron_topkrois_op) {
+    NodeBuilder::get_ops().register_factory<opset6::ExperimentalDetectronTopKROIs>();
+    size_t num_rois = 1;
+    auto input_rois = std::make_shared<op::Parameter>(element::f32, Shape{2, 4});
+    auto input_probs = std::make_shared<op::Parameter>(element::f32, Shape{2});
+    auto topkrois = std::make_shared<op::v6::ExperimentalDetectronTopKROIs>(input_rois, input_probs, num_rois);
+
+    NodeBuilder builder(topkrois);
+    auto g_topkrois = ov::as_type_ptr<opset6::ExperimentalDetectronTopKROIs>(builder.create());
+
+    EXPECT_EQ(g_topkrois->get_max_rois(), topkrois->get_max_rois());
+}
diff --git a/ngraph/test/visitors/op/gather_nd.cpp b/ngraph/test/visitors/op/gather_nd.cpp
index 6195a2e14e434c..5bfba312e0c78e 100644
--- a/ngraph/test/visitors/op/gather_nd.cpp
+++ b/ngraph/test/visitors/op/gather_nd.cpp
@@ -6,6 +6,7 @@
 #include "ngraph/ngraph.hpp"
 #include "ngraph/opsets/opset1.hpp"
 #include "ngraph/opsets/opset5.hpp"
+#include "ngraph/opsets/opset8.hpp"
 #include "util/visitor.hpp"
 
 using namespace std;
@@ -13,7 +14,7 @@ using namespace ngraph;
 using ngraph::test::NodeBuilder;
 using ngraph::test::ValueMap;
 
-TEST(attributes, gather_nd_op) {
+TEST(attributes, gather_nd_v5_op) {
     NodeBuilder::get_ops().register_factory<opset5::GatherND>();
     int batch_dims = 1;
     auto P = make_shared<op::Parameter>(element::f32, Shape{2, 3, 4});
@@ -25,3 +26,16 @@ TEST(attributes, gather_nd_op) {
 
     EXPECT_EQ(g_G->get_batch_dims(), G->get_batch_dims());
 }
+
+TEST(attributes, gather_nd_v8_op) {
+    NodeBuilder::get_ops().register_factory<opset8::GatherND>();
+    int batch_dims = 1;
+    auto P = make_shared<op::Parameter>(element::f32, Shape{2, 3, 4});
+    auto I = make_shared<op::Parameter>(element::i32, Shape{2, 1});
+    auto G = make_shared<op::v8::GatherND>(P, I, batch_dims);
+
+    NodeBuilder builder(G);
+    auto g_G = ov::as_type_ptr<opset8::GatherND>(builder.create());
+
+    EXPECT_EQ(g_G->get_batch_dims(), G->get_batch_dims());
+}
diff --git a/ngraph/test/visitors/op/roll.cpp b/ngraph/test/visitors/op/roll.cpp
new file mode 100644
index 00000000000000..af2a043bbd9aed
--- /dev/null
+++ b/ngraph/test/visitors/op/roll.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "gtest/gtest.h"
+#include "ngraph/ngraph.hpp"
+#include "ngraph/op/util/attr_types.hpp"
+#include "ngraph/opsets/opset7.hpp"
+#include "util/visitor.hpp"
+
+using namespace std;
+using namespace ngraph;
+using ngraph::test::NodeBuilder;
+
+TEST(attributes, roll_op) {
+    NodeBuilder::get_ops().register_factory<opset7::Roll>();
+    const auto A = make_shared<op::Parameter>(element::f32, Shape{4, 3});
+    const auto B = make_shared<op::Constant>(element::i32, Shape{3});
+    const auto C = make_shared<op::Constant>(element::i32, Shape{3});
+
+    const auto roll = make_shared<opset7::Roll>(A, B, C);
+    NodeBuilder builder(roll);
+
+    const auto expected_attr_count = 0;
+    EXPECT_EQ(builder.get_value_map_size(), expected_attr_count);
+}
diff --git a/openvino/conditional_compilation/CMakeLists.txt b/openvino/conditional_compilation/CMakeLists.txt
index 6c786d401a28a7..70fb4484e7692f 100644
--- a/openvino/conditional_compilation/CMakeLists.txt
+++ b/openvino/conditional_compilation/CMakeLists.txt
@@ -10,7 +10,8 @@ add_library(openvino::conditional_compilation ALIAS ${TARGET_NAME})
 
 target_link_libraries(${TARGET_NAME} INTERFACE openvino::itt)
 
-target_include_directories(${TARGET_NAME} INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include)
+target_include_directories(${TARGET_NAME} INTERFACE
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
 
 if(SELECTIVE_BUILD STREQUAL "COLLECT")
     target_compile_definitions(${TARGET_NAME} INTERFACE SELECTIVE_BUILD_ANALYZER)
@@ -45,5 +46,7 @@ elseif(SELECTIVE_BUILD STREQUAL "ON")
     ov_force_include(${TARGET_NAME} INTERFACE ${GENERATED_HEADER})
 endif()
 
+ov_install_static_lib(${TARGET_NAME} ngraph)
+
 file(GLOB_RECURSE hdrs ${CMAKE_CURRENT_SOURCE_DIR}/include/*.h ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_SOURCES ${hdrs})
diff --git a/openvino/itt/CMakeLists.txt b/openvino/itt/CMakeLists.txt
index d45b9857ada43a..fbeab9f3e0d2eb 100644
--- a/openvino/itt/CMakeLists.txt
+++ b/openvino/itt/CMakeLists.txt
@@ -30,6 +30,9 @@ if (CMAKE_COMPILER_IS_GNUCXX)
     target_compile_options(${TARGET_NAME} PRIVATE -Wall)
 endif()
 
-target_include_directories(${TARGET_NAME} PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include)
+target_include_directories(${TARGET_NAME} PUBLIC
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
+
+ov_install_static_lib(${TARGET_NAME} openvino_common)
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
diff --git a/openvino/pp/CMakeLists.txt b/openvino/pp/CMakeLists.txt
index 9583171dcbce60..e2060db6b3fae3 100644
--- a/openvino/pp/CMakeLists.txt
+++ b/openvino/pp/CMakeLists.txt
@@ -8,4 +8,7 @@ add_library(${TARGET_NAME} INTERFACE)
 
 add_library(openvino::pp ALIAS ${TARGET_NAME})
 
-target_include_directories(${TARGET_NAME} INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include)
+target_include_directories(${TARGET_NAME} INTERFACE
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
+
+ov_install_static_lib(${TARGET_NAME} ngraph)
diff --git a/openvino/util/CMakeLists.txt b/openvino/util/CMakeLists.txt
index 54c9082810c67e..547e77783ec59e 100644
--- a/openvino/util/CMakeLists.txt
+++ b/openvino/util/CMakeLists.txt
@@ -33,7 +33,10 @@ add_library(${TARGET_NAME} STATIC ${LIBRARY_SRC} ${PUBLIC_HEADERS})
 add_library(openvino::util ALIAS ${TARGET_NAME})
 
 target_link_libraries(${TARGET_NAME} PRIVATE ${CMAKE_DL_LIBS})
-target_include_directories(${TARGET_NAME} PUBLIC ${UTIL_INCLUDE_DIR})
+target_include_directories(${TARGET_NAME} PUBLIC
+    $<BUILD_INTERFACE:${UTIL_INCLUDE_DIR}>)
+
+ov_install_static_lib(${TARGET_NAME} ngraph)
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
diff --git a/openvino/util/include/openvino/util/common_util.hpp b/openvino/util/include/openvino/util/common_util.hpp
index 6c519f06611b13..60ee9e6e2a181d 100644
--- a/openvino/util/include/openvino/util/common_util.hpp
+++ b/openvino/util/include/openvino/util/common_util.hpp
@@ -37,6 +37,8 @@ std::string to_lower(const std::string& s);
 
 std::string to_upper(const std::string& s);
 
+size_t hash_combine(const std::vector<size_t>& list);
+
 /**
  * @brief trim from start (in place)
  * @param s - string to trim
diff --git a/openvino/util/src/common_util.cpp b/openvino/util/src/common_util.cpp
index b3aba13d41f9fb..82872c486f1040 100644
--- a/openvino/util/src/common_util.cpp
+++ b/openvino/util/src/common_util.cpp
@@ -40,3 +40,11 @@ std::vector<std::string> ov::util::split(const std::string& src, char delimiter,
     }
     return rc;
 }
+
+size_t ov::util::hash_combine(const std::vector<size_t>& list) {
+    size_t seed = 0;
+    for (size_t v : list) {
+        seed ^= v + 0x9e3779b9 + (seed << 6) + (seed >> 2);
+    }
+    return seed;
+}
diff --git a/openvino/util/src/file_util.cpp b/openvino/util/src/file_util.cpp
index 9b0bf1a553f02b..7732fcebe31115 100644
--- a/openvino/util/src/file_util.cpp
+++ b/openvino/util/src/file_util.cpp
@@ -328,7 +328,7 @@ static std::string get_ov_library_path_a() {
 #elif defined(__APPLE__) || defined(__linux__)
     Dl_info info;
     dladdr(reinterpret_cast<void*>(ov::util::get_ov_lib_path), &info);
-    std::string result = get_path_name(std::string(info.dli_fname)).c_str();
+    std::string result = get_path_name(ov::util::get_absolute_file_path(info.dli_fname)).c_str();
     if (!ov::util::ends_with(result, "/lib") && !ov::util::ends_with(result, "/lib/"))
         result = ov::util::path_join({result, "lib"});
     return result;
diff --git a/runtime/bindings/python/CMakeLists.txt b/runtime/bindings/python/CMakeLists.txt
index c3e3786579b19c..5cf2653ff3baa4 100644
--- a/runtime/bindings/python/CMakeLists.txt
+++ b/runtime/bindings/python/CMakeLists.txt
@@ -6,6 +6,10 @@ cmake_minimum_required (VERSION 3.13)
 
 project(OpenVINOPython DESCRIPTION "OpenVINO Runtime Python bindings")
 
+if(NOT DEFINED OpenVINO_SOURCE_DIR)
+    find_package(InferenceEngineDeveloperPackage REQUIRED)
+endif()
+
 add_subdirectory(thirdparty/pybind11 EXCLUDE_FROM_ALL)
 
 set(LIBRARY_OUTPUT_DIRECTORY_BIN ${CMAKE_LIBRARY_OUTPUT_DIRECTORY})
@@ -24,3 +28,7 @@ if(NGRAPH_UNIT_TEST_ENABLE)
     add_subdirectory(tests/mock/pyngraph_fe_mock_api)
     add_dependencies(_pyngraph pybind_mock_frontend)
 endif()
+
+if(InferenceEngineDeveloperPackage_FOUND)
+    ie_cpack(${IE_CPACK_COMPONENTS_ALL})
+endif()
diff --git a/runtime/bindings/python/setup.py b/runtime/bindings/python/setup.py
index 6a4dd6268b335b..83c84174e6e1bf 100644
--- a/runtime/bindings/python/setup.py
+++ b/runtime/bindings/python/setup.py
@@ -33,7 +33,6 @@
     "ngraph.opset5",
     "ngraph.opset6",
     "ngraph.opset7",
-    "ngraph.opset8",
     "ngraph.utils",
     "ngraph.impl",
     "ngraph.impl.op",
@@ -41,6 +40,8 @@
     "ngraph.impl.passes",
     "ngraph.frontend",
     "openvino",
+    # TODO: change the module name according to the description in 69196
+    "openvino.offline_transformations_pybind",
     "openvino.opset1",
     "openvino.opset2",
     "openvino.opset3",
@@ -168,13 +169,13 @@ def build_cmake(self, extension: Extension):
         bin_dir = os.path.join(OPENVINO_ROOT_DIR, "bin")
         if os.environ.get("OpenVINO_DIR") is not None:
             root_dir = PYTHON_API_ROOT_DIR
-            bin_dir = build_dir
 
         self.announce("Configuring cmake project", level=3)
         ext_args = self.cmake_args.split() if self.cmake_args else []
+        ov_build_dir = os.path.join(OPENVINO_ROOT_DIR, "build")
         self.spawn(["cmake", "-S" + root_dir, "-B" + self.build_temp,
                     f"-DCMAKE_BUILD_TYPE={self.config}",
-                    f"-DInferenceEngine_DIR={os.path.join(OPENVINO_ROOT_DIR, 'build')}",
+                    f"-DInferenceEngineDeveloperPackage_DIR={ov_build_dir}",
                     "-DENABLE_PYTHON=ON",
                     "-DNGRAPH_ONNX_FRONTEND_ENABLE=ON"] + ext_args)
 
diff --git a/runtime/bindings/python/src/compatibility/ngraph/__init__.py b/runtime/bindings/python/src/compatibility/ngraph/__init__.py
index 8b12a3c7ff9d82..a19e06c7a1a486 100644
--- a/runtime/bindings/python/src/compatibility/ngraph/__init__.py
+++ b/runtime/bindings/python/src/compatibility/ngraph/__init__.py
@@ -27,164 +27,159 @@
 from ngraph.helpers import function_from_cnn
 from ngraph.helpers import function_to_cnn
 from ngraph.helpers import partial_shape_from_data
-from ngraph.opset8 import absolute
-from ngraph.opset8 import absolute as abs
-from ngraph.opset8 import acos
-from ngraph.opset8 import acosh
-from ngraph.opset8 import adaptive_avg_pool
-from ngraph.opset8 import adaptive_max_pool
-from ngraph.opset8 import add
-from ngraph.opset8 import asin
-from ngraph.opset8 import asinh
-from ngraph.opset8 import assign
-from ngraph.opset8 import atan
-from ngraph.opset8 import atanh
-from ngraph.opset8 import avg_pool
-from ngraph.opset8 import batch_norm_inference
-from ngraph.opset8 import batch_to_space
-from ngraph.opset8 import binary_convolution
-from ngraph.opset8 import broadcast
-from ngraph.opset8 import bucketize
-from ngraph.opset8 import ceiling
-from ngraph.opset8 import ceiling as ceil
-from ngraph.opset8 import clamp
-from ngraph.opset8 import concat
-from ngraph.opset8 import constant
-from ngraph.opset8 import convert
-from ngraph.opset8 import convert_like
-from ngraph.opset8 import convolution
-from ngraph.opset8 import convolution_backprop_data
-from ngraph.opset8 import cos
-from ngraph.opset8 import cosh
-from ngraph.opset8 import ctc_greedy_decoder
-from ngraph.opset8 import ctc_greedy_decoder_seq_len
-from ngraph.opset8 import ctc_loss
-from ngraph.opset8 import cum_sum
-from ngraph.opset8 import cum_sum as cumsum
-from ngraph.opset8 import deformable_convolution
-from ngraph.opset8 import deformable_psroi_pooling
-from ngraph.opset8 import depth_to_space
-from ngraph.opset8 import detection_output
-from ngraph.opset8 import dft
-from ngraph.opset8 import divide
-from ngraph.opset8 import einsum
-from ngraph.opset8 import elu
-from ngraph.opset8 import embedding_bag_offsets_sum
-from ngraph.opset8 import embedding_bag_packed_sum
-from ngraph.opset8 import embedding_segments_sum
-from ngraph.opset8 import extract_image_patches
-from ngraph.opset8 import equal
-from ngraph.opset8 import erf
-from ngraph.opset8 import exp
-from ngraph.opset8 import fake_quantize
-from ngraph.opset8 import floor
-from ngraph.opset8 import floor_mod
-from ngraph.opset8 import gather
-from ngraph.opset8 import gather_elements
-from ngraph.opset8 import gather_nd
-from ngraph.opset8 import gather_tree
-from ngraph.opset8 import gelu
-from ngraph.opset8 import greater
-from ngraph.opset8 import greater_equal
-from ngraph.opset8 import grn
-from ngraph.opset8 import group_convolution
-from ngraph.opset8 import group_convolution_backprop_data
-from ngraph.opset8 import gru_cell
-from ngraph.opset8 import gru_sequence
-from ngraph.opset8 import hard_sigmoid
-from ngraph.opset8 import hsigmoid
-from ngraph.opset8 import hswish
-from ngraph.opset8 import idft
-from ngraph.opset8 import interpolate
-from ngraph.opset8 import less
-from ngraph.opset8 import less_equal
-from ngraph.opset8 import log
-from ngraph.opset8 import logical_and
-from ngraph.opset8 import logical_not
-from ngraph.opset8 import logical_or
-from ngraph.opset8 import logical_xor
-from ngraph.opset8 import log_softmax
-from ngraph.opset8 import loop
-from ngraph.opset8 import lrn
-from ngraph.opset8 import lstm_cell
-from ngraph.opset8 import lstm_sequence
-from ngraph.opset8 import matmul
-from ngraph.opset8 import matrix_nms
-from ngraph.opset8 import max_pool
-from ngraph.opset8 import maximum
-from ngraph.opset8 import minimum
-from ngraph.opset8 import mish
-from ngraph.opset8 import mod
-from ngraph.opset8 import multiclass_nms
-from ngraph.opset8 import multiply
-from ngraph.opset8 import mvn
-from ngraph.opset8 import negative
-from ngraph.opset8 import non_max_suppression
-from ngraph.opset8 import non_zero
-from ngraph.opset8 import normalize_l2
-from ngraph.opset8 import not_equal
-from ngraph.opset8 import one_hot
-from ngraph.opset8 import pad
-from ngraph.opset8 import parameter
-from ngraph.opset8 import power
-from ngraph.opset8 import prelu
-from ngraph.opset8 import prior_box
-from ngraph.opset8 import prior_box_clustered
-from ngraph.opset8 import psroi_pooling
-from ngraph.opset8 import proposal
-from ngraph.opset8 import random_uniform
-from ngraph.opset8 import range
-from ngraph.opset8 import read_value
-from ngraph.opset8 import reduce_l1
-from ngraph.opset8 import reduce_l2
-from ngraph.opset8 import reduce_logical_and
-from ngraph.opset8 import reduce_logical_or
-from ngraph.opset8 import reduce_max
-from ngraph.opset8 import reduce_mean
-from ngraph.opset8 import reduce_min
-from ngraph.opset8 import reduce_prod
-from ngraph.opset8 import reduce_sum
-from ngraph.opset8 import region_yolo
-from ngraph.opset8 import reorg_yolo
-from ngraph.opset8 import relu
-from ngraph.opset8 import reshape
-from ngraph.opset8 import result
-from ngraph.opset8 import reverse_sequence
-from ngraph.opset8 import rnn_cell
-from ngraph.opset8 import rnn_sequence
-from ngraph.opset8 import roi_align
-from ngraph.opset8 import roi_pooling
-from ngraph.opset8 import roll
-from ngraph.opset8 import round
-from ngraph.opset8 import scatter_elements_update
-from ngraph.opset8 import scatter_update
-from ngraph.opset8 import select
-from ngraph.opset8 import selu
-from ngraph.opset8 import shape_of
-from ngraph.opset8 import shuffle_channels
-from ngraph.opset8 import sigmoid
-from ngraph.opset8 import sign
-from ngraph.opset8 import sin
-from ngraph.opset8 import sinh
-from ngraph.opset8 import softmax
-from ngraph.opset8 import softplus
-from ngraph.opset8 import space_to_batch
-from ngraph.opset8 import space_to_depth
-from ngraph.opset8 import split
-from ngraph.opset8 import sqrt
-from ngraph.opset8 import squared_difference
-from ngraph.opset8 import squeeze
-from ngraph.opset8 import strided_slice
-from ngraph.opset8 import subtract
-from ngraph.opset8 import swish
-from ngraph.opset8 import tan
-from ngraph.opset8 import tanh
-from ngraph.opset8 import tensor_iterator
-from ngraph.opset8 import tile
-from ngraph.opset8 import topk
-from ngraph.opset8 import transpose
-from ngraph.opset8 import unsqueeze
-from ngraph.opset8 import variadic_split
+from ngraph.opset7 import absolute
+from ngraph.opset7 import absolute as abs
+from ngraph.opset7 import acos
+from ngraph.opset7 import acosh
+from ngraph.opset7 import add
+from ngraph.opset7 import asin
+from ngraph.opset7 import asinh
+from ngraph.opset7 import assign
+from ngraph.opset7 import atan
+from ngraph.opset7 import atanh
+from ngraph.opset7 import avg_pool
+from ngraph.opset7 import batch_norm_inference
+from ngraph.opset7 import batch_to_space
+from ngraph.opset7 import binary_convolution
+from ngraph.opset7 import broadcast
+from ngraph.opset7 import bucketize
+from ngraph.opset7 import ceiling
+from ngraph.opset7 import ceiling as ceil
+from ngraph.opset7 import clamp
+from ngraph.opset7 import concat
+from ngraph.opset7 import constant
+from ngraph.opset7 import convert
+from ngraph.opset7 import convert_like
+from ngraph.opset7 import convolution
+from ngraph.opset7 import convolution_backprop_data
+from ngraph.opset7 import cos
+from ngraph.opset7 import cosh
+from ngraph.opset7 import ctc_greedy_decoder
+from ngraph.opset7 import ctc_greedy_decoder_seq_len
+from ngraph.opset7 import ctc_loss
+from ngraph.opset7 import cum_sum
+from ngraph.opset7 import cum_sum as cumsum
+from ngraph.opset7 import deformable_convolution
+from ngraph.opset7 import deformable_psroi_pooling
+from ngraph.opset7 import depth_to_space
+from ngraph.opset7 import detection_output
+from ngraph.opset7 import dft
+from ngraph.opset7 import divide
+from ngraph.opset7 import einsum
+from ngraph.opset7 import elu
+from ngraph.opset7 import embedding_bag_offsets_sum
+from ngraph.opset7 import embedding_bag_packed_sum
+from ngraph.opset7 import embedding_segments_sum
+from ngraph.opset7 import extract_image_patches
+from ngraph.opset7 import equal
+from ngraph.opset7 import erf
+from ngraph.opset7 import exp
+from ngraph.opset7 import fake_quantize
+from ngraph.opset7 import floor
+from ngraph.opset7 import floor_mod
+from ngraph.opset7 import gather
+from ngraph.opset7 import gather_elements
+from ngraph.opset7 import gather_nd
+from ngraph.opset7 import gather_tree
+from ngraph.opset7 import gelu
+from ngraph.opset7 import greater
+from ngraph.opset7 import greater_equal
+from ngraph.opset7 import grn
+from ngraph.opset7 import group_convolution
+from ngraph.opset7 import group_convolution_backprop_data
+from ngraph.opset7 import gru_cell
+from ngraph.opset7 import gru_sequence
+from ngraph.opset7 import hard_sigmoid
+from ngraph.opset7 import hsigmoid
+from ngraph.opset7 import hswish
+from ngraph.opset7 import idft
+from ngraph.opset7 import interpolate
+from ngraph.opset7 import less
+from ngraph.opset7 import less_equal
+from ngraph.opset7 import log
+from ngraph.opset7 import logical_and
+from ngraph.opset7 import logical_not
+from ngraph.opset7 import logical_or
+from ngraph.opset7 import logical_xor
+from ngraph.opset7 import log_softmax
+from ngraph.opset7 import loop
+from ngraph.opset7 import lrn
+from ngraph.opset7 import lstm_cell
+from ngraph.opset7 import lstm_sequence
+from ngraph.opset7 import matmul
+from ngraph.opset7 import max_pool
+from ngraph.opset7 import maximum
+from ngraph.opset7 import minimum
+from ngraph.opset7 import mish
+from ngraph.opset7 import mod
+from ngraph.opset7 import multiply
+from ngraph.opset7 import mvn
+from ngraph.opset7 import negative
+from ngraph.opset7 import non_max_suppression
+from ngraph.opset7 import non_zero
+from ngraph.opset7 import normalize_l2
+from ngraph.opset7 import not_equal
+from ngraph.opset7 import one_hot
+from ngraph.opset7 import pad
+from ngraph.opset7 import parameter
+from ngraph.opset7 import power
+from ngraph.opset7 import prelu
+from ngraph.opset7 import prior_box
+from ngraph.opset7 import prior_box_clustered
+from ngraph.opset7 import psroi_pooling
+from ngraph.opset7 import proposal
+from ngraph.opset7 import range
+from ngraph.opset7 import read_value
+from ngraph.opset7 import reduce_l1
+from ngraph.opset7 import reduce_l2
+from ngraph.opset7 import reduce_logical_and
+from ngraph.opset7 import reduce_logical_or
+from ngraph.opset7 import reduce_max
+from ngraph.opset7 import reduce_mean
+from ngraph.opset7 import reduce_min
+from ngraph.opset7 import reduce_prod
+from ngraph.opset7 import reduce_sum
+from ngraph.opset7 import region_yolo
+from ngraph.opset7 import reorg_yolo
+from ngraph.opset7 import relu
+from ngraph.opset7 import reshape
+from ngraph.opset7 import result
+from ngraph.opset7 import reverse_sequence
+from ngraph.opset7 import rnn_cell
+from ngraph.opset7 import rnn_sequence
+from ngraph.opset7 import roi_align
+from ngraph.opset7 import roi_pooling
+from ngraph.opset7 import roll
+from ngraph.opset7 import round
+from ngraph.opset7 import scatter_elements_update
+from ngraph.opset7 import scatter_update
+from ngraph.opset7 import select
+from ngraph.opset7 import selu
+from ngraph.opset7 import shape_of
+from ngraph.opset7 import shuffle_channels
+from ngraph.opset7 import sigmoid
+from ngraph.opset7 import sign
+from ngraph.opset7 import sin
+from ngraph.opset7 import sinh
+from ngraph.opset7 import softmax
+from ngraph.opset7 import softplus
+from ngraph.opset7 import space_to_batch
+from ngraph.opset7 import space_to_depth
+from ngraph.opset7 import split
+from ngraph.opset7 import sqrt
+from ngraph.opset7 import squared_difference
+from ngraph.opset7 import squeeze
+from ngraph.opset7 import strided_slice
+from ngraph.opset7 import subtract
+from ngraph.opset7 import swish
+from ngraph.opset7 import tan
+from ngraph.opset7 import tanh
+from ngraph.opset7 import tensor_iterator
+from ngraph.opset7 import tile
+from ngraph.opset7 import topk
+from ngraph.opset7 import transpose
+from ngraph.opset7 import unsqueeze
+from ngraph.opset7 import variadic_split
 
 
 # Extend Node class to support binary operators
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset8/__init__.py b/runtime/bindings/python/src/compatibility/ngraph/opset8/__init__.py
deleted file mode 100644
index f0d0dfdd2dbf64..00000000000000
--- a/runtime/bindings/python/src/compatibility/ngraph/opset8/__init__.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-from ngraph.opset1.ops import absolute
-from ngraph.opset1.ops import absolute as abs
-from ngraph.opset1.ops import acos
-from ngraph.opset4.ops import acosh
-from ngraph.opset8.ops import adaptive_avg_pool
-from ngraph.opset8.ops import adaptive_max_pool
-from ngraph.opset1.ops import add
-from ngraph.opset1.ops import asin
-from ngraph.opset4.ops import asinh
-from ngraph.opset3.ops import assign
-from ngraph.opset1.ops import atan
-from ngraph.opset4.ops import atanh
-from ngraph.opset1.ops import avg_pool
-from ngraph.opset5.ops import batch_norm_inference
-from ngraph.opset2.ops import batch_to_space
-from ngraph.opset1.ops import binary_convolution
-from ngraph.opset3.ops import broadcast
-from ngraph.opset3.ops import bucketize
-from ngraph.opset1.ops import ceiling
-from ngraph.opset1.ops import ceiling as ceil
-from ngraph.opset1.ops import clamp
-from ngraph.opset1.ops import concat
-from ngraph.opset1.ops import constant
-from ngraph.opset1.ops import convert
-from ngraph.opset1.ops import convert_like
-from ngraph.opset1.ops import convolution
-from ngraph.opset1.ops import convolution_backprop_data
-from ngraph.opset1.ops import cos
-from ngraph.opset1.ops import cosh
-from ngraph.opset1.ops import ctc_greedy_decoder
-from ngraph.opset6.ops import ctc_greedy_decoder_seq_len
-from ngraph.opset4.ops import ctc_loss
-from ngraph.opset3.ops import cum_sum
-from ngraph.opset3.ops import cum_sum as cumsum
-from ngraph.opset8.ops import deformable_convolution
-from ngraph.opset1.ops import deformable_psroi_pooling
-from ngraph.opset1.ops import depth_to_space
-from ngraph.opset1.ops import detection_output
-from ngraph.opset7.ops import dft
-from ngraph.opset1.ops import divide
-from ngraph.opset7.ops import einsum
-from ngraph.opset1.ops import elu
-from ngraph.opset3.ops import embedding_bag_offsets_sum
-from ngraph.opset3.ops import embedding_bag_packed_sum
-from ngraph.opset3.ops import embedding_segments_sum
-from ngraph.opset3.ops import extract_image_patches
-from ngraph.opset1.ops import equal
-from ngraph.opset1.ops import erf
-from ngraph.opset1.ops import exp
-from ngraph.opset1.ops import fake_quantize
-from ngraph.opset1.ops import floor
-from ngraph.opset1.ops import floor_mod
-from ngraph.opset8.ops import gather
-from ngraph.opset6.ops import gather_elements
-from ngraph.opset5.ops import gather_nd
-from ngraph.opset1.ops import gather_tree
-from ngraph.opset7.ops import gelu
-from ngraph.opset1.ops import greater
-from ngraph.opset1.ops import greater_equal
-from ngraph.opset1.ops import grn
-from ngraph.opset1.ops import group_convolution
-from ngraph.opset1.ops import group_convolution_backprop_data
-from ngraph.opset3.ops import gru_cell
-from ngraph.opset5.ops import gru_sequence
-from ngraph.opset1.ops import hard_sigmoid
-from ngraph.opset5.ops import hsigmoid
-from ngraph.opset4.ops import hswish
-from ngraph.opset7.ops import idft
-from ngraph.opset1.ops import interpolate
-from ngraph.opset1.ops import less
-from ngraph.opset1.ops import less_equal
-from ngraph.opset1.ops import log
-from ngraph.opset1.ops import logical_and
-from ngraph.opset1.ops import logical_not
-from ngraph.opset1.ops import logical_or
-from ngraph.opset1.ops import logical_xor
-from ngraph.opset5.ops import log_softmax
-from ngraph.opset5.ops import loop
-from ngraph.opset1.ops import lrn
-from ngraph.opset4.ops import lstm_cell
-from ngraph.opset1.ops import lstm_sequence
-from ngraph.opset1.ops import matmul
-from ngraph.opset8.ops import matrix_nms
-from ngraph.opset8.ops import max_pool
-from ngraph.opset1.ops import maximum
-from ngraph.opset1.ops import minimum
-from ngraph.opset4.ops import mish
-from ngraph.opset1.ops import mod
-from ngraph.opset8.ops import multiclass_nms
-from ngraph.opset1.ops import multiply
-from ngraph.opset6.ops import mvn
-from ngraph.opset1.ops import negative
-from ngraph.opset5.ops import non_max_suppression
-from ngraph.opset3.ops import non_zero
-from ngraph.opset1.ops import normalize_l2
-from ngraph.opset1.ops import not_equal
-from ngraph.opset1.ops import one_hot
-from ngraph.opset1.ops import pad
-from ngraph.opset1.ops import parameter
-from ngraph.opset1.ops import power
-from ngraph.opset1.ops import prelu
-from ngraph.opset1.ops import prior_box
-from ngraph.opset1.ops import prior_box_clustered
-from ngraph.opset1.ops import psroi_pooling
-from ngraph.opset4.ops import proposal
-from ngraph.opset8.ops import random_uniform
-from ngraph.opset1.ops import range
-from ngraph.opset3.ops import read_value
-from ngraph.opset4.ops import reduce_l1
-from ngraph.opset4.ops import reduce_l2
-from ngraph.opset1.ops import reduce_logical_and
-from ngraph.opset1.ops import reduce_logical_or
-from ngraph.opset1.ops import reduce_max
-from ngraph.opset1.ops import reduce_mean
-from ngraph.opset1.ops import reduce_min
-from ngraph.opset1.ops import reduce_prod
-from ngraph.opset1.ops import reduce_sum
-from ngraph.opset1.ops import region_yolo
-from ngraph.opset2.ops import reorg_yolo
-from ngraph.opset1.ops import relu
-from ngraph.opset1.ops import reshape
-from ngraph.opset1.ops import result
-from ngraph.opset1.ops import reverse_sequence
-from ngraph.opset3.ops import rnn_cell
-from ngraph.opset5.ops import rnn_sequence
-from ngraph.opset3.ops import roi_align
-from ngraph.opset2.ops import roi_pooling
-from ngraph.opset7.ops import roll
-from ngraph.opset5.ops import round
-from ngraph.opset3.ops import scatter_elements_update
-from ngraph.opset3.ops import scatter_update
-from ngraph.opset1.ops import select
-from ngraph.opset1.ops import selu
-from ngraph.opset3.ops import shape_of
-from ngraph.opset3.ops import shuffle_channels
-from ngraph.opset1.ops import sigmoid
-from ngraph.opset1.ops import sign
-from ngraph.opset1.ops import sin
-from ngraph.opset1.ops import sinh
-from ngraph.opset1.ops import softmax
-from ngraph.opset4.ops import softplus
-from ngraph.opset2.ops import space_to_batch
-from ngraph.opset1.ops import space_to_depth
-from ngraph.opset1.ops import split
-from ngraph.opset1.ops import sqrt
-from ngraph.opset1.ops import squared_difference
-from ngraph.opset1.ops import squeeze
-from ngraph.opset1.ops import strided_slice
-from ngraph.opset1.ops import subtract
-from ngraph.opset4.ops import swish
-from ngraph.opset1.ops import tan
-from ngraph.opset1.ops import tanh
-from ngraph.opset1.ops import tensor_iterator
-from ngraph.opset1.ops import tile
-from ngraph.opset3.ops import topk
-from ngraph.opset1.ops import transpose
-from ngraph.opset1.ops import unsqueeze
-from ngraph.opset1.ops import variadic_split
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset8/ops.py b/runtime/bindings/python/src/compatibility/ngraph/opset8/ops.py
deleted file mode 100644
index 6c355930b7c021..00000000000000
--- a/runtime/bindings/python/src/compatibility/ngraph/opset8/ops.py
+++ /dev/null
@@ -1,369 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-"""Factory functions for all ngraph ops."""
-from functools import partial
-from typing import Callable, Iterable, List, Optional, Set, Union
-
-import numpy as np
-from ngraph.impl import Node, Shape
-from ngraph.impl.op import Constant, Parameter
-from ngraph.opset_utils import _get_node_factory
-from ngraph.utils.decorators import binary_op, nameable_op, unary_op
-from ngraph.utils.input_validation import (
-    assert_list_of_ints,
-    check_valid_attributes,
-    is_non_negative_value,
-    is_positive_value,
-)
-from ngraph.utils.node_factory import NodeFactory
-from ngraph.utils.tensor_iterator_types import (
-    GraphBody,
-    TensorIteratorSliceInputDesc,
-    TensorIteratorMergedInputDesc,
-    TensorIteratorInvariantInputDesc,
-    TensorIteratorBodyOutputDesc,
-    TensorIteratorConcatOutputDesc,
-)
-from ngraph.utils.types import (
-    NodeInput,
-    NumericData,
-    NumericType,
-    ScalarData,
-    TensorShape,
-    as_node,
-    as_nodes,
-    get_dtype,
-    get_element_type,
-    get_element_type_str,
-    make_constant_node,
-)
-
-_get_node_factory_opset8 = partial(_get_node_factory, "opset8")
-
-
-# -------------------------------------------- ops ------------------------------------------------
-
-
-@nameable_op
-def deformable_convolution(
-        data: NodeInput,
-        offsets: NodeInput,
-        filters: NodeInput,
-        strides: List[int],
-        pads_begin: List[int],
-        pads_end: List[int],
-        dilations: List[int],
-        mask: Optional[NodeInput] = None,
-        auto_pad: str = "EXPLICIT",
-        group: int = 1,
-        deformable_group: int = 1,
-        bilinear_interpolation_pad: bool = False,
-        name: Optional[str] = None,
-) -> Node:
-    """Return a node which performs deformable convolution operation.
-
-    @param data: The node providing data batch tensor.
-    @param offsets: The node providing offset tensor.
-    @param filters: The node providing filters tensor.
-    @param strides: The distance (in pixels) to slide the filter on the feature map over the axes.
-    @param pads_begin: The number of pixels to add to the beginning along each axis.
-    @param pads_end: The number of pixels to add to the end along each axis.
-    @param dilations: The distance in width and height between elements (weights) in the filter.
-    @param mask: The node providing modulation scalar (mask) tensor.
-    @param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
-    @param group: The number of groups which both output and input should be split into.
-    @param deformable_group: The number of groups which deformable values and output should be split
-                             into along the channel axis.
-    @param bilinear_interpolation_pad: The flag that determines the mode of bilinear interpolation
-                                               execution.
-    @param name: The optional new name for output node.
-    @return New node performing deformable convolution operation.
-    """
-    if mask is None:
-        inputs = as_nodes(data, offsets, filters)
-    else:
-        inputs = as_nodes(data, offsets, filters, mask)
-
-    return _get_node_factory_opset8().create(
-        "DeformableConvolution",
-        inputs,
-        {
-            "strides": strides,
-            "pads_begin": pads_begin,
-            "pads_end": pads_end,
-            "dilations": dilations,
-            "auto_pad": auto_pad,
-            "group": group,
-            "deformable_group": deformable_group,
-            "bilinear_interpolation_pad": bilinear_interpolation_pad
-        },
-    )
-
-
-@nameable_op
-def adaptive_avg_pool(
-        data: NodeInput,
-        output_shape: NodeInput
-) -> Node:
-    """Return a node which performs AdaptiveAvgPool operation.
-
-    @param data: The list of input nodes
-    @param output_shape: the shape of spatial dimentions after operation
-    @return: The new node performing AdaptiveAvgPool operation on the data
-    """
-    inputs = as_nodes(data, output_shape)
-    return _get_node_factory_opset8().create("AdaptiveAvgPool", inputs)
-
-
-@nameable_op
-def adaptive_max_pool(
-        data: NodeInput,
-        output_shape: NodeInput,
-        index_element_type: str = "i64"
-) -> Node:
-    """Return a node which performs AdaptiveMaxPool operation.
-
-    @param data: The list of input nodes
-    @param output_shape: the shape of spatial dimentions after operation
-    @param index_element_type: Type of indices output.
-    @return: The new node performing AdaptiveMaxPool operation on the data
-    """
-    inputs = as_nodes(data, output_shape)
-
-    attributes = {
-        "index_element_type": index_element_type,
-    }
-
-    return _get_node_factory_opset8().create("AdaptiveMaxPool", inputs, attributes)
-
-
-@nameable_op
-def multiclass_nms(
-    boxes: NodeInput,
-    scores: NodeInput,
-    sort_result_type: str = "none",
-    sort_result_across_batch: bool = False,
-    output_type: str = "i64",
-    iou_threshold: float = 0.0,
-    score_threshold: float = 0.0,
-    nms_top_k: int = -1,
-    keep_top_k: int = -1,
-    background_class: int = -1,
-    nms_eta: float = 1.0,
-    normalized: bool = True
-) -> Node:
-    """Return a node which performs MulticlassNms.
-
-    @param boxes: Tensor with box coordinates.
-    @param scores: Tensor with box scores.
-    @param sort_result_type: Specifies order of output elements, possible values:
-                             'class': sort selected boxes by class id (ascending)
-                             'score': sort selected boxes by score (descending)
-                             'none': do not guarantee the order.
-    @param sort_result_across_batch: Specifies whenever it is necessary to sort selected boxes
-                                     across batches or not
-    @param output_type: Specifies the output tensor type, possible values:
-                        'i64', 'i32'
-    @param iou_threshold: Specifies intersection over union threshold
-    @param score_threshold: Specifies minimum score to consider box for the processing
-    @param nms_top_k: Specifies maximum number of boxes to be selected per class, -1 meaning
-                      to keep all boxes
-    @param keep_top_k: Specifies maximum number of boxes to be selected per batch element, -1
-                       meaning to keep all boxes
-    @param background_class: Specifies the background class id, -1 meaning to keep all classes
-    @param nms_eta: Specifies eta parameter for adpative NMS, in close range [0, 1.0]
-    @param normalized: Specifies whether boxes are normalized or not
-    @return: The new node which performs MuticlassNms
-    """
-    inputs = as_nodes(boxes, scores)
-
-    attributes = {
-        "sort_result_type": sort_result_type,
-        "sort_result_across_batch": sort_result_across_batch,
-        "output_type": output_type,
-        "iou_threshold": iou_threshold,
-        "score_threshold": score_threshold,
-        "nms_top_k": nms_top_k,
-        "keep_top_k": keep_top_k,
-        "background_class": background_class,
-        "nms_eta": nms_eta,
-        "normalized": normalized
-    }
-
-    return _get_node_factory_opset8().create("MulticlassNms", inputs, attributes)
-
-
-@nameable_op
-def matrix_nms(
-    boxes: NodeInput,
-    scores: NodeInput,
-    sort_result_type: str = "none",
-    sort_result_across_batch: bool = False,
-    output_type: str = "i64",
-    score_threshold: float = 0.0,
-    nms_top_k: int = -1,
-    keep_top_k: int = -1,
-    background_class: int = -1,
-    decay_function: str = "linear",
-    gaussian_sigma: float = 2.0,
-    post_threshold: float = 0.0,
-    normalized: bool = True
-) -> Node:
-    """Return a node which performs MatrixNms.
-
-    @param boxes: Tensor with box coordinates.
-    @param scores: Tensor with box scores.
-    @param sort_result_type: Specifies order of output elements, possible values:
-                             'class': sort selected boxes by class id (ascending)
-                             'score': sort selected boxes by score (descending)
-                             'none': do not guarantee the order.
-    @param sort_result_across_batch: Specifies whenever it is necessary to sort selected boxes
-                                     across batches or not
-    @param output_type: Specifies the output tensor type, possible values:
-                        'i64', 'i32'
-    @param score_threshold: Specifies minimum score to consider box for the processing
-    @param nms_top_k: Specifies maximum number of boxes to be selected per class, -1 meaning
-                      to keep all boxes
-    @param keep_top_k: Specifies maximum number of boxes to be selected per batch element, -1
-                       meaning to keep all boxes
-    @param background_class: Specifies the background class id, -1 meaning to keep all classes
-    @param decay_function: Specifies decay function used to decay scores, possible values:
-                           'gaussian', 'linear'
-    @param gaussian_sigma: Specifies gaussian_sigma parameter for gaussian decay_function
-    @param post_threshold: Specifies threshold to filter out boxes with low confidence score
-                           after decaying
-    @param normalized: Specifies whether boxes are normalized or not
-    @return: The new node which performs MatrixNms
-    """
-    inputs = as_nodes(boxes, scores)
-
-    attributes = {
-        "sort_result_type": sort_result_type,
-        "sort_result_across_batch": sort_result_across_batch,
-        "output_type": output_type,
-        "score_threshold": score_threshold,
-        "nms_top_k": nms_top_k,
-        "keep_top_k": keep_top_k,
-        "background_class": background_class,
-        "decay_function": decay_function,
-        "gaussian_sigma": gaussian_sigma,
-        "post_threshold": post_threshold,
-        "normalized": normalized
-    }
-
-    return _get_node_factory_opset8().create("MatrixNms", inputs, attributes)
-
-
-@nameable_op
-def gather(
-        data: NodeInput,
-        indices: NodeInput,
-        axis: NodeInput,
-        batch_dims: Optional[int] = 0,
-) -> Node:
-    """Return a node which performs Gather with support of negative indices.
-
-    @param data:         N-D tensor with data for gathering
-    @param indices:      N-D tensor with indices by which data is gathered. Negative indices
-    indicate reverse indexing from the end
-    @param axis:         axis along which elements are gathered
-    @param batch_dims:   number of batch dimensions
-    @return:             The new node which performs Gather
-    """
-    inputs = as_nodes(data, indices, axis)
-    attributes = {
-        "batch_dims": batch_dims
-    }
-    return _get_node_factory_opset8().create("Gather", inputs, attributes)
-
-
-@nameable_op
-def max_pool(
-    data: NodeInput,
-    strides: List[int],
-    dilations: List[int],
-    pads_begin: List[int],
-    pads_end: List[int],
-    kernel_shape: TensorShape,
-    rounding_type: str = "floor",
-    auto_pad: Optional[str] = None,
-    index_element_type: Optional[str] = "i64",
-    axis: Optional[int] = 0,
-    name: Optional[str] = None,
-) -> Node:
-    """Perform max pooling operation and return both values and indices of the selected elements.
-
-    @param  data:                The node providing input data.
-    @param  strides:             The distance (in pixels) to slide the filter on the feature map
-                                 over the axes.
-    @param  dilations:           The dilation of filter elements(distance between elements).
-    @param  pads_begin:          The number of pixels to add at the beginning along each axis.
-    @param  pads_end:            The number of pixels to add at the end along each axis.
-    @param  kernel_shape:        The pooling operation kernel shape.
-    @param  rounding_type:       Determines used rounding schema when computing output shape.
-                                 Acceptable values are: ['floor', 'ceil']. Defaults to 'floor'.
-    @param  auto_pad:            Determines how the padding is calculated. Acceptable values:
-                                 [None, 'same_upper', 'same_lower', 'valid']. Defaults to None.
-    @param  index_element_type:  The data type used for the indices output of this operator.
-                                 Defaults to i64.
-    @param  axis:                The first dimension in the data shape used to determine the maximum
-                                 returned index value. The value is the product of all dimensions
-                                 starting at the provided axis. Defaults to 0.
-    @param  name:                The optional name for the created output node.
-
-    @return   The new node performing max pooling operation.
-    """
-    if auto_pad is None:
-        auto_pad = "explicit"
-    return _get_node_factory_opset8().create(
-        "MaxPool",
-        [as_node(data)],
-        {
-            "strides": strides,
-            "dilations": dilations,
-            "pads_begin": pads_begin,
-            "pads_end": pads_end,
-            "kernel": kernel_shape,
-            "rounding_type": rounding_type.upper(),
-            "auto_pad": auto_pad.upper(),
-            "index_element_type": index_element_type,
-            "axis": axis,
-        },
-    )
-
-
-@nameable_op
-def random_uniform(
-        output_shape: NodeInput,
-        min_val: NodeInput,
-        max_val: NodeInput,
-        output_type: str,
-        global_seed: int = 0,
-        op_seed: int = 0
-) -> Node:
-    """Return a node which generates sequence of random values from uniform distribution.
-
-    @param output_shape: Tensor with shape of the output tensor.
-    @param min_val: Tensor with the lower bound on the range of random values to generate.
-    @param max_val: Tensor with the upper bound on the range of random values to generate.
-    @param output_type: Specifies the output tensor type, possible values:
-    'i64', 'i32', 'f64', 'f32', 'f16', 'bf16'.
-    @param global_seed: Specifies global seed value. Required to be a positive integer or 0.
-    @param op_seed: Specifies operational seed value. Required to be a positive integer or 0.
-    @return The new node which performs generation of random values from uniform distribution.
-    """
-    inputs = as_nodes(output_shape, min_val, max_val)
-
-    if global_seed < 0:
-        raise RuntimeError("global_seed should be positive or 0. Got: {}".format(global_seed))
-
-    if op_seed < 0:
-        raise RuntimeError("op_seed should be positive or 0. Got: {}".format(op_seed))
-
-    attributes = {
-        "output_type": output_type,
-        "global_seed": global_seed,
-        "op_seed": op_seed,
-    }
-    return _get_node_factory_opset8().create("RandomUniform", inputs, attributes)
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/node_factory.py b/runtime/bindings/python/src/compatibility/ngraph/utils/node_factory.py
index ffb0c3d861ccc8..83cb9e094b0887 100644
--- a/runtime/bindings/python/src/compatibility/ngraph/utils/node_factory.py
+++ b/runtime/bindings/python/src/compatibility/ngraph/utils/node_factory.py
@@ -12,7 +12,7 @@
 
 from ngraph.exceptions import UserInputError
 
-DEFAULT_OPSET = "opset8"
+DEFAULT_OPSET = "opset7"
 
 
 class NodeFactory(object):
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp b/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp
index db3dccfc44b63f..b028a7f3665e9c 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp
@@ -14,15 +14,15 @@
 namespace py = pybind11;
 
 void regclass_pyngraph_FrontEnd(py::module m) {
-    py::class_<ngraph::frontend::FrontEnd, std::shared_ptr<ngraph::frontend::FrontEnd>> fem(m,
-                                                                                            "FrontEnd",
-                                                                                            py::dynamic_attr(),
-                                                                                            py::module_local());
+    py::class_<ov::frontend::FrontEnd, std::shared_ptr<ov::frontend::FrontEnd>> fem(m,
+                                                                                    "FrontEnd",
+                                                                                    py::dynamic_attr(),
+                                                                                    py::module_local());
     fem.doc() = "ngraph.impl.FrontEnd wraps ngraph::frontend::FrontEnd";
 
     fem.def(
         "load",
-        [](ngraph::frontend::FrontEnd& self, const std::string& s) {
+        [](ov::frontend::FrontEnd& self, const std::string& s) {
             return self.load(s);
         },
         py::arg("path"),
@@ -40,11 +40,12 @@ void regclass_pyngraph_FrontEnd(py::module m) {
                     Loaded input model.
              )");
 
-    fem.def("convert",
-            static_cast<std::shared_ptr<ngraph::Function> (ngraph::frontend::FrontEnd::*)(
-                ngraph::frontend::InputModel::Ptr) const>(&ngraph::frontend::FrontEnd::convert),
-            py::arg("model"),
-            R"(
+    fem.def(
+        "convert",
+        static_cast<std::shared_ptr<ngraph::Function> (ov::frontend::FrontEnd::*)(ov::frontend::InputModel::Ptr) const>(
+            &ov::frontend::FrontEnd::convert),
+        py::arg("model"),
+        R"(
                 Completely convert and normalize entire function, throws if it is not possible.
 
                 Parameters
@@ -59,8 +60,8 @@ void regclass_pyngraph_FrontEnd(py::module m) {
              )");
 
     fem.def("convert",
-            static_cast<void (ngraph::frontend::FrontEnd::*)(std::shared_ptr<ngraph::Function>) const>(
-                &ngraph::frontend::FrontEnd::convert),
+            static_cast<void (ov::frontend::FrontEnd::*)(std::shared_ptr<ngraph::Function>) const>(
+                &ov::frontend::FrontEnd::convert),
             py::arg("function"),
             R"(
                 Completely convert the remaining, not converted part of a function.
@@ -77,7 +78,7 @@ void regclass_pyngraph_FrontEnd(py::module m) {
              )");
 
     fem.def("convert_partially",
-            &ngraph::frontend::FrontEnd::convert_partially,
+            &ov::frontend::FrontEnd::convert_partially,
             py::arg("model"),
             R"(
                 Convert only those parts of the model that can be converted leaving others as-is.
@@ -96,7 +97,7 @@ void regclass_pyngraph_FrontEnd(py::module m) {
              )");
 
     fem.def("decode",
-            &ngraph::frontend::FrontEnd::decode,
+            &ov::frontend::FrontEnd::decode,
             py::arg("model"),
             R"(
                 Convert operations with one-to-one mapping with decoding nodes.
@@ -115,7 +116,7 @@ void regclass_pyngraph_FrontEnd(py::module m) {
              )");
 
     fem.def("normalize",
-            &ngraph::frontend::FrontEnd::normalize,
+            &ov::frontend::FrontEnd::normalize,
             py::arg("function"),
             R"(
                 Runs normalization passes on function that was loaded with partial conversion.
@@ -127,7 +128,7 @@ void regclass_pyngraph_FrontEnd(py::module m) {
              )");
 
     fem.def("get_name",
-            &ngraph::frontend::FrontEnd::get_name,
+            &ov::frontend::FrontEnd::get_name,
             R"(
                 Gets name of this FrontEnd. Can be used by clients
                 if frontend is selected automatically by FrontEndManager::load_by_model.
@@ -138,7 +139,7 @@ void regclass_pyngraph_FrontEnd(py::module m) {
                     Current frontend name. Empty string if not implemented.
             )");
 
-    fem.def("__repr__", [](const ngraph::frontend::FrontEnd& self) -> std::string {
+    fem.def("__repr__", [](const ov::frontend::FrontEnd& self) -> std::string {
         return "<FrontEnd '" + self.get_name() + "'>";
     });
 }
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp b/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp
index 250e80e69e5d74..a4c4d76a7e5235 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp
@@ -14,26 +14,25 @@
 namespace py = pybind11;
 
 void regclass_pyngraph_FrontEndManager(py::module m) {
-    py::class_<ngraph::frontend::FrontEndManager, std::shared_ptr<ngraph::frontend::FrontEndManager>> fem(
-        m,
-        "FrontEndManager",
-        py::dynamic_attr(),
-        py::module_local());
+    py::class_<ov::frontend::FrontEndManager, std::shared_ptr<ov::frontend::FrontEndManager>> fem(m,
+                                                                                                  "FrontEndManager",
+                                                                                                  py::dynamic_attr(),
+                                                                                                  py::module_local());
     fem.doc() = "ngraph.impl.FrontEndManager wraps ngraph::frontend::FrontEndManager";
 
     fem.def(py::init<>());
 
     // Empty pickle dumps are supported as FrontEndManager doesn't have any state
     fem.def(py::pickle(
-        [](const ngraph::frontend::FrontEndManager&) {
+        [](const ov::frontend::FrontEndManager&) {
             return py::make_tuple(0);
         },
         [](py::tuple t) {
-            return ngraph::frontend::FrontEndManager();
+            return ov::frontend::FrontEndManager();
         }));
 
     fem.def("get_available_front_ends",
-            &ngraph::frontend::FrontEndManager::get_available_front_ends,
+            &ov::frontend::FrontEndManager::get_available_front_ends,
             R"(
                 Gets list of registered frontends.
 
@@ -44,7 +43,7 @@ void regclass_pyngraph_FrontEndManager(py::module m) {
              )");
 
     fem.def("load_by_framework",
-            &ngraph::frontend::FrontEndManager::load_by_framework,
+            &ov::frontend::FrontEndManager::load_by_framework,
             py::arg("framework"),
             R"(
                 Loads frontend by name of framework and capabilities.
@@ -62,7 +61,7 @@ void regclass_pyngraph_FrontEndManager(py::module m) {
 
     fem.def(
         "load_by_model",
-        [](const std::shared_ptr<ngraph::frontend::FrontEndManager>& fem, const std::string& model_path) {
+        [](const std::shared_ptr<ov::frontend::FrontEndManager>& fem, const std::string& model_path) {
             return fem->load_by_model(model_path);
         },
         py::arg("model_path"),
@@ -80,66 +79,66 @@ void regclass_pyngraph_FrontEndManager(py::module m) {
                     Frontend interface for further loading of models. 'None' if no suitable frontend is found
             )");
 
-    fem.def("__repr__", [](const ngraph::frontend::FrontEndManager& self) -> std::string {
+    fem.def("__repr__", [](const ov::frontend::FrontEndManager& self) -> std::string {
         return "<FrontEndManager>";
     });
 }
 
 void regclass_pyngraph_GeneralFailureFrontEnd(py::module m) {
-    static py::exception<ngraph::frontend::GeneralFailure> exc(std::move(m), "GeneralFailure");
+    static py::exception<ov::frontend::GeneralFailure> exc(std::move(m), "GeneralFailure");
     py::register_exception_translator([](std::exception_ptr p) {
         try {
             if (p)
                 std::rethrow_exception(p);
-        } catch (const ngraph::frontend::GeneralFailure& e) {
+        } catch (const ov::frontend::GeneralFailure& e) {
             exc(e.what());
         }
     });
 }
 
 void regclass_pyngraph_OpValidationFailureFrontEnd(py::module m) {
-    static py::exception<ngraph::frontend::OpValidationFailure> exc(std::move(m), "OpValidationFailure");
+    static py::exception<ov::frontend::OpValidationFailure> exc(std::move(m), "OpValidationFailure");
     py::register_exception_translator([](std::exception_ptr p) {
         try {
             if (p)
                 std::rethrow_exception(p);
-        } catch (const ngraph::frontend::OpValidationFailure& e) {
+        } catch (const ov::frontend::OpValidationFailure& e) {
             exc(e.what());
         }
     });
 }
 
 void regclass_pyngraph_OpConversionFailureFrontEnd(py::module m) {
-    static py::exception<ngraph::frontend::OpConversionFailure> exc(std::move(m), "OpConversionFailure");
+    static py::exception<ov::frontend::OpConversionFailure> exc(std::move(m), "OpConversionFailure");
     py::register_exception_translator([](std::exception_ptr p) {
         try {
             if (p)
                 std::rethrow_exception(p);
-        } catch (const ngraph::frontend::OpConversionFailure& e) {
+        } catch (const ov::frontend::OpConversionFailure& e) {
             exc(e.what());
         }
     });
 }
 
 void regclass_pyngraph_InitializationFailureFrontEnd(py::module m) {
-    static py::exception<ngraph::frontend::InitializationFailure> exc(std::move(m), "InitializationFailure");
+    static py::exception<ov::frontend::InitializationFailure> exc(std::move(m), "InitializationFailure");
     py::register_exception_translator([](std::exception_ptr p) {
         try {
             if (p)
                 std::rethrow_exception(p);
-        } catch (const ngraph::frontend::InitializationFailure& e) {
+        } catch (const ov::frontend::InitializationFailure& e) {
             exc(e.what());
         }
     });
 }
 
 void regclass_pyngraph_NotImplementedFailureFrontEnd(py::module m) {
-    static py::exception<ngraph::frontend::NotImplementedFailure> exc(std::move(m), "NotImplementedFailure");
+    static py::exception<ov::frontend::NotImplementedFailure> exc(std::move(m), "NotImplementedFailure");
     py::register_exception_translator([](std::exception_ptr p) {
         try {
             if (p)
                 std::rethrow_exception(p);
-        } catch (const ngraph::frontend::NotImplementedFailure& e) {
+        } catch (const ov::frontend::NotImplementedFailure& e) {
             exc(e.what());
         }
     });
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/inputmodel.cpp b/runtime/bindings/python/src/compatibility/pyngraph/frontend/inputmodel.cpp
index 94e5628a917a32..6396c33bf194ae 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/frontend/inputmodel.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/frontend/inputmodel.cpp
@@ -12,14 +12,14 @@
 namespace py = pybind11;
 
 void regclass_pyngraph_InputModel(py::module m) {
-    py::class_<ngraph::frontend::InputModel, std::shared_ptr<ngraph::frontend::InputModel>> im(m,
-                                                                                               "InputModel",
-                                                                                               py::dynamic_attr(),
-                                                                                               py::module_local());
+    py::class_<ov::frontend::InputModel, std::shared_ptr<ov::frontend::InputModel>> im(m,
+                                                                                       "InputModel",
+                                                                                       py::dynamic_attr(),
+                                                                                       py::module_local());
     im.doc() = "ngraph.impl.InputModel wraps ngraph::frontend::InputModel";
 
     im.def("get_place_by_tensor_name",
-           &ngraph::frontend::InputModel::get_place_by_tensor_name,
+           &ov::frontend::InputModel::get_place_by_tensor_name,
            py::arg("tensorName"),
            R"(
                 Returns a tensor place by a tensor name following framework conventions, or
@@ -37,7 +37,7 @@ void regclass_pyngraph_InputModel(py::module m) {
              )");
 
     im.def("get_place_by_operation_name",
-           &ngraph::frontend::InputModel::get_place_by_operation_name,
+           &ov::frontend::InputModel::get_place_by_operation_name,
            py::arg("operationName"),
            R"(
                 Returns an operation place by an operation name following framework conventions, or
@@ -55,7 +55,7 @@ void regclass_pyngraph_InputModel(py::module m) {
              )");
 
     im.def("get_place_by_operation_name_and_input_port",
-           &ngraph::frontend::InputModel::get_place_by_operation_name_and_input_port,
+           &ov::frontend::InputModel::get_place_by_operation_name_and_input_port,
            py::arg("operationName"),
            py::arg("inputPortIndex"),
            R"(
@@ -76,7 +76,7 @@ void regclass_pyngraph_InputModel(py::module m) {
              )");
 
     im.def("get_place_by_operation_name_and_output_port",
-           &ngraph::frontend::InputModel::get_place_by_operation_name_and_output_port,
+           &ov::frontend::InputModel::get_place_by_operation_name_and_output_port,
            py::arg("operationName"),
            py::arg("outputPortIndex"),
            R"(
@@ -97,7 +97,7 @@ void regclass_pyngraph_InputModel(py::module m) {
              )");
 
     im.def("set_name_for_tensor",
-           &ngraph::frontend::InputModel::set_name_for_tensor,
+           &ov::frontend::InputModel::set_name_for_tensor,
            py::arg("tensor"),
            py::arg("newName"),
            R"(
@@ -113,7 +113,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("add_name_for_tensor",
-           &ngraph::frontend::InputModel::add_name_for_tensor,
+           &ov::frontend::InputModel::add_name_for_tensor,
            py::arg("tensor"),
            py::arg("newName"),
            R"(
@@ -129,7 +129,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("set_name_for_operation",
-           &ngraph::frontend::InputModel::set_name_for_operation,
+           &ov::frontend::InputModel::set_name_for_operation,
            py::arg("operation"),
            py::arg("newName"),
            R"(
@@ -145,7 +145,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("free_name_for_tensor",
-           &ngraph::frontend::InputModel::free_name_for_tensor,
+           &ov::frontend::InputModel::free_name_for_tensor,
            py::arg("name"),
            R"(
                 Unassign specified name from tensor place(s).
@@ -157,7 +157,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("free_name_for_operation",
-           &ngraph::frontend::InputModel::free_name_for_operation,
+           &ov::frontend::InputModel::free_name_for_operation,
            py::arg("name"),
            R"(
                 Unassign specified name from operation place(s).
@@ -169,7 +169,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("set_name_for_dimension",
-           &ngraph::frontend::InputModel::set_name_for_dimension,
+           &ov::frontend::InputModel::set_name_for_dimension,
            py::arg("place"),
            py::arg("dimIndex"),
            py::arg("dimName"),
@@ -189,7 +189,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("cut_and_add_new_input",
-           &ngraph::frontend::InputModel::cut_and_add_new_input,
+           &ov::frontend::InputModel::cut_and_add_new_input,
            py::arg("place"),
            py::arg("newName") = std::string(),
            R"(
@@ -206,7 +206,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("cut_and_add_new_output",
-           &ngraph::frontend::InputModel::cut_and_add_new_output,
+           &ov::frontend::InputModel::cut_and_add_new_output,
            py::arg("place"),
            py::arg("newName") = std::string(),
            R"(
@@ -223,7 +223,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("add_output",
-           &ngraph::frontend::InputModel::add_output,
+           &ov::frontend::InputModel::add_output,
            py::arg("place"),
            R"(
                 Assign this place as new output or add necessary nodes to represent a new output.
@@ -235,7 +235,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("remove_output",
-           &ngraph::frontend::InputModel::remove_output,
+           &ov::frontend::InputModel::remove_output,
            py::arg("place"),
            R"(
                 Removes any sinks directly attached to this place with all inbound data flow
@@ -248,7 +248,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("set_partial_shape",
-           &ngraph::frontend::InputModel::set_partial_shape,
+           &ov::frontend::InputModel::set_partial_shape,
            py::arg("place"),
            py::arg("shape"),
            R"(
@@ -267,7 +267,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("get_partial_shape",
-           &ngraph::frontend::InputModel::get_partial_shape,
+           &ov::frontend::InputModel::get_partial_shape,
            py::arg("place"),
            R"(
                 Returns current partial shape used for this place.
@@ -284,7 +284,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("get_inputs",
-           &ngraph::frontend::InputModel::get_inputs,
+           &ov::frontend::InputModel::get_inputs,
            R"(
                 Returns all inputs for a model.
 
@@ -295,7 +295,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("get_outputs",
-           &ngraph::frontend::InputModel::get_outputs,
+           &ov::frontend::InputModel::get_outputs,
            R"(
                 Returns all outputs for a model. An output is a terminal place in a graph where data escapes the flow.
 
@@ -306,7 +306,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("extract_subgraph",
-           &ngraph::frontend::InputModel::extract_subgraph,
+           &ov::frontend::InputModel::extract_subgraph,
            py::arg("inputs"),
            py::arg("outputs"),
            R"(
@@ -322,7 +322,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("override_all_inputs",
-           &ngraph::frontend::InputModel::override_all_inputs,
+           &ov::frontend::InputModel::override_all_inputs,
            py::arg("inputs"),
            R"(
                 Modifies the graph to use new inputs instead of existing ones. New inputs
@@ -335,7 +335,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("override_all_outputs",
-           &ngraph::frontend::InputModel::override_all_outputs,
+           &ov::frontend::InputModel::override_all_outputs,
            py::arg("outputs"),
            R"(
                 Replaces all existing outputs with new ones removing all data flow that
@@ -348,7 +348,7 @@ void regclass_pyngraph_InputModel(py::module m) {
             )");
 
     im.def("set_element_type",
-           &ngraph::frontend::InputModel::set_element_type,
+           &ov::frontend::InputModel::set_element_type,
            py::arg("place"),
            py::arg("type"),
            R"(
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/place.cpp b/runtime/bindings/python/src/compatibility/pyngraph/frontend/place.cpp
index 5414d74529f9b5..a35832a1fa77bf 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/frontend/place.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/frontend/place.cpp
@@ -14,14 +14,14 @@
 namespace py = pybind11;
 
 void regclass_pyngraph_Place(py::module m) {
-    py::class_<ngraph::frontend::Place, std::shared_ptr<ngraph::frontend::Place>> place(m,
-                                                                                        "Place",
-                                                                                        py::dynamic_attr(),
-                                                                                        py::module_local());
+    py::class_<ov::frontend::Place, std::shared_ptr<ov::frontend::Place>> place(m,
+                                                                                "Place",
+                                                                                py::dynamic_attr(),
+                                                                                py::module_local());
     place.doc() = "ngraph.impl.Place wraps ngraph::frontend::Place";
 
     place.def("is_input",
-              &ngraph::frontend::Place::is_input,
+              &ov::frontend::Place::is_input,
               R"(
                 Returns true if this place is input for a model.
 
@@ -32,7 +32,7 @@ void regclass_pyngraph_Place(py::module m) {
              )");
 
     place.def("is_output",
-              &ngraph::frontend::Place::is_output,
+              &ov::frontend::Place::is_output,
               R"(
                 Returns true if this place is output for a model.
 
@@ -43,7 +43,7 @@ void regclass_pyngraph_Place(py::module m) {
              )");
 
     place.def("get_names",
-              &ngraph::frontend::Place::get_names,
+              &ov::frontend::Place::get_names,
               R"(
                 All associated names (synonyms) that identify this place in the graph in a framework specific way.
 
@@ -55,7 +55,7 @@ void regclass_pyngraph_Place(py::module m) {
              )");
 
     place.def("is_equal",
-              &ngraph::frontend::Place::is_equal,
+              &ov::frontend::Place::is_equal,
               py::arg("other"),
               R"(
                 Returns true if another place is the same as this place.
@@ -72,7 +72,7 @@ void regclass_pyngraph_Place(py::module m) {
              )");
 
     place.def("is_equal_data",
-              &ngraph::frontend::Place::is_equal_data,
+              &ov::frontend::Place::is_equal_data,
               py::arg("other"),
               R"(
                 Returns true if another place points to the same data.
@@ -92,7 +92,7 @@ void regclass_pyngraph_Place(py::module m) {
 
     place.def(
         "get_consuming_operations",
-        [](const ngraph::frontend::Place& self, py::object outputName, py::object outputPortIndex) {
+        [](const ov::frontend::Place& self, py::object outputName, py::object outputPortIndex) {
             if (outputName == py::none()) {
                 if (outputPortIndex == py::none()) {
                     return self.get_consuming_operations();
@@ -130,7 +130,7 @@ void regclass_pyngraph_Place(py::module m) {
 
     place.def(
         "get_target_tensor",
-        [](const ngraph::frontend::Place& self, py::object outputName, py::object outputPortIndex) {
+        [](const ov::frontend::Place& self, py::object outputName, py::object outputPortIndex) {
             if (outputName == py::none()) {
                 if (outputPortIndex == py::none()) {
                     return self.get_target_tensor();
@@ -167,7 +167,7 @@ void regclass_pyngraph_Place(py::module m) {
 
     place.def(
         "get_producing_operation",
-        [](const ngraph::frontend::Place& self, py::object inputName, py::object inputPortIndex) {
+        [](const ov::frontend::Place& self, py::object inputName, py::object inputPortIndex) {
             if (inputName == py::none()) {
                 if (inputPortIndex == py::none()) {
                     return self.get_producing_operation();
@@ -203,7 +203,7 @@ void regclass_pyngraph_Place(py::module m) {
              )");
 
     place.def("get_producing_port",
-              &ngraph::frontend::Place::get_producing_port,
+              &ov::frontend::Place::get_producing_port,
               R"(
                 Returns a port that produces data for this place.
 
@@ -215,7 +215,7 @@ void regclass_pyngraph_Place(py::module m) {
 
     place.def(
         "get_input_port",
-        [](const ngraph::frontend::Place& self, py::object inputName, py::object inputPortIndex) {
+        [](const ov::frontend::Place& self, py::object inputName, py::object inputPortIndex) {
             if (inputName == py::none()) {
                 if (inputPortIndex == py::none()) {
                     return self.get_input_port();
@@ -251,7 +251,7 @@ void regclass_pyngraph_Place(py::module m) {
 
     place.def(
         "get_output_port",
-        [](const ngraph::frontend::Place& self, py::object outputName, py::object outputPortIndex) {
+        [](const ov::frontend::Place& self, py::object outputName, py::object outputPortIndex) {
             if (outputName == py::none()) {
                 if (outputPortIndex == py::none()) {
                     return self.get_output_port();
@@ -286,7 +286,7 @@ void regclass_pyngraph_Place(py::module m) {
              )");
 
     place.def("get_consuming_ports",
-              &ngraph::frontend::Place::get_consuming_ports,
+              &ov::frontend::Place::get_consuming_ports,
               R"(
                 Returns all input ports that consume data flows through this place.
 
@@ -298,7 +298,7 @@ void regclass_pyngraph_Place(py::module m) {
 
     place.def(
         "get_source_tensor",
-        [](const ngraph::frontend::Place& self, py::object inputName, py::object inputPortIndex) {
+        [](const ov::frontend::Place& self, py::object inputName, py::object inputPortIndex) {
             if (inputName == py::none()) {
                 if (inputPortIndex == py::none()) {
                     return self.get_source_tensor();
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/node_factory.cpp b/runtime/bindings/python/src/compatibility/pyngraph/node_factory.cpp
index e93e3ce30bbac1..e7d7ee2aa563e3 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/node_factory.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/node_factory.cpp
@@ -82,7 +82,6 @@ class NodeFactory {
             {"opset5", OpsetFunction(ngraph::get_opset5)},
             {"opset6", OpsetFunction(ngraph::get_opset6)},
             {"opset7", OpsetFunction(ngraph::get_opset7)},
-            {"opset8", OpsetFunction(ngraph::get_opset8)},
         };
 
         auto it = s_opsets.find(opset_ver);
diff --git a/runtime/bindings/python/src/openvino/__init__.py b/runtime/bindings/python/src/openvino/__init__.py
index a8ce307765f147..ca73e6b4d6add0 100644
--- a/runtime/bindings/python/src/openvino/__init__.py
+++ b/runtime/bindings/python/src/openvino/__init__.py
@@ -6,46 +6,32 @@
 
 from pkg_resources import get_distribution, DistributionNotFound
 
-__path__ = __import__('pkgutil').extend_path(__path__, __name__)  # type: ignore  # mypy issue #1422
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)  # type: ignore # mypy issue #1422
 
 try:
     __version__ = get_distribution("openvino-core").version
 except DistributionNotFound:
     __version__ = "0.0.0.dev0"
 
-from openvino.ie_api import BlobWrapper
-from openvino.ie_api import infer
-from openvino.ie_api import async_infer
-from openvino.ie_api import get_result
-from openvino.ie_api import blob_from_file
 
+# Openvino pybind bindings and python extended classes
 from openvino.impl import Dimension
 from openvino.impl import Function
 from openvino.impl import Node
 from openvino.impl import PartialShape
 from openvino.impl import Layout
 
-from openvino.pyopenvino import Core
-from openvino.pyopenvino import IENetwork
-from openvino.pyopenvino import ExecutableNetwork
+from openvino.ie_api import Core
+from openvino.ie_api import ExecutableNetwork
+from openvino.ie_api import InferRequest
+from openvino.ie_api import AsyncInferQueue
 from openvino.pyopenvino import Version
 from openvino.pyopenvino import Parameter
-from openvino.pyopenvino import InputInfoPtr
-from openvino.pyopenvino import InputInfoCPtr
-from openvino.pyopenvino import DataPtr
-from openvino.pyopenvino import TensorDesc
-from openvino.pyopenvino import get_version
-from openvino.pyopenvino import StatusCode
-from openvino.pyopenvino import InferQueue
-from openvino.pyopenvino import InferRequest  # TODO: move to ie_api?
-from openvino.pyopenvino import Blob
-from openvino.pyopenvino import PreProcessInfo
-from openvino.pyopenvino import MeanVariant
-from openvino.pyopenvino import ResizeAlgorithm
-from openvino.pyopenvino import ColorFormat
-from openvino.pyopenvino import PreProcessChannel
 from openvino.pyopenvino import Tensor
+from openvino.pyopenvino import ProfilingInfo
+from openvino.pyopenvino import get_version
 
+# Import opsets
 from openvino import opset1
 from openvino import opset2
 from openvino import opset3
@@ -55,6 +41,10 @@
 from openvino import opset7
 from openvino import opset8
 
+# Helper functions for openvino module
+from openvino.ie_api import tensor_from_file
+from openvino.ie_api import compile_model
+
 # Extend Node class to support binary operators
 Node.__add__ = opset8.add
 Node.__sub__ = opset8.subtract
@@ -72,16 +62,3 @@
 Node.__le__ = opset8.less_equal
 Node.__gt__ = opset8.greater
 Node.__ge__ = opset8.greater_equal
-
-# Patching for Blob class
-# flake8: noqa: F811
-# this class will be removed
-Blob = BlobWrapper
-# Patching ExecutableNetwork
-ExecutableNetwork.infer = infer
-# Patching InferRequest
-InferRequest.infer = infer
-InferRequest.async_infer = async_infer
-InferRequest.get_result = get_result
-# Patching InferQueue
-InferQueue.async_infer = async_infer
diff --git a/runtime/bindings/python/src/openvino/ie_api.py b/runtime/bindings/python/src/openvino/ie_api.py
index 925bc2ad5bb545..6300f88ce7c67f 100644
--- a/runtime/bindings/python/src/openvino/ie_api.py
+++ b/runtime/bindings/python/src/openvino/ie_api.py
@@ -2,113 +2,110 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import numpy as np
+import copy
+from typing import Any, List, Union
 
-from openvino.pyopenvino import TBlobFloat32
-from openvino.pyopenvino import TBlobFloat64
-from openvino.pyopenvino import TBlobInt64
-from openvino.pyopenvino import TBlobUint64
-from openvino.pyopenvino import TBlobInt32
-from openvino.pyopenvino import TBlobUint32
-from openvino.pyopenvino import TBlobInt16
-from openvino.pyopenvino import TBlobUint16
-from openvino.pyopenvino import TBlobInt8
-from openvino.pyopenvino import TBlobUint8
-from openvino.pyopenvino import TensorDesc
-from openvino.pyopenvino import InferRequest
-
-
-precision_map = {"FP32": np.float32,
-                 "FP64": np.float64,
-                 "FP16": np.int16,
-                 "BF16": np.int16,
-                 "I16": np.int16,
-                 "I8": np.int8,
-                 "BIN": np.int8,
-                 "I32": np.int32,
-                 "I64": np.int64,
-                 "U8": np.uint8,
-                 "BOOL": np.uint8,
-                 "U16": np.uint16,
-                 "U32": np.uint32,
-                 "U64": np.uint64}
-
-
-def normalize_inputs(py_dict: dict) -> dict:
-    """Normalize a dictionary of inputs to contiguous numpy arrays."""
-    return {k: (np.ascontiguousarray(v) if isinstance(v, np.ndarray) else v)
-            for k, v in py_dict.items()}
-
-# flake8: noqa: D102
-def infer(request: InferRequest, inputs: dict = None) -> dict:
-    results = request._infer(inputs=normalize_inputs(inputs if inputs is not None else {}))
-    return {name: (blob.buffer.copy()) for name, blob in results.items()}
-
-# flake8: noqa: D102
-def get_result(request: InferRequest, name: str) -> np.ndarray:
-    return request.get_blob(name).buffer.copy()
-
-# flake8: noqa: D102
-def async_infer(request: InferRequest, inputs: dict = None, userdata=None) -> None:  # type: ignore
-    request._async_infer(inputs=normalize_inputs(inputs if inputs is not None else {}),
-                         userdata=userdata)
-
-# flake8: noqa: C901
-# Dispatch Blob types on Python side.
-class BlobWrapper:
-    def __new__(cls, tensor_desc: TensorDesc, arr: np.ndarray = None):  # type: ignore
-        arr_size = 0
-        precision = ""
-        if tensor_desc is not None:
-            tensor_desc_size = int(np.prod(tensor_desc.dims))
-            precision = tensor_desc.precision
-            if arr is not None:
-                arr = np.array(arr)  # Keeping array as numpy array
-                arr_size = int(np.prod(arr.shape))
-                if np.isfortran(arr):
-                    arr = arr.ravel(order="F")
-                else:
-                    arr = arr.ravel(order="C")
-                if arr_size != tensor_desc_size:
-                    raise AttributeError(f"Number of elements in provided numpy array "
-                                         f"{arr_size} and required by TensorDesc "
-                                         f"{tensor_desc_size} are not equal")
-                if arr.dtype != precision_map[precision]:
-                    raise ValueError(f"Data type {arr.dtype} of provided numpy array "
-                                     f"doesn't match to TensorDesc precision {precision}")
-                if not arr.flags["C_CONTIGUOUS"]:
-                    arr = np.ascontiguousarray(arr)
-            elif arr is None:
-                arr = np.empty(0, dtype=precision_map[precision])
-        else:
-            raise AttributeError("TensorDesc can't be None")
-
-        if precision in ["FP32"]:
-            return TBlobFloat32(tensor_desc, arr, arr_size)
-        elif precision in ["FP64"]:
-            return TBlobFloat64(tensor_desc, arr, arr_size)
-        elif precision in ["FP16", "BF16"]:
-            return TBlobInt16(tensor_desc, arr.view(dtype=np.int16), arr_size)
-        elif precision in ["I64"]:
-            return TBlobInt64(tensor_desc, arr, arr_size)
-        elif precision in ["U64"]:
-            return TBlobUint64(tensor_desc, arr, arr_size)
-        elif precision in ["I32"]:
-            return TBlobInt32(tensor_desc, arr, arr_size)
-        elif precision in ["U32"]:
-            return TBlobUint32(tensor_desc, arr, arr_size)
-        elif precision in ["I16"]:
-            return TBlobInt16(tensor_desc, arr, arr_size)
-        elif precision in ["U16"]:
-            return TBlobUint16(tensor_desc, arr, arr_size)
-        elif precision in ["I8", "BIN"]:
-            return TBlobInt8(tensor_desc, arr, arr_size)
-        elif precision in ["U8", "BOOL"]:
-            return TBlobUint8(tensor_desc, arr, arr_size)
-        else:
-            raise AttributeError(f"Unsupported precision {precision} for Blob")
-
-# flake8: noqa: D102
-def blob_from_file(path_to_bin_file: str) -> BlobWrapper:
-    array = np.fromfile(path_to_bin_file, dtype=np.uint8)
-    tensor_desc = TensorDesc("U8", array.shape, "C")
-    return BlobWrapper(tensor_desc, array)
+from openvino.pyopenvino import Function
+from openvino.pyopenvino import Core as CoreBase
+from openvino.pyopenvino import ExecutableNetwork as ExecutableNetworkBase
+from openvino.pyopenvino import InferRequest as InferRequestBase
+from openvino.pyopenvino import AsyncInferQueue as AsyncInferQueueBase
+from openvino.pyopenvino import Tensor
+
+from openvino.utils.types import get_dtype
+
+
+def tensor_from_file(path: str) -> Tensor:
+    """Create Tensor from file. Data will be read with dtype of unit8."""
+    return Tensor(np.fromfile(path, dtype=np.uint8))
+
+
+def normalize_inputs(py_dict: dict, py_types: dict) -> dict:
+    """Normalize a dictionary of inputs to Tensors."""
+    for k, val in py_dict.items():
+        try:
+            if isinstance(k, int):
+                ov_type = list(py_types.values())[k]
+            elif isinstance(k, str):
+                ov_type = py_types[k]
+            else:
+                raise TypeError("Incompatible key type for tensor named: {}".format(k))
+        except KeyError:
+            raise KeyError("Port for tensor named {} was not found!".format(k))
+        py_dict[k] = val if isinstance(val, Tensor) else Tensor(np.array(val, get_dtype(ov_type)))
+    return py_dict
+
+
+def get_input_types(obj: Union[InferRequestBase, ExecutableNetworkBase]) -> dict:
+    """Get all precisions from object inputs."""
+    return {i.get_node().get_friendly_name(): i.get_node().get_element_type() for i in obj.inputs}
+
+
+class InferRequest(InferRequestBase):
+    """InferRequest wrapper."""
+
+    def infer(self, inputs: dict = {}) -> List[np.ndarray]:  # noqa: B006
+        """Infer wrapper for InferRequest."""
+        res = super().infer(inputs=normalize_inputs(inputs, get_input_types(self)))
+        # Required to return list since np.ndarray forces all of tensors data to match in
+        # dimensions. This results in errors when running ops like variadic split.
+        return [copy.deepcopy(tensor.data) for tensor in res]
+
+    def start_async(self, inputs: dict = {}, userdata: Any = None) -> None:  # noqa: B006, type: ignore
+        """Asynchronous infer wrapper for InferRequest."""
+        super().start_async(inputs=normalize_inputs(inputs, get_input_types(self)), userdata=userdata)
+
+
+class ExecutableNetwork(ExecutableNetworkBase):
+    """ExecutableNetwork wrapper."""
+
+    def create_infer_request(self) -> InferRequest:
+        """Create new InferRequest object."""
+        return InferRequest(super().create_infer_request())
+
+    def infer_new_request(self, inputs: dict = {}) -> List[np.ndarray]:  # noqa: B006
+        """Infer wrapper for ExecutableNetwork."""
+        res = super().infer_new_request(inputs=normalize_inputs(inputs, get_input_types(self)))
+        # Required to return list since np.ndarray forces all of tensors data to match in
+        # dimensions. This results in errors when running ops like variadic split.
+        return [copy.deepcopy(tensor.data) for tensor in res]
+
+
+class AsyncInferQueue(AsyncInferQueueBase):
+    """AsyncInferQueue wrapper."""
+
+    def __getitem__(self, i: int) -> InferRequest:
+        """Return i-th InferRequest from AsyncInferQueue."""
+        return InferRequest(super().__getitem__(i))
+
+    def start_async(
+        self, inputs: dict = {}, userdata: Any = None  # noqa: B006
+    ) -> None:  # type: ignore
+        """Asynchronous infer wrapper for AsyncInferQueue."""
+        super().start_async(
+            inputs=normalize_inputs(
+                inputs, get_input_types(self[self.get_idle_request_id()])
+            ),
+            userdata=userdata,
+        )
+
+
+class Core(CoreBase):
+    """Core wrapper."""
+
+    def compile_model(
+        self, model: Function, device_name: str, config: dict = {}  # noqa: B006
+    ) -> ExecutableNetwork:
+        """Compile a model from given Function."""
+        return ExecutableNetwork(super().compile_model(model, device_name, config))
+
+    def import_model(
+        self, model_file: str, device_name: str, config: dict = {}  # noqa: B006
+    ) -> ExecutableNetwork:
+        """Compile a model from given model file path."""
+        return ExecutableNetwork(super().import_model(model_file, device_name, config))
+
+
+def compile_model(model_path: str) -> ExecutableNetwork:
+    """Compact method to compile model with AUTO plugin."""
+    return Core().compile_model(model_path, "AUTO")
diff --git a/runtime/bindings/python/src/openvino/impl/__init__.py b/runtime/bindings/python/src/openvino/impl/__init__.py
index 641764122dc5a9..8a4da6705b90b2 100644
--- a/runtime/bindings/python/src/openvino/impl/__init__.py
+++ b/runtime/bindings/python/src/openvino/impl/__init__.py
@@ -49,4 +49,6 @@
 from openvino.pyopenvino import Coordinate
 from openvino.pyopenvino import Output
 from openvino.pyopenvino import Layout
+from openvino.pyopenvino import ConstOutput
 from openvino.pyopenvino import util
+from openvino.pyopenvino import layout_helpers
diff --git a/runtime/bindings/python/src/openvino/offline_transformations_pybind/__init__.py b/runtime/bindings/python/src/openvino/offline_transformations_pybind/__init__.py
new file mode 100644
index 00000000000000..567d667c0724b2
--- /dev/null
+++ b/runtime/bindings/python/src/openvino/offline_transformations_pybind/__init__.py
@@ -0,0 +1,10 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from openvino.pyopenvino.offline_transformations_pybind import apply_moc_transformations
+from openvino.pyopenvino.offline_transformations_pybind import apply_pot_transformations
+from openvino.pyopenvino.offline_transformations_pybind import apply_low_latency_transformation
+from openvino.pyopenvino.offline_transformations_pybind import apply_pruning_transformation
+from openvino.pyopenvino.offline_transformations_pybind import generate_mapping_file
+from openvino.pyopenvino.offline_transformations_pybind import apply_make_stateful_transformation
diff --git a/runtime/bindings/python/src/pyopenvino/CMakeLists.txt b/runtime/bindings/python/src/pyopenvino/CMakeLists.txt
index 305a5687c0fee4..7b377180f9e4c1 100644
--- a/runtime/bindings/python/src/pyopenvino/CMakeLists.txt
+++ b/runtime/bindings/python/src/pyopenvino/CMakeLists.txt
@@ -4,7 +4,7 @@
 project (pyopenvino)
 
 if(NOT DEFINED OpenVINO_SOURCE_DIR)
-    find_package(OpenVINO REQUIRED)
+    find_package(InferenceEngineDeveloperPackage REQUIRED)
 endif()
 
 # PYTHON_VERSION_MAJOR and PYTHON_VERSION_MINOR are defined inside pybind11
@@ -30,8 +30,14 @@ file(GLOB_RECURSE SOURCES *.cpp)
 
 pybind11_add_module(${PROJECT_NAME} MODULE ${SOURCES})
 
+if(TARGET offline_transformations)
+    set(OFFLINE_TRANSFORMATIONS_LIB offline_transformations)
+else()
+    set(OFFLINE_TRANSFORMATIONS_LIB IE::offline_transformations)
+endif()
+
 target_include_directories(${PROJECT_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/..")
-target_link_libraries(${PROJECT_NAME} PRIVATE openvino::runtime)
+target_link_libraries(${PROJECT_NAME} PRIVATE openvino::runtime ${OFFLINE_TRANSFORMATIONS_LIB})
 
 # perform copy
 if(OpenVINO_SOURCE_DIR)
@@ -61,3 +67,7 @@ if(OpenVINO_SOURCE_DIR OR InferenceEngineDeveloperPackage_FOUND)
 
     ie_cpack(pyopenvino_${PYTHON_VERSION})
 endif()
+
+if(TARGET ie_wheel)
+    add_dependencies(ie_wheel ${PROJECT_NAME})
+endif()
diff --git a/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.cpp b/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.cpp
new file mode 100644
index 00000000000000..160a3ef2307c2f
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.cpp
@@ -0,0 +1,192 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+
+#include "pyopenvino/core/async_infer_queue.hpp"
+
+#include <ie_common.h>
+#include <pybind11/functional.h>
+#include <pybind11/stl.h>
+
+#include <chrono>
+#include <condition_variable>
+#include <mutex>
+#include <queue>
+#include <string>
+#include <vector>
+
+#include "pyopenvino/core/common.hpp"
+#include "pyopenvino/core/infer_request.hpp"
+
+namespace py = pybind11;
+
+class AsyncInferQueue {
+public:
+    AsyncInferQueue(std::vector<InferRequestWrapper> requests,
+                    std::queue<size_t> idle_handles,
+                    std::vector<py::object> user_ids)
+        : _requests(requests),
+          _idle_handles(idle_handles),
+          _user_ids(user_ids) {
+        this->set_default_callbacks();
+    }
+
+    ~AsyncInferQueue() {
+        _requests.clear();
+    }
+
+    bool _is_ready() {
+        py::gil_scoped_release release;
+        std::unique_lock<std::mutex> lock(_mutex);
+        _cv.wait(lock, [this] {
+            return !(_idle_handles.empty());
+        });
+
+        return !(_idle_handles.empty());
+    }
+
+    size_t get_idle_request_id() {
+        // Wait for any of _idle_handles
+        py::gil_scoped_release release;
+        std::unique_lock<std::mutex> lock(_mutex);
+        _cv.wait(lock, [this] {
+            return !(_idle_handles.empty());
+        });
+
+        return _idle_handles.front();
+    }
+
+    void wait_all() {
+        // Wait for all requests to return with callback thus updating
+        // _idle_handles so it matches the size of requests
+        py::gil_scoped_release release;
+        std::unique_lock<std::mutex> lock(_mutex);
+        _cv.wait(lock, [this] {
+            return _idle_handles.size() == _requests.size();
+        });
+    }
+
+    void set_default_callbacks() {
+        for (size_t handle = 0; handle < _requests.size(); handle++) {
+            _requests[handle]._request.set_callback([this, handle /* ... */](std::exception_ptr exception_ptr) {
+                _requests[handle]._end_time = Time::now();
+                // Add idle handle to queue
+                _idle_handles.push(handle);
+                // Notify locks in getIdleRequestId() or waitAll() functions
+                _cv.notify_one();
+            });
+        }
+    }
+
+    void set_custom_callbacks(py::function f_callback) {
+        for (size_t handle = 0; handle < _requests.size(); handle++) {
+            _requests[handle]._request.set_callback([this, f_callback, handle](std::exception_ptr exception_ptr) {
+                _requests[handle]._end_time = Time::now();
+                try {
+                    if (exception_ptr) {
+                        std::rethrow_exception(exception_ptr);
+                    }
+                } catch (const std::exception& e) {
+                    throw ov::Exception(e.what());
+                }
+                // Acquire GIL, execute Python function
+                py::gil_scoped_acquire acquire;
+                f_callback(_requests[handle], _user_ids[handle]);
+                // Add idle handle to queue
+                _idle_handles.push(handle);
+                // Notify locks in getIdleRequestId() or waitAll() functions
+                _cv.notify_one();
+            });
+        }
+    }
+
+    std::vector<InferRequestWrapper> _requests;
+    std::queue<size_t> _idle_handles;
+    std::vector<py::object> _user_ids;  // user ID can be any Python object
+    std::mutex _mutex;
+    std::condition_variable _cv;
+};
+
+void regclass_AsyncInferQueue(py::module m) {
+    py::class_<AsyncInferQueue, std::shared_ptr<AsyncInferQueue>> cls(m, "AsyncInferQueue");
+
+    cls.def(py::init([](ov::runtime::ExecutableNetwork& net, size_t jobs) {
+                if (jobs == 0) {
+                    jobs = (size_t)Common::get_optimal_number_of_requests(net);
+                }
+
+                std::vector<InferRequestWrapper> requests;
+                std::queue<size_t> idle_handles;
+                std::vector<py::object> user_ids(jobs);
+
+                for (size_t handle = 0; handle < jobs; handle++) {
+                    auto request = InferRequestWrapper(net.create_infer_request());
+                    // Get Inputs and Outputs info from executable network
+                    request._inputs = net.inputs();
+                    request._outputs = net.outputs();
+
+                    requests.push_back(request);
+                    idle_handles.push(handle);
+                }
+
+                return new AsyncInferQueue(requests, idle_handles, user_ids);
+            }),
+            py::arg("network"),
+            py::arg("jobs") = 0);
+
+    cls.def(
+        "start_async",
+        [](AsyncInferQueue& self, const py::dict inputs, py::object userdata) {
+            // getIdleRequestId function has an intention to block InferQueue
+            // until there is at least one idle (free to use) InferRequest
+            auto handle = self.get_idle_request_id();
+            self._idle_handles.pop();
+            // Set new inputs label/id from user
+            self._user_ids[handle] = userdata;
+            // Update inputs if there are any
+            Common::set_request_tensors(self._requests[handle]._request, inputs);
+            // Now GIL can be released - we are NOT working with Python objects in this block
+            {
+                py::gil_scoped_release release;
+                self._requests[handle]._start_time = Time::now();
+                // Start InferRequest in asynchronus mode
+                self._requests[handle]._request.start_async();
+            }
+        },
+        py::arg("inputs"),
+        py::arg("userdata"));
+
+    cls.def("is_ready", [](AsyncInferQueue& self) {
+        return self._is_ready();
+    });
+
+    cls.def("wait_all", [](AsyncInferQueue& self) {
+        return self.wait_all();
+    });
+
+    cls.def("get_idle_request_id", [](AsyncInferQueue& self) {
+        return self.get_idle_request_id();
+    });
+
+    cls.def("set_callback", [](AsyncInferQueue& self, py::function f_callback) {
+        self.set_custom_callbacks(f_callback);
+    });
+
+    cls.def("__len__", [](AsyncInferQueue& self) {
+        return self._requests.size();
+    });
+
+    cls.def(
+        "__iter__",
+        [](AsyncInferQueue& self) {
+            return py::make_iterator(self._requests.begin(), self._requests.end());
+        },
+        py::keep_alive<0, 1>()); /* Keep set alive while iterator is used */
+
+    cls.def("__getitem__", [](AsyncInferQueue& self, size_t i) {
+        return self._requests[i];
+    });
+
+    cls.def_property_readonly("userdata", [](AsyncInferQueue& self) {
+        return self._user_ids;
+    });
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_network.hpp b/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.hpp
similarity index 77%
rename from runtime/bindings/python/src/pyopenvino/core/ie_network.hpp
rename to runtime/bindings/python/src/pyopenvino/core/async_infer_queue.hpp
index 9cbd5e43456ba9..3ed1122ba126d7 100644
--- a/runtime/bindings/python/src/pyopenvino/core/ie_network.hpp
+++ b/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.hpp
@@ -1,6 +1,5 @@
 // Copyright (C) 2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
-//
 
 #pragma once
 
@@ -8,4 +7,4 @@
 
 namespace py = pybind11;
 
-void regclass_IENetwork(py::module m);
+void regclass_AsyncInferQueue(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/core/common.cpp b/runtime/bindings/python/src/pyopenvino/core/common.cpp
index 098a85f2ffc95a..6ef5774163a20e 100644
--- a/runtime/bindings/python/src/pyopenvino/core/common.cpp
+++ b/runtime/bindings/python/src/pyopenvino/core/common.cpp
@@ -6,6 +6,8 @@
 
 #include <unordered_map>
 
+#define C_CONTIGUOUS py::detail::npy_api::constants::NPY_ARRAY_C_CONTIGUOUS_
+
 namespace Common {
 const std::map<ov::element::Type, py::dtype>& ov_type_to_dtype() {
     static const std::map<ov::element::Type, py::dtype> ov_type_to_dtype_mapping = {
@@ -45,42 +47,130 @@ const std::map<py::str, ov::element::Type>& dtype_to_ov_type() {
     return dtype_to_ov_type_mapping;
 }
 
-InferenceEngine::Layout get_layout_from_string(const std::string& layout) {
-    static const std::unordered_map<std::string, InferenceEngine::Layout> layout_str_to_enum = {
-        {"ANY", InferenceEngine::Layout::ANY},
-        {"NHWC", InferenceEngine::Layout::NHWC},
-        {"NCHW", InferenceEngine::Layout::NCHW},
-        {"NCDHW", InferenceEngine::Layout::NCDHW},
-        {"NDHWC", InferenceEngine::Layout::NDHWC},
-        {"OIHW", InferenceEngine::Layout::OIHW},
-        {"GOIHW", InferenceEngine::Layout::GOIHW},
-        {"OIDHW", InferenceEngine::Layout::OIDHW},
-        {"GOIDHW", InferenceEngine::Layout::GOIDHW},
-        {"SCALAR", InferenceEngine::Layout::SCALAR},
-        {"C", InferenceEngine::Layout::C},
-        {"CHW", InferenceEngine::Layout::CHW},
-        {"HW", InferenceEngine::Layout::HW},
-        {"NC", InferenceEngine::Layout::NC},
-        {"CN", InferenceEngine::Layout::CN},
-        {"BLOCKED", InferenceEngine::Layout::BLOCKED}};
-    return layout_str_to_enum.at(layout);
+ov::runtime::Tensor tensor_from_numpy(py::array& array, bool shared_memory) {
+    // Check if passed array has C-style contiguous memory layout.
+    bool is_contiguous = C_CONTIGUOUS == (array.flags() & C_CONTIGUOUS);
+    auto type = Common::dtype_to_ov_type().at(py::str(array.dtype()));
+    std::vector<size_t> shape(array.shape(), array.shape() + array.ndim());
+
+    // If memory is going to be shared it needs to be contiguous before
+    // passing to the constructor. This case should be handled by advanced
+    // users on their side of the code.
+    if (shared_memory) {
+        if (is_contiguous) {
+            std::vector<size_t> strides(array.strides(), array.strides() + array.ndim());
+            return ov::runtime::Tensor(type, shape, const_cast<void*>(array.data(0)), strides);
+        } else {
+            throw ov::Exception("Tensor with shared memory must be C contiguous!");
+        }
+    }
+    // Convert to contiguous array if not already C-style.
+    if (!is_contiguous) {
+        array = Common::as_contiguous(array, type);
+    }
+    // Create actual Tensor and copy data.
+    auto tensor = ov::runtime::Tensor(type, shape);
+    // If ndim of py::array is 0, array is a numpy scalar. That results in size to be equal to 0.
+    // To gain access to actual raw/low-level data, it is needed to use buffer protocol.
+    py::buffer_info buf = array.request();
+    std::memcpy(tensor.data(), buf.ptr, buf.ndim == 0 ? buf.itemsize : buf.itemsize * buf.size);
+    return tensor;
+}
+
+py::array as_contiguous(py::array& array, ov::element::Type type) {
+    switch (type) {
+    // floating
+    case ov::element::f64:
+        return array.cast<py::array_t<double, py::array::c_style | py::array::forcecast>>();
+    case ov::element::f32:
+        return array.cast<py::array_t<float, py::array::c_style | py::array::forcecast>>();
+    // signed
+    case ov::element::i64:
+        return array.cast<py::array_t<int64_t, py::array::c_style | py::array::forcecast>>();
+    case ov::element::i32:
+        return array.cast<py::array_t<int32_t, py::array::c_style | py::array::forcecast>>();
+    case ov::element::i16:
+        return array.cast<py::array_t<int16_t, py::array::c_style | py::array::forcecast>>();
+    case ov::element::i8:
+        return array.cast<py::array_t<int8_t, py::array::c_style | py::array::forcecast>>();
+    // unsigned
+    case ov::element::u64:
+        return array.cast<py::array_t<uint64_t, py::array::c_style | py::array::forcecast>>();
+    case ov::element::u32:
+        return array.cast<py::array_t<uint32_t, py::array::c_style | py::array::forcecast>>();
+    case ov::element::u16:
+        return array.cast<py::array_t<uint16_t, py::array::c_style | py::array::forcecast>>();
+    case ov::element::u8:
+        return array.cast<py::array_t<uint8_t, py::array::c_style | py::array::forcecast>>();
+    // other
+    case ov::element::boolean:
+        return array.cast<py::array_t<bool, py::array::c_style | py::array::forcecast>>();
+    case ov::element::u1:
+        return array.cast<py::array_t<uint8_t, py::array::c_style | py::array::forcecast>>();
+    // need to create a view on array to cast it correctly
+    case ov::element::f16:
+    case ov::element::bf16:
+        return array.view("int16").cast<py::array_t<int16_t, py::array::c_style | py::array::forcecast>>();
+    default:
+        throw ov::Exception("Tensor cannot be created as contiguous!");
+        break;
+    }
+}
+
+const ov::runtime::Tensor& cast_to_tensor(const py::handle& tensor) {
+    return tensor.cast<const ov::runtime::Tensor&>();
+}
+
+const Containers::TensorNameMap cast_to_tensor_name_map(const py::dict& inputs) {
+    Containers::TensorNameMap result_map;
+    for (auto&& input : inputs) {
+        std::string name;
+        if (py::isinstance<py::str>(input.first)) {
+            name = input.first.cast<std::string>();
+        } else {
+            throw py::type_error("incompatible function arguments!");
+        }
+        if (py::isinstance<ov::runtime::Tensor>(input.second)) {
+            auto tensor = Common::cast_to_tensor(input.second);
+            result_map[name] = tensor;
+        } else {
+            throw ov::Exception("Unable to cast tensor " + name + "!");
+        }
+    }
+    return result_map;
+}
+
+const Containers::TensorIndexMap cast_to_tensor_index_map(const py::dict& inputs) {
+    Containers::TensorIndexMap result_map;
+    for (auto&& input : inputs) {
+        int idx;
+        if (py::isinstance<py::int_>(input.first)) {
+            idx = input.first.cast<int>();
+        } else {
+            throw py::type_error("incompatible function arguments!");
+        }
+        if (py::isinstance<ov::runtime::Tensor>(input.second)) {
+            auto tensor = Common::cast_to_tensor(input.second);
+            result_map[idx] = tensor;
+        } else {
+            throw ov::Exception("Unable to cast tensor " + std::to_string(idx) + "!");
+        }
+    }
+    return result_map;
 }
 
-const std::string& get_layout_from_enum(const InferenceEngine::Layout& layout) {
-    static const std::unordered_map<int, std::string> layout_int_to_str_map = {{0, "ANY"},
-                                                                               {1, "NCHW"},
-                                                                               {2, "NHWC"},
-                                                                               {3, "NCDHW"},
-                                                                               {4, "NDHWC"},
-                                                                               {64, "OIHW"},
-                                                                               {95, "SCALAR"},
-                                                                               {96, "C"},
-                                                                               {128, "CHW"},
-                                                                               {192, "HW"},
-                                                                               {193, "NC"},
-                                                                               {194, "CN"},
-                                                                               {200, "BLOCKED"}};
-    return layout_int_to_str_map.at(layout);
+void set_request_tensors(ov::runtime::InferRequest& request, const py::dict& inputs) {
+    if (!inputs.empty()) {
+        for (auto&& input : inputs) {
+            if (py::isinstance<py::str>(input.first)) {
+                request.set_tensor(input.first.cast<std::string>(), Common::cast_to_tensor(input.second));
+            } else if (py::isinstance<py::int_>(input.first)) {
+                request.set_input_tensor(input.first.cast<size_t>(), Common::cast_to_tensor(input.second));
+            } else {
+                throw py::type_error("Incompatible key type for tensor named: " + input.first.cast<std::string>());
+            }
+        }
+    }
 }
 
 PyObject* parse_parameter(const InferenceEngine::Parameter& param) {
@@ -185,105 +275,13 @@ PyObject* parse_parameter(const InferenceEngine::Parameter& param) {
     }
 }
 
-bool is_TBlob(const py::handle& blob) {
-    if (py::isinstance<InferenceEngine::TBlob<float>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<double>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<int8_t>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<int16_t>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<int32_t>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<int64_t>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<uint8_t>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<uint16_t>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<uint32_t>>(blob)) {
-        return true;
-    } else if (py::isinstance<InferenceEngine::TBlob<uint64_t>>(blob)) {
-        return true;
-    } else {
-        return false;
-    }
-}
-
-const std::shared_ptr<InferenceEngine::Blob> cast_to_blob(const py::handle& blob) {
-    if (py::isinstance<InferenceEngine::TBlob<float>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<float>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<double>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<double>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<int8_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<int8_t>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<int16_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<int16_t>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<int32_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<int32_t>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<int64_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<int64_t>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<uint8_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<uint8_t>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<uint16_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<uint16_t>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<uint32_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<uint32_t>>&>();
-    } else if (py::isinstance<InferenceEngine::TBlob<uint64_t>>(blob)) {
-        return blob.cast<const std::shared_ptr<InferenceEngine::TBlob<uint64_t>>&>();
-    } else {
-        IE_THROW() << "Unsupported data type for when casting to blob!";
-        // return nullptr;
-    }
-}
-
-void blob_from_numpy(const py::handle& arr, InferenceEngine::Blob::Ptr blob) {
-    if (py::isinstance<py::array_t<float>>(arr)) {
-        Common::fill_blob<float>(arr, blob);
-    } else if (py::isinstance<py::array_t<double>>(arr)) {
-        Common::fill_blob<double>(arr, blob);
-    } else if (py::isinstance<py::array_t<int8_t>>(arr)) {
-        Common::fill_blob<int8_t>(arr, blob);
-    } else if (py::isinstance<py::array_t<int16_t>>(arr)) {
-        Common::fill_blob<int16_t>(arr, blob);
-    } else if (py::isinstance<py::array_t<int32_t>>(arr)) {
-        Common::fill_blob<int32_t>(arr, blob);
-    } else if (py::isinstance<py::array_t<int64_t>>(arr)) {
-        Common::fill_blob<int64_t>(arr, blob);
-    } else if (py::isinstance<py::array_t<uint8_t>>(arr)) {
-        Common::fill_blob<uint8_t>(arr, blob);
-    } else if (py::isinstance<py::array_t<uint16_t>>(arr)) {
-        Common::fill_blob<uint16_t>(arr, blob);
-    } else if (py::isinstance<py::array_t<uint32_t>>(arr)) {
-        Common::fill_blob<uint32_t>(arr, blob);
-    } else if (py::isinstance<py::array_t<uint64_t>>(arr)) {
-        Common::fill_blob<uint64_t>(arr, blob);
-    } else {
-        IE_THROW() << "Unsupported data type for when filling blob!";
-    }
-}
-
-void set_request_blobs(InferenceEngine::InferRequest& request, const py::dict& dictonary) {
-    for (auto&& pair : dictonary) {
-        const std::string& name = pair.first.cast<std::string>();
-        if (py::isinstance<py::array>(pair.second)) {
-            Common::blob_from_numpy(pair.second, request.GetBlob(name));
-        } else if (is_TBlob(pair.second)) {
-            request.SetBlob(name, Common::cast_to_blob(pair.second));
-        } else {
-            IE_THROW() << "Unable to set blob " << name << "!";
-        }
-    }
-}
-
-uint32_t get_optimal_number_of_requests(const InferenceEngine::ExecutableNetwork& actual) {
+uint32_t get_optimal_number_of_requests(const ov::runtime::ExecutableNetwork& actual) {
     try {
-        auto parameter_value = actual.GetMetric(METRIC_KEY(SUPPORTED_METRICS));
+        auto parameter_value = actual.get_metric(METRIC_KEY(SUPPORTED_METRICS));
         auto supported_metrics = parameter_value.as<std::vector<std::string>>();
         const std::string key = METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS);
         if (std::find(supported_metrics.begin(), supported_metrics.end(), key) != supported_metrics.end()) {
-            parameter_value = actual.GetMetric(key);
+            parameter_value = actual.get_metric(key);
             if (parameter_value.is<unsigned int>())
                 return parameter_value.as<unsigned int>();
             else
diff --git a/runtime/bindings/python/src/pyopenvino/core/common.hpp b/runtime/bindings/python/src/pyopenvino/core/common.hpp
index 314a8290244581..7e2e0f0dfc95af 100644
--- a/runtime/bindings/python/src/pyopenvino/core/common.hpp
+++ b/runtime/bindings/python/src/pyopenvino/core/common.hpp
@@ -4,53 +4,43 @@
 
 #pragma once
 
-#include <cpp/ie_executable_network.hpp>
-#include <cpp/ie_infer_request.hpp>
-#include <ie_plugin_config.hpp>
-#include <ie_blob.h>
-#include <ie_parameter.hpp>
+#include <string>
+
+#include <pybind11/stl.h>
 #include <pybind11/numpy.h>
 #include <pybind11/pybind11.h>
-#include <string>
+
+#include <ie_plugin_config.hpp>
+#include <ie_parameter.hpp>
+#include <openvino/core/type/element_type.hpp>
 #include "Python.h"
 #include "ie_common.h"
+#include "openvino/runtime/tensor.hpp"
+#include "openvino/runtime/executable_network.hpp"
+#include "openvino/runtime/infer_request.hpp"
+#include "pyopenvino/core/containers.hpp"
 
 namespace py = pybind11;
 
 namespace Common
 {
-    template <typename T>
-    void fill_blob(const py::handle& py_array, InferenceEngine::Blob::Ptr blob)
-    {
-        py::array_t<T> arr = py::cast<py::array>(py_array);
-        if (arr.size() != 0) {
-            // blob->allocate();
-            InferenceEngine::MemoryBlob::Ptr mem_blob = InferenceEngine::as<InferenceEngine::MemoryBlob>(blob);
-            std::copy(
-                arr.data(0), arr.data(0) + arr.size(), mem_blob->rwmap().as<T*>());
-        } else {
-            py::print("Empty array!");
-        }
-    }
-
     const std::map<ov::element::Type, py::dtype>& ov_type_to_dtype();
+
     const std::map<py::str, ov::element::Type>& dtype_to_ov_type();
 
-    InferenceEngine::Layout get_layout_from_string(const std::string& layout);
+    ov::runtime::Tensor tensor_from_numpy(py::array& array, bool shared_memory);
 
-    const std::string& get_layout_from_enum(const InferenceEngine::Layout& layout);
+    py::array as_contiguous(py::array& array, ov::element::Type type);
 
-    PyObject* parse_parameter(const InferenceEngine::Parameter& param);
+    const ov::runtime::Tensor& cast_to_tensor(const py::handle& tensor);
 
-    PyObject* parse_parameter(const InferenceEngine::Parameter& param);
-
-    bool is_TBlob(const py::handle& blob);
+    const Containers::TensorNameMap cast_to_tensor_name_map(const py::dict& inputs);
 
-    const std::shared_ptr<InferenceEngine::Blob> cast_to_blob(const py::handle& blob);
+    const Containers::TensorIndexMap cast_to_tensor_index_map(const py::dict& inputs);
 
-    void blob_from_numpy(const py::handle& _arr, InferenceEngine::Blob::Ptr &blob);
+    void set_request_tensors(ov::runtime::InferRequest& request, const py::dict& inputs);
 
-    void set_request_blobs(InferenceEngine::InferRequest& request, const py::dict& dictonary);
+    PyObject* parse_parameter(const InferenceEngine::Parameter& param);
 
-    uint32_t get_optimal_number_of_requests(const InferenceEngine::ExecutableNetwork& actual);
+    uint32_t get_optimal_number_of_requests(const ov::runtime::ExecutableNetwork& actual);
 }; // namespace Common
diff --git a/runtime/bindings/python/src/pyopenvino/core/containers.cpp b/runtime/bindings/python/src/pyopenvino/core/containers.cpp
index 096b6074325815..12a50b1caf5331 100644
--- a/runtime/bindings/python/src/pyopenvino/core/containers.cpp
+++ b/runtime/bindings/python/src/pyopenvino/core/containers.cpp
@@ -1,51 +1,23 @@
-
 // Copyright (C) 2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
 
 #include "pyopenvino/core/containers.hpp"
 
-#include <pybind11/stl.h>
 #include <pybind11/stl_bind.h>
 
-PYBIND11_MAKE_OPAQUE(Containers::PyInputsDataMap);
-PYBIND11_MAKE_OPAQUE(Containers::PyConstInputsDataMap);
-PYBIND11_MAKE_OPAQUE(Containers::PyOutputsDataMap);
-PYBIND11_MAKE_OPAQUE(Containers::PyResults);
+PYBIND11_MAKE_OPAQUE(Containers::TensorIndexMap);
+PYBIND11_MAKE_OPAQUE(Containers::TensorNameMap);
 
 namespace py = pybind11;
 
 namespace Containers {
 
-void regclass_PyInputsDataMap(py::module m) {
-    auto py_inputs_data_map = py::bind_map<PyInputsDataMap>(m, "PyInputsDataMap");
-
-    py_inputs_data_map.def("keys", [](PyInputsDataMap& self) {
-        return py::make_key_iterator(self.begin(), self.end());
-    });
-}
-
-void regclass_PyConstInputsDataMap(py::module m) {
-    auto py_const_inputs_data_map = py::bind_map<PyConstInputsDataMap>(m, "PyConstInputsDataMap");
-
-    py_const_inputs_data_map.def("keys", [](PyConstInputsDataMap& self) {
-        return py::make_key_iterator(self.begin(), self.end());
-    });
+void regclass_TensorIndexMap(py::module m) {
+    py::bind_map<TensorIndexMap>(m, "TensorIndexMap");
 }
 
-void regclass_PyOutputsDataMap(py::module m) {
-    auto py_outputs_data_map = py::bind_map<PyOutputsDataMap>(m, "PyOutputsDataMap");
-
-    py_outputs_data_map.def("keys", [](PyOutputsDataMap& self) {
-        return py::make_key_iterator(self.begin(), self.end());
-    });
-}
-
-void regclass_PyResults(py::module m) {
-    auto py_results = py::bind_map<PyResults>(m, "PyResults");
-
-    py_results.def("keys", [](PyResults& self) {
-        return py::make_key_iterator(self.begin(), self.end());
-    });
+void regclass_TensorNameMap(py::module m) {
+    py::bind_map<TensorNameMap>(m, "TensorNameMap");
 }
 }  // namespace Containers
diff --git a/runtime/bindings/python/src/pyopenvino/core/containers.hpp b/runtime/bindings/python/src/pyopenvino/core/containers.hpp
index 511d9053ea50fa..e24e7336236cac 100644
--- a/runtime/bindings/python/src/pyopenvino/core/containers.hpp
+++ b/runtime/bindings/python/src/pyopenvino/core/containers.hpp
@@ -4,28 +4,21 @@
 
 #pragma once
 
-#include <pybind11/pybind11.h>
 #include <string>
-#include <ie_input_info.hpp>
-#include "ie_data.h"
-#include "ie_blob.h"
-
-namespace py = pybind11;
+#include <map>
+#include <vector>
 
-namespace Containers {
-    using PyInputsDataMap = std::map<std::string, std::shared_ptr<InferenceEngine::InputInfo>>;
+#include <pybind11/pybind11.h>
 
-    using PyConstInputsDataMap =
-        std::map<std::string, std::shared_ptr<const InferenceEngine::InputInfo>>;
+#include <openvino/runtime/tensor.hpp>
 
-    using PyOutputsDataMap =
-        std::map<std::string, std::shared_ptr<const InferenceEngine::Data>>;
+namespace py = pybind11;
 
-    using PyResults =
-        std::map<std::string, std::shared_ptr<const InferenceEngine::Blob>>;
+namespace Containers {
+    using TensorIndexMap = std::map<size_t, ov::runtime::Tensor>;
+    using TensorNameMap = std::map<std::string, ov::runtime::Tensor>;
+    using InferResults = std::vector<ov::runtime::Tensor>;
 
-    void regclass_PyInputsDataMap(py::module m);
-    void regclass_PyConstInputsDataMap(py::module m);
-    void regclass_PyOutputsDataMap(py::module m);
-    void regclass_PyResults(py::module m);
-}
\ No newline at end of file
+    void regclass_TensorIndexMap(py::module m);
+    void regclass_TensorNameMap(py::module m);
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/core.cpp b/runtime/bindings/python/src/pyopenvino/core/core.cpp
new file mode 100644
index 00000000000000..46eed93d02b46f
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/core.cpp
@@ -0,0 +1,121 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "pyopenvino/core/core.hpp"
+
+#include <ie_extension.h>
+#include <pybind11/stl.h>
+
+#include <openvino/runtime/core.hpp>
+#include <pyopenvino/core/tensor.hpp>
+
+#include "common.hpp"
+
+namespace py = pybind11;
+
+using ConfigMap = std::map<std::string, std::string>;
+
+std::string to_string(py::handle handle) {
+    auto encodedString = PyUnicode_AsUTF8String(handle.ptr());
+    return PyBytes_AsString(encodedString);
+}
+
+void regclass_Core(py::module m) {
+    py::class_<ov::runtime::Core, std::shared_ptr<ov::runtime::Core>> cls(m, "Core");
+
+    cls.def(py::init<const std::string&>(), py::arg("xml_config_file") = "");
+
+    cls.def("set_config",
+            (void (ov::runtime::Core::*)(const ConfigMap&, const std::string&)) & ov::runtime::Core::set_config,
+            py::arg("config"),
+            py::arg("device_name") = "");
+
+    cls.def(
+        "compile_model",
+        (ov::runtime::ExecutableNetwork(
+            ov::runtime::Core::*)(const std::shared_ptr<const ov::Function>&, const std::string&, const ConfigMap&)) &
+            ov::runtime::Core::compile_model,
+        py::arg("model"),
+        py::arg("device_name"),
+        py::arg("config") = py::dict());
+
+    cls.def("compile_model",
+            (ov::runtime::ExecutableNetwork(
+                ov::runtime::Core::*)(const std::string&, const std::string&, const ConfigMap&)) &
+                ov::runtime::Core::compile_model,
+            py::arg("model_path"),
+            py::arg("device_name"),
+            py::arg("config") = py::dict());
+
+    cls.def("get_versions", &ov::runtime::Core::get_versions);
+
+    cls.def("read_model",
+            (std::shared_ptr<ov::Function>(ov::runtime::Core::*)(const std::string&, const std::string&) const) &
+                ov::runtime::Core::read_model,
+            py::arg("model"),
+            py::arg("weights") = "");
+
+    cls.def(
+        "read_model",
+        (std::shared_ptr<ov::Function>(ov::runtime::Core::*)(const std::string&, const ov::runtime::Tensor&) const) &
+            ov::runtime::Core::read_model,
+        py::arg("model"),
+        py::arg("weights"));
+
+    cls.def(
+        "read_model",
+        [](ov::runtime::Core& self, py::object model, py::object weights) {
+            return self.read_model(py::str(model), py::str(weights));
+        },
+        py::arg("model"),
+        py::arg("weights") = "");
+
+    cls.def(
+        "import_model",
+        (ov::runtime::ExecutableNetwork(ov::runtime::Core::*)(std::istream&, const std::string&, const ConfigMap&)) &
+            ov::runtime::Core::import_model,
+        py::arg("model_file"),
+        py::arg("device_name"),
+        py::arg("config") = py::none());
+
+    cls.def(
+        "get_config",
+        [](ov::runtime::Core& self, const std::string& device_name, const std::string& name) -> py::handle {
+            return Common::parse_parameter(self.get_config(device_name, name));
+        },
+        py::arg("device_name"),
+        py::arg("name"));
+
+    cls.def(
+        "get_metric",
+        [](ov::runtime::Core& self, const std::string device_name, const std::string name) -> py::handle {
+            return Common::parse_parameter(self.get_metric(device_name, name));
+        },
+        py::arg("device_name"),
+        py::arg("name"));
+
+    cls.def("register_plugin", &ov::runtime::Core::register_plugin, py::arg("plugin_name"), py::arg("device_name"));
+
+    cls.def("register_plugins", &ov::runtime::Core::register_plugins, py::arg("xml_config_file"));
+
+    cls.def("unload_plugin", &ov::runtime::Core::unload_plugin, py::arg("device_name"));
+
+    cls.def(
+        "query_model",
+        (ov::runtime::SupportedOpsMap(
+            ov::runtime::Core::*)(const std::shared_ptr<const ov::Function>&, const std::string&, const ConfigMap&)) &
+            ov::runtime::Core::query_model,
+        py::arg("model"),
+        py::arg("device_name"),
+        py::arg("config") = py::dict());
+
+    cls.def(
+        "add_extension",
+        [](ov::runtime::Core& self, const std::string& library_path) {
+            return self.add_extension(library_path);
+        },
+        py::arg("library_path"));
+
+    cls.def_property_readonly("available_devices", &ov::runtime::Core::get_available_devices);
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_core.hpp b/runtime/bindings/python/src/pyopenvino/core/core.hpp
similarity index 100%
rename from runtime/bindings/python/src/pyopenvino/core/ie_core.hpp
rename to runtime/bindings/python/src/pyopenvino/core/core.hpp
diff --git a/runtime/bindings/python/src/pyopenvino/core/executable_network.cpp b/runtime/bindings/python/src/pyopenvino/core/executable_network.cpp
new file mode 100644
index 00000000000000..37d6811f38dbce
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/executable_network.cpp
@@ -0,0 +1,97 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/runtime/executable_network.hpp"
+
+#include <pybind11/stl.h>
+
+#include "common.hpp"
+#include "pyopenvino/core/containers.hpp"
+#include "pyopenvino/core/infer_request.hpp"
+
+PYBIND11_MAKE_OPAQUE(Containers::TensorIndexMap);
+PYBIND11_MAKE_OPAQUE(Containers::TensorNameMap);
+
+namespace py = pybind11;
+
+void regclass_ExecutableNetwork(py::module m) {
+    py::class_<ov::runtime::ExecutableNetwork, std::shared_ptr<ov::runtime::ExecutableNetwork>> cls(
+        m,
+        "ExecutableNetwork");
+
+    cls.def(py::init([](ov::runtime::ExecutableNetwork& other) {
+                return other;
+            }),
+            py::arg("other"));
+
+    cls.def("create_infer_request", [](ov::runtime::ExecutableNetwork& self) {
+        return InferRequestWrapper(self.create_infer_request(), self.inputs(), self.outputs());
+    });
+
+    cls.def(
+        "infer_new_request",
+        [](ov::runtime::ExecutableNetwork& self, const py::dict& inputs) {
+            auto request = self.create_infer_request();
+            // Update inputs if there are any
+            Common::set_request_tensors(request, inputs);
+            request.infer();
+
+            Containers::InferResults results;
+            for (const auto out : self.outputs()) {
+                results.push_back(request.get_tensor(out));
+            }
+            return results;
+        },
+        py::arg("inputs"));
+
+    cls.def("export_model", &ov::runtime::ExecutableNetwork::export_model, py::arg("network_model"));
+
+    cls.def(
+        "get_config",
+        [](ov::runtime::ExecutableNetwork& self, const std::string& name) -> py::handle {
+            return Common::parse_parameter(self.get_config(name));
+        },
+        py::arg("name"));
+
+    cls.def(
+        "get_metric",
+        [](ov::runtime::ExecutableNetwork& self, const std::string& name) -> py::handle {
+            return Common::parse_parameter(self.get_metric(name));
+        },
+        py::arg("name"));
+
+    cls.def("get_runtime_function", &ov::runtime::ExecutableNetwork::get_runtime_function);
+
+    cls.def_property_readonly("inputs", &ov::runtime::ExecutableNetwork::inputs);
+
+    cls.def("input",
+            (ov::Output<const ov::Node>(ov::runtime::ExecutableNetwork::*)() const) &
+                ov::runtime::ExecutableNetwork::input);
+
+    cls.def("input",
+            (ov::Output<const ov::Node>(ov::runtime::ExecutableNetwork::*)(size_t) const) &
+                ov::runtime::ExecutableNetwork::input,
+            py::arg("i"));
+
+    cls.def("input",
+            (ov::Output<const ov::Node>(ov::runtime::ExecutableNetwork::*)(const std::string&) const) &
+                ov::runtime::ExecutableNetwork::input,
+            py::arg("tensor_name"));
+
+    cls.def_property_readonly("outputs", &ov::runtime::ExecutableNetwork::outputs);
+
+    cls.def("output",
+            (ov::Output<const ov::Node>(ov::runtime::ExecutableNetwork::*)() const) &
+                ov::runtime::ExecutableNetwork::output);
+
+    cls.def("output",
+            (ov::Output<const ov::Node>(ov::runtime::ExecutableNetwork::*)(size_t) const) &
+                ov::runtime::ExecutableNetwork::output,
+            py::arg("i"));
+
+    cls.def("output",
+            (ov::Output<const ov::Node>(ov::runtime::ExecutableNetwork::*)(const std::string&) const) &
+                ov::runtime::ExecutableNetwork::output,
+            py::arg("tensor_name"));
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_executable_network.hpp b/runtime/bindings/python/src/pyopenvino/core/executable_network.hpp
similarity index 100%
rename from runtime/bindings/python/src/pyopenvino/core/ie_executable_network.hpp
rename to runtime/bindings/python/src/pyopenvino/core/executable_network.hpp
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_blob.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_blob.cpp
deleted file mode 100644
index 11e6c7634a3f23..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_blob.cpp
+++ /dev/null
@@ -1,20 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "ie_blob.h"
-
-#include <ie_blob.h>
-#include <ie_common.h>
-#include <ie_layouts.h>
-
-#include <ie_precision.hpp>
-
-#include "pyopenvino/core/ie_blob.hpp"
-#include "pyopenvino/core/tensor_description.hpp"
-
-namespace py = pybind11;
-
-void regclass_Blob(py::module m) {
-    py::class_<InferenceEngine::Blob, std::shared_ptr<InferenceEngine::Blob>> cls(m, "Blob");
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_blob.hpp b/runtime/bindings/python/src/pyopenvino/core/ie_blob.hpp
deleted file mode 100644
index b5efc4403869b2..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_blob.hpp
+++ /dev/null
@@ -1,56 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-#include <vector>
-
-#include <pybind11/numpy.h>
-#include <pybind11/pybind11.h>
-#include <pybind11/stl.h>
-
-#include "ie_blob.h"
-#include "ie_common.h"
-#include "ie_layouts.h"
-#include "ie_precision.hpp"
-
-#include "pyopenvino/core/tensor_description.hpp"
-
-namespace py = pybind11;
-
-void regclass_Blob(py::module m);
-
-template <typename T>
-void regclass_TBlob(py::module m, std::string typestring)
-{
-    auto pyclass_name = py::detail::c_str((std::string("TBlob") + typestring));
-
-    py::class_<InferenceEngine::TBlob<T>, std::shared_ptr<InferenceEngine::TBlob<T>>> cls(
-            m, pyclass_name);
-
-    cls.def(py::init(
-            [](const InferenceEngine::TensorDesc& tensorDesc, py::array_t<T>& arr, size_t size = 0) {
-                auto blob = InferenceEngine::make_shared_blob<T>(tensorDesc);
-                blob->allocate();
-                if (size != 0) {
-                    std::copy(arr.data(0), arr.data(0) + size, blob->rwmap().template as<T *>());
-                }
-                return blob;
-            }));
-
-    cls.def_property_readonly("buffer", [](InferenceEngine::TBlob<T>& self) {
-        auto blob_ptr = self.buffer().template as<T*>();
-        auto shape = self.getTensorDesc().getDims();
-        return py::array_t<T>(shape, &blob_ptr[0], py::cast(self));
-    });
-
-    cls.def_property_readonly("tensor_desc",
-                              [](InferenceEngine::TBlob<T>& self) { return self.getTensorDesc(); });
-
-    cls.def("__str__", [](InferenceEngine::TBlob<T>& self) -> std::string {
-        std::stringstream ss;
-        auto blob_ptr = self.buffer().template as<T*>();
-        auto shape = self.getTensorDesc().getDims();
-        auto py_arr = py::array_t<T>(shape, &blob_ptr[0], py::cast(self));
-        ss << py_arr;
-        return ss.str();
-    });
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_core.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_core.cpp
deleted file mode 100644
index 9d37bfb00f7fe3..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_core.cpp
+++ /dev/null
@@ -1,160 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/ie_core.hpp"
-
-#include <pybind11/stl.h>
-
-#include <ie_core.hpp>
-
-#include "common.hpp"
-
-namespace py = pybind11;
-
-std::string to_string(py::handle handle) {
-    auto encodedString = PyUnicode_AsUTF8String(handle.ptr());
-    return PyBytes_AsString(encodedString);
-}
-
-void regclass_Core(py::module m) {
-    py::class_<InferenceEngine::Core, std::shared_ptr<InferenceEngine::Core>> cls(m, "Core");
-    cls.def(py::init());
-    cls.def(py::init<const std::string&>());
-
-    cls.def(
-        "set_config",
-        [](InferenceEngine::Core& self, const py::dict& config, const std::string& device_name) {
-            std::map<std::string, std::string> config_map;
-            for (auto item : config) {
-                config_map[to_string(item.first)] = to_string(item.second);
-            }
-            self.SetConfig(config_map, device_name);
-        },
-        py::arg("config"),
-        py::arg("device_name"));
-
-    cls.def(
-        "load_network",
-        [](InferenceEngine::Core& self,
-           const InferenceEngine::CNNNetwork& network,
-           const std::string& device_name,
-           const std::map<std::string, std::string>& config) {
-            return self.LoadNetwork(network, device_name, config);
-        },
-        py::arg("network"),
-        py::arg("device_name"),
-        py::arg("config") = py::dict());
-
-    cls.def(
-        "add_extension",
-        [](InferenceEngine::Core& self, const std::string& extension_path, const std::string& device_name) {
-            auto extension_ptr = InferenceEngine::make_so_pointer<InferenceEngine::IExtension>(extension_path);
-            auto extension = std::dynamic_pointer_cast<InferenceEngine::IExtension>(extension_ptr);
-            self.AddExtension(extension, device_name);
-        },
-        py::arg("extension_path"),
-        py::arg("device_name"));
-
-    cls.def(
-        "get_versions",
-        [](InferenceEngine::Core& self, const std::string& device_name) {
-            return self.GetVersions(device_name);
-        },
-        py::arg("device_name"));
-
-    cls.def(
-        "read_network",
-        [](InferenceEngine::Core& self, py::bytes model, py::bytes weights) {
-            InferenceEngine::MemoryBlob::Ptr weights_blob;
-            if (weights) {
-                std::string weights_bytes = weights;
-                uint8_t* bin = (uint8_t*)weights_bytes.c_str();
-                size_t bin_size = weights_bytes.length();
-                InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8,
-                                                       {bin_size},
-                                                       InferenceEngine::Layout::C);
-                weights_blob = InferenceEngine::make_shared_blob<uint8_t>(tensorDesc);
-                weights_blob->allocate();
-                memcpy(weights_blob->rwmap().as<uint8_t*>(), bin, bin_size);
-            }
-            return self.ReadNetwork(model, weights_blob);
-        },
-        py::arg("model"),
-        py::arg("weights"));
-
-    cls.def(
-        "read_network",
-        [](InferenceEngine::Core& self, const std::string& model, const std::string& weights) {
-            return self.ReadNetwork(model, weights);
-        },
-        py::arg("model"),
-        py::arg("weights") = "");
-
-    cls.def(
-        "read_network",
-        [](InferenceEngine::Core& self, const std::string& model, py::handle blob) {
-            return self.ReadNetwork(model, Common::cast_to_blob(blob));
-        },
-        py::arg("model"),
-        py::arg("blob"));
-
-    cls.def(
-        "read_network",
-        [](InferenceEngine::Core& self, py::object model, py::object weights) {
-            return self.ReadNetwork(py::str(model), py::str(weights));
-        },
-        py::arg("model"),
-        py::arg("weights") = "");
-
-    cls.def(
-        "import_network",
-        [](InferenceEngine::Core& self,
-           const std::string& model_file,
-           const std::string& device_name,
-           const std::map<std::string, std::string>& config) {
-            return self.ImportNetwork(model_file, device_name, config);
-        },
-        py::arg("model_file"),
-        py::arg("device_name"),
-        py::arg("config") = py::none());
-
-    cls.def(
-        "get_config",
-        [](InferenceEngine::Core& self, const std::string& device_name, const std::string& config_name) -> py::handle {
-            return Common::parse_parameter(self.GetConfig(device_name, config_name));
-        },
-        py::arg("device_name"),
-        py::arg("config_name"));
-
-    cls.def(
-        "get_metric",
-        [](InferenceEngine::Core& self, std::string device_name, std::string metric_name) -> py::handle {
-            return Common::parse_parameter(self.GetMetric(device_name, metric_name));
-        },
-        py::arg("device_name"),
-        py::arg("metric_name"));
-
-    cls.def("register_plugin",
-            &InferenceEngine::Core::RegisterPlugin,
-            py::arg("plugin_name"),
-            py::arg("device_name") = py::str());
-
-    cls.def("register_plugins", &InferenceEngine::Core::RegisterPlugins);
-
-    cls.def("unregister_plugin", &InferenceEngine::Core::UnregisterPlugin, py::arg("device_name"));
-
-    cls.def(
-        "query_network",
-        [](InferenceEngine::Core& self,
-           const InferenceEngine::CNNNetwork& network,
-           const std::string& device_name,
-           const std::map<std::string, std::string>& config) {
-            return self.QueryNetwork(network, device_name, config).supportedLayersMap;
-        },
-        py::arg("network"),
-        py::arg("device_name"),
-        py::arg("config") = py::dict());
-
-    cls.def_property_readonly("available_devices", &InferenceEngine::Core::GetAvailableDevices);
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_data.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_data.cpp
deleted file mode 100644
index d1fd3bf760dda4..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_data.cpp
+++ /dev/null
@@ -1,39 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/ie_data.hpp"
-
-#include <ie_data.h>
-#include <pybind11/stl.h>
-
-#include "common.hpp"
-
-namespace py = pybind11;
-
-void regclass_Data(py::module m) {
-    py::class_<InferenceEngine::Data, std::shared_ptr<InferenceEngine::Data>> cls(m, "DataPtr");
-
-    cls.def_property(
-        "layout",
-        [](InferenceEngine::Data& self) {
-            return Common::get_layout_from_enum(self.getLayout());
-        },
-        [](InferenceEngine::Data& self, const std::string& layout) {
-            self.setLayout(Common::get_layout_from_string(layout));
-        });
-
-    cls.def_property(
-        "precision",
-        [](InferenceEngine::Data& self) {
-            return self.getPrecision().name();
-        },
-        [](InferenceEngine::Data& self, const std::string& precision) {
-            self.setPrecision(InferenceEngine::Precision::FromStr(precision));
-        });
-
-    cls.def_property_readonly("shape", &InferenceEngine::Data::getDims);
-
-    cls.def_property_readonly("name", &InferenceEngine::Data::getName);
-    // cls.def_property_readonly("initialized", );
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_executable_network.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_executable_network.cpp
deleted file mode 100644
index 37199110f09e68..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_executable_network.cpp
+++ /dev/null
@@ -1,91 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-#include "pyopenvino/core/ie_executable_network.hpp"
-
-#include <cpp/ie_executable_network.hpp>
-#include <ie_input_info.hpp>
-
-#include "common.hpp"
-#include "pyopenvino/core/containers.hpp"
-#include "pyopenvino/core/ie_infer_request.hpp"
-#include "pyopenvino/core/ie_input_info.hpp"
-
-namespace py = pybind11;
-
-void regclass_ExecutableNetwork(py::module m) {
-    py::class_<InferenceEngine::ExecutableNetwork, std::shared_ptr<InferenceEngine::ExecutableNetwork>> cls(
-        m,
-        "ExecutableNetwork");
-
-    cls.def("create_infer_request", [](InferenceEngine::ExecutableNetwork& self) {
-        auto request = InferRequestWrapper(self.CreateInferRequest());
-        // Get Inputs and Outputs info from executable network
-        request._inputsInfo = self.GetInputsInfo();
-        request._outputsInfo = self.GetOutputsInfo();
-        // request.user_callback_defined = false;
-        return request;
-    });
-
-    cls.def(
-        "_infer",
-        [](InferenceEngine::ExecutableNetwork& self, const py::dict& inputs) {
-            // Create temporary InferRequest
-            auto request = self.CreateInferRequest();
-            // Update inputs if there are any
-            if (!inputs.empty()) {
-                Common::set_request_blobs(request, inputs);  //, self.GetInputsInfo());
-            }
-            // Call Infer function
-            request.Infer();
-            // Get output Blobs and return
-            Containers::PyResults results;
-            InferenceEngine::ConstOutputsDataMap outputsInfo = self.GetOutputsInfo();
-            for (auto& out : outputsInfo) {
-                results[out.first] = request.GetBlob(out.first);
-            }
-            return results;
-        },
-        py::arg("inputs"));
-
-    cls.def("get_exec_graph_info", &InferenceEngine::ExecutableNetwork::GetExecGraphInfo);
-
-    cls.def(
-        "export",
-        [](InferenceEngine::ExecutableNetwork& self, const std::string& modelFileName) {
-            self.Export(modelFileName);
-        },
-        py::arg("model_file"));
-
-    cls.def(
-        "get_config",
-        [](InferenceEngine::ExecutableNetwork& self, const std::string& config_name) -> py::handle {
-            return Common::parse_parameter(self.GetConfig(config_name));
-        },
-        py::arg("config_name"));
-
-    cls.def(
-        "get_metric",
-        [](InferenceEngine::ExecutableNetwork& self, const std::string& metric_name) -> py::handle {
-            return Common::parse_parameter(self.GetMetric(metric_name));
-        },
-        py::arg("metric_name"));
-
-    cls.def_property_readonly("input_info", [](InferenceEngine::ExecutableNetwork& self) {
-        Containers::PyConstInputsDataMap inputs;
-        const InferenceEngine::ConstInputsDataMap& inputsInfo = self.GetInputsInfo();
-        for (const auto& in : inputsInfo) {
-            inputs[in.first] = in.second;
-        }
-        return inputs;
-    });
-
-    cls.def_property_readonly("output_info", [](InferenceEngine::ExecutableNetwork& self) {
-        Containers::PyOutputsDataMap outputs;
-        InferenceEngine::ConstOutputsDataMap outputsInfo = self.GetOutputsInfo();
-        for (auto& out : outputsInfo) {
-            outputs[out.first] = out.second;
-        }
-        return outputs;
-    });
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_infer_queue.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_infer_queue.cpp
deleted file mode 100644
index d54e7cce69c9ff..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_infer_queue.cpp
+++ /dev/null
@@ -1,233 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-#include "pyopenvino/core/ie_infer_queue.hpp"
-
-#include <ie_common.h>
-#include <pybind11/functional.h>
-#include <pybind11/stl.h>
-
-#include <chrono>
-#include <condition_variable>
-#include <cpp/ie_executable_network.hpp>
-#include <cpp/ie_infer_request.hpp>
-#include <ie_iinfer_request.hpp>
-#include <mutex>
-#include <queue>
-#include <string>
-#include <vector>
-
-#include "pyopenvino/core/common.hpp"
-#include "pyopenvino/core/ie_infer_request.hpp"
-
-#define INVALID_ID -1
-
-namespace py = pybind11;
-
-class InferQueue {
-public:
-    InferQueue(std::vector<InferRequestWrapper> requests,
-               std::queue<size_t> idle_handles,
-               std::vector<py::object> user_ids)
-        : _requests(requests),
-          _idle_handles(idle_handles),
-          _user_ids(user_ids) {
-        this->setDefaultCallbacks();
-        _last_id = -1;
-    }
-
-    ~InferQueue() {
-        _requests.clear();
-    }
-
-    bool _is_ready() {
-        py::gil_scoped_release release;
-        std::unique_lock<std::mutex> lock(_mutex);
-        _cv.wait(lock, [this] {
-            return !(_idle_handles.empty());
-        });
-
-        return !(_idle_handles.empty());
-    }
-
-    py::dict _getIdleRequestInfo() {
-        py::gil_scoped_release release;
-        std::unique_lock<std::mutex> lock(_mutex);
-        _cv.wait(lock, [this] {
-            return !(_idle_handles.empty());
-        });
-
-        size_t request_id = _idle_handles.front();
-
-        InferenceEngine::StatusCode status =
-            _requests[request_id]._request.Wait(InferenceEngine::IInferRequest::WaitMode::STATUS_ONLY);
-
-        if (status == InferenceEngine::StatusCode::RESULT_NOT_READY) {
-            status = _requests[request_id]._request.Wait(InferenceEngine::IInferRequest::WaitMode::RESULT_READY);
-        }
-
-        py::dict request_info = py::dict();
-        request_info["id"] = request_id;
-        request_info["status"] = status;
-
-        return request_info;
-    }
-
-    size_t getIdleRequestId() {
-        // Wait for any of _idle_handles
-        py::gil_scoped_release release;
-        std::unique_lock<std::mutex> lock(_mutex);
-        _cv.wait(lock, [this] {
-            return !(_idle_handles.empty());
-        });
-
-        size_t idle_request_id = _idle_handles.front();
-        _idle_handles.pop();
-
-        return idle_request_id;
-    }
-
-    std::vector<InferenceEngine::StatusCode> waitAll() {
-        // Wait for all requests to return with callback thus updating
-        // _idle_handles so it matches the size of requests
-        py::gil_scoped_release release;
-        std::unique_lock<std::mutex> lock(_mutex);
-        _cv.wait(lock, [this] {
-            return _idle_handles.size() == _requests.size();
-        });
-
-        std::vector<InferenceEngine::StatusCode> statuses;
-
-        for (size_t handle = 0; handle < _requests.size(); handle++) {
-            statuses.push_back(_requests[handle]._request.Wait(InferenceEngine::IInferRequest::WaitMode::RESULT_READY));
-        }
-
-        return statuses;
-    }
-
-    void setDefaultCallbacks() {
-        for (size_t handle = 0; handle < _requests.size(); handle++) {
-            _requests[handle]._request.SetCompletionCallback([this, handle /* ... */]() {
-                _requests[handle]._endTime = Time::now();
-                // Add idle handle to queue
-                _idle_handles.push(handle);
-                // Notify locks in getIdleRequestId() or waitAll() functions
-                _cv.notify_one();
-            });
-        }
-    }
-
-    void setCustomCallbacks(py::function f_callback) {
-        for (size_t handle = 0; handle < _requests.size(); handle++) {
-            _requests[handle]._request.SetCompletionCallback([this, f_callback, handle /* ... */]() {
-                _requests[handle]._endTime = Time::now();
-                InferenceEngine::StatusCode statusCode =
-                    _requests[handle]._request.Wait(InferenceEngine::IInferRequest::WaitMode::STATUS_ONLY);
-                if (statusCode == InferenceEngine::StatusCode::RESULT_NOT_READY) {
-                    statusCode = InferenceEngine::StatusCode::OK;
-                }
-                // Acquire GIL, execute Python function
-                py::gil_scoped_acquire acquire;
-                f_callback(_requests[handle], statusCode, _user_ids[handle]);
-                // Add idle handle to queue
-                _idle_handles.push(handle);
-                // Notify locks in getIdleRequestId() or waitAll() functions
-                _cv.notify_one();
-            });
-        }
-    }
-
-    std::vector<InferRequestWrapper> _requests;
-    std::queue<size_t> _idle_handles;
-    std::vector<py::object> _user_ids;  // user ID can be any Python object
-    size_t _last_id;
-    std::mutex _mutex;
-    std::condition_variable _cv;
-};
-
-void regclass_InferQueue(py::module m) {
-    py::class_<InferQueue, std::shared_ptr<InferQueue>> cls(m, "InferQueue");
-
-    cls.def(py::init([](InferenceEngine::ExecutableNetwork& net, size_t jobs) {
-                if (jobs == 0) {
-                    const InferenceEngine::ExecutableNetwork& _net = net;
-                    jobs = (size_t)Common::get_optimal_number_of_requests(_net);
-                }
-
-                std::vector<InferRequestWrapper> requests;
-                std::queue<size_t> idle_handles;
-                std::vector<py::object> user_ids(jobs);
-
-                for (size_t handle = 0; handle < jobs; handle++) {
-                    auto request = InferRequestWrapper(net.CreateInferRequest());
-                    // Get Inputs and Outputs info from executable network
-                    request._inputsInfo = net.GetInputsInfo();
-                    request._outputsInfo = net.GetOutputsInfo();
-
-                    requests.push_back(request);
-                    idle_handles.push(handle);
-                }
-
-                return new InferQueue(requests, idle_handles, user_ids);
-            }),
-            py::arg("network"),
-            py::arg("jobs") = 0);
-
-    cls.def(
-        "_async_infer",
-        [](InferQueue& self, const py::dict inputs, py::object userdata) {
-            // getIdleRequestId function has an intention to block InferQueue
-            // until there is at least one idle (free to use) InferRequest
-            auto handle = self.getIdleRequestId();
-            // Set new inputs label/id from user
-            self._user_ids[handle] = userdata;
-            // Update inputs of picked InferRequest
-            if (!inputs.empty()) {
-                Common::set_request_blobs(self._requests[handle]._request, inputs);
-            }
-            // Now GIL can be released - we are NOT working with Python objects in this block
-            {
-                py::gil_scoped_release release;
-                self._requests[handle]._startTime = Time::now();
-                // Start InferRequest in asynchronus mode
-                self._requests[handle]._request.StartAsync();
-            }
-        },
-        py::arg("inputs"),
-        py::arg("userdata"));
-
-    cls.def("is_ready", [](InferQueue& self) {
-        return self._is_ready();
-    });
-
-    cls.def("wait_all", [](InferQueue& self) {
-        return self.waitAll();
-    });
-
-    cls.def("get_idle_request_info", [](InferQueue& self) {
-        return self._getIdleRequestInfo();
-    });
-
-    cls.def("set_infer_callback", [](InferQueue& self, py::function f_callback) {
-        self.setCustomCallbacks(f_callback);
-    });
-
-    cls.def("__len__", [](InferQueue& self) {
-        return self._requests.size();
-    });
-
-    cls.def(
-        "__iter__",
-        [](InferQueue& self) {
-            return py::make_iterator(self._requests.begin(), self._requests.end());
-        },
-        py::keep_alive<0, 1>()); /* Keep set alive while iterator is used */
-
-    cls.def("__getitem__", [](InferQueue& self, size_t i) {
-        return self._requests[i];
-    });
-
-    cls.def_property_readonly("userdata", [](InferQueue& self) {
-        return self._user_ids;
-    });
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_infer_queue.hpp b/runtime/bindings/python/src/pyopenvino/core/ie_infer_queue.hpp
deleted file mode 100644
index 23aa72fd072496..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_infer_queue.hpp
+++ /dev/null
@@ -1,10 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-#pragma once
-
-#include <pybind11/pybind11.h>
-
-namespace py = pybind11;
-
-void regclass_InferQueue(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_infer_request.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_infer_request.cpp
deleted file mode 100644
index f45ddd6a5cd73d..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_infer_request.cpp
+++ /dev/null
@@ -1,210 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-
-#include "pyopenvino/core/ie_infer_request.hpp"
-
-#include <ie_common.h>
-#include <pybind11/functional.h>
-
-#include <string>
-
-#include "pyopenvino/core/common.hpp"
-#include "pyopenvino/core/containers.hpp"
-#include "pyopenvino/core/ie_executable_network.hpp"
-#include "pyopenvino/core/ie_preprocess_info.hpp"
-
-namespace py = pybind11;
-
-void regclass_InferRequest(py::module m) {
-    py::class_<InferRequestWrapper, std::shared_ptr<InferRequestWrapper>> cls(m, "InferRequest");
-
-    cls.def(
-        "set_batch",
-        [](InferRequestWrapper& self, const int size) {
-            self._request.SetBatch(size);
-        },
-        py::arg("size"));
-
-    cls.def(
-        "get_blob",
-        [](InferRequestWrapper& self, const std::string& name) {
-            return self._request.GetBlob(name);
-        },
-        py::arg("name"));
-
-    cls.def(
-        "set_blob",
-        [](InferRequestWrapper& self, const std::string& name, py::handle& blob) {
-            self._request.SetBlob(name, Common::cast_to_blob(blob));
-        },
-        py::arg("name"),
-        py::arg("blob"));
-
-    cls.def(
-        "set_blob",
-        [](InferRequestWrapper& self,
-           const std::string& name,
-           py::handle& blob,
-           const InferenceEngine::PreProcessInfo& info) {
-            self._request.SetBlob(name, Common::cast_to_blob(blob));
-        },
-        py::arg("name"),
-        py::arg("blob"),
-        py::arg("info"));
-
-    cls.def(
-        "set_input",
-        [](InferRequestWrapper& self, const py::dict& inputs) {
-            Common::set_request_blobs(self._request, inputs);
-        },
-        py::arg("inputs"));
-
-    cls.def(
-        "set_output",
-        [](InferRequestWrapper& self, const py::dict& results) {
-            Common::set_request_blobs(self._request, results);
-        },
-        py::arg("results"));
-
-    cls.def(
-        "_infer",
-        [](InferRequestWrapper& self, const py::dict& inputs) {
-            // Update inputs if there are any
-            if (!inputs.empty()) {
-                Common::set_request_blobs(self._request, inputs);
-            }
-            // Call Infer function
-            self._startTime = Time::now();
-            self._request.Infer();
-            self._endTime = Time::now();
-            // Get output Blobs and return
-            Containers::PyResults results;
-            for (auto& out : self._outputsInfo) {
-                results[out.first] = self._request.GetBlob(out.first);
-            }
-            return results;
-        },
-        py::arg("inputs"));
-
-    cls.def(
-        "_async_infer",
-        [](InferRequestWrapper& self, const py::dict inputs, py::object userdata) {
-            py::gil_scoped_release release;
-            if (!inputs.empty()) {
-                Common::set_request_blobs(self._request, inputs);
-            }
-            // TODO: check for None so next async infer userdata can be updated
-            // if (!userdata.empty())
-            // {
-            //     if (user_callback_defined)
-            //     {
-            //         self._request.SetCompletionCallback([self, userdata]() {
-            //             // py::gil_scoped_acquire acquire;
-            //             auto statusCode = const_cast<InferRequestWrapper&>(self).Wait(
-            //                 InferenceEngine::IInferRequest::WaitMode::STATUS_ONLY);
-            //             self._request.user_callback(self, statusCode, userdata);
-            //             // py::gil_scoped_release release;
-            //         });
-            //     }
-            //     else
-            //     {
-            //         py::print("There is no callback function!");
-            //     }
-            // }
-            self._startTime = Time::now();
-            self._request.StartAsync();
-        },
-        py::arg("inputs"),
-        py::arg("userdata"));
-
-    cls.def("cancel", [](InferRequestWrapper& self) {
-        self._request.Cancel();
-    });
-
-    cls.def(
-        "wait",
-        [](InferRequestWrapper& self, int64_t millis_timeout) {
-            py::gil_scoped_release release;
-            return self._request.Wait(millis_timeout);
-        },
-        py::arg("millis_timeout") = InferenceEngine::IInferRequest::WaitMode::RESULT_READY);
-
-    cls.def(
-        "set_completion_callback",
-        [](InferRequestWrapper& self, py::function f_callback, py::object userdata) {
-            self._request.SetCompletionCallback([&self, f_callback, userdata]() {
-                self._endTime = Time::now();
-                InferenceEngine::StatusCode statusCode =
-                    self._request.Wait(InferenceEngine::IInferRequest::WaitMode::STATUS_ONLY);
-                if (statusCode == InferenceEngine::StatusCode::RESULT_NOT_READY) {
-                    statusCode = InferenceEngine::StatusCode::OK;
-                }
-                // Acquire GIL, execute Python function
-                py::gil_scoped_acquire acquire;
-                f_callback(self, statusCode, userdata);
-            });
-        },
-        py::arg("f_callback"),
-        py::arg("userdata"));
-
-    cls.def("get_perf_counts", [](InferRequestWrapper& self) {
-        std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> perfMap;
-        perfMap = self._request.GetPerformanceCounts();
-        py::dict perf_map;
-
-        for (auto it : perfMap) {
-            py::dict profile_info;
-            switch (it.second.status) {
-            case InferenceEngine::InferenceEngineProfileInfo::EXECUTED:
-                profile_info["status"] = "EXECUTED";
-                break;
-            case InferenceEngine::InferenceEngineProfileInfo::NOT_RUN:
-                profile_info["status"] = "NOT_RUN";
-                break;
-            case InferenceEngine::InferenceEngineProfileInfo::OPTIMIZED_OUT:
-                profile_info["status"] = "OPTIMIZED_OUT";
-                break;
-            default:
-                profile_info["status"] = "UNKNOWN";
-            }
-            profile_info["exec_type"] = it.second.exec_type;
-            profile_info["layer_type"] = it.second.layer_type;
-            profile_info["cpu_time"] = it.second.cpu_uSec;
-            profile_info["real_time"] = it.second.realTime_uSec;
-            profile_info["execution_index"] = it.second.execution_index;
-            perf_map[it.first.c_str()] = profile_info;
-        }
-        return perf_map;
-    });
-
-    cls.def(
-        "preprocess_info",
-        [](InferRequestWrapper& self, const std::string& name) {
-            return self._request.GetPreProcess(name);
-        },
-        py::arg("name"));
-
-    //    cls.def_property_readonly("preprocess_info", [](InferRequestWrapper& self) {
-    //
-    //    });
-
-    cls.def_property_readonly("input_blobs", [](InferRequestWrapper& self) {
-        Containers::PyResults input_blobs;
-        for (auto& in : self._inputsInfo) {
-            input_blobs[in.first] = self._request.GetBlob(in.first);
-        }
-        return input_blobs;
-    });
-
-    cls.def_property_readonly("output_blobs", [](InferRequestWrapper& self) {
-        Containers::PyResults output_blobs;
-        for (auto& out : self._outputsInfo) {
-            output_blobs[out.first] = self._request.GetBlob(out.first);
-        }
-        return output_blobs;
-    });
-
-    cls.def_property_readonly("latency", [](InferRequestWrapper& self) {
-        return self.getLatency();
-    });
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_infer_request.hpp b/runtime/bindings/python/src/pyopenvino/core/ie_infer_request.hpp
deleted file mode 100644
index 13afbac440360d..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_infer_request.hpp
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <chrono>
-
-#include <pybind11/pybind11.h>
-
-#include <cpp/ie_executable_network.hpp>
-#include <cpp/ie_infer_request.hpp>
-#include <ie_input_info.hpp>
-
-namespace py = pybind11;
-
-typedef std::chrono::high_resolution_clock Time;
-typedef std::chrono::nanoseconds ns;
-
-class InferRequestWrapper {
-public:
-    InferRequestWrapper(InferenceEngine::InferRequest request)
-        : _request(request)
-    {
-    }
-    // ~InferRequestWrapper() = default;
-
-    // bool user_callback_defined;
-    // py::function user_callback;
-
-    double getLatency() {
-        auto execTime = std::chrono::duration_cast<ns>(_endTime - _startTime);
-        return static_cast<double>(execTime.count()) * 0.000001;
-    }
-
-    InferenceEngine::InferRequest _request;
-    InferenceEngine::ConstInputsDataMap _inputsInfo;
-    InferenceEngine::ConstOutputsDataMap _outputsInfo;
-    Time::time_point _startTime;
-    Time::time_point _endTime;
-};
-
-void regclass_InferRequest(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_input_info.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_input_info.cpp
deleted file mode 100644
index d47020b537a098..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_input_info.cpp
+++ /dev/null
@@ -1,78 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/ie_input_info.hpp"
-
-#include <pybind11/stl.h>
-
-#include <ie_input_info.hpp>
-
-#include "common.hpp"
-
-namespace py = pybind11;
-
-class ConstInputInfoWrapper {
-public:
-    ConstInputInfoWrapper() = default;
-    ~ConstInputInfoWrapper() = default;
-    const InferenceEngine::InputInfo& cref() const {
-        return value;
-    }
-
-protected:
-    const InferenceEngine::InputInfo& ref() {
-        return this->value;
-    }
-    const InferenceEngine::InputInfo value = InferenceEngine::InputInfo();
-};
-
-void regclass_InputInfo(py::module m) {
-    // Workaround for constant class
-    py::class_<ConstInputInfoWrapper, std::shared_ptr<ConstInputInfoWrapper>> cls_const(m, "InputInfoCPtr");
-
-    cls_const.def(py::init<>());
-
-    cls_const.def_property_readonly("input_data", [](const ConstInputInfoWrapper& self) {
-        return self.cref().getInputData();
-    });
-    cls_const.def_property_readonly("precision", [](const ConstInputInfoWrapper& self) {
-        return self.cref().getPrecision().name();
-    });
-    cls_const.def_property_readonly("tensor_desc", [](const ConstInputInfoWrapper& self) {
-        return self.cref().getTensorDesc();
-    });
-    cls_const.def_property_readonly("name", [](const ConstInputInfoWrapper& self) {
-        return self.cref().name();
-    });
-    // Mutable version
-    py::class_<InferenceEngine::InputInfo, std::shared_ptr<InferenceEngine::InputInfo>> cls(m, "InputInfoPtr");
-
-    cls.def(py::init<>());
-
-    cls.def_property("input_data",
-                     &InferenceEngine::InputInfo::getInputData,
-                     &InferenceEngine::InputInfo::setInputData);
-    cls.def_property(
-        "layout",
-        [](InferenceEngine::InputInfo& self) {
-            return Common::get_layout_from_enum(self.getLayout());
-        },
-        [](InferenceEngine::InputInfo& self, const std::string& layout) {
-            self.setLayout(Common::get_layout_from_string(layout));
-        });
-    cls.def_property(
-        "precision",
-        [](InferenceEngine::InputInfo& self) {
-            return self.getPrecision().name();
-        },
-        [](InferenceEngine::InputInfo& self, const std::string& precision) {
-            self.setPrecision(InferenceEngine::Precision::FromStr(precision));
-        });
-    cls.def_property_readonly("tensor_desc", &InferenceEngine::InputInfo::getTensorDesc);
-    cls.def_property_readonly("name", &InferenceEngine::InputInfo::name);
-    cls.def_property_readonly("preprocess_info", [](InferenceEngine::InputInfo& self) {
-        InferenceEngine::PreProcessInfo& preprocess = self.getPreProcess();
-        return preprocess;
-    });
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_network.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_network.cpp
deleted file mode 100644
index b57765f19cf55c..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_network.cpp
+++ /dev/null
@@ -1,96 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/ie_network.hpp"
-
-#include <cpp/ie_cnn_network.h>
-#include <pybind11/stl.h>
-#include <pybind11/stl_bind.h>
-
-#include <ie_input_info.hpp>
-
-#include "openvino/core/function.hpp"
-#include "pyopenvino/core/containers.hpp"
-#include "pyopenvino/core/ie_input_info.hpp"
-
-namespace py = pybind11;
-
-void regclass_IENetwork(py::module m) {
-    py::class_<InferenceEngine::CNNNetwork, std::shared_ptr<InferenceEngine::CNNNetwork>> cls(m, "IENetwork");
-    cls.def(py::init());
-
-    cls.def(py::init([](std::shared_ptr<ov::Function>& function) {
-        InferenceEngine::CNNNetwork cnnNetwork(function);
-        return std::make_shared<InferenceEngine::CNNNetwork>(cnnNetwork);
-    }));
-
-    cls.def("reshape",
-            [](InferenceEngine::CNNNetwork& self, const std::map<std::string, std::vector<size_t>>& input_shapes) {
-                self.reshape(input_shapes);
-            });
-
-    cls.def(
-        "add_outputs",
-        [](InferenceEngine::CNNNetwork& self, py::handle& outputs) {
-            int i = 0;
-            py::list _outputs;
-            if (!py::isinstance<py::list>(outputs)) {
-                if (py::isinstance<py::str>(outputs)) {
-                    _outputs.append(outputs.cast<py::str>());
-                } else if (py::isinstance<py::tuple>(outputs)) {
-                    _outputs.append(outputs.cast<py::tuple>());
-                }
-            } else {
-                _outputs = outputs.cast<py::list>();
-            }
-            for (py::handle output : _outputs) {
-                if (py::isinstance<py::str>(_outputs[i])) {
-                    self.addOutput(output.cast<std::string>(), 0);
-                } else if (py::isinstance<py::tuple>(output)) {
-                    py::tuple output_tuple = output.cast<py::tuple>();
-                    self.addOutput(output_tuple[0].cast<std::string>(), output_tuple[1].cast<int>());
-                } else {
-                    IE_THROW() << "Incorrect type " << output.get_type() << "for layer to add at index " << i
-                               << ". Expected string with layer name or tuple with two elements: layer name as "
-                                  "first element and port id as second";
-                }
-                i++;
-            }
-        },
-        py::arg("outputs"));
-    cls.def("add_output", &InferenceEngine::CNNNetwork::addOutput, py::arg("layer_name"), py::arg("output_index") = 0);
-
-    cls.def(
-        "serialize",
-        [](InferenceEngine::CNNNetwork& self, const std::string& path_to_xml, const std::string& path_to_bin) {
-            self.serialize(path_to_xml, path_to_bin);
-        },
-        py::arg("path_to_xml"),
-        py::arg("path_to_bin") = "");
-
-    cls.def("get_function", [](InferenceEngine::CNNNetwork& self) {
-        return self.getFunction();
-    });
-
-    cls.def("get_ov_name_for_tensor", &InferenceEngine::CNNNetwork::getOVNameForTensor, py::arg("orig_name"));
-
-    cls.def_property("batch_size",
-                     &InferenceEngine::CNNNetwork::getBatchSize,
-                     &InferenceEngine::CNNNetwork::setBatchSize);
-
-    cls.def_property_readonly("input_info", [](InferenceEngine::CNNNetwork& self) {
-        Containers::PyInputsDataMap inputs;
-        const InferenceEngine::InputsDataMap& inputsInfo = self.getInputsInfo();
-        for (auto& in : inputsInfo) {
-            inputs[in.first] = in.second;
-        }
-        return inputs;
-    });
-
-    cls.def_property_readonly("outputs", [](InferenceEngine::CNNNetwork& self) {
-        return self.getOutputsInfo();
-    });
-
-    cls.def_property_readonly("name", &InferenceEngine::CNNNetwork::getName);
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_preprocess_info.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_preprocess_info.cpp
deleted file mode 100644
index 7accf2f2b66075..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_preprocess_info.cpp
+++ /dev/null
@@ -1,70 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/ie_preprocess_info.hpp"
-
-#include <ie_common.h>
-#include <pybind11/stl.h>
-
-#include <ie_preprocess.hpp>
-
-#include "pyopenvino/core/common.hpp"
-
-namespace py = pybind11;
-
-void regclass_PreProcessInfo(py::module m) {
-    py::class_<InferenceEngine::PreProcessChannel, std::shared_ptr<InferenceEngine::PreProcessChannel>>(
-        m,
-        "PreProcessChannel")
-        .def_readwrite("std_scale", &InferenceEngine::PreProcessChannel::stdScale)
-        .def_readwrite("mean_value", &InferenceEngine::PreProcessChannel::meanValue)
-        .def_readwrite("mean_data", &InferenceEngine::PreProcessChannel::meanData);
-
-    py::class_<InferenceEngine::PreProcessInfo> cls(m, "PreProcessInfo");
-
-    cls.def(py::init());
-    cls.def("__getitem__", [](InferenceEngine::PreProcessInfo& self, size_t& index) {
-        return self[index];
-    });
-    cls.def("get_number_of_channels", &InferenceEngine::PreProcessInfo::getNumberOfChannels);
-    cls.def("init", &InferenceEngine::PreProcessInfo::init);
-    cls.def("set_mean_image", [](InferenceEngine::PreProcessInfo& self, py::handle meanImage) {
-        self.setMeanImage(Common::cast_to_blob(meanImage));
-    });
-    cls.def("set_mean_image_for_channel",
-            [](InferenceEngine::PreProcessInfo& self, py::handle meanImage, const size_t channel) {
-                self.setMeanImageForChannel(Common::cast_to_blob(meanImage), channel);
-            });
-    cls.def_property("mean_variant",
-                     &InferenceEngine::PreProcessInfo::getMeanVariant,
-                     &InferenceEngine::PreProcessInfo::setVariant);
-    cls.def_property("resize_algorithm",
-                     &InferenceEngine::PreProcessInfo::getResizeAlgorithm,
-                     &InferenceEngine::PreProcessInfo::setResizeAlgorithm);
-    cls.def_property("color_format",
-                     &InferenceEngine::PreProcessInfo::getColorFormat,
-                     &InferenceEngine::PreProcessInfo::setColorFormat);
-
-    py::enum_<InferenceEngine::MeanVariant>(m, "MeanVariant")
-        .value("MEAN_IMAGE", InferenceEngine::MeanVariant::MEAN_IMAGE)
-        .value("MEAN_VALUE", InferenceEngine::MeanVariant::MEAN_VALUE)
-        .value("NONE", InferenceEngine::MeanVariant::NONE)
-        .export_values();
-
-    py::enum_<InferenceEngine::ResizeAlgorithm>(m, "ResizeAlgorithm")
-        .value("NO_RESIZE", InferenceEngine::ResizeAlgorithm::NO_RESIZE)
-        .value("RESIZE_BILINEAR", InferenceEngine::ResizeAlgorithm::RESIZE_BILINEAR)
-        .value("RESIZE_AREA", InferenceEngine::ResizeAlgorithm::RESIZE_AREA)
-        .export_values();
-
-    py::enum_<InferenceEngine::ColorFormat>(m, "ColorFormat")
-        .value("RAW", InferenceEngine::ColorFormat::RAW)
-        .value("RGB", InferenceEngine::ColorFormat::RGB)
-        .value("BGR", InferenceEngine::ColorFormat::BGR)
-        .value("RGBX", InferenceEngine::ColorFormat::RGBX)
-        .value("BGRX", InferenceEngine::ColorFormat::BGRX)
-        .value("NV12", InferenceEngine::ColorFormat::NV12)
-        .value("I420", InferenceEngine::ColorFormat::I420)
-        .export_values();
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_version.cpp b/runtime/bindings/python/src/pyopenvino/core/ie_version.cpp
deleted file mode 100644
index 158cda68ceaaef..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/ie_version.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/ie_version.hpp"
-
-#include <ie_version.hpp>
-
-namespace py = pybind11;
-
-void regclass_Version(py::module m) {
-    py::class_<InferenceEngine::Version> cls(m, "Version");
-
-    cls.def_readonly("build_number", &InferenceEngine::Version::buildNumber);
-    cls.def_readonly("description", &InferenceEngine::Version::description);
-    cls.def_readwrite("api_version", &InferenceEngine::Version::apiVersion);
-
-    cls.def_property_readonly("major", [](InferenceEngine::Version& self) {
-        return IE_VERSION_MAJOR;
-    });
-
-    cls.def_property_readonly("minor", [](InferenceEngine::Version& self) {
-        return IE_VERSION_MINOR;
-    });
-}
\ No newline at end of file
diff --git a/runtime/bindings/python/src/pyopenvino/core/infer_request.cpp b/runtime/bindings/python/src/pyopenvino/core/infer_request.cpp
new file mode 100644
index 00000000000000..65ed43e8499097
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/infer_request.cpp
@@ -0,0 +1,270 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+
+#include "pyopenvino/core/infer_request.hpp"
+
+#include <ie_common.h>
+#include <pybind11/functional.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+#include <string>
+
+#include "pyopenvino/core/common.hpp"
+#include "pyopenvino/core/containers.hpp"
+
+PYBIND11_MAKE_OPAQUE(Containers::TensorIndexMap);
+PYBIND11_MAKE_OPAQUE(Containers::TensorNameMap);
+
+namespace py = pybind11;
+
+void regclass_InferRequest(py::module m) {
+    py::class_<InferRequestWrapper, std::shared_ptr<InferRequestWrapper>> cls(m, "InferRequest");
+
+    cls.def(py::init([](InferRequestWrapper& other) {
+                return other;
+            }),
+            py::arg("other"));
+
+    cls.def(
+        "set_tensors",
+        [](InferRequestWrapper& self, const py::dict& inputs) {
+            auto tensor_map = Common::cast_to_tensor_name_map(inputs);
+            for (auto&& input : tensor_map) {
+                self._request.set_tensor(input.first, input.second);
+            }
+        },
+        py::arg("inputs"));
+
+    cls.def(
+        "set_output_tensors",
+        [](InferRequestWrapper& self, const py::dict& outputs) {
+            auto outputs_map = Common::cast_to_tensor_index_map(outputs);
+            for (auto&& output : outputs_map) {
+                self._request.set_output_tensor(output.first, output.second);
+            }
+        },
+        py::arg("outputs"));
+
+    cls.def(
+        "set_input_tensors",
+        [](InferRequestWrapper& self, const py::dict& inputs) {
+            auto inputs_map = Common::cast_to_tensor_index_map(inputs);
+            for (auto&& input : inputs_map) {
+                self._request.set_input_tensor(input.first, input.second);
+            }
+        },
+        py::arg("inputs"));
+
+    cls.def(
+        "infer",
+        [](InferRequestWrapper& self, const py::dict& inputs) {
+            // Update inputs if there are any
+            Common::set_request_tensors(self._request, inputs);
+            // Call Infer function
+            self._start_time = Time::now();
+            self._request.infer();
+            self._end_time = Time::now();
+            Containers::InferResults results;
+            for (auto& out : self._outputs) {
+                results.push_back(self._request.get_tensor(out));
+            }
+            return results;
+        },
+        py::arg("inputs"));
+
+    cls.def(
+        "start_async",
+        [](InferRequestWrapper& self, const py::dict& inputs, py::object& userdata) {
+            // Update inputs if there are any
+            Common::set_request_tensors(self._request, inputs);
+            if (!userdata.is(py::none())) {
+                if (self.user_callback_defined) {
+                    self.userdata = userdata;
+                } else {
+                    PyErr_WarnEx(PyExc_RuntimeWarning, "There is no callback function!", 1);
+                }
+            }
+            py::gil_scoped_release release;
+            self._start_time = Time::now();
+            self._request.start_async();
+        },
+        py::arg("inputs"),
+        py::arg("userdata"));
+
+    cls.def("cancel", [](InferRequestWrapper& self) {
+        self._request.cancel();
+    });
+
+    cls.def("wait", [](InferRequestWrapper& self) {
+        py::gil_scoped_release release;
+        self._request.wait();
+    });
+
+    cls.def(
+        "wait_for",
+        [](InferRequestWrapper& self, const int timeout) {
+            py::gil_scoped_release release;
+            return self._request.wait_for(std::chrono::milliseconds(timeout));
+        },
+        py::arg("timeout"));
+
+    cls.def(
+        "set_callback",
+        [](InferRequestWrapper& self, py::function f_callback, py::object& userdata) {
+            self.userdata = userdata;
+            self.user_callback_defined = true;
+            self._request.set_callback([&self, f_callback](std::exception_ptr exception_ptr) {
+                self._end_time = Time::now();
+                try {
+                    if (exception_ptr) {
+                        std::rethrow_exception(exception_ptr);
+                    }
+                } catch (const std::exception& e) {
+                    throw ov::Exception("Caught exception: " + std::string(e.what()));
+                }
+                // Acquire GIL, execute Python function
+                py::gil_scoped_acquire acquire;
+                f_callback(self.userdata);
+            });
+        },
+        py::arg("f_callback"),
+        py::arg("userdata"));
+
+    cls.def(
+        "get_tensor",
+        [](InferRequestWrapper& self, const std::string& name) {
+            return self._request.get_tensor(name);
+        },
+        py::arg("name"));
+
+    cls.def(
+        "get_tensor",
+        [](InferRequestWrapper& self, const ov::Output<const ov::Node>& port) {
+            return self._request.get_tensor(port);
+        },
+        py::arg("port"));
+
+    cls.def(
+        "get_tensor",
+        [](InferRequestWrapper& self, const ov::Output<ov::Node>& port) {
+            return self._request.get_tensor(port);
+        },
+        py::arg("port"));
+
+    cls.def(
+        "get_input_tensor",
+        [](InferRequestWrapper& self, size_t idx) {
+            return self._request.get_input_tensor(idx);
+        },
+        py::arg("idx"));
+
+    cls.def("get_input_tensor", [](InferRequestWrapper& self) {
+        return self._request.get_input_tensor();
+    });
+
+    cls.def(
+        "get_output_tensor",
+        [](InferRequestWrapper& self, size_t idx) {
+            return self._request.get_output_tensor(idx);
+        },
+        py::arg("idx"));
+
+    cls.def("get_output_tensor", [](InferRequestWrapper& self) {
+        return self._request.get_output_tensor();
+    });
+
+    cls.def(
+        "set_tensor",
+        [](InferRequestWrapper& self, const std::string& name, const ov::runtime::Tensor& tensor) {
+            self._request.set_tensor(name, tensor);
+        },
+        py::arg("name"),
+        py::arg("tensor"));
+
+    cls.def(
+        "set_tensor",
+        [](InferRequestWrapper& self, const ov::Output<const ov::Node>& port, const ov::runtime::Tensor& tensor) {
+            self._request.set_tensor(port, tensor);
+        },
+        py::arg("port"),
+        py::arg("tensor"));
+
+    cls.def(
+        "set_tensor",
+        [](InferRequestWrapper& self, const ov::Output<ov::Node>& port, const ov::runtime::Tensor& tensor) {
+            self._request.set_tensor(port, tensor);
+        },
+        py::arg("port"),
+        py::arg("tensor"));
+
+    cls.def(
+        "set_input_tensor",
+        [](InferRequestWrapper& self, size_t idx, const ov::runtime::Tensor& tensor) {
+            self._request.set_input_tensor(idx, tensor);
+        },
+        py::arg("idx"),
+        py::arg("tensor"));
+
+    cls.def(
+        "set_input_tensor",
+        [](InferRequestWrapper& self, const ov::runtime::Tensor& tensor) {
+            self._request.set_input_tensor(tensor);
+        },
+        py::arg("tensor"));
+
+    cls.def(
+        "set_output_tensor",
+        [](InferRequestWrapper& self, size_t idx, const ov::runtime::Tensor& tensor) {
+            self._request.set_output_tensor(idx, tensor);
+        },
+        py::arg("idx"),
+        py::arg("tensor"));
+
+    cls.def(
+        "set_output_tensor",
+        [](InferRequestWrapper& self, const ov::runtime::Tensor& tensor) {
+            self._request.set_output_tensor(tensor);
+        },
+        py::arg("tensor"));
+
+    cls.def("get_profiling_info", [](InferRequestWrapper& self) {
+        return self._request.get_profiling_info();
+    });
+
+    cls.def_property_readonly("userdata", [](InferRequestWrapper& self) {
+        return self.userdata;
+    });
+
+    cls.def_property_readonly("inputs", [](InferRequestWrapper& self) {
+        return self._inputs;
+    });
+
+    cls.def_property_readonly("outputs", [](InferRequestWrapper& self) {
+        return self._outputs;
+    });
+
+    cls.def_property_readonly("input_tensors", [](InferRequestWrapper& self) {
+        std::vector<ov::runtime::Tensor> tensors;
+        for (auto&& node : self._inputs) {
+            tensors.push_back(self._request.get_tensor(node));
+        }
+        return tensors;
+    });
+
+    cls.def_property_readonly("output_tensors", [](InferRequestWrapper& self) {
+        std::vector<ov::runtime::Tensor> tensors;
+        for (auto&& node : self._outputs) {
+            tensors.push_back(self._request.get_tensor(node));
+        }
+        return tensors;
+    });
+
+    cls.def_property_readonly("latency", [](InferRequestWrapper& self) {
+        return self.get_latency();
+    });
+
+    cls.def_property_readonly("profiling_info", [](InferRequestWrapper& self) {
+        return self._request.get_profiling_info();
+    });
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/infer_request.hpp b/runtime/bindings/python/src/pyopenvino/core/infer_request.hpp
new file mode 100644
index 00000000000000..143df8f200cb73
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/infer_request.hpp
@@ -0,0 +1,51 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <chrono>
+
+#include <pybind11/pybind11.h>
+
+#include <openvino/runtime/infer_request.hpp>
+
+namespace py = pybind11;
+
+typedef std::chrono::high_resolution_clock Time;
+typedef std::chrono::nanoseconds ns;
+
+class InferRequestWrapper {
+public:
+    InferRequestWrapper(ov::runtime::InferRequest request)
+        : _request(request)
+    {
+        // AsyncInferQueue uses this constructor - setting callback for computing a latency will be done there
+    }
+
+    InferRequestWrapper(ov::runtime::InferRequest request, const std::vector<ov::Output<const ov::Node>>& inputs, const std::vector<ov::Output<const ov::Node>>& outputs)
+        : _request(request), _inputs(inputs), _outputs(outputs)
+    {
+        _request.set_callback([this](std::exception_ptr exception_ptr) {
+            _end_time = Time::now();
+        });
+    }
+    // ~InferRequestWrapper() = default;
+
+    bool user_callback_defined = false;
+    py::object userdata;
+
+    double get_latency() {
+        auto execTime = std::chrono::duration_cast<ns>(_end_time - _start_time);
+        return static_cast<double>(execTime.count()) * 0.000001;
+    }
+
+    ov::runtime::InferRequest _request;
+    std::vector<ov::Output<const ov::Node>> _inputs;
+    std::vector<ov::Output<const ov::Node>> _outputs;
+
+    Time::time_point _start_time;
+    Time::time_point _end_time;
+};
+
+void regclass_InferRequest(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/core/offline_transformations.cpp b/runtime/bindings/python/src/pyopenvino/core/offline_transformations.cpp
new file mode 100644
index 00000000000000..f58bcc6eedfcd6
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/offline_transformations.cpp
@@ -0,0 +1,84 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "pyopenvino/core/offline_transformations.hpp"
+
+#include <pybind11/stl.h>
+
+#include <generate_mapping_file.hpp>
+#include <openvino/pass/make_stateful.hpp>
+#include <pot_transformations.hpp>
+#include <pruning.hpp>
+#include <transformations/common_optimizations/moc_transformations.hpp>
+
+#include "openvino/pass/low_latency.hpp"
+#include "openvino/pass/manager.hpp"
+
+namespace py = pybind11;
+
+void regmodule_offline_transformations(py::module m) {
+    // TODO: change the submodule name according to the description in 69196
+    py::module m_offline_transformations =
+        m.def_submodule("offline_transformations_pybind", "Offline transformations module");
+
+    m_offline_transformations.def(
+        "apply_moc_transformations",
+        [](std::shared_ptr<ov::Function> function, bool cf) {
+            ov::pass::Manager manager;
+            manager.register_pass<ngraph::pass::MOCTransformations>(cf);
+            manager.run_passes(function);
+        },
+        py::arg("function"),
+        py::arg("cf"));
+
+    m_offline_transformations.def(
+        "apply_pot_transformations",
+        [](std::shared_ptr<ov::Function> function, std::string device) {
+            ov::pass::Manager manager;
+            manager.register_pass<ngraph::pass::POTTransformations>(std::move(device));
+            manager.run_passes(function);
+        },
+        py::arg("function"),
+        py::arg("device"));
+
+    m_offline_transformations.def(
+        "apply_low_latency_transformation",
+        [](std::shared_ptr<ov::Function> function, bool use_const_initializer = true) {
+            ov::pass::Manager manager;
+            manager.register_pass<ov::pass::LowLatency2>(use_const_initializer);
+            manager.run_passes(function);
+        },
+        py::arg("function"),
+        py::arg("use_const_initializer") = true);
+
+    m_offline_transformations.def(
+        "apply_pruning_transformation",
+        [](std::shared_ptr<ngraph::Function> function) {
+            ov::pass::Manager manager;
+            manager.register_pass<ngraph::pass::Pruning>();
+            manager.run_passes(function);
+        },
+        py::arg("function"));
+
+    m_offline_transformations.def(
+        "generate_mapping_file",
+        [](std::shared_ptr<ov::Function> function, std::string path, bool extract_names) {
+            ov::pass::Manager manager;
+            manager.register_pass<ngraph::pass::GenerateMappingFile>(path, extract_names);
+            manager.run_passes(function);
+        },
+        py::arg("function"),
+        py::arg("path"),
+        py::arg("extract_names"));
+
+    m_offline_transformations.def(
+        "apply_make_stateful_transformation",
+        [](std::shared_ptr<ov::Function> function, const std::map<std::string, std::string>& param_res_names) {
+            ngraph::pass::Manager manager;
+            manager.register_pass<ov::pass::MakeStateful>(param_res_names);
+            manager.run_passes(function);
+        },
+        py::arg("function"),
+        py::arg("param_res_names"));
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_input_info.hpp b/runtime/bindings/python/src/pyopenvino/core/offline_transformations.hpp
similarity index 74%
rename from runtime/bindings/python/src/pyopenvino/core/ie_input_info.hpp
rename to runtime/bindings/python/src/pyopenvino/core/offline_transformations.hpp
index 69d17221bc271d..50d4210218a749 100644
--- a/runtime/bindings/python/src/pyopenvino/core/ie_input_info.hpp
+++ b/runtime/bindings/python/src/pyopenvino/core/offline_transformations.hpp
@@ -8,4 +8,4 @@
 
 namespace py = pybind11;
 
-void regclass_InputInfo(py::module m);
+void regmodule_offline_transformations(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/core/profiling_info.cpp b/runtime/bindings/python/src/pyopenvino/core/profiling_info.cpp
new file mode 100644
index 00000000000000..9619e070386017
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/profiling_info.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "pyopenvino/core/profiling_info.hpp"
+
+#include <pybind11/chrono.h>
+
+#include "openvino/runtime/profiling_info.hpp"
+
+namespace py = pybind11;
+
+void regclass_ProfilingInfo(py::module m) {
+    py::class_<ov::runtime::ProfilingInfo, std::shared_ptr<ov::runtime::ProfilingInfo>> cls(m, "ProfilingInfo");
+    cls.def(py::init<>())
+        .def_readwrite("status", &ov::runtime::ProfilingInfo::status)
+        .def_readwrite("real_time", &ov::runtime::ProfilingInfo::real_time)
+        .def_readwrite("cpu_time", &ov::runtime::ProfilingInfo::cpu_time)
+        .def_readwrite("node_name", &ov::runtime::ProfilingInfo::node_name)
+        .def_readwrite("exec_type", &ov::runtime::ProfilingInfo::exec_type)
+        .def_readwrite("node_type", &ov::runtime::ProfilingInfo::node_type);
+
+    py::enum_<ov::runtime::ProfilingInfo::Status>(cls, "Status")
+        .value("NOT_RUN", ov::runtime::ProfilingInfo::Status::NOT_RUN)
+        .value("OPTIMIZED_OUT", ov::runtime::ProfilingInfo::Status::OPTIMIZED_OUT)
+        .value("EXECUTED", ov::runtime::ProfilingInfo::Status::EXECUTED)
+        .export_values();
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_data.hpp b/runtime/bindings/python/src/pyopenvino/core/profiling_info.hpp
similarity index 78%
rename from runtime/bindings/python/src/pyopenvino/core/ie_data.hpp
rename to runtime/bindings/python/src/pyopenvino/core/profiling_info.hpp
index 6b1459714ecb0d..023935f620e913 100644
--- a/runtime/bindings/python/src/pyopenvino/core/ie_data.hpp
+++ b/runtime/bindings/python/src/pyopenvino/core/profiling_info.hpp
@@ -8,4 +8,4 @@
 
 namespace py = pybind11;
 
-void regclass_Data(py::module m);
+void regclass_ProfilingInfo(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/core/tensor.cpp b/runtime/bindings/python/src/pyopenvino/core/tensor.cpp
index a0d6952f04e0bb..1f1146c045c624 100644
--- a/runtime/bindings/python/src/pyopenvino/core/tensor.cpp
+++ b/runtime/bindings/python/src/pyopenvino/core/tensor.cpp
@@ -10,28 +10,13 @@
 #include "openvino/runtime/tensor.hpp"
 #include "pyopenvino/core/common.hpp"
 
-#define C_CONTIGUOUS py::detail::npy_api::constants::NPY_ARRAY_C_CONTIGUOUS_
-
 namespace py = pybind11;
 
 void regclass_Tensor(py::module m) {
     py::class_<ov::runtime::Tensor, std::shared_ptr<ov::runtime::Tensor>> cls(m, "Tensor");
 
     cls.def(py::init([](py::array& array, bool shared_memory) {
-                auto type = Common::dtype_to_ov_type().at(py::str(array.dtype()));
-                std::vector<size_t> shape(array.shape(), array.shape() + array.ndim());
-                if (shared_memory) {
-                    if (C_CONTIGUOUS == (array.flags() & C_CONTIGUOUS)) {
-                        std::vector<size_t> strides(array.strides(), array.strides() + array.ndim());
-                        return ov::runtime::Tensor(type, shape, const_cast<void*>(array.data(0)), strides);
-                    } else {
-                        IE_THROW() << "Tensor with shared memory must be C contiguous!";
-                    }
-                }
-                array = py::module::import("numpy").attr("ascontiguousarray")(array).cast<py::array>();
-                auto tensor = ov::runtime::Tensor(type, shape);
-                std::memcpy(tensor.data(), array.data(0), array.nbytes());
-                return tensor;
+                return Common::tensor_from_numpy(array, shared_memory);
             }),
             py::arg("array"),
             py::arg("shared_memory") = false);
diff --git a/runtime/bindings/python/src/pyopenvino/core/tensor_description.cpp b/runtime/bindings/python/src/pyopenvino/core/tensor_description.cpp
deleted file mode 100644
index b9382f34e5cb2e..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/tensor_description.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "pyopenvino/core/tensor_description.hpp"
-
-#include <ie_common.h>
-#include <ie_layouts.h>
-#include <pybind11/stl.h>
-
-#include <ie_precision.hpp>
-
-#include "common.hpp"
-
-namespace py = pybind11;
-using namespace InferenceEngine;
-
-void regclass_TensorDecription(py::module m) {
-    py::class_<TensorDesc, std::shared_ptr<TensorDesc>> cls(m, "TensorDesc");
-    cls.def(py::init<const Precision&, const SizeVector&, Layout>());
-    cls.def(py::init([](const std::string& precision, const SizeVector& dims, const std::string& layout) {
-        return TensorDesc(Precision::FromStr(precision), dims, Common::get_layout_from_string(layout));
-    }));
-
-    cls.def_property(
-        "layout",
-        [](TensorDesc& self) {
-            return Common::get_layout_from_enum(self.getLayout());
-        },
-        [](TensorDesc& self, const std::string& layout) {
-            self.setLayout(Common::get_layout_from_string(layout));
-        });
-
-    cls.def_property(
-        "precision",
-        [](TensorDesc& self) {
-            return self.getPrecision().name();
-        },
-        [](TensorDesc& self, const std::string& precision) {
-            self.setPrecision(InferenceEngine::Precision::FromStr(precision));
-        });
-
-    cls.def_property(
-        "dims",
-        [](TensorDesc& self) {
-            return self.getDims();
-        },
-        [](TensorDesc& self, const SizeVector& dims) {
-            self.setDims(dims);
-        });
-
-    cls.def(
-        "__eq__",
-        [](const TensorDesc& a, const TensorDesc b) {
-            return a == b;
-        },
-        py::is_operator());
-}
diff --git a/runtime/bindings/python/src/pyopenvino/core/tensor_description.hpp b/runtime/bindings/python/src/pyopenvino/core/tensor_description.hpp
deleted file mode 100644
index 806c7b9d3b8792..00000000000000
--- a/runtime/bindings/python/src/pyopenvino/core/tensor_description.hpp
+++ /dev/null
@@ -1,11 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <pybind11/pybind11.h>
-
-namespace py = pybind11;
-
-void regclass_TensorDecription(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/core/version.cpp b/runtime/bindings/python/src/pyopenvino/core/version.cpp
new file mode 100644
index 00000000000000..bed253697fda06
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/core/version.cpp
@@ -0,0 +1,24 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/core/version.hpp"
+
+#include <pybind11/stl.h>
+
+namespace py = pybind11;
+
+void regclass_Version(py::module m) {
+    py::class_<ov::Version> cls(m, "Version");
+
+    cls.def_readonly("build_number", &ov::Version::buildNumber);
+    cls.def_readonly("description", &ov::Version::description);
+
+    cls.def_property_readonly("major", [](ov::Version& self) {
+        return OPENVINO_VERSION_MAJOR;
+    });
+
+    cls.def_property_readonly("minor", [](ov::Version& self) {
+        return OPENVINO_VERSION_MINOR;
+    });
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_version.hpp b/runtime/bindings/python/src/pyopenvino/core/version.hpp
similarity index 100%
rename from runtime/bindings/python/src/pyopenvino/core/ie_version.hpp
rename to runtime/bindings/python/src/pyopenvino/core/version.hpp
diff --git a/runtime/bindings/python/src/pyopenvino/graph/function.cpp b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
index 3496e88d0149dd..27e3d18b0ec079 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/function.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
@@ -7,6 +7,7 @@
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 
+#include "openvino/core/partial_shape.hpp"
 #include "openvino/op/parameter.hpp"  // ov::op::v0::Parameter
 #include "openvino/op/sink.hpp"
 #include "pyopenvino/graph/function.hpp"
@@ -15,6 +16,16 @@ namespace py = pybind11;
 
 static const char* CAPSULE_NAME = "ngraph_function";
 
+void set_tensor_names(const ov::ParameterVector& parameters) {
+    for (const auto& param : parameters) {
+        ov::Output<ov::Node> p = param;
+        if (p.get_node()->output(0).get_names().empty()) {
+            std::unordered_set<std::string> p_names({p.get_node()->get_friendly_name()});
+            p.get_node()->output(0).set_names(p_names);
+        }
+    }
+}
+
 void regclass_graph_Function(py::module m) {
     py::class_<ov::Function, std::shared_ptr<ov::Function>> function(m, "Function", py::module_local());
     function.doc() = "openvino.impl.Function wraps ov::Function";
@@ -53,12 +64,15 @@ void regclass_graph_Function(py::module m) {
                         String to set as function's friendly name.
                  )");
 
-    function.def(py::init<const std::vector<std::shared_ptr<ov::Node>>&,
-                          const std::vector<std::shared_ptr<ov::op::v0::Parameter>>&,
-                          const std::string&>(),
+    function.def(py::init([](const std::vector<std::shared_ptr<ov::Node>>& results,
+                             const ov::ParameterVector& parameters,
+                             const std::string& name) {
+                     set_tensor_names(parameters);
+                     return std::make_shared<ov::Function>(results, parameters, name);
+                 }),
                  py::arg("results"),
                  py::arg("parameters"),
-                 py::arg("name"),
+                 py::arg("name") = "",
                  R"(
                     Create user-defined Function which is a representation of a model.
 
@@ -74,12 +88,15 @@ void regclass_graph_Function(py::module m) {
                         String to set as function's friendly name.
                  )");
 
-    function.def(py::init<const std::shared_ptr<ov::Node>&,
-                          const std::vector<std::shared_ptr<ov::op::v0::Parameter>>&,
-                          const std::string&>(),
+    function.def(py::init([](const std::shared_ptr<ov::Node>& results,
+                             const ov::ParameterVector& parameters,
+                             const std::string& name) {
+                     set_tensor_names(parameters);
+                     return std::make_shared<ov::Function>(results, parameters, name);
+                 }),
                  py::arg("result"),
                  py::arg("parameters"),
-                 py::arg("name"),
+                 py::arg("name") = "",
                  R"(
                     Create user-defined Function which is a representation of a model.
 
@@ -94,6 +111,41 @@ void regclass_graph_Function(py::module m) {
                     name : str
                         String to set as function's friendly name.
                  )");
+
+    function.def(
+        "reshape",
+        [](ov::Function& self, const std::map<std::string, ov::PartialShape>& partial_shapes) {
+            self.reshape(partial_shapes);
+        },
+        py::arg("partial_shapes"),
+        R"(
+                Parameters
+                ----------
+                partial_shapes : Dict[string, PartialShape]
+                    Index of Output.
+
+                Returns
+                ----------
+                reshape : void
+             )");
+
+    function.def(
+        "reshape",
+        [](ov::Function& self, const std::map<ov::Output<ov::Node>, ov::PartialShape>& partial_shapes) {
+            self.reshape(partial_shapes);
+        },
+        py::arg("partial_shapes"),
+        R"(
+                Parameters
+                ----------
+                partial_shapes : Dict[Output, PartialShape]
+                    Index of Output.
+
+                Returns
+                ----------
+                reshape : void
+             )");
+
     function.def("get_output_size",
                  &ov::Function::get_output_size,
                  R"(
@@ -264,6 +316,42 @@ void regclass_graph_Function(py::module m) {
                     ----------
                     is_dynamic : bool
                  )");
+    function.def("input", (ov::Output<ov::Node>(ov::Function::*)()) & ov::Function::input);
+
+    function.def("input", (ov::Output<ov::Node>(ov::Function::*)(size_t)) & ov::Function::input, py::arg("i"));
+
+    function.def("input",
+                 (ov::Output<ov::Node>(ov::Function::*)(const std::string&)) & ov::Function::input,
+                 py::arg("tensor_name"));
+
+    function.def("input", (ov::Output<const ov::Node>(ov::Function::*)() const) & ov::Function::input);
+
+    function.def("input",
+                 (ov::Output<const ov::Node>(ov::Function::*)(size_t) const) & ov::Function::input,
+                 py::arg("i"));
+
+    function.def("input",
+                 (ov::Output<const ov::Node>(ov::Function::*)(const std::string&) const) & ov::Function::input,
+                 py::arg("tensor_name"));
+
+    function.def("output", (ov::Output<ov::Node>(ov::Function::*)()) & ov::Function::output);
+
+    function.def("output", (ov::Output<ov::Node>(ov::Function::*)(size_t)) & ov::Function::output, py::arg("i"));
+
+    function.def("output",
+                 (ov::Output<ov::Node>(ov::Function::*)(const std::string&)) & ov::Function::output,
+                 py::arg("tensor_name"));
+
+    function.def("output", (ov::Output<const ov::Node>(ov::Function::*)() const) & ov::Function::output);
+
+    function.def("output",
+                 (ov::Output<const ov::Node>(ov::Function::*)(size_t) const) & ov::Function::output,
+                 py::arg("i"));
+
+    function.def("output",
+                 (ov::Output<const ov::Node>(ov::Function::*)(const std::string&) const) & ov::Function::output,
+                 py::arg("tensor_name"));
+
     function.def("__repr__", [](const ov::Function& self) {
         std::string class_name = py::cast(self).get_type().attr("__name__").cast<std::string>();
         std::stringstream shapes_ss;
@@ -309,6 +397,16 @@ void regclass_graph_Function(py::module m) {
         return pybind_capsule;
     });
 
+    function.def_property_readonly("inputs",
+                                   (std::vector<ov::Output<ov::Node>>(ov::Function::*)()) & ov::Function::inputs);
+    function.def_property_readonly(
+        "inputs",
+        (std::vector<ov::Output<const ov::Node>>(ov::Function::*)() const) & ov::Function::inputs);
+    function.def_property_readonly("outputs",
+                                   (std::vector<ov::Output<ov::Node>>(ov::Function::*)()) & ov::Function::outputs);
+    function.def_property_readonly(
+        "outputs",
+        (std::vector<ov::Output<const ov::Node>>(ov::Function::*)() const) & ov::Function::outputs);
     function.def_property_readonly("name", &ov::Function::get_name);
     function.def_property("friendly_name", &ov::Function::get_friendly_name, &ov::Function::set_friendly_name);
 }
diff --git a/runtime/bindings/python/src/pyopenvino/graph/layout_helpers.cpp b/runtime/bindings/python/src/pyopenvino/graph/layout_helpers.cpp
new file mode 100644
index 00000000000000..ff7eedaa07c5d4
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/graph/layout_helpers.cpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "pyopenvino/graph/layout_helpers.hpp"
+
+#include <pybind11/pybind11.h>
+
+#include "openvino/core/layout.hpp"
+
+namespace py = pybind11;
+
+void regmodule_graph_layout_helpers(py::module m) {
+    py::module mod = m.def_submodule("layout_helpers", "openvino.impl.layout_helpers");
+
+    mod.def("has_batch", &ov::layout::has_batch, py::arg("layout"));
+    mod.def("batch_idx", &ov::layout::batch_idx, py::arg("layout"));
+    mod.def("has_channels", &ov::layout::has_channels, py::arg("layout"));
+    mod.def("channels_idx", &ov::layout::channels_idx, py::arg("layout"));
+    mod.def("has_depth", &ov::layout::has_depth, py::arg("layout"));
+    mod.def("depth_idx", &ov::layout::depth_idx, py::arg("layout"));
+    mod.def("has_height", &ov::layout::has_height, py::arg("layout"));
+    mod.def("height_idx", &ov::layout::height_idx, py::arg("layout"));
+    mod.def("has_width", &ov::layout::has_width, py::arg("layout"));
+    mod.def("width_idx", &ov::layout::width_idx, py::arg("layout"));
+}
diff --git a/runtime/bindings/python/src/pyopenvino/core/ie_preprocess_info.hpp b/runtime/bindings/python/src/pyopenvino/graph/layout_helpers.hpp
similarity index 75%
rename from runtime/bindings/python/src/pyopenvino/core/ie_preprocess_info.hpp
rename to runtime/bindings/python/src/pyopenvino/graph/layout_helpers.hpp
index cc762ada0cb623..573316b3af1189 100644
--- a/runtime/bindings/python/src/pyopenvino/core/ie_preprocess_info.hpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/layout_helpers.hpp
@@ -8,4 +8,4 @@
 
 namespace py = pybind11;
 
-void regclass_PreProcessInfo(py::module m);
\ No newline at end of file
+void regmodule_graph_layout_helpers(py::module m);
diff --git a/runtime/bindings/python/src/pyopenvino/graph/node_output.cpp b/runtime/bindings/python/src/pyopenvino/graph/node_output.cpp
index 56ac60e3ba9f64..8d1cfcec5bb7d0 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/node_output.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/node_output.cpp
@@ -11,68 +11,5 @@
 
 namespace py = pybind11;
 
-void regclass_graph_Output(py::module m) {
-    py::class_<ov::Output<ov::Node>, std::shared_ptr<ov::Output<ov::Node>>> output(m, "Output", py::dynamic_attr());
-    output.doc() = "openvino.impl.Output wraps ov::Output<Node>";
-
-    output.def("get_node",
-               &ov::Output<ov::Node>::get_node,
-               R"(
-                Get node referenced by this output handle.
-
-                Returns
-                ----------
-                get_node : Node
-                    Node object referenced by this output handle.
-               )");
-    output.def("get_index",
-               &ov::Output<ov::Node>::get_index,
-               R"(
-                The index of the output referred to by this output handle.
-
-                Returns
-                ----------
-                get_index : int
-                    Index value as integer.
-               )");
-    output.def("get_element_type",
-               &ov::Output<ov::Node>::get_element_type,
-               R"(
-                The element type of the output referred to by this output handle.
-
-                Returns
-                ----------
-                get_element_type : Type
-                    Type of the output.
-               )");
-    output.def("get_shape",
-               &ov::Output<ov::Node>::get_shape,
-               R"(
-                The shape of the output referred to by this output handle.
-
-                Returns
-                ----------
-                get_shape : Shape
-                    Shape of the output.
-               )");
-    output.def("get_partial_shape",
-               &ov::Output<ov::Node>::get_partial_shape,
-               R"(
-                The partial shape of the output referred to by this output handle.
-
-                Returns
-                ----------
-                get_partial_shape : PartialShape
-                    PartialShape of the output.
-               )");
-    output.def("get_target_inputs",
-               &ov::Output<ov::Node>::get_target_inputs,
-               R"(
-                A set containing handles for all inputs targeted by the output
-                referenced by this output handle.
-                Returns
-                ----------
-                get_target_inputs : Set[Input]
-                    Set of Inputs.
-               )");
-}
+template void regclass_graph_Output<ov::Node>(py::module m, std::string typestring);
+template void regclass_graph_Output<const ov::Node>(py::module m, std::string typestring);
diff --git a/runtime/bindings/python/src/pyopenvino/graph/node_output.hpp b/runtime/bindings/python/src/pyopenvino/graph/node_output.hpp
index 9934c628b2e098..a88722ebc18448 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/node_output.hpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/node_output.hpp
@@ -5,7 +5,80 @@
 #pragma once
 
 #include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include "openvino/core/node_output.hpp"
 
 namespace py = pybind11;
 
-void regclass_graph_Output(py::module m);
+template <typename VT>
+void regclass_graph_Output(py::module m, std::string typestring)
+{
+    auto pyclass_name = py::detail::c_str((typestring + std::string("Output")));
+    auto docs = py::detail::c_str((std::string("openvino.impl.") + typestring + std::string("Output wraps ov::Output<") + typestring + std::string(" ov::Node >")));
+    py::class_<ov::Output<VT>, std::shared_ptr<ov::Output<VT>>> output(m,
+                                                                       pyclass_name,
+                                                                       py::dynamic_attr());
+    output.doc() = docs;
+
+    output.def("get_node",
+               &ov::Output<VT>::get_node,
+               R"(
+                Get node referenced by this output handle.
+
+                Returns
+                ----------
+                get_node : Node or const Node
+                    Node object referenced by this output handle.
+               )");
+    output.def("get_index",
+               &ov::Output<VT>::get_index,
+               R"(
+                The index of the output referred to by this output handle.
+
+                Returns
+                ----------
+                get_index : int
+                    Index value as integer.
+               )");
+    output.def("get_element_type",
+               &ov::Output<VT>::get_element_type,
+               R"(
+                The element type of the output referred to by this output handle.
+
+                Returns
+                ----------
+                get_element_type : Type
+                    Type of the output.
+               )");
+    output.def("get_shape",
+               &ov::Output<VT>::get_shape,
+               R"(
+                The shape of the output referred to by this output handle.
+
+                Returns
+                ----------
+                get_shape : Shape
+                    Shape of the output.
+               )");
+    output.def("get_partial_shape",
+               &ov::Output<VT>::get_partial_shape,
+               R"(
+                The partial shape of the output referred to by this output handle.
+
+                Returns
+                ----------
+                get_partial_shape : PartialShape
+                    PartialShape of the output.
+               )");
+    output.def("get_target_inputs",
+               &ov::Output<VT>::get_target_inputs,
+               R"(
+                A set containing handles for all inputs targeted by the output
+                referenced by this output handle.
+                Returns
+                ----------
+                get_target_inputs : Set[Input]
+                    Set of Inputs.
+               )");
+}
diff --git a/runtime/bindings/python/src/pyopenvino/pyopenvino.cpp b/runtime/bindings/python/src/pyopenvino/pyopenvino.cpp
index 4cd1d4765aa28e..5422948aa160e6 100644
--- a/runtime/bindings/python/src/pyopenvino/pyopenvino.cpp
+++ b/runtime/bindings/python/src/pyopenvino/pyopenvino.cpp
@@ -1,11 +1,10 @@
 // Copyright (C) 2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 
-#include <ie_common.h>
 #include <pybind11/pybind11.h>
 
-#include <ie_iinfer_request.hpp>
-#include <ie_version.hpp>
+#include <openvino/core/node.hpp>
+#include <openvino/core/version.hpp>
 #include <string>
 
 #include "pyopenvino/graph/axis_set.hpp"
@@ -20,22 +19,19 @@
 #if defined(NGRAPH_ONNX_FRONTEND_ENABLE)
 #    include "pyopenvino/graph/onnx_import/onnx_import.hpp"
 #endif
+#include "pyopenvino/core/async_infer_queue.hpp"
 #include "pyopenvino/core/containers.hpp"
-#include "pyopenvino/core/ie_blob.hpp"
-#include "pyopenvino/core/ie_core.hpp"
-#include "pyopenvino/core/ie_data.hpp"
-#include "pyopenvino/core/ie_executable_network.hpp"
-#include "pyopenvino/core/ie_infer_queue.hpp"
-#include "pyopenvino/core/ie_infer_request.hpp"
-#include "pyopenvino/core/ie_input_info.hpp"
-#include "pyopenvino/core/ie_network.hpp"
+#include "pyopenvino/core/core.hpp"
+#include "pyopenvino/core/executable_network.hpp"
 #include "pyopenvino/core/ie_parameter.hpp"
-#include "pyopenvino/core/ie_preprocess_info.hpp"
-#include "pyopenvino/core/ie_version.hpp"
+#include "pyopenvino/core/infer_request.hpp"
+#include "pyopenvino/core/offline_transformations.hpp"
+#include "pyopenvino/core/profiling_info.hpp"
 #include "pyopenvino/core/tensor.hpp"
-#include "pyopenvino/core/tensor_description.hpp"
+#include "pyopenvino/core/version.hpp"
 #include "pyopenvino/graph/dimension.hpp"
 #include "pyopenvino/graph/layout.hpp"
+#include "pyopenvino/graph/layout_helpers.hpp"
 #include "pyopenvino/graph/ops/constant.hpp"
 #include "pyopenvino/graph/ops/parameter.hpp"
 #include "pyopenvino/graph/ops/result.hpp"
@@ -52,9 +48,9 @@
 namespace py = pybind11;
 
 std::string get_version() {
-    auto version = InferenceEngine::GetInferenceEngineVersion();
-    std::string version_str = std::to_string(version->apiVersion.major) + ".";
-    version_str += std::to_string(version->apiVersion.minor) + ".";
+    auto version = ov::get_openvino_version();
+    std::string version_str = std::to_string(OPENVINO_VERSION_MAJOR) + ".";
+    version_str += std::to_string(OPENVINO_VERSION_MINOR) + ".";
     version_str += version->buildNumber;
     return version_str;
 }
@@ -62,26 +58,6 @@ std::string get_version() {
 PYBIND11_MODULE(pyopenvino, m) {
     m.doc() = "Package openvino.pyopenvino which wraps openvino C++ APIs";
     m.def("get_version", &get_version);
-    py::enum_<InferenceEngine::StatusCode>(m, "StatusCode")
-        .value("OK", InferenceEngine::StatusCode::OK)
-        .value("GENERAL_ERROR", InferenceEngine::StatusCode::GENERAL_ERROR)
-        .value("NOT_IMPLEMENTED", InferenceEngine::StatusCode::NOT_IMPLEMENTED)
-        .value("NETWORK_NOT_LOADED", InferenceEngine::StatusCode::NETWORK_NOT_LOADED)
-        .value("PARAMETER_MISMATCH", InferenceEngine::StatusCode::PARAMETER_MISMATCH)
-        .value("NOT_FOUND", InferenceEngine::StatusCode::NOT_FOUND)
-        .value("OUT_OF_BOUNDS", InferenceEngine::StatusCode::OUT_OF_BOUNDS)
-        .value("UNEXPECTED", InferenceEngine::StatusCode::UNEXPECTED)
-        .value("REQUEST_BUSY", InferenceEngine::StatusCode::REQUEST_BUSY)
-        .value("RESULT_NOT_READY", InferenceEngine::StatusCode::RESULT_NOT_READY)
-        .value("NOT_ALLOCATED", InferenceEngine::StatusCode::NOT_ALLOCATED)
-        .value("INFER_NOT_STARTED", InferenceEngine::StatusCode::INFER_NOT_STARTED)
-        .value("NETWORK_NOT_READ", InferenceEngine::StatusCode::NETWORK_NOT_READ)
-        .export_values();
-
-    py::enum_<InferenceEngine::IInferRequest::WaitMode>(m, "WaitMode")
-        .value("RESULT_READY", InferenceEngine::IInferRequest::WaitMode::RESULT_READY)
-        .value("STATUS_ONLY", InferenceEngine::IInferRequest::WaitMode::STATUS_ONLY)
-        .export_values();
 
     regclass_graph_PyRTMap(m);
     regmodule_graph_types(m);
@@ -91,7 +67,6 @@ PYBIND11_MODULE(pyopenvino, m) {
     regclass_graph_PartialShape(m);
     regclass_graph_Node(m);
     regclass_graph_Input(m);
-    regclass_graph_Output(m);
     regclass_graph_NodeFactory(m);
     regclass_graph_Strides(m);
     regclass_graph_CoordinateDiff(m);
@@ -102,6 +77,7 @@ PYBIND11_MODULE(pyopenvino, m) {
     regclass_graph_op_Constant(m_op);
     regclass_graph_op_Parameter(m_op);
     regclass_graph_op_Result(m_op);
+
 #if defined(NGRAPH_ONNX_FRONTEND_ENABLE)
     regmodule_graph_onnx_import(m);
 #endif
@@ -109,44 +85,25 @@ PYBIND11_MODULE(pyopenvino, m) {
     regclass_graph_Function(m);
     regmodule_graph_passes(m);
     regmodule_graph_util(m);
+    regmodule_graph_layout_helpers(m);
     regclass_graph_Variant(m);
     regclass_graph_VariantWrapper<std::string>(m, std::string("String"));
     regclass_graph_VariantWrapper<int64_t>(m, std::string("Int"));
+    regclass_graph_Output<ov::Node>(m, std::string(""));
+    regclass_graph_Output<const ov::Node>(m, std::string("Const"));
 
     regclass_Core(m);
-    regclass_IENetwork(m);
-
-    regclass_Data(m);
-    regclass_TensorDecription(m);
-
-    // Blob will be removed
-    // Registering template of Blob
-    regclass_Blob(m);
-    // Registering specific types of Blobs
-    regclass_TBlob<float>(m, "Float32");
-    regclass_TBlob<double>(m, "Float64");
-    regclass_TBlob<int64_t>(m, "Int64");
-    regclass_TBlob<uint64_t>(m, "Uint64");
-    regclass_TBlob<int32_t>(m, "Int32");
-    regclass_TBlob<uint32_t>(m, "Uint32");
-    regclass_TBlob<int16_t>(m, "Int16");
-    regclass_TBlob<uint16_t>(m, "Uint16");
-    regclass_TBlob<int8_t>(m, "Int8");
-    regclass_TBlob<uint8_t>(m, "Uint8");
-
     regclass_Tensor(m);
-
     // Registering specific types of containers
-    Containers::regclass_PyInputsDataMap(m);
-    Containers::regclass_PyConstInputsDataMap(m);
-    Containers::regclass_PyOutputsDataMap(m);
-    Containers::regclass_PyResults(m);
+    Containers::regclass_TensorIndexMap(m);
+    Containers::regclass_TensorNameMap(m);
 
     regclass_ExecutableNetwork(m);
     regclass_InferRequest(m);
     regclass_Version(m);
     regclass_Parameter(m);
-    regclass_InputInfo(m);
-    regclass_InferQueue(m);
-    regclass_PreProcessInfo(m);
+    regclass_AsyncInferQueue(m);
+    regclass_ProfilingInfo(m);
+
+    regmodule_offline_transformations(m);
 }
diff --git a/runtime/bindings/python/tests/__init__.py b/runtime/bindings/python/tests/__init__.py
index 274b91d09fec20..48c46843e7ed58 100644
--- a/runtime/bindings/python/tests/__init__.py
+++ b/runtime/bindings/python/tests/__init__.py
@@ -24,8 +24,9 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 
 skip_segfault = pytest.mark.skip(reason="Segmentation fault error")
 xfail_accuracy = xfail_test(reason="Accuracy")
+xfail_issue_FLOAT_LIKE = xfail_test(reason="Use of bfloat16 or float16")
 xfail_issue_69444 = xfail_test(reason="failed with accuracy issue")
-skip_issue_69443 = pytest.mark.skip(reason="Segmentation fault due to empty pads_begin, pads_end")
+xfail_issue_69443 = xfail_test(reason="Error in ref. implementation due to the empty pads_begin, pads_end")
 skip_issue_67415 = pytest.mark.skip(reason="RuntimeError: Unsupported data type for when filling blob!")
 xfail_issue_67415 = xfail_test(reason="RuntimeError: Unsupported data type for when filling blob!")
 xfail_issue_33488 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations: "
@@ -47,7 +48,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
                                       "TfIdfVectorizer")
 xfail_issue_33581 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations: "
                                       "GatherElements")
-xfail_issue_33633 = xfail_test(reason="MaxPool: dilations unsupported")
 xfail_issue_35923 = xfail_test(reason="RuntimeError: PReLU without weights is not supported")
 xfail_issue_35927 = xfail_test(reason="RuntimeError: B has zero dimension that is not allowable")
 xfail_issue_36486 = xfail_test(reason="RuntimeError: HardSigmoid operation should be converted "
@@ -93,8 +93,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 xfail_issue_44968 = xfail_test(reason="Expected: Unsupported dynamic op: Squeeze")
 xfail_issue_47323 = xfail_test(reason="RuntimeError: The plugin does not support FP64")
 xfail_issue_47337 = xfail_test(reason="RuntimeError: Unsupported dynamic ops: v1::OneHot")
-xfail_issue_33593 = xfail_test(reason="Current implementation of MaxPool doesn't support indices output")
-xfail_issue_55760 = xfail_test(reason="RuntimeError: Reversed axis have axes above the source space shape")
 
 # Model MSFT issues:
 xfail_issue_37957 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations: "
@@ -108,9 +106,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
                                       "/openvino/ngraph/src/ngraph/op/util/elementwise_args.cpp:48:")
 xfail_issue_39658 = xfail_test(reason="RuntimeError: Tile operation has a form that is not supported."
                                       " z should be converted to TileIE operation.")
-xfail_issue_39659 = xfail_test(reason="RuntimeError: Broadcast operation has a form that is not supported."
-                                      " y should be converted to Tile operation.")
-xfail_issue_45344 = xfail_test(reason="Unsupported dynamic ops: v3::NonMaxSuppressionIE3")
 xfail_issue_39662 = xfail_test(reason="RuntimeError: 'ScatterElementsUpdate' layer with name 'y' have "
                                       "indices value that points to non-existing output tensor element")
 
@@ -139,7 +134,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 xfail_issue_63039 = xfail_test(reason="Result mismatches with UINT8 operations")
 xfail_issue_63043 = xfail_test(reason="Recurrent node expects constants as W, R, B inputs.")
 xfail_issue_63044 = xfail_test(reason="ONNX opset 14 operation: Trilu")
-xfail_issue_63045 = xfail_test(reason="Maxpool with strides, padding and dilations fail")
 
 skip_rng_tests = pytest.mark.skip(reason="Tests use random number generator with no seed.")
 xfail_issue_63136 = xfail_test(reason="Unsupported operation: CastLike")
@@ -147,3 +141,4 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 xfail_issue_63138 = xfail_test(reason="Missing ONNX Shape-15 support")
 xfail_issue_63643 = xfail_test(reason="RuntimeError: Unsupported operation of type: Convolution name")
 xfail_issue_54663 = xfail_test(reason="Disabled until MaxPool-8 is supported on CPU")
+xfail_issue_68212 = xfail_test(reason="Unsupported reading model with bytes streams")
diff --git a/runtime/bindings/python/tests/conftest.py b/runtime/bindings/python/tests/conftest.py
index 865ae43552e0ec..9efb9a9ecaab1e 100644
--- a/runtime/bindings/python/tests/conftest.py
+++ b/runtime/bindings/python/tests/conftest.py
@@ -3,6 +3,7 @@
 
 import os
 import pytest
+import numpy as np
 
 import tests
 
@@ -15,6 +16,19 @@ def image_path():
     return path_to_img
 
 
+def read_image():
+    import cv2
+    n, c, h, w = (1, 3, 32, 32)
+    image = cv2.imread(image_path())
+    if image is None:
+        raise FileNotFoundError("Input image not found")
+
+    image = cv2.resize(image, (h, w)) / 255
+    image = image.transpose((2, 0, 1)).astype(np.float32)
+    image = image.reshape((n, c, h, w))
+    return image
+
+
 def model_path(is_myriad=False):
     path_to_repo = os.environ["MODELS_PATH"]
     if not is_myriad:
@@ -78,6 +92,8 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "skip_on_hetero: Skip test on HETERO")
     config.addinivalue_line("markers", "skip_on_template: Skip test on TEMPLATE")
     config.addinivalue_line("markers", "onnx_coverage: Collect ONNX operator coverage")
+    config.addinivalue_line("markers", "template_extension")
+    config.addinivalue_line("markers", "dynamic_library: Runs tests only in dynamic libraries case")
 
 
 def pytest_collection_modifyitems(config, items):
diff --git a/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.cpp b/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.cpp
index dd760e262462a9..580dd5d58daacc 100644
--- a/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.cpp
+++ b/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.cpp
@@ -8,7 +8,7 @@
 #include "frontend_manager/frontend_manager_defs.hpp"
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 extern "C" MOCK_API FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
diff --git a/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.hpp b/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.hpp
index fef78ede66732b..d7a02f48904204 100644
--- a/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.hpp
+++ b/runtime/bindings/python/tests/mock/mock_py_ngraph_frontend/mock_py_frontend.hpp
@@ -18,7 +18,7 @@
 // OK to have 'using' in mock header
 
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 ////////////////////////////////
 
diff --git a/runtime/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp b/runtime/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
index 54e022b978fe63..b806d1fddc5720 100644
--- a/runtime/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
+++ b/runtime/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp
@@ -9,7 +9,7 @@
 
 namespace py = pybind11;
 using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov::frontend;
 
 static void register_mock_frontend_stat(py::module m) {
     m.def(
diff --git a/runtime/bindings/python/tests/runtime.py b/runtime/bindings/python/tests/runtime.py
index cc5c49620a6c5e..a1c0bae384b965 100644
--- a/runtime/bindings/python/tests/runtime.py
+++ b/runtime/bindings/python/tests/runtime.py
@@ -7,11 +7,11 @@
 from typing import Dict, List, Union
 
 import numpy as np
-from openvino import Core, IENetwork, Blob, DataPtr
+
+from openvino import Core
 
 from openvino.exceptions import UserInputError
 from openvino.impl import Function, Node, PartialShape, Type
-from openvino.opset1.ops import result
 from openvino.utils.types import NumericData, get_shape, get_dtype
 
 import tests
@@ -32,46 +32,6 @@ def get_runtime():
         return runtime()
 
 
-def _convert_inputs(cnn_network: IENetwork) -> None:
-    """WA converts unsupported input images formats."""
-    precision_map = {
-        "FP64": "FP32",
-        "I64": "I32",
-        "U32": "I32",
-    }
-
-    for cnn_input in cnn_network.input_info:
-        try:
-            _precision = precision_map[cnn_network.input_info[cnn_input].precision]
-            cnn_network.input_info[cnn_input].precision = _precision
-        except KeyError:
-            pass
-
-
-def _convert_val(val):
-    """WA converts unsupported input values."""
-    if type(val) is np.ndarray:
-        if val.dtype == np.float64:
-            return np.array(val, dtype=np.float32)
-        elif val.dtype == np.int64:
-            return np.array(val, dtype=np.int32)
-        return np.array(val)
-
-    return np.array(val, dtype=np.float32)
-
-
-def apply_ng_type(output: DataPtr, ng_type: Type):
-    ng_ie_supported_type_map = {
-        Type.boolean.get_type_name(): "BOOL",
-        Type.f32.get_type_name(): "FP32",
-        Type.i8.get_type_name(): "I8",
-        Type.i32.get_type_name(): "I32",
-        Type.u8.get_type_name(): "U8",
-    }
-    if ng_type.get_type_name() in ng_ie_supported_type_map:
-        output.precision = ng_ie_supported_type_map[ng_type.get_type_name()]
-
-
 class Runtime(object):
     """Represents an nGraph runtime environment."""
 
@@ -120,25 +80,6 @@ def __repr__(self) -> str:
         params_string = ", ".join([param.name for param in self.parameters])
         return "<Computation: {}({})>".format(self.function.get_name(), params_string)
 
-    def _get_ie_output_blob_name(self, outputs: Dict, ng_result: result) -> str:
-        if len(self.results) == 1:
-            return next(iter(outputs.keys()))
-        else:
-            prev_layer = ng_result.input(0).get_source_output()
-            out_name = prev_layer.get_node().get_friendly_name()
-            if prev_layer.get_node().get_output_size() != 1:
-                out_name += "." + str(prev_layer.get_index())
-            return out_name
-
-    def _get_ie_output_blob_buffer(self, output_blobs: Dict[str, Blob], ng_result: result) -> np.ndarray:
-        out_name = self._get_ie_output_blob_name(output_blobs, ng_result)
-        out_blob = output_blobs[out_name]
-
-        if out_blob.tensor_desc.layout == "SCALAR":
-            return out_blob.buffer.reshape(())
-        else:
-            return out_blob.buffer
-
     def convert_buffers(self, source_buffers, target_dtypes):
         converted_buffers = []
         for i in range(len(source_buffers)):
@@ -157,35 +98,24 @@ def __call__(self, *input_values: NumericData) -> List[NumericData]:
             raise UserInputError(
                 "Expected %s params, received not enough %s values.", len(self.parameters), len(input_values)
             )
-        # ignore not needed input values
-        input_values = input_values[:len(self.parameters)]
-
-        input_values = [_convert_val(input_value) for input_value in input_values]
-        input_shapes = [get_shape(input_value) for input_value in input_values]
 
         param_names = [param.friendly_name for param in self.parameters]
+        input_shapes = [get_shape(input_value) for input_value in input_values]
 
         if self.network_cache.get(str(input_shapes)) is None:
-            cnn_network = IENetwork(self.function)
+            function = self.function
             if self.function.is_dynamic():
-                cnn_network.reshape(dict(zip(param_names, input_shapes)))
-            # Convert unsupported inputs of the network
-            _convert_inputs(cnn_network)
-            self.network_cache[str(input_shapes)] = cnn_network
+                function.reshape(dict(zip(param_names, [PartialShape(i) for i in input_shapes])))
+            self.network_cache[str(input_shapes)] = function
         else:
-            cnn_network = self.network_cache[str(input_shapes)]
-
-        # set output blobs precission based on nG results
-        for ng_result in self.results:
-            ie_out_name = self._get_ie_output_blob_name(cnn_network.outputs, ng_result)
-            apply_ng_type(cnn_network.outputs[ie_out_name], ng_result.get_output_element_type(0))
+            function = self.network_cache[str(input_shapes)]
 
-        executable_network = self.runtime.backend.load_network(cnn_network, self.runtime.backend_name)
+        executable_network = self.runtime.backend.compile_model(function, self.runtime.backend_name)
 
         for parameter, input in zip(self.parameters, input_values):
             parameter_shape = parameter.get_output_partial_shape(0)
-            input_shape = PartialShape(input.shape)
-            if len(input.shape) > 0 and not parameter_shape.compatible(input_shape):
+            input_shape = PartialShape([]) if isinstance(input, (int, float)) else PartialShape(input.shape)
+            if not parameter_shape.compatible(input_shape):
                 raise UserInputError(
                     "Provided tensor's shape: %s does not match the expected: %s.",
                     input_shape,
@@ -193,13 +123,16 @@ def __call__(self, *input_values: NumericData) -> List[NumericData]:
                 )
 
         request = executable_network.create_infer_request()
-        request.infer(dict(zip(param_names, input_values)))
-
-        # Set order of output blobs compatible with nG Function
-        result_buffers = [self._get_ie_output_blob_buffer(request.output_blobs, result)
-                          for result in self.results]
-
-        # Since OV overwrite result data type we have to convert results to the original one.
+        result_buffers = request.infer(dict(zip(param_names, input_values)))
+        # # Note: other methods to get result_buffers from request
+        # # First call infer with no return value:
+        # request.infer(dict(zip(param_names, input_values)))
+        # # Now use any of following options:
+        # result_buffers = [request.get_tensor(n).data for n in request.outputs]
+        # result_buffers = [request.get_output_tensor(i).data for i in range(len(request.outputs))]
+        # result_buffers = [t.data for t in request.output_tensors]
+
+        # # Since OV overwrite result data type we have to convert results to the original one.
         original_dtypes = [get_dtype(result.get_output_element_type(0)) for result in self.results]
         converted_buffers = self.convert_buffers(result_buffers, original_dtypes)
         return converted_buffers
diff --git a/runtime/bindings/python/tests/test_inference_engine/helpers.py b/runtime/bindings/python/tests/test_inference_engine/helpers.py
deleted file mode 100644
index db48c1e9298da7..00000000000000
--- a/runtime/bindings/python/tests/test_inference_engine/helpers.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (C) 2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import numpy as np
-import cv2
-import os
-
-
-def image_path():
-    path_to_repo = os.environ["DATA_PATH"]
-    path_to_img = os.path.join(path_to_repo, "validation_set", "224x224", "dog.bmp")
-    return path_to_img
-
-
-def model_path(is_myriad=False):
-    path_to_repo = os.environ["MODELS_PATH"]
-    if not is_myriad:
-        test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
-        test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
-    else:
-        test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
-        test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
-    return (test_xml, test_bin)
-
-
-def read_image():
-    n, c, h, w = (1, 3, 32, 32)
-    image = cv2.imread(image_path())
-    if image is None:
-        raise FileNotFoundError("Input image not found")
-
-    image = cv2.resize(image, (h, w)) / 255
-    image = image.transpose((2, 0, 1)).astype(np.float32)
-    image = image.reshape((n, c, h, w))
-    return image
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_core.py b/runtime/bindings/python/tests/test_inference_engine/test_core.py
index 59ed993278c8aa..400a03b7f5abbf 100644
--- a/runtime/bindings/python/tests/test_inference_engine/test_core.py
+++ b/runtime/bindings/python/tests/test_inference_engine/test_core.py
@@ -8,160 +8,131 @@
 from pathlib import Path
 
 import openvino.opset8 as ov
-from openvino import Core, IENetwork, ExecutableNetwork, blob_from_file
-from openvino.impl import Function, Shape, Type
-from openvino.impl.op import Parameter
-from openvino import TensorDesc, Blob
+from openvino import Function, Core, ExecutableNetwork, Tensor, tensor_from_file, compile_model
+
+from ..conftest import model_path, model_onnx_path, plugins_path, read_image
 
-from ..conftest import model_path, model_onnx_path, plugins_path
 
 test_net_xml, test_net_bin = model_path()
 test_net_onnx = model_onnx_path()
 plugins_xml, plugins_win_xml, plugins_osx_xml = plugins_path()
 
 
-def test_blobs():
-    input_shape = [1, 3, 4, 4]
-    input_data_float32 = (np.random.rand(*input_shape) - 0.5).astype(np.float32)
-
-    td = TensorDesc("FP32", input_shape, "NCHW")
-
-    input_blob_float32 = Blob(td, input_data_float32)
-
-    assert np.all(np.equal(input_blob_float32.buffer, input_data_float32))
+def test_compact_api_xml():
+    img = read_image()
 
-    input_data_int16 = (np.random.rand(*input_shape) + 0.5).astype(np.int16)
+    model = compile_model(test_net_xml)
+    assert(isinstance(model, ExecutableNetwork))
+    results = model.infer_new_request({"data": img})
+    assert np.argmax(results) == 2
 
-    td = TensorDesc("I16", input_shape, "NCHW")
 
-    input_blob_i16 = Blob(td, input_data_int16)
+def test_compact_api_onnx():
+    img = read_image()
 
-    assert np.all(np.equal(input_blob_i16.buffer, input_data_int16))
+    model = compile_model(test_net_onnx)
+    assert(isinstance(model, ExecutableNetwork))
+    results = model.infer_new_request({"data": img})
+    assert np.argmax(results) == 2
 
 
-@pytest.mark.skip(reason="Fix")
-def test_ie_core_class():
+def test_core_class():
     input_shape = [1, 3, 4, 4]
     param = ov.parameter(input_shape, np.float32, name="parameter")
     relu = ov.relu(param, name="relu")
     func = Function([relu], [param], "test")
     func.get_ordered_ops()[2].friendly_name = "friendly"
 
-    cnn_network = IENetwork(func)
+    core = Core()
+    model = core.compile_model(func, "CPU", {})
 
-    ie_core = Core()
-    ie_core.set_config({}, device_name="CPU")
-    executable_network = ie_core.load_network(cnn_network, "CPU", {})
-
-    td = TensorDesc("FP32", input_shape, "NCHW")
-
-    # from IPython import embed; embed()
-
-    request = executable_network.create_infer_request()
-    input_data = np.random.rand(*input_shape) - 0.5
+    request = model.create_infer_request()
+    input_data = np.random.rand(*input_shape).astype(np.float32) - 0.5
 
     expected_output = np.maximum(0.0, input_data)
 
-    input_blob = Blob(td, input_data)
-
-    request.set_input({"parameter": input_blob})
-    request.infer()
-
-    result = request.get_blob("relu").buffer
+    input_tensor = Tensor(input_data)
+    results = request.infer({"parameter": input_tensor})
 
-    assert np.allclose(result, expected_output)
+    assert np.allclose(results, expected_output)
 
 
-def test_load_network(device):
+def test_compile_model(device):
     ie = Core()
-    net = ie.read_network(model=test_net_xml, weights=test_net_bin)
-    exec_net = ie.load_network(net, device)
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = ie.compile_model(func, device)
     assert isinstance(exec_net, ExecutableNetwork)
 
 
-def test_read_network():
-    ie_core = Core()
-    net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
-    assert isinstance(net, IENetwork)
+def test_read_model_from_ir():
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    assert isinstance(func, Function)
 
-    net = ie_core.read_network(model=test_net_xml)
-    assert isinstance(net, IENetwork)
+    func = core.read_model(model=test_net_xml)
+    assert isinstance(func, Function)
 
 
-def test_read_network_from_blob():
-    ie_core = Core()
+def test_read_model_from_tensor():
+    core = Core()
     model = open(test_net_xml).read()
-    blob = blob_from_file(test_net_bin)
-    net = ie_core.read_network(model=model, blob=blob)
-    assert isinstance(net, IENetwork)
+    tensor = tensor_from_file(test_net_bin)
+    func = core.read_model(model=model, weights=tensor)
+    assert isinstance(func, Function)
 
 
-def test_read_network_from_blob_valid():
-    ie_core = Core()
-    model = open(test_net_xml).read()
-    blob = blob_from_file(test_net_bin)
-    net = ie_core.read_network(model=model, blob=blob)
-    ref_net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
-    assert net.name == ref_net.name
-    assert net.batch_size == ref_net.batch_size
-    ii_net = net.input_info
-    ii_net2 = ref_net.input_info
-    o_net = net.outputs
-    o_net2 = ref_net.outputs
-    assert ii_net.keys() == ii_net2.keys()
-    assert o_net.keys() == o_net2.keys()
+def test_read_model_as_path():
+    core = Core()
+    func = core.read_model(model=Path(test_net_xml), weights=Path(test_net_bin))
+    assert isinstance(func, Function)
 
+    func = core.read_model(model=test_net_xml, weights=Path(test_net_bin))
+    assert isinstance(func, Function)
 
-def test_read_network_as_path():
-    ie_core = Core()
-    net = ie_core.read_network(model=Path(test_net_xml), weights=Path(test_net_bin))
-    assert isinstance(net, IENetwork)
+    func = core.read_model(model=Path(test_net_xml))
+    assert isinstance(func, Function)
 
-    net = ie_core.read_network(model=test_net_xml, weights=Path(test_net_bin))
-    assert isinstance(net, IENetwork)
 
-    net = ie_core.read_network(model=Path(test_net_xml))
-    assert isinstance(net, IENetwork)
+def test_read_model_from_onnx():
+    core = Core()
+    func = core.read_model(model=test_net_onnx)
+    assert isinstance(func, Function)
 
 
-def test_read_network_from_onnx():
-    ie_core = Core()
-    net = ie_core.read_network(model=test_net_onnx)
-    assert isinstance(net, IENetwork)
+def test_read_model_from_onnx_as_path():
+    core = Core()
+    func = core.read_model(model=Path(test_net_onnx))
+    assert isinstance(func, Function)
 
 
-def test_read_network_from_onnx_as_path():
-    ie_core = Core()
-    net = ie_core.read_network(model=Path(test_net_onnx))
-    assert isinstance(net, IENetwork)
-
-
-def test_read_net_from_buffer():
-    ie_core = Core()
-    with open(test_net_bin, "rb") as f:
-        bin = f.read()
-    with open(model_path()[0], "rb") as f:
-        xml = f.read()
-    net = ie_core.read_network(model=xml, weights=bin)
-    assert isinstance(net, IENetwork)
+# @pytest.mark.xfail("68212")
+# def test_read_net_from_buffer():
+#     core = Core()
+#     with open(test_net_bin, "rb") as f:
+#         bin = f.read()
+#     with open(model_path()[0], "rb") as f:
+#         xml = f.read()
+#     func = core.read_model(model=xml, weights=bin)
+#     assert isinstance(func, IENetwork)
 
 
+@pytest.mark.xfail("68212")
 def test_net_from_buffer_valid():
-    ie_core = Core()
+    core = Core()
     with open(test_net_bin, "rb") as f:
         bin = f.read()
     with open(model_path()[0], "rb") as f:
         xml = f.read()
-    net = ie_core.read_network(model=xml, weights=bin)
-    ref_net = ie_core.read_network(model=test_net_xml, weights=test_net_bin)
-    assert net.name == ref_net.name
-    assert net.batch_size == ref_net.batch_size
-    ii_net = net.input_info
-    ii_net2 = ref_net.input_info
-    o_net = net.outputs
-    o_net2 = ref_net.outputs
-    assert ii_net.keys() == ii_net2.keys()
-    assert o_net.keys() == o_net2.keys()
+    func = core.read_model(model=xml, weights=bin)
+    ref_func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    assert func.name == func.name
+    assert func.batch_size == ref_func.batch_size
+    ii_func = func.input_info
+    ii_func2 = ref_func.input_info
+    o_func = func.outputs
+    o_func2 = ref_func.outputs
+    assert ii_func.keys() == ii_func2.keys()
+    assert o_func.keys() == o_func2.keys()
 
 
 def test_get_version(device):
@@ -230,28 +201,29 @@ def test_get_metric_str():
                                    f"metric must be string but {type(param)} is returned"
 
 
-def test_query_network(device):
+def test_query_model(device):
     ie = Core()
-    net = ie.read_network(model=test_net_xml, weights=test_net_bin)
-    query_res = ie.query_network(network=net, device_name=device)
-    func_net = net.get_function()
-    ops_net = func_net.get_ordered_ops()
-    ops_net_names = [op.friendly_name for op in ops_net]
-    assert [key for key in query_res.keys() if key not in ops_net_names] == [], \
-        "Not all network layers present in query_network results"
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    query_res = ie.query_model(model=func, device_name=device)
+    ops_func = func.get_ordered_ops()
+    ops_func_names = [op.friendly_name for op in ops_func]
+    assert [key for key in query_res.keys() if key not in ops_func_names] == [], \
+        "Not all network layers present in query_model results"
     assert next(iter(set(query_res.values()))) == device, "Wrong device for some layers"
 
 
+@pytest.mark.dynamic_library
 @pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU", reason="Device independent test")
 def test_register_plugin():
     ie = Core()
     ie.register_plugin("MKLDNNPlugin", "BLA")
-    net = ie.read_network(model=test_net_xml, weights=test_net_bin)
-    exec_net = ie.load_network(net, "BLA")
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = ie.compile_model(func, "BLA")
     assert isinstance(exec_net, ExecutableNetwork), \
         "Cannot load the network to the registered plugin with name 'BLA'"
 
 
+@pytest.mark.dynamic_library
 @pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU", reason="Device independent test")
 def test_register_plugins():
     ie = Core()
@@ -262,21 +234,92 @@ def test_register_plugins():
     elif platform == "win32":
         ie.register_plugins(plugins_win_xml)
 
-    net = ie.read_network(model=test_net_xml, weights=test_net_bin)
-    exec_net = ie.load_network(net, "CUSTOM")
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = ie.compile_model(func, "CUSTOM")
     assert isinstance(exec_net,
                       ExecutableNetwork), "Cannot load the network to " \
                                           "the registered plugin with name 'CUSTOM' " \
-                                          "registred in the XML file"
+                                          "registered in the XML file"
 
 
-def test_create_IENetwork_from_nGraph():
-    element_type = Type.f32
-    param = Parameter(element_type, Shape([1, 3, 22, 22]))
-    relu = ov.relu(param)
-    func = Function([relu], [param], "test")
-    cnnNetwork = IENetwork(func)
-    assert cnnNetwork is not None
-    func2 = cnnNetwork.get_function()
-    assert func2 is not None
-    assert len(func2.get_ops()) == 3
+@pytest.mark.skip(reason="Need to figure out if it's expected behaviour (fails with C++ API as well")
+def test_unregister_plugin(device):
+    ie = Core()
+    ie.unload_plugin(device)
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    with pytest.raises(RuntimeError) as e:
+        ie.load_network(func, device)
+    assert f"Device with '{device}' name is not registered in the InferenceEngine" in str(e.value)
+
+
+@pytest.mark.xfail("68212")
+@pytest.mark.template_extension
+def test_add_extension(device):
+    model = bytes(b"""<net name="Network" version="10">
+    <layers>
+        <layer name="in1" type="Parameter" id="0" version="opset1">
+            <data element_type="f32" shape="2,2,2,1"/>
+            <output>
+                <port id="0" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </output>
+        </layer>
+        <layer name="operation" id="1" type="Template" version="custom_opset">
+            <data  add="11"/>
+            <input>
+                <port id="1" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </input>
+            <output>
+                <port id="2" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </output>
+        </layer>
+        <layer name="output" type="Result" id="2" version="opset1">
+            <input>
+                <port id="0" precision="FP32">
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>2</dim>
+                    <dim>1</dim>
+                </port>
+            </input>
+        </layer>
+    </layers>
+    <edges>
+        <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
+        <edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
+    </edges>
+</net>""")
+
+    core = Core()
+    if platform == "win32":
+        core.add_extension(extension_path="template_extension.dll")
+    else:
+        core.add_extension(extension_path="libtemplate_extension.so")
+    func = core.read_model(model=model, init_from_buffer=True)
+    assert isinstance(func, Function)
+
+    # input_blob = next(iter(network.input_info))
+    # n, c, h, w = network.input_info[input_blob].input_data.shape
+
+    # input_values = np.ndarray(buffer=np.array([1, 2, 3, 4, 5, 6, 7, 8]), shape = (n, c, h, w), dtype=int)
+    # expected = np.ndarray(buffer=np.array([12, 13, 14, 15, 16, 17, 18, 19]),
+    # shape = (n, c, h, w), dtype=int)
+    #
+    # exec_network = core.compile_model(func, device)
+    # computed = exec_network.infer_new_request(inputs={input_blob : input_values})
+    # output_blob = next(iter(network.outputs))
+    # assert np.allclose(expected, computed[output_blob], atol=1e-2, rtol=1e-2)
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_executable_network.py b/runtime/bindings/python/tests/test_inference_engine/test_executable_network.py
new file mode 100644
index 00000000000000..76d6775fcb45fe
--- /dev/null
+++ b/runtime/bindings/python/tests/test_inference_engine/test_executable_network.py
@@ -0,0 +1,282 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import pytest
+import numpy as np
+
+from ..conftest import model_path, read_image
+from openvino.impl import Function, ConstOutput, Shape
+
+from openvino import Core, Tensor
+
+is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
+test_net_xml, test_net_bin = model_path(is_myriad)
+
+
+def test_get_metric(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    network_name = exec_net.get_metric("NETWORK_NAME")
+    assert network_name == "test_model"
+
+
+@pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU", reason="Device dependent test")
+def test_get_config(device):
+    core = Core()
+    if core.get_metric(device, "FULL_DEVICE_NAME") == "arm_compute::NEON":
+        pytest.skip("Can't run on ARM plugin due-to CPU dependent test")
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    config = exec_net.get_config("PERF_COUNT")
+    assert config == "NO"
+
+
+def test_get_runtime_function(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    runtime_func = exec_net.get_runtime_function()
+    assert isinstance(runtime_func, Function)
+
+
+@pytest.mark.skip(reason="After infer will be implemented")
+def test_export_import():
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, "CPU")
+    exported_net_file = "exported_model.bin"
+    exec_net.export_model(network_model=exported_net_file)
+    assert os.path.exists(exported_net_file)
+    exec_net = core.import_network(exported_net_file, "CPU")
+    os.remove(exported_net_file)
+    img = read_image()
+    res = exec_net.infer({"data": img})
+    assert np.argmax(res["fc_out"][0]) == 3
+    del exec_net
+    del core
+
+
+def test_get_input_i(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.input(0)
+    input_node = input.get_node()
+    name = input_node.friendly_name
+    assert isinstance(input, ConstOutput)
+    assert name == "data"
+
+
+def test_get_input_tensor_name(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.input("data")
+    input_node = input.get_node()
+    name = input_node.friendly_name
+    assert isinstance(input, ConstOutput)
+    assert name == "data"
+
+
+def test_get_input(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.input()
+    input_node = input.get_node()
+    name = input_node.friendly_name
+    assert isinstance(input, ConstOutput)
+    assert name == "data"
+
+
+def test_get_output_i(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    output = exec_net.output(0)
+    assert isinstance(output, ConstOutput)
+
+
+def test_get_output(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    output = exec_net.output()
+    assert isinstance(output, ConstOutput)
+
+
+def test_input_set_friendly_name(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.input("data")
+    input_node = input.get_node()
+    input_node.set_friendly_name("input_1")
+    name = input_node.friendly_name
+    assert isinstance(input, ConstOutput)
+    assert name == "input_1"
+
+
+def test_output_set_friendly_name(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    output = exec_net.output(0)
+    output_node = output.get_node()
+    output_node.set_friendly_name("output_1")
+    name = output_node.friendly_name
+    assert isinstance(output, ConstOutput)
+    assert name == "output_1"
+
+
+def test_outputs(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    outputs = exec_net.outputs
+    assert isinstance(outputs, list)
+    assert len(outputs) == 1
+
+
+def test_outputs_items(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    outputs = exec_net.outputs
+    assert isinstance(outputs[0], ConstOutput)
+
+
+def test_output_type(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    output = exec_net.output(0)
+    output_type = output.get_element_type().get_type_name()
+    assert output_type == "f32"
+
+
+def test_output_shape(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    output = exec_net.output(0)
+    expected_shape = Shape([1, 10])
+    assert str(output.get_shape()) == str(expected_shape)
+
+
+def test_input_get_index(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    input = exec_net.input(0)
+    expected_idx = 0
+    assert input.get_index() == expected_idx
+
+
+def test_inputs(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    inputs = exec_net.inputs
+    assert isinstance(inputs, list)
+    assert len(inputs) == 1
+
+
+def test_inputs_items(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    inputs = exec_net.inputs
+    assert isinstance(inputs[0], ConstOutput)
+
+
+def test_inputs_get_friendly_name(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    inputs = exec_net.inputs
+    input_0 = inputs[0]
+    node = input_0.get_node()
+    name = node.friendly_name
+    assert name == "data"
+
+
+def test_inputs_set_friendly_name(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    inputs = exec_net.inputs
+    input_0 = inputs[0]
+    node = input_0.get_node()
+    node.set_friendly_name("input_0")
+    name = node.friendly_name
+    assert name == "input_0"
+
+
+def test_inputs_docs(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    inputs = exec_net.inputs
+    input_0 = inputs[0]
+    expected_string = "openvino.impl.ConstOutput wraps ov::Output<Const ov::Node >"
+    assert input_0.__doc__ == expected_string
+
+
+def test_infer_new_request_numpy(device):
+    ie = Core()
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    img = read_image()
+    exec_net = ie.compile_model(func, device)
+    res = exec_net.infer_new_request({"data": img})
+    assert np.argmax(res) == 2
+
+
+def test_infer_new_request_tensor_numpy_copy(device):
+    ie = Core()
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    img = read_image()
+    tensor = Tensor(img)
+    exec_net = ie.compile_model(func, device)
+    res_tensor = exec_net.infer_new_request({"data": tensor})
+    res_img = exec_net.infer_new_request({"data": tensor})
+    assert np.argmax(res_tensor) == 2
+    assert np.argmax(res_tensor) == np.argmax(res_img)
+
+
+def test_infer_tensor_numpy_shared_memory(device):
+    ie = Core()
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    img = read_image()
+    img = np.ascontiguousarray(img)
+    tensor = Tensor(img, shared_memory=True)
+    exec_net = ie.compile_model(func, device)
+    res_tensor = exec_net.infer_new_request({"data": tensor})
+    res_img = exec_net.infer_new_request({"data": tensor})
+    assert np.argmax(res_tensor) == 2
+    assert np.argmax(res_tensor) == np.argmax(res_img)
+
+
+def test_infer_new_request_wrong_port_name(device):
+    ie = Core()
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    img = read_image()
+    tensor = Tensor(img)
+    exec_net = ie.compile_model(func, device)
+    with pytest.raises(KeyError) as e:
+        exec_net.infer_new_request({"_data_": tensor})
+    assert "Port for tensor named _data_ was not found!" in str(e.value)
+
+
+def test_infer_tensor_wrong_input_data(device):
+    ie = Core()
+    func = ie.read_model(model=test_net_xml, weights=test_net_bin)
+    img = read_image()
+    img = np.ascontiguousarray(img)
+    tensor = Tensor(img, shared_memory=True)
+    exec_net = ie.compile_model(func, device)
+    with pytest.raises(TypeError) as e:
+        exec_net.infer_new_request({0.: tensor})
+    assert "Incompatible key type for tensor named: 0." in str(e.value)
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py b/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py
index 518a11cb37dead..cf9a17f459fc5d 100644
--- a/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py
+++ b/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py
@@ -4,138 +4,197 @@
 import numpy as np
 import os
 import pytest
+import datetime
+import time
 
-from tests.test_inference_engine.helpers import model_path, read_image
-from openvino import Core, Blob, TensorDesc, StatusCode
-
+from ..conftest import model_path, read_image
+from openvino import Core, AsyncInferQueue, Tensor, ProfilingInfo
 
 is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
 test_net_xml, test_net_bin = model_path(is_myriad)
 
 
-def test_get_perf_counts(device):
-    ie_core = Core()
-    net = ie_core.read_network(test_net_xml, test_net_bin)
-    ie_core.set_config({"PERF_COUNT": "YES"}, device)
-    exec_net = ie_core.load_network(net, device)
+def test_get_profiling_info(device):
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    core.set_config({"PERF_COUNT": "YES"}, device)
+    exec_net = core.compile_model(func, device)
     img = read_image()
     request = exec_net.create_infer_request()
-    td = TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
-    input_blob = Blob(td, img)
-    request.set_input({"data": input_blob})
-    request.infer()
-    pc = request.get_perf_counts()
-    assert pc["29"]["status"] == "EXECUTED"
-    assert pc["29"]["layer_type"] == "FullyConnected"
-    del exec_net
-    del ie_core
-    del net
-
-
-@pytest.mark.skipif(os.environ.get("TEST_DEVICE", "CPU") != "CPU",
-                    reason=f"Can't run test on device {os.environ.get('TEST_DEVICE', 'CPU')}, "
-                           "Dynamic batch fully supported only on CPU")
-@pytest.mark.skip(reason="Fix")
-def test_set_batch_size(device):
-    ie_core = Core()
-    ie_core.set_config({"DYN_BATCH_ENABLED": "YES"}, device)
-    net = ie_core.read_network(test_net_xml, test_net_bin)
-    net.batch_size = 10
-    data = np.ones(shape=net.input_info["data"].input_data.shape)
-    exec_net = ie_core.load_network(net, device)
-    data[0] = read_image()[0]
-    request = exec_net.create_infer_request()
-    request.set_batch(1)
-    td = TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
-    input_blob = Blob(td, data)
-    request.set_input({"data": input_blob})
-    request.infer()
-    assert np.allclose(int(round(request.output_blobs["fc_out"].buffer[0][2])), 1), \
-        "Incorrect data for 1st batch"
-    del exec_net
-    del ie_core
-    del net
-
-
-@pytest.mark.skip(reason="Fix")
-def test_set_zero_batch_size(device):
-    ie_core = Core()
-    net = ie_core.read_network(test_net_xml, test_net_bin)
-    exec_net = ie_core.load_network(net, device)
-    request = exec_net.create_infer_request()
-    with pytest.raises(ValueError) as e:
-        request.set_batch(0)
-    assert "Batch size should be positive integer number but 0 specified" in str(e.value)
-    del exec_net
-    del ie_core
-    del net
-
-
-@pytest.mark.skip(reason="Fix")
-def test_set_negative_batch_size(device):
-    ie_core = Core()
-    net = ie_core.read_network(test_net_xml, test_net_bin)
-    exec_net = ie_core.load_network(net, device)
-    request = exec_net.create_infer_request()
-    with pytest.raises(ValueError) as e:
-        request.set_batch(-1)
-    assert "Batch size should be positive integer number but -1 specified" in str(e.value)
-    del exec_net
-    del ie_core
-    del net
+    request.infer({0: img})
+    assert request.latency > 0
+    prof_info = request.get_profiling_info()
+    soft_max_node = next(node for node in prof_info if node.node_name == "fc_out")
+    assert soft_max_node.node_type == "Softmax"
+    assert soft_max_node.status == ProfilingInfo.Status.OPTIMIZED_OUT
+    assert isinstance(soft_max_node.real_time, datetime.timedelta)
+    assert isinstance(soft_max_node.cpu_time, datetime.timedelta)
+    assert isinstance(soft_max_node.exec_type, str)
+
+
+def test_tensor_setter(device):
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    exec_net_1 = core.compile_model(model=func, device_name=device)
+    exec_net_2 = core.compile_model(model=func, device_name=device)
 
+    img = read_image()
+    tensor = Tensor(img)
 
-def test_blob_setter(device):
-    ie_core = Core()
-    net = ie_core.read_network(test_net_xml, test_net_bin)
-    exec_net_1 = ie_core.load_network(network=net, device_name=device)
+    request1 = exec_net_1.create_infer_request()
+    request1.set_tensor("data", tensor)
+    t1 = request1.get_tensor("data")
 
-    net.input_info["data"].layout = "NHWC"
-    exec_net_2 = ie_core.load_network(network=net, device_name=device)
+    assert np.allclose(tensor.data, t1.data, atol=1e-2, rtol=1e-2)
 
-    img = read_image()
+    res = request1.infer({0: tensor})
+    res_1 = np.sort(res[0])
+    t2 = request1.get_tensor("fc_out")
+    assert np.allclose(t2.data, res[0].data, atol=1e-2, rtol=1e-2)
 
-    request1 = exec_net_1.create_infer_request()
-    tensor_desc = TensorDesc("FP32", [1, 3, img.shape[2], img.shape[3]], "NCHW")
-    img_blob1 = Blob(tensor_desc, img)
-    request1.set_input({"data": img_blob1})
-    request1.infer()
-    res_1 = np.sort(request1.get_blob("fc_out").buffer)
-
-    img = np.transpose(img, axes=(0, 2, 3, 1)).astype(np.float32)
-    tensor_desc = TensorDesc("FP32", [1, 3, 32, 32], "NHWC")
-    img_blob = Blob(tensor_desc, img)
     request = exec_net_2.create_infer_request()
-    request.set_blob("data", img_blob)
-    request.infer()
-    res_2 = np.sort(request.get_blob("fc_out").buffer)
+    res = request.infer({"data": tensor})
+    res_2 = np.sort(request.get_tensor("fc_out").data)
     assert np.allclose(res_1, res_2, atol=1e-2, rtol=1e-2)
 
+    request.set_tensor("data", tensor)
+    t3 = request.get_tensor("data")
+    assert np.allclose(t3.data, t1.data, atol=1e-2, rtol=1e-2)
+
+
+def test_set_tensors(device):
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    exec_net = core.compile_model(func, device)
+
+    data1 = read_image()
+    tensor1 = Tensor(data1)
+    data2 = np.ones(shape=(1, 10), dtype=np.float32)
+    tensor2 = Tensor(data2)
+    data3 = np.ones(shape=(1, 3, 32, 32), dtype=np.float32)
+    tensor3 = Tensor(data3)
+    data4 = np.zeros(shape=(1, 10), dtype=np.float32)
+    tensor4 = Tensor(data4)
+
+    request = exec_net.create_infer_request()
+    request.set_tensors({"data": tensor1, "fc_out": tensor2})
+    t1 = request.get_tensor("data")
+    t2 = request.get_tensor("fc_out")
+    assert np.allclose(tensor1.data, t1.data, atol=1e-2, rtol=1e-2)
+    assert np.allclose(tensor2.data, t2.data, atol=1e-2, rtol=1e-2)
+
+    request.set_output_tensors({0: tensor2})
+    output_node = exec_net.outputs[0]
+    t3 = request.get_tensor(output_node)
+    assert np.allclose(tensor2.data, t3.data, atol=1e-2, rtol=1e-2)
+
+    request.set_input_tensors({0: tensor1})
+    output_node = exec_net.inputs[0]
+    t4 = request.get_tensor(output_node)
+    assert np.allclose(tensor1.data, t4.data, atol=1e-2, rtol=1e-2)
+
+    output_node = exec_net.inputs[0]
+    request.set_tensor(output_node, tensor3)
+    t5 = request.get_tensor(output_node)
+    assert np.allclose(tensor3.data, t5.data, atol=1e-2, rtol=1e-2)
+
+    request.set_input_tensor(tensor3)
+    t6 = request.get_tensor(request.inputs[0])
+    assert np.allclose(tensor3.data, t6.data, atol=1e-2, rtol=1e-2)
+
+    request.set_input_tensor(0, tensor1)
+    t7 = request.get_tensor(request.inputs[0])
+    assert np.allclose(tensor1.data, t7.data, atol=1e-2, rtol=1e-2)
+
+    request.set_output_tensor(tensor2)
+    t8 = request.get_tensor(request.outputs[0])
+    assert np.allclose(tensor2.data, t8.data, atol=1e-2, rtol=1e-2)
+
+    request.set_output_tensor(0, tensor4)
+    t9 = request.get_tensor(request.outputs[0])
+    assert np.allclose(tensor4.data, t9.data, atol=1e-2, rtol=1e-2)
+
 
 def test_cancel(device):
-    ie_core = Core()
-    net = ie_core.read_network(test_net_xml, test_net_bin)
-    exec_net = ie_core.load_network(net, device)
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    exec_net = core.compile_model(func, device)
     img = read_image()
-    td = TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
-    input_blob = Blob(td, img)
     request = exec_net.create_infer_request()
 
-    def callback(req, code, array):
-        array.append(42)
-
-    data = []
-    request.set_completion_callback(callback, data)
-    request.set_input({"data": input_blob})
-    request.async_infer()
+    request.start_async({0: img})
     request.cancel()
     with pytest.raises(RuntimeError) as e:
         request.wait()
     assert "[ INFER_CANCELLED ]" in str(e.value)
-    # check if callback has executed
-    assert data == [42]
 
-    request.async_infer()
-    status = request.wait()
-    assert status == StatusCode.OK
-    assert data == [42, 42]
+    request.start_async({"data": img})
+    request.cancel()
+    with pytest.raises(RuntimeError) as e:
+        request.wait_for(1)
+    assert "[ INFER_CANCELLED ]" in str(e.value)
+
+
+def test_start_async(device):
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    exec_net = core.compile_model(func, device)
+    img = read_image()
+    jobs = 3
+    requests = []
+    for _ in range(jobs):
+        requests.append(exec_net.create_infer_request())
+
+    def callback(callbacks_info):
+        time.sleep(0.01)
+        callbacks_info["finished"] += 1
+
+    callbacks_info = {}
+    callbacks_info["finished"] = 0
+    for request in requests:
+        request.set_callback(callback, callbacks_info)
+        request.start_async({0: img})
+    for request in requests:
+        request.wait()
+        assert request.latency > 0
+    assert callbacks_info["finished"] == jobs
+
+
+def test_infer_mixed_keys(device):
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    core.set_config({"PERF_COUNT": "YES"}, device)
+    model = core.compile_model(func, device)
+
+    img = read_image()
+    tensor = Tensor(img)
+
+    data2 = np.ones(shape=img.shape, dtype=np.float32)
+    tensor2 = Tensor(data2)
+
+    request = model.create_infer_request()
+    res = request.infer({0: tensor2, "data": tensor})
+    assert np.argmax(res) == 2
+
+
+def test_infer_queue(device):
+    jobs = 8
+    num_request = 4
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    exec_net = core.compile_model(func, device)
+    infer_queue = AsyncInferQueue(exec_net, num_request)
+    jobs_done = [{"finished": False, "latency": 0} for _ in range(jobs)]
+
+    def callback(request, job_id):
+        jobs_done[job_id]["finished"] = True
+        jobs_done[job_id]["latency"] = request.latency
+
+    img = read_image()
+    infer_queue.set_callback(callback)
+    assert infer_queue.is_ready
+    for i in range(jobs):
+        infer_queue.start_async({"data": img}, i)
+    infer_queue.wait_all()
+    assert all(job["finished"] for job in jobs_done)
+    assert all(job["latency"] > 0 for job in jobs_done)
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_output_const_node.py b/runtime/bindings/python/tests/test_inference_engine/test_output_const_node.py
new file mode 100644
index 00000000000000..d7d1a2eea68a36
--- /dev/null
+++ b/runtime/bindings/python/tests/test_inference_engine/test_output_const_node.py
@@ -0,0 +1,83 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+from ..conftest import model_path
+from openvino.impl import ConstOutput, Shape, PartialShape, Type
+
+from openvino import Core
+
+is_myriad = os.environ.get("TEST_DEVICE") == "MYRIAD"
+test_net_xml, test_net_bin = model_path(is_myriad)
+
+
+def model_path(is_myriad=False):
+    path_to_repo = os.environ["MODELS_PATH"]
+    if not is_myriad:
+        test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.xml")
+        test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp32.bin")
+    else:
+        test_xml = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.xml")
+        test_bin = os.path.join(path_to_repo, "models", "test_model", "test_model_fp16.bin")
+    return (test_xml, test_bin)
+
+
+def test_const_output_type(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    node = exec_net.input(0)
+    assert isinstance(node, ConstOutput)
+
+
+def test_const_output_docs(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    node = exec_net.input(0)
+    exptected_string = "openvino.impl.ConstOutput wraps ov::Output<Const ov::Node >"
+    assert node.__doc__ == exptected_string
+
+
+def test_const_output_get_index(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    node = exec_net.input("data")
+    assert node.get_index() == 0
+
+
+def test_const_output_get_element_type(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    node = exec_net.input("data")
+    assert node.get_element_type() == Type.f32
+
+
+def test_const_output_get_shape(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    node = exec_net.input("data")
+    expected_shape = Shape([1, 3, 32, 32])
+    assert str(node.get_shape()) == str(expected_shape)
+
+
+def test_const_output_get_partial_shape(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    node = exec_net.input("data")
+    expected_partial_shape = PartialShape([1, 3, 32, 32])
+    assert node.get_partial_shape() == expected_partial_shape
+
+
+def test_const_output_get_target_inputs(device):
+    core = Core()
+    func = core.read_model(model=test_net_xml, weights=test_net_bin)
+    exec_net = core.compile_model(func, device)
+    outputs = exec_net.outputs
+    for node in outputs:
+        assert isinstance(node.get_target_inputs(), set)
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_tensor.py b/runtime/bindings/python/tests/test_inference_engine/test_tensor.py
index d840bfa71f4a34..607364d9bc6ae6 100644
--- a/runtime/bindings/python/tests/test_inference_engine/test_tensor.py
+++ b/runtime/bindings/python/tests/test_inference_engine/test_tensor.py
@@ -4,7 +4,7 @@
 import numpy as np
 import pytest
 
-from tests.test_inference_engine.helpers import read_image
+from ..conftest import read_image
 from openvino import Tensor
 import openvino as ov
 
diff --git a/runtime/bindings/python/tests/test_ngraph/test_basic.py b/runtime/bindings/python/tests/test_ngraph/test_basic.py
index 02fb9f4ca3acdb..011abee486eb95 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_basic.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_basic.py
@@ -12,13 +12,11 @@
 from openvino.pyopenvino import VariantInt, VariantString
 
 from openvino.exceptions import UserInputError
-from openvino.impl import Function, PartialShape, Shape, Type
+from openvino.impl import Function, PartialShape, Shape, Type, layout_helpers
 from openvino.impl.op import Parameter
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
 
-from tests import skip_issue_67415
-
 
 def test_ngraph_function_api():
     shape = [2, 2]
@@ -35,6 +33,12 @@ def test_ngraph_function_api():
     assert op_types == ["Parameter", "Parameter", "Parameter", "Add", "Multiply", "Result"]
     assert len(function.get_ops()) == 6
     assert function.get_output_size() == 1
+    assert ["A", "B", "C"] == [input.get_node().friendly_name for input in function.inputs]
+    assert ["Result"] == [output.get_node().get_type_name() for output in function.outputs]
+    assert function.input(0).get_node().friendly_name == "A"
+    assert function.output(0).get_node().get_type_name() == "Result"
+    assert function.input(tensor_name="A").get_node().friendly_name == "A"
+    assert function.output().get_node().get_type_name() == "Result"
     assert function.get_output_op(0).get_type_name() == "Result"
     assert function.get_output_element_type(0) == parameter_a.get_element_type()
     assert list(function.get_output_shape(0)) == [2, 2]
@@ -48,7 +52,7 @@ def test_ngraph_function_api():
     "dtype",
     [
         np.float32,
-        pytest.param(np.float64, marks=skip_issue_67415),
+        np.float64,
         np.int8,
         np.int16,
         np.int32,
@@ -173,7 +177,7 @@ def test_convert_to_float(destination_type, rand_range, in_dtype, expected_type)
 )
 def test_convert_to_int(destination_type, expected_type):
     np.random.seed(133391)
-    input_data = (np.ceil(-8 + np.random.rand(2, 3, 4) * 16)).astype(np.float32)
+    input_data = (np.ceil(-8 + np.random.rand(2, 3, 4) * 16)).astype(expected_type)
     expected = np.array(input_data, dtype=expected_type)
     result = run_op_node([input_data], ops.convert, destination_type)
     assert np.allclose(result, expected)
@@ -195,7 +199,7 @@ def test_convert_to_int(destination_type, expected_type):
 )
 def test_convert_to_uint(destination_type, expected_type):
     np.random.seed(133391)
-    input_data = np.ceil(np.random.rand(2, 3, 4) * 16).astype(np.float32)
+    input_data = np.ceil(np.random.rand(2, 3, 4) * 16).astype(expected_type)
     expected = np.array(input_data, dtype=expected_type)
     result = run_op_node([input_data], ops.convert, destination_type)
     assert np.allclose(result, expected)
@@ -534,3 +538,56 @@ def test_layout():
     layout = ov.Layout("N...C")
     assert layout == "N...C"
     assert layout != "NC?"
+
+
+def test_layout_helpers():
+    layout = ov.Layout("NCHWD")
+    assert(layout_helpers.has_batch(layout))
+    assert(layout_helpers.has_channels(layout))
+    assert(layout_helpers.has_depth(layout))
+    assert(layout_helpers.has_height(layout))
+    assert(layout_helpers.has_width(layout))
+
+    assert layout_helpers.batch_idx(layout) == 0
+    assert layout_helpers.channels_idx(layout) == 1
+    assert layout_helpers.height_idx(layout) == 2
+    assert layout_helpers.width_idx(layout) == 3
+    assert layout_helpers.depth_idx(layout) == 4
+
+    layout = ov.Layout("N...C")
+    assert(layout_helpers.has_batch(layout))
+    assert(layout_helpers.has_channels(layout))
+    assert not(layout_helpers.has_depth(layout))
+    assert not(layout_helpers.has_height(layout))
+    assert not (layout_helpers.has_width(layout))
+
+    assert layout_helpers.batch_idx(layout) == 0
+    assert layout_helpers.channels_idx(layout) == -1
+
+    with pytest.raises(RuntimeError):
+        layout_helpers.height_idx(layout)
+
+    with pytest.raises(RuntimeError):
+        layout_helpers.width_idx(layout)
+
+    with pytest.raises(RuntimeError):
+        layout_helpers.depth_idx(layout)
+
+    layout = ov.Layout("NC?")
+    assert(layout_helpers.has_batch(layout))
+    assert(layout_helpers.has_channels(layout))
+    assert not(layout_helpers.has_depth(layout))
+    assert not(layout_helpers.has_height(layout))
+    assert not (layout_helpers.has_width(layout))
+
+    assert layout_helpers.batch_idx(layout) == 0
+    assert layout_helpers.channels_idx(layout) == 1
+
+    with pytest.raises(RuntimeError):
+        layout_helpers.height_idx(layout)
+
+    with pytest.raises(RuntimeError):
+        layout_helpers.width_idx(layout)
+
+    with pytest.raises(RuntimeError):
+        layout_helpers.depth_idx(layout)
diff --git a/runtime/bindings/python/tests/test_ngraph/test_data_movement.py b/runtime/bindings/python/tests/test_ngraph/test_data_movement.py
index 0baac90c011e0a..624427210a888b 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_data_movement.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_data_movement.py
@@ -8,8 +8,6 @@
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
 
-from tests import xfail_issue_67415
-
 
 def test_reverse_sequence():
     input_data = np.array(
@@ -180,7 +178,6 @@ def test_pad_constant():
     assert np.allclose(result, expected)
 
 
-@xfail_issue_67415
 def test_select():
     cond = np.array([[False, False], [True, False], [True, True]])
     then_node = np.array([[-1, 0], [1, 2], [3, 4]], dtype=np.int32)
diff --git a/runtime/bindings/python/tests/test_ngraph/test_ops.py b/runtime/bindings/python/tests/test_ngraph/test_ops.py
index 4e4ce9005cfce3..4719466c451380 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_ops.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_ops.py
@@ -10,7 +10,6 @@
 from openvino.impl.op import Constant, Parameter
 from tests.runtime import get_runtime
 
-from tests import xfail_issue_67415
 from tests import xfail_issue_54663
 
 
@@ -523,7 +522,6 @@ def test_axisset():
     assert set(tuple_axisset) == set(set_axisset)
 
 
-@xfail_issue_67415
 def test_select():
     element_type = Type.f32
     A = Parameter(Type.boolean, Shape([1, 2]))
diff --git a/runtime/bindings/python/tests/test_ngraph/test_ops_binary.py b/runtime/bindings/python/tests/test_ngraph/test_ops_binary.py
index 7a910eae3c5b58..8e965c9e3f01c3 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_ops_binary.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_ops_binary.py
@@ -10,8 +10,6 @@
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
 
-from tests import xfail_issue_67415
-
 
 @pytest.mark.parametrize(
     "ng_api_helper,numpy_function",
@@ -84,7 +82,6 @@ def test_binary_op_with_scalar(ng_api_helper, numpy_function):
     assert np.allclose(result, expected)
 
 
-@xfail_issue_67415
 @pytest.mark.parametrize(
     "ng_api_helper,numpy_function",
     [(ov.logical_and, np.logical_and), (ov.logical_or, np.logical_or), (ov.logical_xor, np.logical_xor)],
@@ -107,7 +104,6 @@ def test_binary_logical_op(ng_api_helper, numpy_function):
     assert np.allclose(result, expected)
 
 
-@xfail_issue_67415
 @pytest.mark.parametrize(
     "ng_api_helper,numpy_function",
     [(ov.logical_and, np.logical_and), (ov.logical_or, np.logical_or), (ov.logical_xor, np.logical_xor)],
diff --git a/runtime/bindings/python/tests/test_ngraph/test_ops_unary.py b/runtime/bindings/python/tests/test_ngraph/test_ops_unary.py
index e304914180210a..7ce4f98e94535a 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_ops_unary.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_ops_unary.py
@@ -9,8 +9,6 @@
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
 
-from tests import xfail_issue_67415
-
 
 @pytest.mark.parametrize(
     "ng_api_fn, numpy_fn, range_start, range_end",
@@ -79,7 +77,6 @@ def test_unary_op_scalar(ng_api_fn, numpy_fn, input_data):
     assert np.allclose(result, expected)
 
 
-@xfail_issue_67415
 @pytest.mark.parametrize(
     "input_data", [(np.array([True, False, True, False])), (np.array([True])), (np.array([False]))]
 )
diff --git a/runtime/bindings/python/tests/test_ngraph/test_reduction.py b/runtime/bindings/python/tests/test_ngraph/test_reduction.py
index 12334fbe82fa89..8ac606eaa855ec 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_reduction.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_reduction.py
@@ -10,8 +10,6 @@
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
 
-from tests import xfail_issue_67415
-
 
 @pytest.mark.parametrize(
     "ng_api_helper, numpy_function, reduction_axes",
@@ -40,7 +38,6 @@ def test_reduction_ops(ng_api_helper, numpy_function, reduction_axes):
     assert np.allclose(result, expected)
 
 
-@xfail_issue_67415
 @pytest.mark.parametrize(
     "ng_api_helper, numpy_function, reduction_axes",
     [
diff --git a/runtime/bindings/python/tests/test_ngraph/test_sequence_processing.py b/runtime/bindings/python/tests/test_ngraph/test_sequence_processing.py
index ebef086f174954..c394225230e8bd 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_sequence_processing.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_sequence_processing.py
@@ -6,8 +6,7 @@
 import openvino.opset8 as ov
 from tests.runtime import get_runtime
 from tests.test_ngraph.util import run_op_node
-from tests import (xfail_issue_47337,
-                   xfail_accuracy)
+from tests import (xfail_issue_47337)
 
 
 def test_onehot():
@@ -35,7 +34,6 @@ def test_one_hot():
     assert np.allclose(result, excepted)
 
 
-@xfail_accuracy
 def test_range():
     start = 5
     stop = 35
diff --git a/runtime/bindings/python/tests/test_onnx/test_backend.py b/runtime/bindings/python/tests/test_onnx/test_backend.py
index 63b7e527eb3753..2af5e0f9b4399f 100644
--- a/runtime/bindings/python/tests/test_onnx/test_backend.py
+++ b/runtime/bindings/python/tests/test_onnx/test_backend.py
@@ -6,17 +6,15 @@
 import onnx.backend.test
 from tests import (
     BACKEND_NAME,
+    xfail_issue_FLOAT_LIKE,
     skip_rng_tests,
-    xfail_issue_67415,
     xfail_issue_33488,
     xfail_issue_33538,
     xfail_issue_33581,
     xfail_issue_33589,
-    xfail_issue_33593,
     xfail_issue_33595,
     xfail_issue_33596,
     xfail_issue_33606,
-    xfail_issue_33633,
     xfail_issue_33651,
     xfail_issue_38091,
     xfail_issue_38699,
@@ -29,7 +27,6 @@
     xfail_issue_38734,
     xfail_issue_38735,
     xfail_issue_39658,
-    xfail_issue_39659,
     xfail_issue_39662,
     xfail_issue_44854,
     xfail_issue_44858,
@@ -37,25 +34,22 @@
     xfail_issue_44965,
     xfail_issue_44968,
     xfail_issue_45180,
-    xfail_issue_45344,
     xfail_issue_47323,
     xfail_issue_47337,
     xfail_issue_48052,
     xfail_issue_49207,
     xfail_issue_49750,
     xfail_issue_52463,
-    xfail_issue_55760,
     xfail_issue_58033,
     xfail_issue_63033,
     xfail_issue_63036,
     xfail_issue_63039,
     xfail_issue_63043,
     xfail_issue_63044,
-    xfail_issue_63045,
     xfail_issue_63136,
     xfail_issue_63137,
     xfail_issue_63138,
-    skip_issue_69443,
+    xfail_issue_69443,
     xfail_issue_69444,
 )
 from tests.test_onnx.utils.onnx_backend import OpenVinoTestBackend
@@ -108,49 +102,18 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
 
 tests_expected_to_fail = [
     (
-        xfail_issue_67415,
-        "OnnxBackendNodeModelTest.test_and2d_cpu",
-        "OnnxBackendNodeModelTest.test_and3d_cpu",
-        "OnnxBackendNodeModelTest.test_and4d_cpu",
-        "OnnxBackendNodeModelTest.test_and_bcast3v1d_cpu",
-        "OnnxBackendNodeModelTest.test_and_bcast3v2d_cpu",
-        "OnnxBackendNodeModelTest.test_and_bcast4v2d_cpu",
-        "OnnxBackendNodeModelTest.test_and_bcast4v3d_cpu",
-        "OnnxBackendNodeModelTest.test_and_bcast4v4d_cpu",
+        xfail_issue_FLOAT_LIKE,
+        "OnnxBackendNodeModelTest.test_cast_BFLOAT16_to_FLOAT_cpu",
         "OnnxBackendNodeModelTest.test_cast_FLOAT16_to_DOUBLE_cpu",
         "OnnxBackendNodeModelTest.test_cast_FLOAT16_to_FLOAT_cpu",
+        "OnnxBackendNodeModelTest.test_cast_FLOAT_to_BFLOAT16_cpu",
+        "OnnxBackendNodeModelTest.test_castlike_BFLOAT16_to_FLOAT_expanded_cpu",
         "OnnxBackendNodeModelTest.test_castlike_FLOAT16_to_DOUBLE_expanded_cpu",
         "OnnxBackendNodeModelTest.test_castlike_FLOAT16_to_FLOAT_expanded_cpu",
-        "OnnxBackendNodeModelTest.test_if_cpu",
+        "OnnxBackendNodeModelTest.test_castlike_FLOAT_to_BFLOAT16_expanded_cpu",
         "OnnxBackendNodeModelTest.test_max_float16_cpu",
         "OnnxBackendNodeModelTest.test_min_float16_cpu",
         "OnnxBackendNodeModelTest.test_mod_mixed_sign_float16_cpu",
-        "OnnxBackendNodeModelTest.test_not_2d_cpu",
-        "OnnxBackendNodeModelTest.test_not_3d_cpu",
-        "OnnxBackendNodeModelTest.test_not_4d_cpu",
-        "OnnxBackendNodeModelTest.test_or2d_cpu",
-        "OnnxBackendNodeModelTest.test_or3d_cpu",
-        "OnnxBackendNodeModelTest.test_or4d_cpu",
-        "OnnxBackendNodeModelTest.test_or_bcast3v1d_cpu",
-        "OnnxBackendNodeModelTest.test_or_bcast3v2d_cpu",
-        "OnnxBackendNodeModelTest.test_or_bcast4v2d_cpu",
-        "OnnxBackendNodeModelTest.test_or_bcast4v3d_cpu",
-        "OnnxBackendNodeModelTest.test_or_bcast4v4d_cpu",
-        "OnnxBackendNodeModelTest.test_where_example_cpu",
-        "OnnxBackendNodeModelTest.test_where_long_example_cpu",
-        "OnnxBackendNodeModelTest.test_xor2d_cpu",
-        "OnnxBackendNodeModelTest.test_xor3d_cpu",
-        "OnnxBackendNodeModelTest.test_xor4d_cpu",
-        "OnnxBackendNodeModelTest.test_xor_bcast3v1d_cpu",
-        "OnnxBackendNodeModelTest.test_xor_bcast3v2d_cpu",
-        "OnnxBackendNodeModelTest.test_xor_bcast4v2d_cpu",
-        "OnnxBackendNodeModelTest.test_xor_bcast4v3d_cpu",
-        "OnnxBackendNodeModelTest.test_xor_bcast4v4d_cpu",
-        "OnnxBackendNodeModelTest.test_compress_0_cpu",
-        "OnnxBackendNodeModelTest.test_compress_1_cpu",
-        "OnnxBackendNodeModelTest.test_compress_default_axis_cpu",
-        "OnnxBackendNodeModelTest.test_compress_negative_axis_cpu",
-        "OnnxBackendNodeModelTest.test_nonzero_example_cpu",
     ),
     (
         xfail_issue_49207,
@@ -165,7 +128,7 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_lstm_with_peepholes_cpu",
     ),
     (
-        skip_issue_69443,
+        xfail_issue_69443,
         "OnnxBackendNodeModelTest.test_constant_pad_cpu",
         "OnnxBackendNodeModelTest.test_edge_pad_cpu",
         "OnnxBackendNodeModelTest.test_reflect_pad_cpu",
@@ -175,36 +138,11 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_tile_cpu",
         "OnnxBackendNodeModelTest.test_tile_precomputed_cpu",
     ),
-    (
-        xfail_issue_39659,
-        "OnnxBackendNodeModelTest.test_constantofshape_int_shape_zero_cpu",
-    ),
-    (
-        xfail_issue_45344,
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_center_point_box_format_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_flipped_coordinates_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_identical_boxes_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_limit_output_size_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_single_box_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_suppress_by_IOU_and_scores_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_suppress_by_IOU_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_two_batches_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_two_classes_cpu",
-    ),
     (
         xfail_issue_39662,
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_two_classes_cpu",
         "OnnxBackendNodeModelTest.test_scatter_elements_with_negative_indices_cpu",
         "OnnxBackendNodeModelTest.test_gather_negative_indices_cpu",
     ),
-    (xfail_issue_33633, "OnnxBackendNodeModelTest.test_maxpool_2d_dilations_cpu"),
-    (
-        xfail_issue_55760,
-        "OnnxBackendNodeModelTest.test_argmax_negative_axis_keepdims_example_select_last_index_cpu",
-        "OnnxBackendNodeModelTest.test_argmin_negative_axis_keepdims_example_select_last_index_cpu",
-        "OnnxBackendNodeModelTest.test_argmax_negative_axis_keepdims_random_select_last_index_cpu",
-        "OnnxBackendNodeModelTest.test_argmin_negative_axis_keepdims_random_select_last_index_cpu",
-    ),
     (
         xfail_issue_38091,
         "OnnxBackendNodeModelTest.test_gather_negative_indices_cpu",
@@ -299,15 +237,6 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_resize_downsample_scales_cubic_align_corners_cpu",
         "OnnxBackendNodeModelTest.test_resize_downsample_scales_cubic_A_n0p5_exclude_outside_cpu",
         "OnnxBackendNodeModelTest.test_resize_upsample_scales_cubic_A_n0p5_exclude_outside_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_cubic_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_floor_align_corners_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_ceil_half_pixel_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_cubic_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_linear_pytorch_half_pixel_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_nearest_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_nearest_tf_half_pixel_for_nn_cpu",
     ),
     (
         xfail_issue_33581,
@@ -401,11 +330,6 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_squeeze_cpu",
         "OnnxBackendNodeModelTest.test_squeeze_negative_axes_cpu",
     ),
-    (
-        xfail_issue_33593,
-        "OnnxBackendNodeModelTest.test_maxpool_with_argmax_2d_precomputed_strides_cpu",
-        "OnnxBackendNodeModelTest.test_maxpool_with_argmax_2d_precomputed_pads_cpu",
-    ),
     (xfail_issue_58033, "OnnxBackendNodeModelTest.test_einsum_batch_diagonal_cpu"),
     (
         xfail_issue_63033,
@@ -450,11 +374,6 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_triu_square_neg_cpu",
         "OnnxBackendNodeModelTest.test_triu_zero_cpu",
     ),
-    (
-        xfail_issue_63045,
-        "OnnxBackendPyTorchConvertedModelTest.test_MaxPool1d_stride_padding_dilation_cpu",
-        "OnnxBackendPyTorchConvertedModelTest.test_MaxPool2d_stride_padding_dilation_cpu",
-    ),
     (
         skip_rng_tests,
         "OnnxBackendNodeModelTest.test_bernoulli_cpu",
diff --git a/runtime/bindings/python/tests/test_onnx/test_onnx_external_data.py b/runtime/bindings/python/tests/test_onnx/test_onnx_external_data.py
index cbeb316c79ad6c..8fd4fb89ca582d 100644
--- a/runtime/bindings/python/tests/test_onnx/test_onnx_external_data.py
+++ b/runtime/bindings/python/tests/test_onnx/test_onnx_external_data.py
@@ -12,9 +12,7 @@
 def test_import_onnx_with_external_data():
     model_path = os.path.join(os.path.dirname(__file__), "models/external_data.onnx")
     ie = Core()
-    network = ie.read_network(model=model_path)
-
-    ng_function = network.get_function()
+    func = ie.read_model(model=model_path)
 
     dtype = np.float32
     value_a = np.array([1.0, 3.0, 5.0], dtype=dtype)
@@ -22,6 +20,6 @@ def test_import_onnx_with_external_data():
     # third input [5.0, 1.0, 3.0] read from external file
 
     runtime = get_runtime()
-    computation = runtime.computation(ng_function)
+    computation = runtime.computation(func)
     result = computation(value_a, value_b)
     assert np.allclose(result, np.array([3.0, 3.0, 3.0], dtype=dtype))
diff --git a/runtime/bindings/python/tests/test_onnx/test_onnx_import.py b/runtime/bindings/python/tests/test_onnx/test_onnx_import.py
index 2886ff592f3ef3..7f5387d20284f9 100644
--- a/runtime/bindings/python/tests/test_onnx/test_onnx_import.py
+++ b/runtime/bindings/python/tests/test_onnx/test_onnx_import.py
@@ -15,9 +15,7 @@
 def test_import_onnx_function():
     model_path = os.path.join(os.path.dirname(__file__), "models/add_abc.onnx")
     ie = Core()
-    network = ie.read_network(model=model_path)
-
-    ng_function = network.get_function()
+    func = ie.read_model(model=model_path)
 
     dtype = np.float32
     value_a = np.array([1.0], dtype=dtype)
@@ -25,7 +23,7 @@ def test_import_onnx_function():
     value_c = np.array([3.0], dtype=dtype)
 
     runtime = get_runtime()
-    computation = runtime.computation(ng_function)
+    computation = runtime.computation(func)
     result = computation(value_a, value_b, value_c)
     assert np.allclose(result, np.array([6], dtype=dtype))
 
@@ -49,5 +47,19 @@ def test_simple_graph():
 
     runtime = get_runtime()
     computation = runtime.computation(ng_model_function)
-    assert np.array_equal(computation(1, 2, 3)[0], np.array([6.0], dtype=np.float32))
-    assert np.array_equal(computation(4, 5, 6)[0], np.array([15.0], dtype=np.float32))
+    assert np.array_equal(
+        computation(
+            np.array([1], dtype=np.float32),
+            np.array([2], dtype=np.float32),
+            np.array([3], dtype=np.float32),
+        )[0],
+        np.array([6.0], dtype=np.float32),
+    )
+    assert np.array_equal(
+        computation(
+            np.array([4], dtype=np.float32),
+            np.array([5], dtype=np.float32),
+            np.array([6], dtype=np.float32),
+        )[0],
+        np.array([15.0], dtype=np.float32),
+    )
diff --git a/runtime/bindings/python/tests/test_onnx/test_ops_convpool.py b/runtime/bindings/python/tests/test_onnx/test_ops_convpool.py
index 6637a06463b19c..6d431fea9e235d 100644
--- a/runtime/bindings/python/tests/test_onnx/test_ops_convpool.py
+++ b/runtime/bindings/python/tests/test_onnx/test_ops_convpool.py
@@ -369,7 +369,7 @@ def test_pool_max(ndarray_1x1x4x4):
     x = ndarray_1x1x4x4
     y = np.array([[16, 18], [24, 26]], dtype=np.float32).reshape([1, 1, 2, 2])
 
-    ng_results = run_node(node, [x])
+    ng_results = run_node(node, [x], opset_version=7)
     assert np.array_equal(ng_results, [y])
 
 
diff --git a/runtime/bindings/python/tests/test_onnx/test_ops_logical.py b/runtime/bindings/python/tests/test_onnx/test_ops_logical.py
index aabd4304277bf7..150d50673586ef 100644
--- a/runtime/bindings/python/tests/test_onnx/test_ops_logical.py
+++ b/runtime/bindings/python/tests/test_onnx/test_ops_logical.py
@@ -7,15 +7,13 @@
 
 from tests.test_onnx.utils import run_node
 
-from tests import xfail_issue_67415
-
 
 @pytest.mark.parametrize(
     "onnx_op, numpy_func, data_type",
     [
-        pytest.param("And", np.logical_and, np.bool, marks=xfail_issue_67415),
-        pytest.param("Or", np.logical_or, np.bool, marks=xfail_issue_67415),
-        pytest.param("Xor", np.logical_xor, np.bool, marks=xfail_issue_67415),
+        pytest.param("And", np.logical_and, np.bool),
+        pytest.param("Or", np.logical_or, np.bool),
+        pytest.param("Xor", np.logical_xor, np.bool),
         pytest.param("Equal", np.equal, np.int32),
         pytest.param("Greater", np.greater, np.int32),
         pytest.param("Less", np.less, np.int32),
@@ -37,7 +35,6 @@ def test_logical(onnx_op, numpy_func, data_type):
     assert np.array_equal(ng_results, [expected_output])
 
 
-@xfail_issue_67415
 def test_logical_not():
     input_data = np.array([[False, True, True], [False, True, False], [False, False, True]])
     expected_output = np.logical_not(input_data)
diff --git a/runtime/bindings/python/tests/test_onnx/test_ops_random.py b/runtime/bindings/python/tests/test_onnx/test_ops_random.py
new file mode 100644
index 00000000000000..11388971e8759d
--- /dev/null
+++ b/runtime/bindings/python/tests/test_onnx/test_ops_random.py
@@ -0,0 +1,53 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import onnx
+import onnx.mapping
+
+from tests.test_onnx.utils import run_node
+
+
+def test_random_uniform():
+    low = 90.0
+    high = 100.0
+
+    node = onnx.helper.make_node(
+        "RandomUniform",
+        inputs=[],
+        outputs=["y"],
+        high=high,
+        low=low,
+        seed=10.0,
+        shape=(30, 30),
+    )
+
+    result = run_node(node, [])[0]
+
+    assert result.shape == (30, 30)
+    assert len(np.unique(result)) == 900
+    assert np.max(result) < high
+    assert np.min(result) > low
+    assert np.isclose(np.mean(result), np.mean(np.array([low, high])), rtol=0.001)
+
+
+def test_random_normal():
+    mean = 100.0
+    scale = 10.0
+
+    node = onnx.helper.make_node(
+        "RandomNormal",
+        inputs=[],
+        outputs=["y"],
+        mean=mean,
+        scale=scale,
+        seed=10.0,
+        shape=(30, 30),
+    )
+
+    result = run_node(node, [])[0]
+
+    assert result.shape == (30, 30)
+    assert len(np.unique(result)) == 900
+    assert np.allclose(np.mean(result), mean, rtol=0.05)
+    assert np.allclose(np.std(result), scale, rtol=0.05)
diff --git a/runtime/bindings/python/tests/test_onnx/test_ops_unary.py b/runtime/bindings/python/tests/test_onnx/test_ops_unary.py
index 041466663d23a5..201458c629a60b 100644
--- a/runtime/bindings/python/tests/test_onnx/test_ops_unary.py
+++ b/runtime/bindings/python/tests/test_onnx/test_ops_unary.py
@@ -11,8 +11,6 @@
 from tests.runtime import get_runtime
 from tests.test_onnx.utils import get_node_model, import_onnx_model, run_model, run_node
 
-from tests import skip_issue_67415
-
 
 @pytest.mark.parametrize(
     "input_data",
@@ -333,7 +331,6 @@ def test_cast_to_bool(val_type, input_data):
     assert np.allclose(result, expected)
 
 
-@skip_issue_67415
 @pytest.mark.parametrize(
     "val_type, range_start, range_end, in_dtype",
     [
@@ -359,7 +356,7 @@ def test_cast_to_float(val_type, range_start, range_end, in_dtype):
 )
 def test_cast_to_int(val_type):
     np.random.seed(133391)
-    input_data = np.ceil(-8 + np.random.rand(2, 3, 4) * 16)
+    input_data = np.ceil(-8 + np.random.rand(2, 3, 4) * 16).astype(val_type)
     expected = np.array(input_data, dtype=val_type)
 
     model = get_node_model("Cast", input_data, opset=6, to=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[val_type])
@@ -372,7 +369,7 @@ def test_cast_to_int(val_type):
 )
 def test_cast_to_uint(val_type):
     np.random.seed(133391)
-    input_data = np.ceil(np.random.rand(2, 3, 4) * 16)
+    input_data = np.ceil(np.random.rand(2, 3, 4) * 16).astype(val_type)
     expected = np.array(input_data, dtype=val_type)
 
     model = get_node_model("Cast", input_data, opset=6, to=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[val_type])
diff --git a/runtime/bindings/python/tests/test_onnx/test_zoo_models.py b/runtime/bindings/python/tests/test_onnx/test_zoo_models.py
index 2042d7a0f6d303..ee16fa4724a5b0 100644
--- a/runtime/bindings/python/tests/test_onnx/test_zoo_models.py
+++ b/runtime/bindings/python/tests/test_onnx/test_zoo_models.py
@@ -77,6 +77,7 @@ def tinyyolov3_post_processing(outputs : Sequence[Any]) -> Sequence[Any]:
     "resnet34-v2-7": {"atol": 0.001, "rtol": 0.001},
     "vgg16-7": {"atol": 0.001, "rtol": 0.001},
     "vgg19-bn-7": {"atol": 0.001, "rtol": 0.001},
+    "vgg19-7": {"atol": 0.001, "rtol": 0.001},
     "tinyyolov2-7": {"atol": 0.001, "rtol": 0.001},
     "tinyyolov2-8": {"atol": 0.001, "rtol": 0.001},
     "candy-8": {"atol": 0.001, "rtol": 0.001},
@@ -115,6 +116,12 @@ def tinyyolov3_post_processing(outputs : Sequence[Any]) -> Sequence[Any]:
     "test_retinanet_resnet101": {"atol": 1.3e-06},
 }
 
+def tolerance_map_key_in_model_path(path):
+    for key in tolerance_map:
+        if key in path:
+            return key
+    return None
+
 zoo_models = []
 # rglob doesn't work for symlinks, so models have to be physically somwhere inside "MODELS_ROOT_DIR"
 for path in Path(MODELS_ROOT_DIR).rglob("*.onnx"):
@@ -127,6 +134,12 @@ def tinyyolov3_post_processing(outputs : Sequence[Any]) -> Sequence[Any]:
             # updated model looks now:
             # {"model_name": path, "model_file": file, "dir": mdir, "atol": ..., "rtol": ...}
             model.update(tolerance_map[basedir])
+        else:
+            # some models have the same stem, have to check if any of the keys from tolerance_map
+            # is found in the full model path
+            model_key = tolerance_map_key_in_model_path(str(path))
+            if model_key is not None:
+                model.update(tolerance_map[model_key])
         if basedir in post_processing:
             model.update(post_processing[basedir])
         zoo_models.append(model)
diff --git a/runtime/bindings/python/tests/test_onnx/utils/onnx_helpers.py b/runtime/bindings/python/tests/test_onnx/utils/onnx_helpers.py
index 53c5487d31476f..570c82619eda03 100644
--- a/runtime/bindings/python/tests/test_onnx/utils/onnx_helpers.py
+++ b/runtime/bindings/python/tests/test_onnx/utils/onnx_helpers.py
@@ -5,7 +5,7 @@
 import onnx
 from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
 
-from openvino import Core, Blob, TensorDesc
+from openvino import Core, Tensor
 from openvino.impl import Function
 
 
@@ -21,9 +21,7 @@ def np_dtype_to_tensor_type(data_type: np.dtype) -> int:
 def import_onnx_model(model: onnx.ModelProto) -> Function:
     onnx.checker.check_model(model)
     model_byte_string = model.SerializeToString()
-
     ie = Core()
-    ie_network = ie.read_network(bytes(model_byte_string), Blob(TensorDesc("U8", [], "C")))
+    func = ie.read_model(bytes(model_byte_string), Tensor(type=np.uint8, shape=[]))
 
-    ng_function = ie_network.get_function()
-    return ng_function
+    return func
diff --git a/runtime/bindings/python/tests/test_transformations/test_offline_api.py b/runtime/bindings/python/tests/test_transformations/test_offline_api.py
new file mode 100644
index 00000000000000..72b72c811ec159
--- /dev/null
+++ b/runtime/bindings/python/tests/test_transformations/test_offline_api.py
@@ -0,0 +1,62 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# TODO: change the module name according to the description in 69196
+from openvino.offline_transformations_pybind import apply_moc_transformations, apply_pot_transformations, \
+    apply_low_latency_transformation, apply_pruning_transformation, apply_make_stateful_transformation
+
+from openvino import Function, PartialShape
+import openvino as ov
+
+
+def get_test_function():
+    param = ov.opset8.parameter(PartialShape([1, 3, 22, 22]), name="parameter")
+    relu = ov.opset8.relu(param)
+    res = ov.opset8.result(relu, name="result")
+    return Function([res], [param], "test")
+
+
+def test_moc_transformations():
+    function = get_test_function()
+
+    apply_moc_transformations(function, False)
+
+    assert function is not None
+    assert len(function.get_ops()) == 3
+
+
+def test_pot_transformations():
+    function = get_test_function()
+
+    apply_pot_transformations(function, "GNA")
+
+    assert function is not None
+    assert len(function.get_ops()) == 3
+
+
+def test_low_latency_transformation():
+    function = get_test_function()
+
+    apply_low_latency_transformation(function, True)
+
+    assert function is not None
+    assert len(function.get_ops()) == 3
+
+
+def test_pruning_transformation():
+    function = get_test_function()
+
+    apply_pruning_transformation(function)
+
+    assert function is not None
+    assert len(function.get_ops()) == 3
+
+
+def test_make_stateful_transformations():
+    function = get_test_function()
+
+    apply_make_stateful_transformation(function, {"parameter": "result"})
+
+    assert function is not None
+    assert len(function.get_parameters()) == 0
+    assert len(function.get_results()) == 0
diff --git a/runtime/bindings/python/tests_compatibility/__init__.py b/runtime/bindings/python/tests_compatibility/__init__.py
index b0348b293e8079..714761dc1f62b2 100644
--- a/runtime/bindings/python/tests_compatibility/__init__.py
+++ b/runtime/bindings/python/tests_compatibility/__init__.py
@@ -25,7 +25,7 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 skip_segfault = pytest.mark.skip(reason="Segmentation fault error")
 xfail_accuracy = xfail_test(reason="Accuracy")
 xfail_issue_69444 = xfail_test(reason="failed with accuracy issue")
-skip_issue_69443 = pytest.mark.skip(reason="Segmentation fault due to empty pads_begin, pads_end")
+xfail_issue_69443 = xfail_test(reason="Segmentation fault due to empty pads_begin, pads_end")
 xfail_issue_67415 = xfail_test(reason="RuntimeError: Unsupported data type for when filling blob!")
 xfail_issue_33488 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations: "
                                       "MaxUnpool")
@@ -46,7 +46,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
                                       "TfIdfVectorizer")
 xfail_issue_33581 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations: "
                                       "GatherElements")
-xfail_issue_33633 = xfail_test(reason="MaxPool: dilations unsupported")
 xfail_issue_35923 = xfail_test(reason="RuntimeError: PReLU without weights is not supported")
 xfail_issue_35927 = xfail_test(reason="RuntimeError: B has zero dimension that is not allowable")
 xfail_issue_36486 = xfail_test(reason="RuntimeError: HardSigmoid operation should be converted "
@@ -99,8 +98,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 xfail_issue_44968 = xfail_test(reason="Expected: Unsupported dynamic op: Squeeze")
 xfail_issue_47323 = xfail_test(reason="RuntimeError: The plugin does not support FP64")
 xfail_issue_47337 = xfail_test(reason="RuntimeError: Unsupported dynamic ops: v1::OneHot")
-xfail_issue_33593 = xfail_test(reason="Current implementation of MaxPool doesn't support indices output")
-xfail_issue_55760 = xfail_test(reason="RuntimeError: Reversed axis have axes above the source space shape")
 
 # Model MSFT issues:
 xfail_issue_37957 = xfail_test(reason="RuntimeError: nGraph does not support the following ONNX operations: "
@@ -114,9 +111,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
                                       "/openvino/ngraph/src/ngraph/op/util/elementwise_args.cpp:48:")
 xfail_issue_39658 = xfail_test(reason="RuntimeError: Tile operation has a form that is not supported."
                                       " z should be converted to TileIE operation.")
-xfail_issue_39659 = xfail_test(reason="RuntimeError: Broadcast operation has a form that is not supported."
-                                      " y should be converted to Tile operation.")
-xfail_issue_45344 = xfail_test(reason="Unsupported dynamic ops: v3::NonMaxSuppressionIE3")
 xfail_issue_39662 = xfail_test(reason="RuntimeError: 'ScatterElementsUpdate' layer with name 'y' have "
                                       "indices value that points to non-existing output tensor element")
 
@@ -145,7 +139,6 @@ def xfail_test(reason="Mark the test as expected to fail", strict=True):
 xfail_issue_63039 = xfail_test(reason="Result mismatches with UINT8 operations")
 xfail_issue_63043 = xfail_test(reason="Recurrent node expects constants as W, R, B inputs.")
 xfail_issue_63044 = xfail_test(reason="ONNX opset 14 operation: Trilu")
-xfail_issue_63045 = xfail_test(reason="Maxpool with strides, padding and dilations fail")
 
 skip_rng_tests = pytest.mark.skip(reason="Tests use random number generator with no seed.")
 xfail_issue_63136 = xfail_test(reason="Unsupported operation: CastLike")
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_adaptive_pool.py b/runtime/bindings/python/tests_compatibility/test_ngraph/test_adaptive_pool.py
deleted file mode 100644
index ba7fe7b28f3508..00000000000000
--- a/runtime/bindings/python/tests_compatibility/test_ngraph/test_adaptive_pool.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import ngraph as ng
-import numpy as np
-from tests_compatibility.runtime import get_runtime
-
-
-def test_adaptive_avg_pool():
-    runtime = get_runtime()
-    input = np.reshape([0.0, 4, 1, 3, -2, -5, -2,
-                        -2, 1, -3, 1, -3, -4, 0,
-                        -2, 1, -1, -2, 3, -1, -3,
-
-                        -1, -2, 3, 4, -3, -4, 1,
-                        2, 0, -4, -5, -2, -2, -3,
-                        2, 3, 1, -5, 2, -4, -2], (2, 3, 7))
-    input_tensor = ng.constant(input)
-    output_shape = ng.constant(np.array([3], dtype=np.int32))
-
-    adaptive_pool_node = ng.adaptive_avg_pool(input_tensor, output_shape)
-    computation = runtime.computation(adaptive_pool_node)
-    adaptive_pool_results = computation()
-    expected_results = np.reshape([1.66666663, 0.66666669, -3.,
-                                   -1.33333337, -1.66666663, -2.33333325,
-                                   -0.66666669, 0., -0.33333334,
-
-                                   0., 1.33333337, -2.,
-                                   -0.66666669, -3.66666675, -2.33333325,
-                                   2., -0.66666669, -1.33333337], (2, 3, 3))
-
-    assert np.allclose(adaptive_pool_results, expected_results)
-
-
-def test_adaptive_max_pool():
-    runtime = get_runtime()
-    input = np.reshape([0, 4, 1, 3, -2, -5, -2,
-                        -2, 1, -3, 1, -3, -4, 0,
-                        -2, 1, -1, -2, 3, -1, -3,
-
-                        -1, -2, 3, 4, -3, -4, 1,
-                        2, 0, -4, -5, -2, -2, -3,
-                        2, 3, 1, -5, 2, -4, -2], (2, 3, 7))
-    input_tensor = ng.constant(input)
-    output_shape = ng.constant(np.array([3], dtype=np.int32))
-
-    adaptive_pool_node = ng.adaptive_max_pool(input_tensor, output_shape)
-    computation = runtime.computation(adaptive_pool_node)
-    adaptive_pool_results = computation()
-    expected_results = np.reshape([4, 3, -2,
-                                   1, 1, 0,
-                                   1, 3, 3,
-
-                                   3, 4, 1,
-                                   2, -2, -2,
-                                   3, 2, 2], (2, 3, 3))
-
-    expected_indices = np.reshape([1, 3, 4,
-                                   1, 3, 6,
-                                   1, 4, 4,
-
-                                   2, 3, 6,
-                                   0, 4, 4,
-                                   1, 4, 4], (2, 3, 3))
-
-    assert np.allclose(adaptive_pool_results, [expected_results, expected_indices])
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_create_op.py b/runtime/bindings/python/tests_compatibility/test_ngraph/test_create_op.py
index 673d7a2ebf10b4..d8cc5d8a19e11c 100644
--- a/runtime/bindings/python/tests_compatibility/test_ngraph/test_create_op.py
+++ b/runtime/bindings/python/tests_compatibility/test_ngraph/test_create_op.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 import pytest
-from _pyngraph import PartialShape, Dimension
+from _pyngraph import PartialShape
 
 import ngraph as ng
 import ngraph.opset1 as ng_opset1
@@ -23,33 +23,6 @@
 ]
 
 
-@pytest.mark.parametrize("dtype", [np.float32, np.float64])
-def test_adaptive_avg_pool(dtype):
-    data = ng.parameter([2, 24, 34, 62], name="input", dtype=dtype)
-    output_shape = ng.constant(np.array([16, 16], dtype=np.int32))
-
-    node = ng.adaptive_avg_pool(data, output_shape)
-
-    assert node.get_type_name() == "AdaptiveAvgPool"
-    assert node.get_output_size() == 1
-    assert list(node.get_output_shape(0)) == [2, 24, 16, 16]
-
-
-@pytest.mark.parametrize("dtype", [np.float32, np.float64])
-@pytest.mark.parametrize("ind_type", ["i32", "i64"])
-def test_adaptive_max_pool(dtype, ind_type):
-    data = ng.parameter([2, 24, 34, 62], name="input", dtype=dtype)
-    output_shape = ng.constant(np.array([16, 16], dtype=np.int32))
-
-    node = ng.adaptive_max_pool(data, output_shape, ind_type)
-
-    assert node.get_type_name() == "AdaptiveMaxPool"
-    assert node.get_output_size() == 2
-    assert list(node.get_output_shape(0)) == [2, 24, 16, 16]
-    assert list(node.get_output_shape(1)) == [2, 24, 16, 16]
-    assert node.get_output_element_type(1) == Type.i32 if ind_type == "i32" else Type.i64
-
-
 @pytest.mark.parametrize("dtype", [np.float32, np.float64])
 def test_binary_convolution(dtype):
     strides = np.array([1, 1])
@@ -67,7 +40,14 @@ def test_binary_convolution(dtype):
     parameter_input1 = ng.parameter(input1_shape, name="Input1", dtype=dtype)
 
     node = ng.binary_convolution(
-        parameter_input0, parameter_input1, strides, pads_begin, pads_end, dilations, mode, pad_value,
+        parameter_input0,
+        parameter_input1,
+        strides,
+        pads_begin,
+        pads_end,
+        dilations,
+        mode,
+        pad_value,
     )
 
     assert node.get_type_name() == "BinaryConvolution"
@@ -91,26 +71,30 @@ def test_ctc_greedy_decoder(dtype):
     assert list(node.get_output_shape(0)) == expected_shape
 
 
-@pytest.mark.parametrize("fp_dtype, int_dtype, int_ci, int_sl, merge_repeated, blank_index",
-                         [
-                             (np.float32, np.int32, "i32", "i32", True, True),
-                             (np.float32, np.int32, "i64", "i32", True, True),
-                             (np.float32, np.int32, "i32", "i64", True, True),
-                             (np.float32, np.int32, "i64", "i64", True, True),
-                             (np.float64, np.int64, "i32", "i32", False, True),
-                             (np.float64, np.int64, "i64", "i32", False, True),
-                             (np.float64, np.int64, "i32", "i64", False, True),
-                             (np.float64, np.int64, "i64", "i64", False, True),
-                             (np.float32, np.int32, "i32", "i32", True, False),
-                             (np.float32, np.int32, "i64", "i32", True, False),
-                             (np.float32, np.int32, "i32", "i64", True, False),
-                             (np.float32, np.int32, "i64", "i64", True, False),
-                             (np.float64, np.int64, "i32", "i32", False, False),
-                             (np.float64, np.int64, "i64", "i32", False, False),
-                             (np.float64, np.int64, "i32", "i64", False, False),
-                             (np.float64, np.int64, "i64", "i64", False, False)
-                         ],)
-def test_ctc_greedy_decoder_seq_len(fp_dtype, int_dtype, int_ci, int_sl, merge_repeated, blank_index):
+@pytest.mark.parametrize(
+    "fp_dtype, int_dtype, int_ci, int_sl, merge_repeated, blank_index",
+    [
+        (np.float32, np.int32, "i32", "i32", True, True),
+        (np.float32, np.int32, "i64", "i32", True, True),
+        (np.float32, np.int32, "i32", "i64", True, True),
+        (np.float32, np.int32, "i64", "i64", True, True),
+        (np.float64, np.int64, "i32", "i32", False, True),
+        (np.float64, np.int64, "i64", "i32", False, True),
+        (np.float64, np.int64, "i32", "i64", False, True),
+        (np.float64, np.int64, "i64", "i64", False, True),
+        (np.float32, np.int32, "i32", "i32", True, False),
+        (np.float32, np.int32, "i64", "i32", True, False),
+        (np.float32, np.int32, "i32", "i64", True, False),
+        (np.float32, np.int32, "i64", "i64", True, False),
+        (np.float64, np.int64, "i32", "i32", False, False),
+        (np.float64, np.int64, "i64", "i32", False, False),
+        (np.float64, np.int64, "i32", "i64", False, False),
+        (np.float64, np.int64, "i64", "i64", False, False),
+    ],
+)
+def test_ctc_greedy_decoder_seq_len(
+    fp_dtype, int_dtype, int_ci, int_sl, merge_repeated, blank_index
+):
     input0_shape = [8, 20, 128]
     input1_shape = [8]
     input2_shape = [1]
@@ -123,7 +107,12 @@ def test_ctc_greedy_decoder_seq_len(fp_dtype, int_dtype, int_ci, int_sl, merge_r
         parameter_input2 = ng.parameter(input2_shape, name="Input2", dtype=int_dtype)
 
     node = ng.ctc_greedy_decoder_seq_len(
-        parameter_input0, parameter_input1, parameter_input2, merge_repeated, int_ci, int_sl
+        parameter_input0,
+        parameter_input1,
+        parameter_input2,
+        merge_repeated,
+        int_ci,
+        int_sl,
     )
 
     assert node.get_type_name() == "CTCGreedyDecoderSeqLen"
@@ -148,7 +137,13 @@ def test_deformable_convolution_opset1(dtype):
     parameter_input2 = ng.parameter(input2_shape, name="Input2", dtype=dtype)
 
     node = ng_opset1.deformable_convolution(
-        parameter_input0, parameter_input1, parameter_input2, strides, pads_begin, pads_end, dilations,
+        parameter_input0,
+        parameter_input1,
+        parameter_input2,
+        strides,
+        pads_begin,
+        pads_end,
+        dilations,
     )
 
     assert node.get_type_name() == "DeformableConvolution"
@@ -173,35 +168,13 @@ def test_deformable_convolution(dtype):
     parameter_input2 = ng.parameter(input2_shape, name="Input2", dtype=dtype)
 
     node = ng.deformable_convolution(
-        parameter_input0, parameter_input1, parameter_input2, strides, pads_begin, pads_end, dilations,
-    )
-
-    assert node.get_type_name() == "DeformableConvolution"
-    assert node.get_output_size() == 1
-    assert list(node.get_output_shape(0)) == expected_shape
-
-
-@pytest.mark.parametrize("dtype", np_types)
-def test_deformable_convolution_mask(dtype):
-    strides = np.array([1, 1])
-    pads_begin = np.array([0, 0])
-    pads_end = np.array([0, 0])
-    dilations = np.array([1, 1])
-
-    input0_shape = [1, 1, 9, 9]
-    input1_shape = [1, 18, 7, 7]
-    input2_shape = [1, 1, 3, 3]
-    input3_shape = [1, 9, 7, 7]
-    expected_shape = [1, 1, 7, 7]
-
-    parameter_input0 = ng.parameter(input0_shape, name="Input0", dtype=dtype)
-    parameter_input1 = ng.parameter(input1_shape, name="Input1", dtype=dtype)
-    parameter_input2 = ng.parameter(input2_shape, name="Input2", dtype=dtype)
-    parameter_input3 = ng.parameter(input3_shape, name="Input3", dtype=dtype)
-
-    node = ng.deformable_convolution(
-        parameter_input0, parameter_input1, parameter_input2, strides,
-        pads_begin, pads_end, dilations, parameter_input3
+        parameter_input0,
+        parameter_input1,
+        parameter_input2,
+        strides,
+        pads_begin,
+        pads_end,
+        dilations,
     )
 
     assert node.get_type_name() == "DeformableConvolution"
@@ -277,7 +250,9 @@ def test_gather_tree(dtype):
     parameter_input2 = ng.parameter(input2_shape, name="Input2", dtype=dtype)
     parameter_input3 = ng.parameter(input3_shape, name="Input3", dtype=dtype)
 
-    node = ng.gather_tree(parameter_input0, parameter_input1, parameter_input2, parameter_input3)
+    node = ng.gather_tree(
+        parameter_input0, parameter_input1, parameter_input2, parameter_input3
+    )
 
     assert node.get_type_name() == "GatherTree"
     assert node.get_output_size() == 1
@@ -307,7 +282,13 @@ def test_lstm_cell_operator(dtype):
     expected_shape = [1, 128]
 
     node_default = ng.lstm_cell(
-        parameter_X, parameter_H_t, parameter_C_t, parameter_W, parameter_R, parameter_B, hidden_size,
+        parameter_X,
+        parameter_H_t,
+        parameter_C_t,
+        parameter_W,
+        parameter_R,
+        parameter_B,
+        hidden_size,
     )
 
     assert node_default.get_type_name() == "LSTMCell"
@@ -363,7 +344,13 @@ def test_lstm_cell_operator_opset1(dtype):
     expected_shape = [1, 128]
 
     node_default = ng_opset1.lstm_cell(
-        parameter_X, parameter_H_t, parameter_C_t, parameter_W, parameter_R, parameter_B, hidden_size,
+        parameter_X,
+        parameter_H_t,
+        parameter_C_t,
+        parameter_W,
+        parameter_R,
+        parameter_B,
+        hidden_size,
     )
 
     assert node_default.get_type_name() == "LSTMCell"
@@ -612,7 +599,9 @@ def test_gru_cell_operator():
 
     expected_shape = [1, 128]
 
-    node_default = ng.gru_cell(parameter_X, parameter_H_t, parameter_W, parameter_R, parameter_B, hidden_size)
+    node_default = ng.gru_cell(
+        parameter_X, parameter_H_t, parameter_W, parameter_R, parameter_B, hidden_size
+    )
 
     assert node_default.get_type_name() == "GRUCell"
     assert node_default.get_output_size() == 1
@@ -820,8 +809,10 @@ def test_loop():
     ti_inputs = [iter_cnt, data, initial_cma, one]
     body_const_condition = ng.constant(True, dtype=np.bool)
 
-    graph_body = GraphBody([body_timestep, body_data_in, body_prev_cma, body_const_one],
-                           [curr_cma, cma_hist, body_const_condition])
+    graph_body = GraphBody(
+        [body_timestep, body_data_in, body_prev_cma, body_const_one],
+        [curr_cma, cma_hist, body_const_condition],
+    )
     ti_slice_input_desc = [
         # timestep
         # input_idx, body_param_idx, start, stride, part_size, end, axis
@@ -926,7 +917,9 @@ def test_region_yolo():
     end_axis = 3
     do_softmax = False
 
-    node = ng.region_yolo(data, num_coords, num_classes, num_regions, do_softmax, mask, axis, end_axis)
+    node = ng.region_yolo(
+        data, num_coords, num_classes, num_regions, do_softmax, mask, axis, end_axis
+    )
 
     assert node.get_type_name() == "RegionYolo"
     assert node.get_output_size() == 1
@@ -996,7 +989,9 @@ def test_embedding_segments_sum_with_some_opt_inputs():
 def test_embedding_bag_packed_sum():
     emb_table = ng.parameter([5, 2], name="emb_table", dtype=np.float32)
     indices = ng.parameter([3, 3], name="indices", dtype=np.int64)
-    per_sample_weights = ng.parameter([3, 3], name="per_sample_weights", dtype=np.float32)
+    per_sample_weights = ng.parameter(
+        [3, 3], name="per_sample_weights", dtype=np.float32
+    )
 
     # only 1 out of 3 optional inputs
     node = ng.embedding_bag_packed_sum(emb_table, indices, per_sample_weights)
@@ -1048,7 +1043,7 @@ def test_prior_box(int_dtype, fp_dtype):
         "offset": fp_dtype(0),
         "min_size": np.array([2, 3], dtype=fp_dtype),
         "aspect_ratio": np.array([1.5, 2.0, 2.5], dtype=fp_dtype),
-        "scale_all_sizes": False
+        "scale_all_sizes": False,
     }
 
     layer_shape = ng.constant(np.array([32, 32], dtype=int_dtype), int_dtype)
@@ -1120,7 +1115,9 @@ def test_detection_output(int_dtype, fp_dtype):
     aux_class_preds = ng.parameter([4, 4], fp_dtype, "aux_class_preds")
     aux_box_preds = ng.parameter([4, 8], fp_dtype, "aux_box_preds")
 
-    node = ng.detection_output(box_logits, class_preds, proposals, attributes, aux_class_preds, aux_box_preds)
+    node = ng.detection_output(
+        box_logits, class_preds, proposals, attributes, aux_class_preds, aux_box_preds
+    )
 
     assert node.get_type_name() == "DetectionOutput"
     assert node.get_output_size() == 1
@@ -1158,7 +1155,10 @@ def test_proposal(int_dtype, fp_dtype):
 
     assert node.get_type_name() == "Proposal"
     assert node.get_output_size() == 2
-    assert list(node.get_output_shape(0)) == [batch_size * attributes["post_nms_topn"], 5]
+    assert list(node.get_output_shape(0)) == [
+        batch_size * attributes["post_nms_topn"],
+        5,
+    ]
 
 
 def test_tensor_iterator():
@@ -1193,7 +1193,10 @@ def test_tensor_iterator():
     iter_cnt = ng.range(zero, np.int32(16), np.int32(1))
     ti_inputs = [iter_cnt, data, initial_cma, one]
 
-    graph_body = GraphBody([body_timestep, body_data_in, body_prev_cma, body_const_one], [curr_cma, cma_hist])
+    graph_body = GraphBody(
+        [body_timestep, body_data_in, body_prev_cma, body_const_one],
+        [curr_cma, cma_hist],
+    )
     ti_slice_input_desc = [
         # timestep
         # input_idx, body_param_idx, start, stride, part_size, end, axis
@@ -1551,7 +1554,7 @@ def test_gru_sequence_operator_bidirectional(dtype):
         activation_alpha,
         activation_beta,
         clip,
-        linear_before_reset
+        linear_before_reset,
     )
 
     assert node_param.get_type_name() == "GRUSequence"
@@ -1617,7 +1620,7 @@ def test_gru_sequence_operator_reverse(dtype):
         activation_alpha,
         activation_beta,
         clip,
-        linear_before_reset
+        linear_before_reset,
     )
 
     assert node_param.get_type_name() == "GRUSequence"
@@ -1683,7 +1686,7 @@ def test_gru_sequence_operator_forward(dtype):
         activation_alpha,
         activation_beta,
         clip,
-        linear_before_reset
+        linear_before_reset,
     )
 
     assert node.get_type_name() == "GRUSequence"
@@ -1873,53 +1876,3 @@ def test_rnn_sequence_operator_forward(dtype):
 
     assert node.get_type_name() == "RNNSequence"
     assert node.get_output_size() == 2
-
-
-def test_multiclass_nms():
-    boxes_data = np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.1, 1.0, 1.1,
-                           0.0, -0.1, 1.0, 0.9, 0.0, 10.0, 1.0, 11.0,
-                           0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0], dtype="float32")
-    boxes_data = boxes_data.reshape([1, 6, 4])
-    box = ng.constant(boxes_data, dtype=np.float)
-    scores_data = np.array([0.9, 0.75, 0.6, 0.95, 0.5, 0.3,
-                            0.95, 0.75, 0.6, 0.80, 0.5, 0.3], dtype="float32")
-    scores_data = scores_data.reshape([1, 2, 6])
-    score = ng.constant(scores_data, dtype=np.float)
-
-    nms_node = ng.multiclass_nms(box, score, output_type="i32", nms_top_k=3,
-                                 iou_threshold=0.5, score_threshold=0.0, sort_result_type="classid",
-                                 nms_eta=1.0)
-
-    assert nms_node.get_type_name() == "MulticlassNms"
-    assert nms_node.get_output_size() == 3
-    assert nms_node.outputs()[0].get_partial_shape() == PartialShape([Dimension(0, 6), Dimension(6)])
-    assert nms_node.outputs()[1].get_partial_shape() == PartialShape([Dimension(0, 6), Dimension(1)])
-    assert list(nms_node.outputs()[2].get_shape()) == [1, ]
-    assert nms_node.get_output_element_type(0) == Type.f32
-    assert nms_node.get_output_element_type(1) == Type.i32
-    assert nms_node.get_output_element_type(2) == Type.i32
-
-
-def test_matrix_nms():
-    boxes_data = np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.1, 1.0, 1.1,
-                           0.0, -0.1, 1.0, 0.9, 0.0, 10.0, 1.0, 11.0,
-                           0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0], dtype="float32")
-    boxes_data = boxes_data.reshape([1, 6, 4])
-    box = ng.constant(boxes_data, dtype=np.float)
-    scores_data = np.array([0.9, 0.75, 0.6, 0.95, 0.5, 0.3,
-                            0.95, 0.75, 0.6, 0.80, 0.5, 0.3], dtype="float32")
-    scores_data = scores_data.reshape([1, 2, 6])
-    score = ng.constant(scores_data, dtype=np.float)
-
-    nms_node = ng.matrix_nms(box, score, output_type="i32", nms_top_k=3,
-                             score_threshold=0.0, sort_result_type="score", background_class=0,
-                             decay_function="linear", gaussian_sigma=2.0, post_threshold=0.0)
-
-    assert nms_node.get_type_name() == "MatrixNms"
-    assert nms_node.get_output_size() == 3
-    assert nms_node.outputs()[0].get_partial_shape() == PartialShape([Dimension(0, 6), Dimension(6)])
-    assert nms_node.outputs()[1].get_partial_shape() == PartialShape([Dimension(0, 6), Dimension(1)])
-    assert list(nms_node.outputs()[2].get_shape()) == [1, ]
-    assert nms_node.get_output_element_type(0) == Type.f32
-    assert nms_node.get_output_element_type(1) == Type.i32
-    assert nms_node.get_output_element_type(2) == Type.i32
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_pooling.py b/runtime/bindings/python/tests_compatibility/test_ngraph/test_pooling.py
index 423c519272b4ed..38f4fded429986 100644
--- a/runtime/bindings/python/tests_compatibility/test_ngraph/test_pooling.py
+++ b/runtime/bindings/python/tests_compatibility/test_ngraph/test_pooling.py
@@ -26,14 +26,18 @@ def test_avg_pool_2d(_ndarray_1x1x4x4):
     exclude_pad = True
     expected = [[[[13.5, 15.5], [21.5, 23.5]]]]
 
-    avg_pool_node = ng.avg_pool(param, strides, pads_begin, pads_end, kernel_shape, exclude_pad)
+    avg_pool_node = ng.avg_pool(
+        param, strides, pads_begin, pads_end, kernel_shape, exclude_pad
+    )
     computation = runtime.computation(avg_pool_node, param)
     result = computation(input_data)
     assert np.allclose(result, expected)
 
     expected = [[[[13.5, 14.5, 15.5], [17.5, 18.5, 19.5], [21.5, 22.5, 23.5]]]]
     strides = [1, 1]
-    avg_pool_node = ng.avg_pool(param, strides, pads_begin, pads_end, kernel_shape, exclude_pad)
+    avg_pool_node = ng.avg_pool(
+        param, strides, pads_begin, pads_end, kernel_shape, exclude_pad
+    )
     computation = runtime.computation(avg_pool_node, param)
     result = computation(input_data)
     assert np.allclose(result, expected)
@@ -44,14 +48,18 @@ def test_avg_pool_2d(_ndarray_1x1x4x4):
     exclude_pad = True
 
     expected = [[[[11.0, 12.5, 14.0], [17.0, 18.5, 20.0], [23.0, 24.5, 26.0]]]]
-    avg_pool_node = ng.avg_pool(param, strides, pads_begin, pads_end, kernel_shape, exclude_pad)
+    avg_pool_node = ng.avg_pool(
+        param, strides, pads_begin, pads_end, kernel_shape, exclude_pad
+    )
     computation = runtime.computation(avg_pool_node, param)
     result = computation(input_data)
     assert np.allclose(result, expected)
 
     exclude_pad = False
     expected = [[[[2.75, 6.25, 3.5], [8.5, 18.5, 10.0], [5.75, 12.25, 6.5]]]]
-    avg_pool_node = ng.avg_pool(param, strides, pads_begin, pads_end, kernel_shape, exclude_pad)
+    avg_pool_node = ng.avg_pool(
+        param, strides, pads_begin, pads_end, kernel_shape, exclude_pad
+    )
     computation = runtime.computation(avg_pool_node, param)
     result = computation(input_data)
     assert np.allclose(result, expected)
@@ -69,7 +77,9 @@ def test_avg_pooling_3d(_ndarray_1x1x4x4):
     pads_end = [0] * spatial_dim_count
     exclude_pad = True
 
-    avgpool = ng.avg_pool(param, strides, pads_begin, pads_end, kernel_shape, exclude_pad)
+    avgpool = ng.avg_pool(
+        param, strides, pads_begin, pads_end, kernel_shape, exclude_pad
+    )
     comp = rt.computation(avgpool, param)
     result = comp(data)
     result_ref = [[[[[13.5, 15.5], [21.5, 23.5]], [[13.5, 15.5], [21.5, 23.5]]]]]
@@ -85,35 +95,20 @@ def test_max_pool_basic():
     #          [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
     data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
     strides = [1, 1]
-    dilations = [1, 1]
     pads_begin = [0, 0]
     pads_end = [0, 0]
     kernel_shape = [2, 2]
-    rounding_type = "floor"
-    auto_pad = None
-    index_et = "i32"
 
     data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
-    maxpool_node = ng.max_pool(
-        data_node,
-        strides,
-        dilations,
-        pads_begin,
-        pads_end,
-        kernel_shape,
-        rounding_type,
-        auto_pad,
-        index_et,
-    )
+    maxpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
     comp = rt.computation(maxpool_node, data_node)
+
     result = comp(data)
 
     expected = np.array(
         [[[[5.5, 6.5, 7.5], [9.5, 10.5, 11.5], [13.5, 14.5, 15.5]]]], dtype=np.float32
     )
-    expected_idx = np.array([[[[5, 6, 7], [9, 10, 11], [13, 14, 15]]]], dtype=np.int32)
-    assert np.allclose(result[0], expected)
-    assert np.allclose(result[1], expected_idx)
+    assert np.allclose(result, expected)
 
 
 def test_max_pool_strides():
@@ -125,33 +120,17 @@ def test_max_pool_strides():
     #          [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
     data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
     strides = [2, 1]
-    dilations = [1, 1]
     pads_begin = [0, 0]
     pads_end = [0, 0]
     kernel_shape = [2, 2]
-    rounding_type = "floor"
-    auto_pad = None
-    index_et = "i32"
 
     data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
-    maxpool_node = ng.max_pool(
-        data_node,
-        strides,
-        dilations,
-        pads_begin,
-        pads_end,
-        kernel_shape,
-        rounding_type,
-        auto_pad,
-        index_et,
-    )
+    maxpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
     comp = rt.computation(maxpool_node, data_node)
     result = comp(data)
 
     expected = np.array([[[[5.5, 6.5, 7.5], [13.5, 14.5, 15.5]]]], dtype=np.float32)
-    expected_idx = np.array([[[[5, 6, 7], [13, 14, 15]]]], dtype=np.int32)
-    assert np.allclose(result[0], expected)
-    assert np.allclose(result[1], expected_idx)
+    assert np.allclose(result, expected)
 
 
 def test_max_pool_kernel_shape1x1():
@@ -163,31 +142,16 @@ def test_max_pool_kernel_shape1x1():
     #          [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
     data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
     strides = [1, 1]
-    dilations = [1, 1]
     pads_begin = [0, 0]
     pads_end = [0, 0]
     kernel_shape = [1, 1]
-    rounding_type = "floor"
-    auto_pad = None
-    index_et = "i32"
 
     data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
-    maxpool_node = ng.max_pool(
-        data_node,
-        strides,
-        dilations,
-        pads_begin,
-        pads_end,
-        kernel_shape,
-        rounding_type,
-        auto_pad,
-        index_et,
-    )
+    maxpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
     comp = rt.computation(maxpool_node, data_node)
     result = comp(data)
 
-    assert np.allclose(result[0], data)
-    assert np.allclose(result[1], np.arange(0, 16, dtype=np.int32).reshape((1, 1, 4, 4)))
+    assert np.allclose(result, data)
 
 
 def test_max_pool_kernel_shape3x3():
@@ -199,31 +163,17 @@ def test_max_pool_kernel_shape3x3():
     #          [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
     data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
     strides = [1, 1]
-    dilations = [1, 1]
     pads_begin = [0, 0]
     pads_end = [0, 0]
     kernel_shape = [3, 3]
-    rounding_type = "floor"
-    auto_pad = None
-    index_et = "i32"
 
     data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
-    maxpool_node = ng.max_pool(
-        data_node,
-        strides,
-        dilations,
-        pads_begin,
-        pads_end,
-        kernel_shape,
-        rounding_type,
-        auto_pad,
-        index_et,
-    )
+    maxpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
     comp = rt.computation(maxpool_node, data_node)
     result = comp(data)
 
     expected = np.array([[[[10.5, 11.5], [14.5, 15.5]]]], dtype=np.float32)
-    assert np.allclose(result[0], expected)
+    assert np.allclose(result, expected)
 
 
 def test_max_pool_non_zero_pads():
@@ -235,7 +185,6 @@ def test_max_pool_non_zero_pads():
     #          [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
     data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
     strides = [1, 1]
-    dilations = [1, 1]
     pads_begin = [1, 1]
     pads_end = [1, 1]
     #  0   0  ,  0  ,  0  ,  0,    0
@@ -245,22 +194,9 @@ def test_max_pool_non_zero_pads():
     #  0 [12.5, 13.5, 14.5, 15.5], 0
     #  0   0  ,  0  ,  0  ,  0,    0
     kernel_shape = [2, 2]
-    rounding_type = "floor"
-    auto_pad = None
-    index_et = "i32"
 
     data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
-    maxpool_node = ng.max_pool(
-        data_node,
-        strides,
-        dilations,
-        pads_begin,
-        pads_end,
-        kernel_shape,
-        rounding_type,
-        auto_pad,
-        index_et,
-    )
+    maxpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
     comp = rt.computation(maxpool_node, data_node)
     result = comp(data)
 
@@ -278,22 +214,7 @@ def test_max_pool_non_zero_pads():
         ],
         dtype=np.float32,
     )
-    expected_idx = np.array(
-        [
-            [
-                [
-                    [0, 1, 2, 3, 3],
-                    [4, 5, 6, 7, 7],
-                    [8, 9, 10, 11, 11],
-                    [12, 13, 14, 15, 15],
-                    [12, 13, 14, 15, 15],
-                ]
-            ]
-        ],
-        dtype=np.int32,
-    )
-    assert np.allclose(result[0], expected)
-    assert np.allclose(result[1], expected_idx)
+    assert np.allclose(result, expected)
 
 
 def test_max_pool_same_upper_auto_pads():
@@ -305,7 +226,6 @@ def test_max_pool_same_upper_auto_pads():
     #          [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
     data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
     strides = [1, 1]
-    dilations = [1, 1]
     pads_begin = [0, 0]
     pads_end = [0, 0]
     # [ 0.5,  1.5,  2.5,  3.5], 0,
@@ -315,20 +235,10 @@ def test_max_pool_same_upper_auto_pads():
     #   0  ,  0  ,  0  ,  0,    0
     kernel_shape = [2, 2]
     auto_pad = "same_upper"
-    rounding_type = "floor"
-    index_et = "i32"
 
     data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
     maxpool_node = ng.max_pool(
-        data_node,
-        strides,
-        dilations,
-        pads_begin,
-        pads_end,
-        kernel_shape,
-        rounding_type,
-        auto_pad,
-        index_et,
+        data_node, strides, pads_begin, pads_end, kernel_shape, auto_pad=auto_pad
     )
     comp = rt.computation(maxpool_node, data_node)
     result = comp(data)
@@ -346,21 +256,7 @@ def test_max_pool_same_upper_auto_pads():
         ],
         dtype=np.float32,
     )
-    expected_idx = np.array(
-        [
-            [
-                [
-                    [5, 6, 7, 7],
-                    [9, 10, 11, 11],
-                    [13, 14, 15, 15],
-                    [13, 14, 15, 15],
-                ]
-            ]
-        ],
-        dtype=np.int32,
-    )
-    assert np.allclose(result[0], expected)
-    assert np.allclose(result[1], expected_idx)
+    assert np.allclose(result, expected)
 
 
 def test_max_pool_same_lower_auto_pads():
@@ -372,7 +268,6 @@ def test_max_pool_same_lower_auto_pads():
     #          [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
     data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
     strides = [1, 1]
-    dilations = [1, 1]
     pads_begin = [0, 0]
     pads_end = [0, 0]
     #  0   0  ,  0  ,  0  ,  0,
@@ -382,20 +277,10 @@ def test_max_pool_same_lower_auto_pads():
     #  0 [12.5, 13.5, 14.5, 15.5],
     kernel_shape = [2, 2]
     auto_pad = "same_lower"
-    rounding_type = "floor"
-    index_et = "i32"
 
     data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
     maxpool_node = ng.max_pool(
-        data_node,
-        strides,
-        dilations,
-        pads_begin,
-        pads_end,
-        kernel_shape,
-        rounding_type,
-        auto_pad,
-        index_et,
+        data_node, strides, pads_begin, pads_end, kernel_shape, auto_pad=auto_pad
     )
     comp = rt.computation(maxpool_node, data_node)
     result = comp(data)
@@ -413,18 +298,4 @@ def test_max_pool_same_lower_auto_pads():
         ],
         dtype=np.float32,
     )
-    expected_idx = np.array(
-        [
-            [
-                [
-                    [0, 1, 2, 3],
-                    [4, 5, 6, 7],
-                    [8, 9, 10, 11],
-                    [12, 13, 14, 15],
-                ]
-            ]
-        ],
-        dtype=np.int32,
-    )
-    assert np.allclose(result[0], expected)
-    assert np.allclose(result[1], expected_idx)
+    assert np.allclose(result, expected)
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py b/runtime/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py
deleted file mode 100644
index c82654c7167f07..00000000000000
--- a/runtime/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import ngraph as ng
-import numpy as np
-from tests_compatibility.runtime import get_runtime
-
-
-def test_random_uniform():
-    runtime = get_runtime()
-    input_tensor = ng.constant(np.array([2, 4, 3], dtype=np.int32))
-    min_val = ng.constant(np.array([-2.7], dtype=np.float32))
-    max_val = ng.constant(np.array([3.5], dtype=np.float32))
-
-    random_uniform_node = ng.random_uniform(input_tensor, min_val, max_val,
-                                            output_type="f32", global_seed=7461,
-                                            op_seed=1546)
-    computation = runtime.computation(random_uniform_node)
-    random_uniform_results = computation()
-    expected_results = np.array([[[2.8450181, -2.3457108, 2.2134445],
-                                  [-1.0436587, 0.79548645, 1.3023183],
-                                  [0.34447956, -2.0267959, 1.3989122],
-                                  [0.9607613, 1.5363653, 3.117298]],
-
-                                 [[1.570041, 2.2782724, 2.3193843],
-                                  [3.3393657, 0.63299894, 0.41231918],
-                                  [3.1739233, 0.03919673, -0.2136085],
-                                  [-1.4519991, -2.277353, 2.630727]]], dtype=np.float32)
-
-    assert np.allclose(random_uniform_results, expected_results)
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py b/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py
index ec31562e71909b..ed3b1647e0ed73 100644
--- a/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py
+++ b/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py
@@ -11,11 +11,9 @@
     xfail_issue_33538,
     xfail_issue_33581,
     xfail_issue_33589,
-    xfail_issue_33593,
     xfail_issue_33595,
     xfail_issue_33596,
     xfail_issue_33606,
-    xfail_issue_33633,
     xfail_issue_33651,
     xfail_issue_38091,
     xfail_issue_38699,
@@ -28,7 +26,6 @@
     xfail_issue_38734,
     xfail_issue_38735,
     xfail_issue_39658,
-    xfail_issue_39659,
     xfail_issue_39662,
     xfail_issue_44854,
     xfail_issue_44858,
@@ -36,25 +33,22 @@
     xfail_issue_44965,
     xfail_issue_44968,
     xfail_issue_45180,
-    xfail_issue_45344,
     xfail_issue_47323,
     xfail_issue_47337,
     xfail_issue_48052,
     xfail_issue_49207,
     xfail_issue_49750,
     xfail_issue_52463,
-    xfail_issue_55760,
     xfail_issue_58033,
     xfail_issue_63033,
     xfail_issue_63036,
     xfail_issue_63039,
     xfail_issue_63043,
     xfail_issue_63044,
-    xfail_issue_63045,
     xfail_issue_63136,
     xfail_issue_63137,
     xfail_issue_63138,
-    skip_issue_69443,
+    xfail_issue_69443,
     xfail_issue_69444,
 )
 from tests_compatibility.test_onnx.utils.onnx_backend import OpenVinoTestBackend
@@ -119,7 +113,7 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_lstm_with_peepholes_cpu",
     ),
     (
-        skip_issue_69443,
+        xfail_issue_69443,
         "OnnxBackendNodeModelTest.test_constant_pad_cpu",
         "OnnxBackendNodeModelTest.test_edge_pad_cpu",
         "OnnxBackendNodeModelTest.test_reflect_pad_cpu",
@@ -129,36 +123,11 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_tile_cpu",
         "OnnxBackendNodeModelTest.test_tile_precomputed_cpu",
     ),
-    (
-        xfail_issue_39659,
-        "OnnxBackendNodeModelTest.test_constantofshape_int_shape_zero_cpu",
-    ),
-    (
-        xfail_issue_45344,
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_center_point_box_format_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_flipped_coordinates_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_identical_boxes_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_limit_output_size_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_single_box_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_suppress_by_IOU_and_scores_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_suppress_by_IOU_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_two_batches_cpu",
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_two_classes_cpu",
-    ),
     (
         xfail_issue_39662,
-        "OnnxBackendNodeModelTest.test_nonmaxsuppression_two_classes_cpu",
         "OnnxBackendNodeModelTest.test_scatter_elements_with_negative_indices_cpu",
         "OnnxBackendNodeModelTest.test_gather_negative_indices_cpu",
     ),
-    (xfail_issue_33633, "OnnxBackendNodeModelTest.test_maxpool_2d_dilations_cpu"),
-    (
-        xfail_issue_55760,
-        "OnnxBackendNodeModelTest.test_argmax_negative_axis_keepdims_example_select_last_index_cpu",
-        "OnnxBackendNodeModelTest.test_argmin_negative_axis_keepdims_example_select_last_index_cpu",
-        "OnnxBackendNodeModelTest.test_argmax_negative_axis_keepdims_random_select_last_index_cpu",
-        "OnnxBackendNodeModelTest.test_argmin_negative_axis_keepdims_random_select_last_index_cpu",
-    ),
     (
         xfail_issue_38091,
         "OnnxBackendNodeModelTest.test_gather_negative_indices_cpu",
@@ -253,15 +222,6 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_resize_downsample_scales_cubic_align_corners_cpu",
         "OnnxBackendNodeModelTest.test_resize_downsample_scales_cubic_A_n0p5_exclude_outside_cpu",
         "OnnxBackendNodeModelTest.test_resize_upsample_scales_cubic_A_n0p5_exclude_outside_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_cubic_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_floor_align_corners_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_nearest_ceil_half_pixel_cpu",
-        "OnnxBackendNodeModelTest.test_resize_upsample_sizes_cubic_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_linear_pytorch_half_pixel_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_nearest_cpu",
-        "OnnxBackendNodeModelTest.test_resize_downsample_sizes_nearest_tf_half_pixel_for_nn_cpu",
     ),
     (
         xfail_issue_33581,
@@ -355,11 +315,6 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_squeeze_cpu",
         "OnnxBackendNodeModelTest.test_squeeze_negative_axes_cpu",
     ),
-    (
-        xfail_issue_33593,
-        "OnnxBackendNodeModelTest.test_maxpool_with_argmax_2d_precomputed_strides_cpu",
-        "OnnxBackendNodeModelTest.test_maxpool_with_argmax_2d_precomputed_pads_cpu",
-    ),
     (xfail_issue_58033, "OnnxBackendNodeModelTest.test_einsum_batch_diagonal_cpu"),
     (
         xfail_issue_63033,
@@ -404,11 +359,6 @@ def expect_fail(test_case_path, xfail):  # type: (str) -> None
         "OnnxBackendNodeModelTest.test_triu_square_neg_cpu",
         "OnnxBackendNodeModelTest.test_triu_zero_cpu",
     ),
-    (
-        xfail_issue_63045,
-        "OnnxBackendPyTorchConvertedModelTest.test_MaxPool1d_stride_padding_dilation_cpu",
-        "OnnxBackendPyTorchConvertedModelTest.test_MaxPool2d_stride_padding_dilation_cpu",
-    ),
     (
         skip_rng_tests,
         "OnnxBackendNodeModelTest.test_bernoulli_cpu",
diff --git a/samples/c/common/opencv_c_wrapper/CMakeLists.txt b/samples/c/common/opencv_c_wrapper/CMakeLists.txt
index 45b2b3e9ed0025..9550b217edf594 100644
--- a/samples/c/common/opencv_c_wrapper/CMakeLists.txt
+++ b/samples/c/common/opencv_c_wrapper/CMakeLists.txt
@@ -28,3 +28,9 @@ set_target_properties(${TARGET_NAME} PROPERTIES FOLDER c_samples)
 if(COMMAND add_clang_format_target AND NOT IE_SAMPLE_EXCLUDE_CLANG_FORMAT)
     add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 endif()
+
+install(
+    TARGETS ${TARGET_NAME}
+    RUNTIME DESTINATION samples_bin/ COMPONENT samples_bin EXCLUDE_FROM_ALL
+    LIBRARY DESTINATION samples_bin/ COMPONENT samples_bin EXCLUDE_FROM_ALL
+)
diff --git a/samples/c/hello_classification/main.c b/samples/c/hello_classification/main.c
index b4c3f4ed79c921..1f2831fa918612 100644
--- a/samples/c/hello_classification/main.c
+++ b/samples/c/hello_classification/main.c
@@ -122,15 +122,19 @@ int main(int argc, char** argv) {
     // -------------------------------------
 
     IEStatusCode status = ie_core_create("", &core);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_create status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // Step 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin
     // files) or ONNX (.onnx file) format
     status = ie_core_read_network(core, input_model, NULL, &network);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_read_network status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // check the network topology
     status = ie_network_get_inputs_number(network, &network_input_size);
     if (status != OK || network_input_size != 1) {
@@ -140,7 +144,7 @@ int main(int argc, char** argv) {
 
     status = ie_network_get_outputs_number(network, &network_output_size);
     if (status != OK || network_output_size != 1) {
-        printf("Sample supports topologies with 1 output only\n");
+        fprintf(stderr, "Sample supports topologies with 1 output only\n");
         goto err;
     }
     // -----------------------------------------------------------------------------------------------------
@@ -151,8 +155,10 @@ int main(int argc, char** argv) {
     // -----------------------------------------------------
 
     status = ie_network_get_input_name(network, 0, &input_name);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_get_input_name status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     /* Mark input as resizable by setting of a resize algorithm.
      * In this case we will be able to set an input blob of any shape to an infer
      * request. Resize and layout conversions are executed automatically during
@@ -160,15 +166,19 @@ int main(int argc, char** argv) {
     status |= ie_network_set_input_resize_algorithm(network, input_name, RESIZE_BILINEAR);
     status |= ie_network_set_input_layout(network, input_name, NHWC);
     status |= ie_network_set_input_precision(network, input_name, U8);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_set_input_* status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     // --------------------------- Prepare output blobs
     // ----------------------------------------------------
     status |= ie_network_get_output_name(network, 0, &output_name);
     status |= ie_network_set_output_precision(network, output_name, FP32);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_get_output_* status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     // -----------------------------------------------------------------------------------------------------
 
@@ -176,15 +186,19 @@ int main(int argc, char** argv) {
     // ------------------------------------------
     ie_config_t config = {NULL, NULL, NULL};
     status = ie_core_load_network(core, network, device_name, &config, &exe_network);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_load_network status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 5. Create infer request
     // -------------------------------------------------
     status = ie_exec_network_create_infer_request(exe_network, &infer_request);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_exec_network_create_infer_request status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 6. Prepare input
@@ -201,28 +215,34 @@ int main(int argc, char** argv) {
     // memory
     status = ie_blob_make_memory_from_preallocated(&tensorDesc, img.mat_data, size, &imgBlob);
     if (status != OK) {
+        fprintf(stderr, "ERROR ie_blob_make_memory_from_preallocated status %d, line %d\n", status, __LINE__);
         image_free(&img);
         goto err;
     }
     // infer_request accepts input blob of any size
 
     status = ie_infer_request_set_blob(infer_request, input_name, imgBlob);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_set_blob status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 7. Do inference
     // --------------------------------------------------------
     /* Running the request synchronously */
     status = ie_infer_request_infer(infer_request);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_infer status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 8. Process output
     // ------------------------------------------------------
     status = ie_infer_request_get_blob(infer_request, output_name, &output_blob);
     if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_get_blob status %d, line %d\n", status, __LINE__);
         image_free(&img);
         goto err;
     }
diff --git a/samples/c/hello_nv12_input_classification/main.c b/samples/c/hello_nv12_input_classification/main.c
index 9effcdef06a97a..02715bb0709486 100644
--- a/samples/c/hello_nv12_input_classification/main.c
+++ b/samples/c/hello_nv12_input_classification/main.c
@@ -178,8 +178,10 @@ int main(int argc, char** argv) {
     }
 
     size_t input_width = 0, input_height = 0, img_size = 0;
-    if (!is_supported_image_size(argv[3], &input_width, &input_height))
+    if (!is_supported_image_size(argv[3], &input_width, &input_height)) {
+        fprintf(stderr, "ERROR is_supported_image_size, line %d\n", __LINE__);
         return EXIT_FAILURE;
+    }
 
     const char* input_model = argv[1];
     const char* input_image_path = argv[2];
@@ -196,15 +198,19 @@ int main(int argc, char** argv) {
     // --------------------------- Step 1. Initialize inference engine core
     // -------------------------------------
     IEStatusCode status = ie_core_create("", &core);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_create status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // Step 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin
     // files) or ONNX (.onnx file) format
     status = ie_core_read_network(core, input_model, NULL, &network);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_read_network status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 3. Configure input & output
@@ -212,8 +218,10 @@ int main(int argc, char** argv) {
     // --------------------------- Prepare input blobs
     // -----------------------------------------------------
     status = ie_network_get_input_name(network, 0, &input_name);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_get_input_name status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     /* Mark input as resizable by setting of a resize algorithm.
      * In this case we will be able to set an input blob of any shape to an infer
@@ -226,15 +234,19 @@ int main(int argc, char** argv) {
     // pre-processing
     status |= ie_network_set_color_format(network, input_name, NV12);
 
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_set_input_* status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     // --------------------------- Prepare output blobs
     // ----------------------------------------------------
     status |= ie_network_get_output_name(network, 0, &output_name);
     status |= ie_network_set_output_precision(network, output_name, FP32);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_set_output_* status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     // -----------------------------------------------------------------------------------------------------
 
@@ -242,15 +254,19 @@ int main(int argc, char** argv) {
     // ------------------------------------------
     ie_config_t config = {NULL, NULL, NULL};
     status = ie_core_load_network(core, network, device_name, &config, &exe_network);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_load_network status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 5. Create infer request
     // -------------------------------------------------
     status = ie_exec_network_create_infer_request(exe_network, &infer_request);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_exec_network_create_infer_request status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 6. Prepare input
@@ -258,10 +274,14 @@ int main(int argc, char** argv) {
     // size converted to NV12 data size: height(NV12) = 3 / 2 * logical height
     img_size = input_width * (input_height * 3 / 2);
     img_data = (unsigned char*)calloc(img_size, sizeof(unsigned char));
-    if (NULL == img_data)
+    if (NULL == img_data) {
+        fprintf(stderr, "ERROR calloc returned NULL, line %d\n", __LINE__);
         goto err;
-    if (img_size != read_image_from_file(input_image_path, img_data, img_size))
+    }
+    if (img_size != read_image_from_file(input_image_path, img_data, img_size)) {
+        fprintf(stderr, "ERROR ie_exec_network_create_infer_request `img_size` missmatch, line %d\n", __LINE__);
         goto err;
+    }
 
     // --------------------------- Create a blob to hold the NV12 input data
     // ------------------------------- Create tensor descriptors for Y and UV
@@ -279,27 +299,35 @@ int main(int argc, char** argv) {
     status |= ie_blob_make_memory_from_preallocated(&uv_tensor, img_data + y_plane_size, uv_plane_size, &uv_blob);
     // Create NV12Blob from Y and UV blobs
     status |= ie_blob_make_memory_nv12(y_blob, uv_blob, &nv12_blob);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_blob_make_memory_* status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     status = ie_infer_request_set_blob(infer_request, input_name, nv12_blob);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_set_blob status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 7. Do inference
     // --------------------------------------------------------
     /* Running the request synchronously */
     status = ie_infer_request_infer(infer_request);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_infer status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 8. Process output
     // ------------------------------------------------------
     status = ie_infer_request_get_blob(infer_request, output_name, &output_blob);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_get_blob status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     size_t class_num;
     struct classify_res* cls = output_blob_to_classify_res(output_blob, &class_num);
diff --git a/samples/c/object_detection_sample_ssd/main.c b/samples/c/object_detection_sample_ssd/main.c
index c71d643950d022..8899fdb460234f 100644
--- a/samples/c/object_detection_sample_ssd/main.c
+++ b/samples/c/object_detection_sample_ssd/main.c
@@ -69,6 +69,7 @@ int ParseAndCheckCommandLine(int argc, char* argv[]) {
             config_msg = optarg;
             break;
         default:
+            fprintf(stderr, "Unknown argument `%c`. Please use -h option.\n", opt);
             return -1;
         }
     }
@@ -76,11 +77,11 @@ int ParseAndCheckCommandLine(int argc, char* argv[]) {
     if (help)
         return -1;
     if (input_model == NULL) {
-        printf("Model is required but not set. Please set -m option.\n");
+        fprintf(stderr, "Model is required but not set. Please set -m option.\n");
         return -1;
     }
     if (img_msg == NULL) {
-        printf("Input is required but not set.Please set -i option.\n");
+        fprintf(stderr, "Input is required but not set.Please set -i option.\n");
         return -1;
     }
 
@@ -96,14 +97,14 @@ int ParseAndCheckCommandLine(int argc, char* argv[]) {
 void readInputFilesArgument(const char* arg) {
     struct stat sb;
     if (stat(arg, &sb) != 0) {
-        printf("%sFile %s cannot be opened!\n", warn, arg);
+        fprintf(stderr, "%sFile %s cannot be opened!\n", warn, arg);
         return;
     }
     if (S_ISDIR(sb.st_mode)) {
         DIR* dp;
         dp = opendir(arg);
         if (dp == NULL) {
-            printf("%sFile %s cannot be opened!\n", warn, arg);
+            fprintf(stderr, "%sFile %s cannot be opened!\n", warn, arg);
             return;
         }
 
@@ -189,6 +190,7 @@ void parseInputFilesArguments(int argc, char** argv) {
 ie_config_t* parseConfig(const char* config_file, char comment) {
     FILE* file = fopen(config_file, "r");
     if (!file) {
+        fprintf(stderr, "ERROR file `%s` opening failure\n", config_file);
         return NULL;
     }
 
@@ -323,7 +325,7 @@ int main(int argc, char** argv) {
     /** This file_paths stores paths to the processed images **/
     parseInputFilesArguments(argc, argv_temp);
     if (!file_num) {
-        printf("No suitable images were found\n");
+        fprintf(stderr, "No suitable images were found\n");
         free(argv_temp);
         return EXIT_FAILURE;
     }
@@ -334,16 +336,20 @@ int main(int argc, char** argv) {
 
     printf("%sLoading Inference Engine\n", info);
     IEStatusCode status = ie_core_create("", &core);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_create status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     // ------------------------------ Get Available Devices
     // ------------------------------------------------------
     ie_core_versions_t ver;
     printf("%sDevice info: \n", info);
     status = ie_core_get_versions(core, device_name, &ver);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_get_versions status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     for (i = 0; i < ver.num_vers; ++i) {
         printf("         %s\n", ver.versions[i].device_name);
         printf("         %s version ......... %zu.%zu\n",
@@ -358,8 +364,10 @@ int main(int argc, char** argv) {
         // Custom CPU extension is loaded as a shared library and passed as a
         // pointer to base extension
         status = ie_core_add_extension(core, custom_ex_library_msg, "CPU");
-        if (status != OK)
+        if (status != OK) {
+            fprintf(stderr, "ERROR ie_core_add_extension status %d, line %d\n", status, __LINE__);
             goto err;
+        }
         printf("%sCustom extension loaded: %s\n", info, custom_ex_library_msg);
     }
 
@@ -369,8 +377,10 @@ int main(int argc, char** argv) {
         // description
         ie_config_t cfg = {"CONFIG_FILE", custom_plugin_cfg_msg, NULL};
         status = ie_core_set_config(core, &cfg, device_name);
-        if (status != OK)
+        if (status != OK) {
+            fprintf(stderr, "ERROR ie_core_set_config status %d, line %d\n", status, __LINE__);
             goto err;
+        }
         printf("%sConfig for device plugin custom extension loaded: %s\n", info, custom_plugin_cfg_msg);
     }
     // -----------------------------------------------------------------------------------------------------
@@ -380,8 +390,10 @@ int main(int argc, char** argv) {
     printf("%sLoading network:\n", info);
     printf("\t%s\n", input_model);
     status = ie_core_read_network(core, input_model, NULL, &network);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_core_read_network status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 3. Configure input & output
@@ -394,7 +406,7 @@ int main(int argc, char** argv) {
     size_t input_num = 0;
     status = ie_network_get_inputs_number(network, &input_num);
     if (status != OK || (input_num != 1 && input_num != 2)) {
-        printf("Sample supports topologies only with 1 or 2 inputs\n");
+        fprintf(stderr, "Sample supports topologies only with 1 or 2 inputs\n");
         goto err;
     }
 
@@ -435,7 +447,7 @@ int main(int argc, char** argv) {
 
             status = ie_network_set_input_precision(network, name, FP32);
             if (status != OK || (input_dim.dims[1] != 3 && input_dim.dims[1] != 6)) {
-                printf("Invalid input info. Should be 3 or 6 values length\n");
+                fprintf(stderr, "Invalid input info. Should be 3 or 6 values length\n");
                 goto err;
             }
         }
@@ -464,7 +476,7 @@ int main(int argc, char** argv) {
     for (i = 0; i < file_num; ++i) {
         c_mat_t img = {NULL, 0, 0, 0, 0, 0};
         if (image_read(file_paths[i], &img) == -1) {
-            printf("%sImage %s cannot be read!\n", warn, file_paths[i]);
+            fprintf(stderr, "%sImage %s cannot be read!\n", warn, file_paths[i]);
             continue;
         }
         /** Store image data **/
@@ -504,7 +516,7 @@ int main(int argc, char** argv) {
     }
 
     if (!image_num) {
-        printf("Valid input images were not found!\n");
+        fprintf(stderr, "Valid input images were not found!\n");
         free(originalImages);
         free(images);
         goto err;
@@ -512,8 +524,10 @@ int main(int argc, char** argv) {
 
     input_shapes_t shapes;
     status = ie_network_get_input_shapes(network, &shapes);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_get_input_shapes status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     /** Using ie_network_reshape() to set the batch size equal to the number of
      * input images **/
@@ -521,14 +535,18 @@ int main(int argc, char** argv) {
      * **/
     shapes.shapes[0].shape.dims[0] = image_num;
     status = ie_network_reshape(network, shapes);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_reshape status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     ie_network_input_shapes_free(&shapes);
 
     input_shapes_t shapes2;
     status = ie_network_get_input_shapes(network, &shapes2);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_get_input_shapes status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     size_t batchSize = shapes2.shapes[0].shape.dims[0];
     ie_network_input_shapes_free(&shapes2);
     printf("%sBatch size is %zu\n", info, batchSize);
@@ -541,20 +559,24 @@ int main(int argc, char** argv) {
     status = ie_network_get_outputs_number(network, &output_num);
 
     if (status != OK || !output_num) {
-        printf("Can't find a DetectionOutput layer in the topology\n");
+        fprintf(stderr, "Can't find a DetectionOutput layer in the topology\n");
         goto err;
     }
 
     status = ie_network_get_output_name(network, output_num - 1, &output_name);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_get_output_name status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     dimensions_t output_dim;
     status = ie_network_get_output_dims(network, output_name, &output_dim);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_get_output_dims status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     if (output_dim.ranks != 4) {
-        printf("Incorrect output dimensions for SSD model\n");
+        fprintf(stderr, "Incorrect output dimensions for SSD model\n");
         goto err;
     }
 
@@ -569,8 +591,10 @@ int main(int argc, char** argv) {
     /** Set the precision of output data provided by the user, should be called
      * before load of the network to the device **/
     status = ie_network_set_output_precision(network, output_name, FP32);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_network_set_output_precision status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 4. Loading model to the device
@@ -581,13 +605,16 @@ int main(int argc, char** argv) {
         status = ie_core_load_network(core, network, device_name, config, &exe_network);
         config_free(config);
         if (status != OK) {
+            fprintf(stderr, "ERROR ie_core_load_network status %d, line %d\n", status, __LINE__);
             goto err;
         }
     } else {
         ie_config_t cfg = {NULL, NULL, NULL};
         status = ie_core_load_network(core, network, device_name, &cfg, &exe_network);
-        if (status != OK)
+        if (status != OK) {
+            fprintf(stderr, "ERROR ie_core_load_network status %d, line %d\n", status, __LINE__);
             goto err;
+        }
     }
 
     // -----------------------------------------------------------------------------------------------------
@@ -596,8 +623,10 @@ int main(int argc, char** argv) {
     // -------------------------------------------------
     printf("%sCreate infer request\n", info);
     status = ie_exec_network_create_infer_request(exe_network, &infer_request);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_exec_network_create_infer_request status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 6. Prepare input
@@ -605,22 +634,28 @@ int main(int argc, char** argv) {
 
     /** Creating input blob **/
     status = ie_infer_request_get_blob(infer_request, imageInputName, &imageInput);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_get_blob status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     /** Filling input tensor with images. First b channel, then g and r channels
      * **/
     dimensions_t input_tensor_dims;
     status = ie_blob_get_dims(imageInput, &input_tensor_dims);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_blob_get_dims status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     size_t num_channels = input_tensor_dims.dims[1];
     size_t image_size = input_tensor_dims.dims[3] * input_tensor_dims.dims[2];
 
     ie_blob_buffer_t blob_buffer;
     status = ie_blob_get_buffer(imageInput, &blob_buffer);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_blob_get_buffer status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     unsigned char* data = (unsigned char*)(blob_buffer.buffer);
 
     /** Iterate over all input images **/
@@ -651,6 +686,7 @@ int main(int argc, char** argv) {
         ie_blob_buffer_t info_blob_buffer;
         status |= ie_blob_get_buffer(input2, &info_blob_buffer);
         if (status != OK) {
+            fprintf(stderr, "ERROR ie_blob_get_buffer status %d, line %d\n", status, __LINE__);
             ie_blob_free(&input2);
             goto err;
         }
@@ -672,8 +708,10 @@ int main(int argc, char** argv) {
     printf("%sStart inference\n", info);
     status = ie_infer_request_infer_async(infer_request);
     status |= ie_infer_request_wait(infer_request, -1);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_infer_async status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     // -----------------------------------------------------------------------------------------------------
 
     // --------------------------- Step 8. Process output
@@ -681,13 +719,17 @@ int main(int argc, char** argv) {
     printf("%sProcessing output blobs\n", info);
 
     status = ie_infer_request_get_blob(infer_request, output_name, &output_blob);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_infer_request_get_blob status %d, line %d\n", status, __LINE__);
         goto err;
+    }
 
     ie_blob_buffer_t output_blob_buffer;
     status = ie_blob_get_cbuffer(output_blob, &output_blob_buffer);
-    if (status != OK)
+    if (status != OK) {
+        fprintf(stderr, "ERROR ie_blob_get_cbuffer status %d, line %d\n", status, __LINE__);
         goto err;
+    }
     const float* detection = (float*)(output_blob_buffer.cbuffer);
 
     int** classes = (int**)calloc(image_num, sizeof(int*));
diff --git a/scripts/demo/run_sample_benchmark_app.bat b/scripts/demo/run_sample_benchmark_app.bat
index b9499bcca4e076..18f78faea58d70 100644
--- a/scripts/demo/run_sample_benchmark_app.bat
+++ b/scripts/demo/run_sample_benchmark_app.bat
@@ -4,12 +4,18 @@
 @echo off
 setlocal enabledelayedexpansion
 
+set ROOT_DIR=%~dp0
+
 set TARGET=CPU
 set BUILD_FOLDER=%USERPROFILE%\Documents\Intel\OpenVINO
 
 :: command line arguments parsing
 :input_arguments_loop
 if not "%1"=="" (
+    if "%1"=="-b" (
+        set BUILD_FOLDER=%2
+        shift
+    )
     if "%1"=="-d" (
         set TARGET=%2
         echo target = !TARGET!
@@ -25,6 +31,7 @@ if not "%1"=="" (
         echo.
         echo Options:
         echo    -help                      Print help message
+        echo    -b BUILD_FOLDER            Specify the sample build directory
         echo    -d DEVICE                  Specify the target device to infer on; CPU, GPU, HDDL or MYRIAD are acceptable. Sample will look for a suitable plugin for device specified
         echo    -sample-options OPTIONS    Specify command line arguments for the sample
         exit /b
@@ -33,12 +40,12 @@ if not "%1"=="" (
     goto :input_arguments_loop
 )
 
+set "SOLUTION_DIR64=%BUILD_FOLDER%\inference_engine_cpp_samples_build"
+
 IF "%SAMPLE_OPTIONS%"=="" (
     set SAMPLE_OPTIONS=-niter 1000 
 )
 
-set ROOT_DIR=%~dp0
-
 set TARGET_PRECISION=FP16
 
 echo target_precision = !TARGET_PRECISION!
@@ -158,81 +165,21 @@ if "%PROCESSOR_ARCHITECTURE%" == "AMD64" (
     set "PLATFORM=Win32"
 )
 
-set VSWHERE="false"
-if exist "%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" (
-    set VSWHERE="true"
-    cd /d "%ProgramFiles(x86)%\Microsoft Visual Studio\Installer"
-) else if exist "%ProgramFiles%\Microsoft Visual Studio\Installer\vswhere.exe" (
-    set VSWHERE="true"
-    cd /d "%ProgramFiles%\Microsoft Visual Studio\Installer"
-) else (
-    echo "vswhere tool is not found"
-)
-
-if !VSWHERE! == "true" (
-    for /f "usebackq tokens=*" %%i in (`vswhere -latest -products * -requires Microsoft.Component.MSBuild -property installationPath`) do (
-        set VS_PATH=%%i
-    )
-    if exist "!VS_PATH!\MSBuild\14.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=!VS_PATH!\MSBuild\14.0\Bin\MSBuild.exe"
-    )
-    if exist "!VS_PATH!\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=!VS_PATH!\MSBuild\15.0\Bin\MSBuild.exe"
-    )
-    if exist "!VS_PATH!\MSBuild\Current\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=!VS_PATH!\MSBuild\Current\Bin\MSBuild.exe"
-    )
-    for /f "usebackq tokens=1 delims=." %%i in (`vswhere -latest -products * -requires Microsoft.Component.MSBuild -property installationVersion`) do (
-        set VS_MAJOR_VER=%%i
-    )
-    if "!VS_MAJOR_VER!"=="16" set "MSBUILD_VERSION=16 2019"
-    if "!VS_MAJOR_VER!"=="15" set "MSBUILD_VERSION=15 2017"
-)
-
-if "!MSBUILD_BIN!" == "" (
-    if exist "C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=14 2015"
-    )
-    if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\MSBuild\15.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=15 2017"
-    )
-    if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=15 2017"
-    )
-    if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\MSBuild\15.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=15 2017"
-    )
-) else (
-    if not "!MSBUILD_BIN:2019=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=16 2019"
-    if not "!MSBUILD_BIN:2017=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=15 2017"
-    if not "!MSBUILD_BIN:2015=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=14 2015"
-)
-
-if "!MSBUILD_BIN!" == "" (
-    echo Build tools for Visual Studio 2015 / 2017 / 2019 cannot be found. If you use Visual Studio 2017, please download and install build tools from https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017
-    GOTO errorHandling
-)
-
-set "SOLUTION_DIR64=%BUILD_FOLDER%\inference_engine_cpp_samples_build"
-
-echo Creating Visual Studio !MSBUILD_VERSION! %PLATFORM% files in %SOLUTION_DIR64%... && ^
 if exist "%SOLUTION_DIR64%\CMakeCache.txt" del "%SOLUTION_DIR64%\CMakeCache.txt"
-cd /d "%INTEL_OPENVINO_DIR%\samples\cpp" && cmake -E make_directory "%SOLUTION_DIR64%" && cd /d "%SOLUTION_DIR64%" && cmake -G "Visual Studio !MSBUILD_VERSION!" -A %PLATFORM% "%INTEL_OPENVINO_DIR%\samples\cpp"
+
+cd /d "%INTEL_OPENVINO_DIR%\samples\cpp" && cmake -E make_directory "%SOLUTION_DIR64%" && cd /d "%SOLUTION_DIR64%" && cmake -G "Visual Studio 16 2019" -A %PLATFORM% "%INTEL_OPENVINO_DIR%\samples\cpp"
 if ERRORLEVEL 1 GOTO errorHandling
 
 CALL :delay 7
 
 echo.
-echo ###############^|^| Build Inference Engine samples using MS Visual Studio (MSBuild.exe) ^|^|###############
+echo ###############^|^| Build Inference Engine samples using cmake ^|^|###############
 echo.
+
 CALL :delay 3
-echo "!MSBUILD_BIN!" Samples.sln /p:Configuration=Release /t:cpp_samples\benchmark_app /clp:ErrorsOnly /m
-"!MSBUILD_BIN!" Samples.sln /p:Configuration=Release /t:cpp_samples\benchmark_app /clp:ErrorsOnly /m
 
+echo cmake --build . --config Release --target benchmark_app
+cmake --build . --config Release --target benchmark_app
 if ERRORLEVEL 1 GOTO errorHandling
 
 CALL :delay 7
diff --git a/scripts/demo/run_sample_benchmark_app.sh b/scripts/demo/run_sample_benchmark_app.sh
index 9e273d6c2a515e..d7b1077afcca76 100755
--- a/scripts/demo/run_sample_benchmark_app.sh
+++ b/scripts/demo/run_sample_benchmark_app.sh
@@ -6,6 +6,7 @@
 echo -ne "\e[0;33mWARNING: If you get an error when running the sample in the Docker container, you may need to install additional packages. To do this, run the container as root (-u 0) and run install_openvino_dependencies.sh script. If you get a package-independent error, try setting additional parameters using -sample-options.\e[0m\n"
 
 ROOT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]-$0}" )" && pwd )"
+build_dir="$HOME/inference_engine_cpp_samples_build"
 
 . "$ROOT_DIR/utils.sh"
 
@@ -14,6 +15,7 @@ usage() {
     echo
     echo "Options:"
     echo "  -help                     Print help message"
+    echo "  -b BUILD_DIR              Specify the sample build directory"
     echo "  -d DEVICE                 Specify the target device to infer on; CPU, GPU, HDDL or MYRIAD are acceptable. Sample will look for a suitable plugin for device specified"
     echo "  -sample-options OPTIONS   Specify command line arguments for the sample"
     echo
@@ -30,6 +32,10 @@ do
 key="$1"
 
 case $key in
+    -b | --build_dir)
+    build_dir="$2/inference_engine_cpp_samples_build"
+    shift
+    ;;
     -h | -help | --help)
     usage
     ;;
@@ -58,9 +64,9 @@ target_precision="FP16"
 
 echo -ne "target_precision = ${target_precision}\n"
 
-models_path="$HOME/openvino_models/models"
-models_cache="$HOME/openvino_models/cache"
-irs_path="$HOME/openvino_models/ir"
+models_path="$build_dir/../openvino_models/models"
+models_cache="$build_dir/../openvino_models/cache"
+irs_path="$build_dir/../openvino_models/ir"
 
 model_name="squeezenet1.1"
 
@@ -163,7 +169,6 @@ if [ "$OS_PATH" == "x86_64" ]; then
 fi
 
 samples_path="${INTEL_OPENVINO_DIR}/samples/cpp"
-build_dir="$HOME/inference_engine_cpp_samples_build"
 binaries_dir="${build_dir}/${OS_PATH}/Release"
 
 if [ -e "$build_dir/CMakeCache.txt" ]; then
diff --git a/scripts/demo/run_sample_squeezenet.bat b/scripts/demo/run_sample_squeezenet.bat
index 293168cb9e710d..2ccb9e7c18b022 100644
--- a/scripts/demo/run_sample_squeezenet.bat
+++ b/scripts/demo/run_sample_squeezenet.bat
@@ -4,12 +4,18 @@
 @echo off
 setlocal enabledelayedexpansion
 
+set ROOT_DIR=%~dp0
+
 set TARGET=CPU
 set BUILD_FOLDER=%USERPROFILE%\Documents\Intel\OpenVINO
 
 :: command line arguments parsing
 :input_arguments_loop
 if not "%1"=="" (
+    if "%1"=="-b" (
+        set BUILD_FOLDER=%2
+        shift
+    )
     if "%1"=="-d" (
         set TARGET=%2
         echo target = !TARGET!
@@ -25,6 +31,7 @@ if not "%1"=="" (
         echo.
         echo Options:
         echo    -help                      Print help message
+        echo    -b BUILD_FOLDER            Specify the sample build directory
         echo    -d DEVICE                  Specify the target device to infer on; CPU, GPU, HDDL or MYRIAD are acceptable. Sample will look for a suitable plugin for device specified
         echo    -sample-options OPTIONS    Specify command line arguments for the sample
         exit /b
@@ -33,7 +40,7 @@ if not "%1"=="" (
     goto :input_arguments_loop
 )
 
-set ROOT_DIR=%~dp0
+set "SOLUTION_DIR64=%BUILD_FOLDER%\inference_engine_cpp_samples_build"
 
 set TARGET_PRECISION=FP16
 
@@ -154,81 +161,21 @@ if "%PROCESSOR_ARCHITECTURE%" == "AMD64" (
     set "PLATFORM=Win32"
 )
 
-set VSWHERE="false"
-if exist "%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" (
-    set VSWHERE="true"
-    cd /d "%ProgramFiles(x86)%\Microsoft Visual Studio\Installer"
-) else if exist "%ProgramFiles%\Microsoft Visual Studio\Installer\vswhere.exe" (
-    set VSWHERE="true"
-    cd /d "%ProgramFiles%\Microsoft Visual Studio\Installer"
-) else (
-    echo "vswhere tool is not found"
-)
-
-if !VSWHERE! == "true" (
-    for /f "usebackq tokens=*" %%i in (`vswhere -latest -products * -requires Microsoft.Component.MSBuild -property installationPath`) do (
-        set VS_PATH=%%i
-    )
-    if exist "!VS_PATH!\MSBuild\14.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=!VS_PATH!\MSBuild\14.0\Bin\MSBuild.exe"
-    )
-    if exist "!VS_PATH!\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=!VS_PATH!\MSBuild\15.0\Bin\MSBuild.exe"
-    )
-    if exist "!VS_PATH!\MSBuild\Current\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=!VS_PATH!\MSBuild\Current\Bin\MSBuild.exe"
-    )
-    for /f "usebackq tokens=1 delims=." %%i in (`vswhere -latest -products * -requires Microsoft.Component.MSBuild -property installationVersion`) do (
-        set VS_MAJOR_VER=%%i
-    )
-    if "!VS_MAJOR_VER!"=="16" set "MSBUILD_VERSION=16 2019"
-    if "!VS_MAJOR_VER!"=="15" set "MSBUILD_VERSION=15 2017"
-)
-
-if "!MSBUILD_BIN!" == "" (
-    if exist "C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\MSBuild\14.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=14 2015"
-    )
-    if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\MSBuild\15.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=15 2017"
-    )
-    if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=15 2017"
-    )
-    if exist "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\MSBuild\15.0\Bin\MSBuild.exe" (
-        set "MSBUILD_BIN=C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\MSBuild\15.0\Bin\MSBuild.exe"
-        set "MSBUILD_VERSION=15 2017"
-    )
-) else (
-    if not "!MSBUILD_BIN:2019=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=16 2019"
-    if not "!MSBUILD_BIN:2017=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=15 2017"
-    if not "!MSBUILD_BIN:2015=!"=="!MSBUILD_BIN!" set "MSBUILD_VERSION=14 2015"
-)
-
-if "!MSBUILD_BIN!" == "" (
-    echo Build tools for Visual Studio 2015 / 2017 / 2019 cannot be found. If you use Visual Studio 2017, please download and install build tools from https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017
-    GOTO errorHandling
-)
-
-set "SOLUTION_DIR64=%BUILD_FOLDER%\inference_engine_cpp_samples_build"
-
-echo Creating Visual Studio !MSBUILD_VERSION! %PLATFORM% files in %SOLUTION_DIR64%... && ^
 if exist "%SOLUTION_DIR64%\CMakeCache.txt" del "%SOLUTION_DIR64%\CMakeCache.txt"
-cd /d "%INTEL_OPENVINO_DIR%\samples\cpp" && cmake -E make_directory "%SOLUTION_DIR64%" && cd /d "%SOLUTION_DIR64%" && cmake -G "Visual Studio !MSBUILD_VERSION!" -A %PLATFORM% "%INTEL_OPENVINO_DIR%\samples\cpp"
+
+cd /d "%INTEL_OPENVINO_DIR%\samples\cpp" && cmake -E make_directory "%SOLUTION_DIR64%" && cd /d "%SOLUTION_DIR64%" && cmake -G "Visual Studio 16 2019" -A %PLATFORM% "%INTEL_OPENVINO_DIR%\samples\cpp"
 if ERRORLEVEL 1 GOTO errorHandling
 
 CALL :delay 7
 
 echo.
-echo ###############^|^| Build Inference Engine samples using MS Visual Studio (MSBuild.exe) ^|^|###############
+echo ###############^|^| Build Inference Engine samples using cmake ^|^|###############
 echo.
+
 CALL :delay 3
-echo "!MSBUILD_BIN!" Samples.sln /p:Configuration=Release /t:cpp_samples\classification_sample_async /clp:ErrorsOnly /m
-"!MSBUILD_BIN!" Samples.sln /p:Configuration=Release /t:cpp_samples\classification_sample_async /clp:ErrorsOnly /m
 
+echo cmake --build . --config Release --target classification_sample_async
+cmake --build . --config Release --target classification_sample_async
 if ERRORLEVEL 1 GOTO errorHandling
 
 CALL :delay 7
diff --git a/scripts/demo/run_sample_squeezenet.sh b/scripts/demo/run_sample_squeezenet.sh
index 3d1b12a1fec6fc..1ce3a61cb9d5c7 100755
--- a/scripts/demo/run_sample_squeezenet.sh
+++ b/scripts/demo/run_sample_squeezenet.sh
@@ -6,6 +6,7 @@
 echo -ne "\e[0;33mWARNING: If you get an error when running the sample in the Docker container, you may need to install additional packages. To do this, run the container as root (-u 0) and run install_openvino_dependencies.sh script. If you get a package-independent error, try setting additional parameters using -sample-options.\e[0m\n"
 
 ROOT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]-$0}" )" && pwd )"
+build_dir="$HOME/inference_engine_cpp_samples_build"
 
 . "$ROOT_DIR/utils.sh"
 
@@ -14,6 +15,7 @@ usage() {
     echo
     echo "Options:"
     echo "  -help                     Print help message"
+    echo "  -b BUILD_DIR              Specify the sample build directory"
     echo "  -d DEVICE                 Specify the target device to infer on; CPU, GPU, HDDL or MYRIAD are acceptable. Sample will look for a suitable plugin for device specified"
     echo "  -sample-options OPTIONS   Specify command line arguments for the sample"
     echo
@@ -30,6 +32,10 @@ do
 key="$1"
 
 case $key in
+    -b | --build_dir)
+    build_dir="$2/inference_engine_cpp_samples_build"
+    shift
+    ;;
     -h | -help | --help)
     usage
     ;;
@@ -54,9 +60,9 @@ target_precision="FP16"
 
 echo -ne "target_precision = ${target_precision}\n"
 
-models_path="$HOME/openvino_models/models"
-models_cache="$HOME/openvino_models/cache"
-irs_path="$HOME/openvino_models/ir"
+models_path="$build_dir/../openvino_models/models"
+models_cache="$build_dir/../openvino_models/cache"
+irs_path="$build_dir/../openvino_models/ir"
 
 model_name="squeezenet1.1"
 
diff --git a/tests/fuzz/src/import_pdpd-fuzzer.cc b/tests/fuzz/src/import_pdpd-fuzzer.cc
index b25338c4356f83..5d76b9974a4ffc 100644
--- a/tests/fuzz/src/import_pdpd-fuzzer.cc
+++ b/tests/fuzz/src/import_pdpd-fuzzer.cc
@@ -11,8 +11,8 @@ const char split_sequence[] = {'F', 'U', 'Z', 'Z', '_', 'N', 'E', 'X',
                                'T', '_', 'F', 'I', 'E', 'L', 'D'};
 const char *PDPD = "paddle";
 
-using namespace ngraph;
-using namespace ngraph::frontend;
+using namespace ov;
+using namespace ov::frontend;
 
 extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
   /// split input buffer to model and params
@@ -23,10 +23,10 @@ extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
   const void *params_buf = tokenizer.next(&params_size);
 
   try {
-    ngraph::frontend::FrontEndManager frontend_manager = FrontEndManager();
-    ngraph::frontend::FrontEnd::Ptr frontend =
+    ov::frontend::FrontEndManager frontend_manager = FrontEndManager();
+    ov::frontend::FrontEnd::Ptr frontend =
         frontend_manager.load_by_framework(PDPD);
-    ngraph::frontend::InputModel::Ptr input_model;
+    ov::frontend::InputModel::Ptr input_model;
     std::stringstream model;
     std::stringstream params;
     model << std::string((const char *)model_buf, model_size);
@@ -37,9 +37,9 @@ extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
       input_model = frontend->load(in_model, in_params);
     } else
       input_model = frontend->load(in_model);
-    std::shared_ptr<ngraph::Function> function = frontend->convert(input_model);
+    std::shared_ptr<ov::Function> function = frontend->convert(input_model);
   } catch (const std::exception&) {
     return 0;  // fail gracefully on expected exceptions
   }
   return 0;
-}
\ No newline at end of file
+}
diff --git a/tests/layer_tests/common/constants.py b/tests/layer_tests/common/constants.py
index cf570fc40c2d88..26e6942d791db7 100644
--- a/tests/layer_tests/common/constants.py
+++ b/tests/layer_tests/common/constants.py
@@ -4,14 +4,6 @@
 import os
 
 
-if 'MO_ROOT' in os.environ:
-    mo_bin = os.environ['MO_ROOT']
-    if not os.path.exists(mo_bin):
-        raise EnvironmentError(
-            "Environment variable MO_ROOT points to non existing path {}".format(mo_bin))
-else:
-    raise EnvironmentError("MO_ROOT variable is not set")
-
 if os.environ.get('OUTPUT_DIR') is not None:
     out_path = os.environ['OUTPUT_DIR']
 else:
diff --git a/tests/layer_tests/common/layer_test_class.py b/tests/layer_tests/common/layer_test_class.py
index c6e51d2c1f86dd..d501f535e94037 100644
--- a/tests/layer_tests/common/layer_test_class.py
+++ b/tests/layer_tests/common/layer_test_class.py
@@ -24,14 +24,15 @@ def produce_model_path(self, framework_model, save_path):
     def get_framework_results(self, inputs_dict, model_path):
         pass
 
-    def _test(self, framework_model, ref_net, ie_device, precision, ir_version, temp_dir, infer_timeout=60,
-              enabled_transforms='', disabled_transforms='', **kwargs):
+    def _test(self, framework_model, ref_net, ie_device, precision, ir_version, temp_dir, use_new_frontend=False,
+              infer_timeout=60, enabled_transforms='', disabled_transforms='', **kwargs):
         """
         :param enabled_transforms/disabled_transforms: string with idxs of transforms that should be enabled/disabled.
                                                        Example: "transform_1,transform_2"
         """
         model_path = self.produce_model_path(framework_model=framework_model, save_path=temp_dir)
 
+        self.use_new_frontend = use_new_frontend
         # TODO Pass environment variables via subprocess environment
         os.environ['MO_ENABLED_TRANSFORMS'] = enabled_transforms
         os.environ['MO_DISABLED_TRANSFORMS'] = disabled_transforms
@@ -50,6 +51,9 @@ def _test(self, framework_model, ref_net, ie_device, precision, ir_version, temp
         if 'input_names' in kwargs and len(kwargs['input_names']):
             mo_params.update(dict(input=','.join(kwargs['input_names'])))
 
+        if use_new_frontend:
+            mo_params["use_new_frontend"] = True
+
         exit_code, stderr = generate_ir(**mo_params)
 
         del os.environ['MO_ENABLED_TRANSFORMS']
diff --git a/tests/layer_tests/common/tf_layer_test_class.py b/tests/layer_tests/common/tf_layer_test_class.py
index ab19c29e9701f8..18ce17913d2f1f 100644
--- a/tests/layer_tests/common/tf_layer_test_class.py
+++ b/tests/layer_tests/common/tf_layer_test_class.py
@@ -4,10 +4,33 @@
 import os
 
 from common.layer_test_class import CommonLayerTest
-
 from common.utils.tf_utils import summarize_graph
 
 
+def transpose_nchw_to_nhwc(data, use_new_frontend):
+    if use_new_frontend:
+        return data
+
+    if len(data.shape) == 4:  # reshaping for 4D tensors
+        return data.transpose(0, 2, 3, 1)
+    elif len(data.shape) == 5:  # reshaping for 5D tensors
+        return data.transpose(0, 2, 3, 4, 1)
+    else:
+        return data
+
+
+def transpose_nhwc_to_nchw(data, use_new_frontend):
+    if use_new_frontend:
+        return data
+
+    if len(data.shape) == 4:  # reshaping for 4D tensors
+        return data.transpose(0, 3, 1, 2)  # 2, 0, 1
+    elif len(data.shape) == 5:  # reshaping for 5D tensors
+        return data.transpose(0, 4, 1, 2, 3)  # 3, 0, 1, 2
+    else:
+        return data
+
+
 def save_to_pb(tf_model, path_to_saved_tf_model):
     import tensorflow as tf
     tf.io.write_graph(tf_model, path_to_saved_tf_model, 'model.pb', False)
@@ -41,21 +64,11 @@ def get_framework_results(self, inputs_dict, model_path):
                 input = dict()
                 for key in inputs_dict.keys():
                     data = inputs_dict.get(key)
-                    if len(data.shape) == 4:        # reshaping for 4D tensors
-                        input[key+':0'] = data.transpose(0, 2, 3, 1)
-                    elif len(data.shape) == 5:      # reshaping for 5D tensors
-                        input[key+':0'] = data.transpose(0, 2, 3, 4, 1)
-                    else:
-                        input[key+':0'] = data
+                    input[key + ':0'] = transpose_nchw_to_nhwc(data, self.use_new_frontend)
                 tf_res = sess.run([out + ":0" for out in outputs_list], input)
 
                 result = dict()
                 for i, output in enumerate(outputs_list):
                     _tf_res = tf_res[i]
-                    if len(_tf_res.shape) == 4:      # reshaping for 4D tensors
-                        result[output] = _tf_res.transpose(0, 3, 1, 2) # 2, 0, 1
-                    elif len(_tf_res.shape) == 5:    # reshaping for 5D tensors
-                        result[output] = _tf_res.transpose(0, 4, 1, 2, 3)  # 3, 0, 1, 2
-                    else:
-                        result[output] = _tf_res
+                    result[output] = transpose_nhwc_to_nchw(_tf_res, self.use_new_frontend)
                 return result
diff --git a/tests/layer_tests/common/utils/common_utils.py b/tests/layer_tests/common/utils/common_utils.py
index f92daf51c26bcc..e5fc5d93f3773b 100644
--- a/tests/layer_tests/common/utils/common_utils.py
+++ b/tests/layer_tests/common/utils/common_utils.py
@@ -2,24 +2,24 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import logging
-import os
 import subprocess
 import sys
+from pathlib import Path
 
+import mo
 import numpy as np
 
-
 logger = logging.getLogger(__name__)
 
 
 def generate_ir(coverage=False, **kwargs):
-    # Get default mo args
-    mo = os.path.join(os.environ.get("MO_ROOT"), "mo.py")
+    mo_path = Path(mo.__file__).parent
+    mo_runner = mo_path.joinpath('main.py').as_posix()
     if coverage:
-        params = [sys.executable, '-m', 'coverage', 'run', '-p', '--source={}'.format(os.environ.get("MO_ROOT")),
-                  '--omit=*_test.py', mo]
+        params = [sys.executable, '-m', 'coverage', 'run', '-p', '--source={}'.format(mo_path.parent),
+                  '--omit=*_test.py', mo_runner]
     else:
-        params = [sys.executable, mo]
+        params = [sys.executable, mo_runner]
     for key, value in kwargs.items():
         if key == "batch":
             params.extend(("-b", str(value)))
diff --git a/tests/layer_tests/common/utils/tf_utils.py b/tests/layer_tests/common/utils/tf_utils.py
index 7900586390cc1d..cae4cb0f17d039 100644
--- a/tests/layer_tests/common/utils/tf_utils.py
+++ b/tests/layer_tests/common/utils/tf_utils.py
@@ -7,6 +7,7 @@
 import tensorflow as tf
 import numpy as np
 
+from mo.ops.op import PermuteAttrs
 
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
@@ -107,3 +108,23 @@ def summarize_graph(model_path, output_nodes_for_freeze=None, reshape_net=None):
                 result['inputs'][layer]['shape'] = scoring_res[layer].shape
 
     return result
+
+
+def permute_nhwc_to_nchw(shape, use_new_frontend=False):
+    if use_new_frontend:
+        return shape
+    perm = PermuteAttrs.get_nhwc_to_nchw_permutation(len(shape)).perm
+    new_shape = np.array(shape)[perm]
+    return new_shape
+
+
+def permute_nchw_to_nhwc(shape, use_new_frontend=False):
+    if use_new_frontend:
+        return shape
+    perm = PermuteAttrs.get_nchw_to_nhwc_permutation(len(shape)).perm
+    new_shape = np.array(shape)[perm]
+    return new_shape
+
+
+def permute_axis(axis, permutation_inv):
+    return permutation_inv[axis]
diff --git a/tests/layer_tests/conftest.py b/tests/layer_tests/conftest.py
index 350fd22a3b20f5..94b3deb5c1e039 100644
--- a/tests/layer_tests/conftest.py
+++ b/tests/layer_tests/conftest.py
@@ -59,6 +59,11 @@ def pytest_addoption(parser):
         required=True,
         action="store",
         help="Version of IR to generate by Model Optimizer")
+    parser.addoption(
+        "--use_new_frontend",
+        required=False,
+        action="store_true",
+        help="Use Model Optimizer with new FrontEnd")
 
 
 @pytest.fixture(scope="session")
@@ -67,6 +72,12 @@ def ir_version(request):
     return request.config.getoption('ir_version')
 
 
+@pytest.fixture(scope="session")
+def use_new_frontend(request):
+    """Fixture function for command-line option."""
+    return request.config.getoption('use_new_frontend')
+
+
 @pytest.fixture(scope="function")
 def temp_dir(request):
     """Create directory for test purposes."""
diff --git a/tests/layer_tests/tensorflow_tests/permutation_utils.py b/tests/layer_tests/tensorflow_tests/permutation_utils.py
deleted file mode 100644
index c26c7b690f72ec..00000000000000
--- a/tests/layer_tests/tensorflow_tests/permutation_utils.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import numpy as np
-
-from mo.ops.op import PermuteAttrs
-
-
-def permute_nhwc_to_nchw(shape):
-    perm = PermuteAttrs.get_nhwc_to_nchw_permutation(len(shape)).perm
-    new_shape = np.array(shape)[perm]
-    return new_shape
-
-
-def permute_nchw_to_nhwc(shape):
-    perm = PermuteAttrs.get_nchw_to_nhwc_permutation(len(shape)).perm
-    new_shape = np.array(shape)[perm]
-    return new_shape
-
-
-def permute_axis(axis, permutation_inv):
-    return permutation_inv[axis]
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Add.py b/tests/layer_tests/tensorflow_tests/test_tf_Add.py
index 46508f610d7a6f..c629841b3804c0 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Add.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Add.py
@@ -5,10 +5,11 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestAdd(CommonTFLayerTest):
-    def create_add_placeholder_const_net(self, x_shape, y_shape, ir_version):
+    def create_add_placeholder_const_net(self, x_shape, y_shape, ir_version, use_new_frontend):
         """
             Tensorflow net                  IR net
 
@@ -30,11 +31,9 @@ def create_add_placeholder_const_net(self, x_shape, y_shape, ir_version):
         with tf.compat.v1.Session() as sess:
             tf_x_shape = x_shape.copy()
             tf_y_shape = y_shape.copy()
-            # reshaping
-            if len(tf_x_shape) >= 3:
-                tf_x_shape.append(tf_x_shape.pop(1))
-            if len(tf_y_shape) >= 3:
-                tf_y_shape.append(tf_y_shape.pop(1))
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            tf_y_shape = permute_nchw_to_nhwc(tf_y_shape, use_new_frontend)
 
             x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
             constant_value = np.random.randint(-256, 256, tf_y_shape).astype(np.float32)
@@ -55,23 +54,6 @@ def create_add_placeholder_const_net(self, x_shape, y_shape, ir_version):
         #   Moreover, do not forget to validate ALL layer attributes!!!
         #
 
-        if len(add_shape) >= 3:
-            # Permute add_shape to (N,C,...) format
-            order = [0, len(add_shape) - 1] + list(range(1, len(add_shape) - 1))
-            add_shape = [add_shape[i] for i in order]
-
-        y_shape_to_compare = tf_y_shape.copy()
-        while len(y_shape_to_compare) < len(x_shape):
-            # Expand shape of constant with 1
-            y_shape_to_compare = [1] + y_shape_to_compare
-            constant_value = np.expand_dims(constant_value, axis=0)
-
-        if len(y_shape_to_compare) >= 3:
-            # Permute constant_value to (N,C,...) format for correct further reshape
-            order = [0, len(y_shape_to_compare) - 1] + list(range(1, len(y_shape_to_compare) - 1))
-            y_shape_to_compare = [y_shape_to_compare[i] for i in order]
-            constant_value = np.transpose(constant_value, order)
-
         ref_net = None
 
         return tf_net, ref_net
@@ -87,9 +69,9 @@ def create_add_placeholder_const_net(self, x_shape, y_shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [
         # Power
@@ -104,9 +86,9 @@ def test_add_placeholder_const_1D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         # Power
@@ -122,9 +104,9 @@ def test_add_placeholder_const_2D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         # Power
@@ -140,9 +122,9 @@ def test_add_placeholder_const_3D(self, params, ie_device, precision, ir_version
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         # Power
@@ -159,9 +141,9 @@ def test_add_placeholder_const_4D(self, params, ie_device, precision, ir_version
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     ###############################################################################################
     #                                                                                             #
@@ -176,9 +158,9 @@ def test_add_placeholder_const_5D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_broadcast_1D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_broadcast_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_broadcast_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_2D = [
         # Power
@@ -195,9 +177,9 @@ def test_add_placeholder_const_broadcast_1D(self, params, ie_device, precision,
 
     @pytest.mark.parametrize("params", test_data_broadcast_2D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_broadcast_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_broadcast_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_3D = [
         # Power
@@ -218,9 +200,9 @@ def test_add_placeholder_const_broadcast_2D(self, params, ie_device, precision,
 
     @pytest.mark.parametrize("params", test_data_broadcast_3D)
     @pytest.mark.nightly
-    def test_add_placeholder_const_broadcast_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_broadcast_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_4D = [
         # Power
@@ -236,9 +218,9 @@ def test_add_placeholder_const_broadcast_3D(self, params, ie_device, precision,
         # Eltwise
         dict(x_shape=[1, 3, 1, 1], y_shape=[3, 1]),
         # Eltwise
-        dict(x_shape=[1, 3, 1, 2], y_shape=[3, 1, 2]),
+        dict(x_shape=[1, 2, 1, 3], y_shape=[3, 1, 2]),
         # Eltwise
-        dict(x_shape=[1, 3, 1, 2], y_shape=[1, 3, 2]),
+        dict(x_shape=[1, 2, 1, 3], y_shape=[1, 3, 2]),
         # Eltwise
         dict(x_shape=[1, 3, 100, 224], y_shape=[1, 1, 1, 224]),
         # Eltwise
@@ -248,9 +230,9 @@ def test_add_placeholder_const_broadcast_3D(self, params, ie_device, precision,
     @pytest.mark.parametrize("params", test_data_broadcast_4D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_add_placeholder_const_broadcast_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_broadcast_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version=ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_5D = [
         # Power
@@ -264,7 +246,7 @@ def test_add_placeholder_const_broadcast_4D(self, params, ie_device, precision,
         # Eltwise
         dict(x_shape=[1, 3, 1, 1, 1], y_shape=[3, 1]),
         # Eltwise
-        dict(x_shape=[1, 3, 1, 1, 2], y_shape=[1, 3, 2]),
+        dict(x_shape=[1, 2, 1, 1, 3], y_shape=[1, 3, 2]),
         # Eltwise
         dict(x_shape=[1, 3, 5, 1, 2], y_shape=[5, 3, 2, 1]),
         # Eltwise
@@ -276,6 +258,7 @@ def test_add_placeholder_const_broadcast_4D(self, params, ie_device, precision,
     @pytest.mark.parametrize("params", test_data_broadcast_5D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_add_placeholder_const_broadcast_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version), ie_device, precision,
-                   ir_version=ir_version, temp_dir=temp_dir)
+    def test_add_placeholder_const_broadcast_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        # we do not perform transpose in the test in case of new frontend
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend), ie_device, precision,
+                   ir_version=ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_BatchToSpace.py b/tests/layer_tests/tensorflow_tests/test_tf_BatchToSpace.py
index f7d46dfcef4dba..c07727dbd7dd9b 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_BatchToSpace.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_BatchToSpace.py
@@ -7,7 +7,7 @@
 
 
 class TestBatchToSpace(CommonTFLayerTest):
-    def create_batch_to_space_net(self, in_shape, crops_value, block_shape_value, out_shape, ir_version):
+    def create_batch_to_space_net(self, in_shape, crops_value, block_shape_value, out_shape, ir_version, use_new_frontend):
         """
             Tensorflow net               IR net
 
@@ -28,7 +28,7 @@ def create_batch_to_space_net(self, in_shape, crops_value, block_shape_value, ou
             x = tf.compat.v1.placeholder(tf.float32, in_shape, 'Input')
             crops = tf.constant(crops_value)
             block_shape = tf.constant(block_shape_value)
-            tf.batch_to_space(x, block_shape, crops, name='Operation')
+            tf.batch_to_space_nd(x, block_shape, crops, name='Operation')
 
             tf.compat.v1.global_variables_initializer()
             tf_net = sess.graph_def
@@ -61,9 +61,9 @@ def create_batch_to_space_net(self, in_shape, crops_value, block_shape_value, ou
 
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_batch_to_space_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_batch_to_space_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_batch_to_space_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_batch_to_space_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         dict(in_shape=[72, 2, 1, 4, 2], block_shape_value=[3, 4, 2], crops_value=[[1, 2], [0, 0], [3, 0]],
@@ -75,6 +75,6 @@ def test_batch_to_space_4D(self, params, ie_device, precision, ir_version, temp_
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_batch_to_space_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_batch_to_space_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_batch_to_space_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_batch_to_space_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_BiasAdd.py b/tests/layer_tests/tensorflow_tests/test_tf_BiasAdd.py
index b7e997195f58ba..938702142f7f32 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_BiasAdd.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_BiasAdd.py
@@ -4,10 +4,11 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestBiasAdd(CommonTFLayerTest):
-    def create_bias_add_placeholder_const_net(self, shape, ir_version):
+    def create_bias_add_placeholder_const_net(self, shape, ir_version, use_new_frontend):
         """
             Tensorflow net                      IR net
 
@@ -29,9 +30,8 @@ def create_bias_add_placeholder_const_net(self, shape, ir_version):
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
             tf_x_shape = shape.copy()
-            # reshaping
-            if len(tf_x_shape) >= 3:
-                tf_x_shape.append(tf_x_shape.pop(1))
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
             tf_y_shape = tf_x_shape[-1:]
 
             x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
@@ -56,7 +56,7 @@ def create_bias_add_placeholder_const_net(self, shape, ir_version):
 
         return tf_net, ref_net
 
-    def create_bias_add_2_consts_net(self, shape, ir_version):
+    def create_bias_add_2_consts_net(self, shape, ir_version, use_new_frontend):
         """
             Tensorflow net                         IR net
 
@@ -82,9 +82,8 @@ def create_bias_add_2_consts_net(self, shape, ir_version):
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
             tf_x_shape = shape.copy()
-            # reshaping
-            if len(tf_x_shape) >= 3:
-                tf_x_shape.append(tf_x_shape.pop(1))
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
             tf_y_shape = tf_x_shape[-1:]
 
             constant_value_x = np.random.randint(-256, 256, tf_x_shape).astype(np.float32)
@@ -93,13 +92,10 @@ def create_bias_add_2_consts_net(self, shape, ir_version):
             y = tf.constant(constant_value_y)
 
             add = tf.nn.bias_add(x, y, name="Operation")
-            add_shape = add.shape.as_list()
-            add_value = add.eval()
 
             placeholder = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')  # Input_1 in graph_def
 
             concat = tf.concat([placeholder, add], axis=tf_concat_axis, name='Operation')
-            concat_shape = concat.shape.as_list()
 
             tf.compat.v1.global_variables_initializer()
             tf_net = sess.graph_def
@@ -110,15 +106,6 @@ def create_bias_add_2_consts_net(self, shape, ir_version):
         #   Moreover, do not forget to validate ALL layer attributes!!!
         #
 
-        # Format axis to positive value
-        concat_ax = tf_concat_axis if tf_concat_axis >= 0 else tf_concat_axis + len(shape)
-        if len(shape) >= 3:
-            # Permute shapes to (N,C,...) format
-            order = [0, len(concat_shape) - 1] + list(range(1, len(concat_shape) - 1))
-            concat_shape = [concat_shape[i] for i in order]
-            concat_ax = order.index(concat_ax)
-            add_value = np.transpose(add_value, order)
-
         ref_net = None
 
         return tf_net, ref_net
@@ -130,15 +117,15 @@ def create_bias_add_2_consts_net(self, shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_bias_add_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_bias_add_2_consts_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_2_consts_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         pytest.param(dict(shape=[1, 1, 224]), marks=pytest.mark.xfail(reason="*-19053")),
@@ -147,15 +134,15 @@ def test_bias_add_2_consts_2D(self, params, ie_device, precision, ir_version, te
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_bias_add_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_bias_add_2_consts_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_2_consts_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         dict(shape=[1, 1, 100, 224]),
@@ -165,15 +152,15 @@ def test_bias_add_2_consts_3D(self, params, ie_device, precision, ir_version, te
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_bias_add_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_bias_add_2_consts_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_2_consts_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         dict(shape=[1, 1, 50, 100, 224]),
@@ -183,12 +170,12 @@ def test_bias_add_2_consts_4D(self, params, ie_device, precision, ir_version, te
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_bias_add_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_bias_add_2_consts_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bias_add_2_consts_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_BinaryOps.py b/tests/layer_tests/tensorflow_tests/test_tf_BinaryOps.py
new file mode 100644
index 00000000000000..5625ffe0aa1ffd
--- /dev/null
+++ b/tests/layer_tests/tensorflow_tests/test_tf_BinaryOps.py
@@ -0,0 +1,129 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
+
+
+def generate_input(op_type, size):
+    narrow_borders = ["Pow"]
+
+    logical_type = ['LogicalAnd', 'LogicalOr', 'LogicalXor']
+
+    # usual function domain
+    lower = -256
+    upper = 256
+
+    # specific domains
+    if op_type in narrow_borders:
+        lower = 0
+        upper = 16
+
+    if op_type in logical_type:
+        return np.random.randint(0, 1, size).astype(np.bool)
+    elif op_type in narrow_borders:
+        return np.random.uniform(lower, upper, size).astype(np.float32)
+    else:
+        return np.random.uniform(lower, upper, size).astype(np.float32)
+
+
+class TestBinaryOps(CommonTFLayerTest):
+    def _prepare_input(self, inputs_dict):
+        for input in inputs_dict.keys():
+            inputs_dict[input] = generate_input(self.current_op_type, inputs_dict[input])
+        return inputs_dict
+
+    def create_add_placeholder_const_net(self, x_shape, y_shape, ir_version, op_type, use_new_frontend):
+        """
+            Tensorflow net                  IR net
+
+            Placeholder->BinaryOp       =>       Placeholder->Eltwise or Power or ScaleShift
+                         /                                     /
+            Const-------/                         Const-------/
+
+        """
+
+        self.current_op_type = op_type
+
+        #
+        #   Create Tensorflow model
+        #
+        import tensorflow as tf
+
+        op_type_to_tf = {
+            'Add': tf.math.add,
+            'Sub': tf.math.subtract,
+            'Mul': tf.math.multiply,
+            'Div': tf.math.divide,
+            'RealDiv': tf.realdiv,
+            'SquaredDifference': tf.math.squared_difference,
+            'Pow': tf.math.pow,
+            'Maximum': tf.math.maximum,
+            'Minimum': tf.math.minimum,
+            'Equal': tf.math.equal,
+            'NotEqual': tf.math.not_equal,
+            'Mod': tf.math.mod,
+            'Greater': tf.math.greater,
+            'GreaterEqual': tf.math.greater_equal,
+            'Less': tf.math.less,
+            'LessEqual': tf.math.less_equal,
+            'LogicalAnd': tf.math.logical_and,
+            'LogicalOr': tf.math.logical_or,
+            'LogicalXor': tf.math.logical_xor,
+            'FloorMod': tf.math.floormod,
+        }
+
+        type = np.float32
+        if op_type in ["LogicalAnd", "LogicalOr", "LogicalXor"]:
+            type = np.bool
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            tf_x_shape = x_shape.copy()
+            tf_y_shape = y_shape.copy()
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            tf_y_shape = permute_nchw_to_nhwc(tf_y_shape, use_new_frontend)
+
+            x = tf.compat.v1.placeholder(type, tf_x_shape, 'Input')
+            constant_value = generate_input(op_type, tf_y_shape)
+            if (constant_value == 0).all():
+                # Avoid elimination of the layer from IR
+                constant_value = constant_value + 1
+            y = tf.constant(constant_value, dtype=type)
+
+            op = op_type_to_tf[op_type](x, y, name="Operation")
+
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        #
+        #   Create reference IR net
+        #   Please, specify 'type': 'Input' for input node
+        #   Moreover, do not forget to validate ALL layer attributes!!!
+        #
+
+        ref_net = None
+
+        return tf_net, ref_net
+
+    test_data_precommits = [dict(x_shape=[2, 3, 4], y_shape=[2, 3, 4]),
+                            dict(x_shape=[2, 3, 4, 5], y_shape=[2, 3, 4, 5])]
+
+    @pytest.mark.parametrize("params", test_data_precommits)
+    @pytest.mark.parametrize("op_type",
+                             ['Add', 'Sub', 'Mul', 'Div', 'RealDiv', 'SquaredDifference', 'Pow', 'Maximum', 'Minimum',
+                                 'Equal', 'NotEqual', 'Mod', 'Greater', 'GreaterEqual', 'Less', 'LessEqual',
+                                 'LogicalAnd', 'LogicalOr', 'LogicalXor', 'FloorMod'])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_binary_op(self, params, ie_device, precision, ir_version, temp_dir, op_type, use_new_frontend):
+        if ie_device == 'GPU' and precision == "FP16":
+            pytest.skip("BinaryOps tests temporary skipped on GPU with FP16 precision."
+                        "Several tests don't pass accuracy checks.")
+        self._test(*self.create_add_placeholder_const_net(**params, ir_version=ir_version, op_type=op_type,
+                                                          use_new_frontend=use_new_frontend), ie_device, precision,
+                   ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Bucketize.py b/tests/layer_tests/tensorflow_tests/test_tf_Bucketize.py
index 10d0bc6de74386..3a1d6725dee98b 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Bucketize.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Bucketize.py
@@ -10,7 +10,7 @@
 
 
 class TestBucketize(CommonTFLayerTest):
-    def create_bucketize_net(self, input_shape, input_type, boundaries_size, ir_version):
+    def create_bucketize_net(self, input_shape, input_type, boundaries_size, ir_version, use_new_frontend):
         """
             Tensorflow net:                     IR net:
                  Input            =>      Input        Boundaries
@@ -30,7 +30,7 @@ def create_bucketize_net(self, input_shape, input_type, boundaries_size, ir_vers
         # create reference IR net
         ref_net = None
 
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             nodes_attributes = {
                 'input': {'kind': 'op', 'type': 'Parameter'},
                 'input_data': {'shape': input_shape, 'kind': 'data'},
@@ -64,9 +64,9 @@ def create_bucketize_net(self, input_shape, input_type, boundaries_size, ir_vers
 
     @pytest.mark.parametrize("params", test_data_float32)
     @pytest.mark.nightly
-    def test_bucketize_float32(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bucketize_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bucketize_float32(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bucketize_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_int32 = [
         dict(input_shape=[5], input_type=tf.int32, boundaries_size=1),
@@ -78,6 +78,6 @@ def test_bucketize_float32(self, params, ie_device, precision, ir_version, temp_
 
     @pytest.mark.parametrize("params", test_data_int32)
     @pytest.mark.nightly
-    def test_bucketize_int32(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_bucketize_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_bucketize_int32(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_bucketize_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Ceil.py b/tests/layer_tests/tensorflow_tests/test_tf_Ceil.py
deleted file mode 100644
index c516a598b88f64..00000000000000
--- a/tests/layer_tests/tensorflow_tests/test_tf_Ceil.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import pytest
-
-from common.layer_test_class import check_ir_version
-from common.tf_layer_test_class import CommonTFLayerTest
-from unit_tests.utils.graph import build_graph
-
-
-class TestCeil(CommonTFLayerTest):
-    def create_ceil_net(self, shape, ir_version):
-        """
-            Tensorflow net                 IR net
-
-            Input->Ceil       =>       Input->Ceil
-
-        """
-
-        #
-        #   Create Tensorflow model
-        #
-
-        import tensorflow as tf
-
-        tf.compat.v1.reset_default_graph()
-
-        # Create the graph and model
-        with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 3:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
-
-            tf.math.ceil(input, name='Operation')
-
-            tf.compat.v1.global_variables_initializer()
-            tf_net = sess.graph_def
-
-        ref_net = None
-
-        if check_ir_version(10, None, ir_version):
-            nodes_attributes = {
-                'input': {'kind': 'op', 'type': 'Parameter'},
-                'input_data': {'shape': shape, 'kind': 'data'},
-                'Ceiling': {'kind': 'op', 'type': 'Ceiling'},
-                'Ceiling_data': {'shape': shape, 'kind': 'data'},
-                'result': {'kind': 'op', 'type': 'Result'}
-            }
-
-            ref_net = build_graph(nodes_attributes,
-                                  [('input', 'input_data'),
-                                   ('input_data', 'Ceiling'),
-                                   ('Ceiling', 'Ceiling_data'),
-                                   ('Ceiling_data', 'result')
-                                   ])
-
-        return tf_net, ref_net
-
-    test_data_precommit = [dict(shape=[3, 2, 3, 7, 6])]
-
-    @pytest.mark.parametrize("params", test_data_precommit)
-    @pytest.mark.precommit
-    def test_ceil_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_ceil_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
-
-    test_data = [dict(shape=[1]),
-                 dict(shape=[2, 5]),
-                 dict(shape=[5, 3, 7, 4]),
-                 dict(shape=[3, 2, 3, 7, 6])]
-
-    @pytest.mark.parametrize("params", test_data)
-    @pytest.mark.nightly
-    def test_ceil(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_ceil_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Concat.py b/tests/layer_tests/tensorflow_tests/test_tf_Concat.py
index cfa6ca0e958c35..2fb5f54e84bc03 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Concat.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Concat.py
@@ -4,10 +4,10 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
-
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 class TestConcat(CommonTFLayerTest):
-    def create_concat_net(self, shape, axis, ir_version):
+    def create_concat_net(self, shape, axis, ir_version, use_new_frontend):
         """
             Tensorflow net               IR net
 
@@ -28,15 +28,14 @@ def create_concat_net(self, shape, axis, ir_version):
 
             ax = axis
 
-            input_shape_x = shape.copy()
-            # reshaping
-            if len(input_shape_x) >= 3:
-                input_shape_x.append(input_shape_x.pop(1))
+            tf_x_shape = shape.copy()
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
 
             # TODO: add concat with const inputs to check fusing (as in ONNX)
 
-            x = tf.compat.v1.placeholder(tf.float32, input_shape_x, 'Input')
-            y = tf.compat.v1.placeholder(tf.float32, input_shape_x, 'Input')  # Input_1 in graph_def
+            x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
+            y = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')  # Input_1 in graph_def
 
             concat = tf.concat([x, y], axis=ax, name='Operation')
             concat_shape = concat.shape.as_list()
@@ -50,14 +49,6 @@ def create_concat_net(self, shape, axis, ir_version):
         #   Moreover, do not forget to validate ALL layer attributes!!!
         #
 
-        # Format axis to positive value
-        concat_ax = axis if axis >= 0 else axis + len(shape)
-        if len(shape) >= 3:
-            # Permute shape to (N,C,...) format and compute correct axis value
-            order = [0, len(concat_shape) - 1] + list(range(1, len(concat_shape) - 1))
-            concat_shape = [concat_shape[i] for i in order]
-            concat_ax = order.index(concat_ax)
-
         ref_net = None
 
         return tf_net, ref_net
@@ -69,18 +60,18 @@ def create_concat_net(self, shape, axis, ir_version):
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_concat_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_concat_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_concat_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_concat_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [dict(shape=[1, 224], axis=0),
                     dict(shape=[1, 224], axis=-1)]
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_concat_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_concat_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_concat_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_concat_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [pytest.param(dict(shape=[1, 3, 224], axis=0), marks=pytest.mark.xfail(reason="*-19053")),
                     pytest.param(dict(shape=[1, 3, 224], axis=-1), marks=pytest.mark.xfail(reason="*-19053")),
@@ -88,9 +79,9 @@ def test_concat_2D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_concat_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_concat_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_concat_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_concat_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [dict(shape=[1, 3, 100, 224], axis=0),
                     dict(shape=[1, 3, 100, 224], axis=-1),
@@ -99,9 +90,9 @@ def test_concat_3D(self, params, ie_device, precision, ir_version, temp_dir):
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_concat_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_concat_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_concat_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_concat_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [dict(shape=[1, 3, 50, 100, 224], axis=0),
                     dict(shape=[1, 3, 50, 100, 224], axis=-1),
@@ -109,6 +100,6 @@ def test_concat_4D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_concat_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_concat_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_concat_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_concat_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_ELU.py b/tests/layer_tests/tensorflow_tests/test_tf_ELU.py
deleted file mode 100644
index 7e183cf9c3438d..00000000000000
--- a/tests/layer_tests/tensorflow_tests/test_tf_ELU.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import pytest
-from common.layer_test_class import check_ir_version
-from common.tf_layer_test_class import CommonTFLayerTest
-from unit_tests.utils.graph import build_graph
-
-
-class TestELU(CommonTFLayerTest):
-    def create_elu_net(self, shape, ir_version):
-        """
-            Tensorflow net                 IR net
-
-            Input->ELU       =>       Input->ELU
-
-        """
-
-        #
-        #   Create Tensorflow model
-        #
-
-        import tensorflow as tf
-
-        tf.compat.v1.reset_default_graph()
-
-        # Create the graph and model
-        with tf.compat.v1.Session() as sess:
-
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 4:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
-
-            tf.nn.elu(input, name='Operation')
-
-            tf.compat.v1.global_variables_initializer()
-            tf_net = sess.graph_def
-
-        #
-        #   Create reference IR net
-        #   Please, specify 'type': 'Input' for input node
-        #   Moreover, do not forget to validate ALL layer attributes!!!
-        #
-
-        ref_net = None
-
-        if check_ir_version(10, None, ir_version):
-            nodes_attributes = {
-                'input': {'kind': 'op', 'type': 'Parameter'},
-                'input_data': {'shape': shape, 'kind': 'data'},
-                'ELU': {'kind': 'op', 'type': 'Elu'},
-                'ELU_data': {'shape': shape, 'kind': 'data'},
-                'result': {'kind': 'op', 'type': 'Result'}
-            }
-
-            ref_net = build_graph(nodes_attributes,
-                                  [('input', 'input_data'),
-                                   ('input_data', 'ELU'),
-                                   ('ELU', 'ELU_data'),
-                                   ('ELU_data', 'result')
-                                   ])
-
-        return tf_net, ref_net
-
-    test_data_precommit = [dict(shape=[4, 6, 8, 10, 12])]
-
-    @pytest.mark.parametrize("params", test_data_precommit)
-    @pytest.mark.precommit
-    def test_elu_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        if ie_device == 'GPU':
-            pytest.skip("5D tensors is not supported on GPU")
-        self._test(*self.create_elu_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
-
-    test_data = [dict(shape=[10, 12]),
-                 dict(shape=[8, 10, 12]),
-                 dict(shape=[6, 8, 10, 12]),
-                 dict(shape=[4, 6, 8, 10, 12])]
-
-    @pytest.mark.parametrize("params", test_data)
-    @pytest.mark.nightly
-    def test_elu(self, params, ie_device, precision, ir_version, temp_dir):
-        if ie_device == 'GPU':
-            pytest.skip("5D tensors is not supported on GPU")
-        self._test(*self.create_elu_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Eltwise.py b/tests/layer_tests/tensorflow_tests/test_tf_Eltwise.py
index d6880f3e0a1e31..7277f044743f76 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Eltwise.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Eltwise.py
@@ -4,10 +4,11 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestEltwise(CommonTFLayerTest):
-    def create_eltwise_net(self, shape, operation, ir_version):
+    def create_eltwise_net(self, shape, operation, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -26,13 +27,12 @@ def create_eltwise_net(self, shape, operation, ir_version):
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
 
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 4:
-                shapes.append(shapes.pop(1))
+            tf_x_shape = shape.copy()
 
-            x = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
-            y = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')     # Input_1 in graph_def
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+
+            x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
+            y = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')     # Input_1 in graph_def
 
             if operation == 'sum':
                 tf.add(x, y, name='Operation')
@@ -62,9 +62,9 @@ def create_eltwise_net(self, shape, operation, ir_version):
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_eltwise(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_eltwise_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_eltwise(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_eltwise_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = []
     for operation in ['sum', 'max', 'mul']:
@@ -72,8 +72,8 @@ def test_eltwise(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.precommit
-    def test_eltwise_5D_precommit(self, params, ie_device, precision, ir_version, temp_dir):
+    def test_eltwise_5D_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
         if ie_device == 'GPU':
             pytest.skip("5D tensors is not supported on GPU")
-        self._test(*self.create_eltwise_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+        self._test(*self.create_eltwise_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_FakeQuantize.py b/tests/layer_tests/tensorflow_tests/test_tf_FakeQuantize.py
index d4d8fe91b845cb..501fe70cedf81d 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_FakeQuantize.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_FakeQuantize.py
@@ -35,7 +35,7 @@ def _prepare_input(self, inputs_dict, kwargs):
             expected_nudged_input_max + expected_step
         ])}
 
-    def create_fake_quantize_net(self, il, ih, num_bits, narrow_range, nudged_il, nudged_ih, expected_step, ir_version):
+    def create_fake_quantize_net(self, il, ih, num_bits, narrow_range, nudged_il, nudged_ih, expected_step, ir_version, use_new_frontend):
         # original tf model
         import tensorflow as tf
         tf.compat.v1.reset_default_graph()
@@ -50,7 +50,7 @@ def create_fake_quantize_net(self, il, ih, num_bits, narrow_range, nudged_il, nu
 
         # reference graph to compare with IR
         ref_net = None
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             levels = 2 ** num_bits - int(narrow_range)
 
             # data (shape, value) -> const (shape, vale) -> data (shape, no value)
@@ -120,6 +120,6 @@ def create_fake_quantize_net(self, il, ih, num_bits, narrow_range, nudged_il, nu
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_fake_quantize(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_fake_quantize_net(**params, ir_version=ir_version), ie_device, precision, ir_version,
-                   kwargs_to_prepare_input=params, temp_dir=temp_dir)
+    def test_fake_quantize(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_fake_quantize_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend), ie_device, precision, ir_version,
+                   kwargs_to_prepare_input=params, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Floor.py b/tests/layer_tests/tensorflow_tests/test_tf_Floor.py
deleted file mode 100644
index 9c47f1f5910bcc..00000000000000
--- a/tests/layer_tests/tensorflow_tests/test_tf_Floor.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import pytest
-
-from common.layer_test_class import check_ir_version
-from common.tf_layer_test_class import CommonTFLayerTest
-from unit_tests.utils.graph import build_graph
-
-
-class TestFloor(CommonTFLayerTest):
-    def create_floor_net(self, shape, ir_version):
-        """
-            Tensorflow net                 IR net
-
-            Input->Floor       =>       Input->Floor
-
-        """
-
-        #
-        #   Create Tensorflow model
-        #
-
-        import tensorflow as tf
-
-        tf.compat.v1.reset_default_graph()
-
-        # Create the graph and model
-        with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 3:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
-
-            tf.floor(input, name='Operation')
-
-            tf.compat.v1.global_variables_initializer()
-            tf_net = sess.graph_def
-
-        ref_net = None
-
-        if check_ir_version(10, None, ir_version):
-            nodes_attributes = {
-                'input': {'kind': 'op', 'type': 'Parameter'},
-                'input_data': {'shape': shape, 'kind': 'data'},
-                'Floor': {'kind': 'op', 'type': 'Floor'},
-                'Floor_data': {'shape': shape, 'kind': 'data'},
-                'result': {'kind': 'op', 'type': 'Result'}
-            }
-
-            ref_net = build_graph(nodes_attributes,
-                                  [('input', 'input_data'),
-                                   ('input_data', 'Floor'),
-                                   ('Floor', 'Floor_data'),
-                                   ('Floor_data', 'result')
-                                   ])
-
-        return tf_net, ref_net
-
-    test_data_precommit = [dict(shape=[3, 2, 3, 7, 6])]
-
-    @pytest.mark.parametrize("params", test_data_precommit)
-    @pytest.mark.precommit
-    def test_floor_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_floor_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
-
-    test_data = [dict(shape=[1]),
-                 dict(shape=[2, 5]),
-                 dict(shape=[5, 3, 7, 4]),
-                 dict(shape=[3, 2, 3, 7, 6])]
-
-    @pytest.mark.parametrize("params", test_data)
-    @pytest.mark.nightly
-    def test_floor(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_floor_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Gather.py b/tests/layer_tests/tensorflow_tests/test_tf_Gather.py
index 403b33ab1a37af..955ce2c4d95899 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Gather.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Gather.py
@@ -11,7 +11,7 @@ class TestGather(CommonTFLayerTest):
     def create_indices_constant(self):
         pass
 
-    def create_gather_net(self, data_shape, indices, axis, batch_dims, **kwargs):
+    def create_gather_net(self, data_shape, indices, axis, batch_dims, use_new_frontend, **kwargs):
         import tensorflow as tf
 
         tf.compat.v1.reset_default_graph()
@@ -36,9 +36,9 @@ def create_gather_net(self, data_shape, indices, axis, batch_dims, **kwargs):
 
     @pytest.mark.parametrize("params", test_data_precommit)
     @pytest.mark.precommit
-    def test_gather(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_gather_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_gather(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_gather_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_nightly = [
         dict(data_shape=[2, 3], axis=1, indices=[0, 2], batch_dims=0),
@@ -56,6 +56,6 @@ def test_gather(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_nightly)
     @pytest.mark.nightly
-    def test_gather_nightly(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_gather_net(**params),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_gather_nightly(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_gather_net(**params, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Identity.py b/tests/layer_tests/tensorflow_tests/test_tf_Identity.py
index 6e963e85c4e766..3858249ef6afd5 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Identity.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Identity.py
@@ -5,11 +5,12 @@
 
 from common.layer_test_class import check_ir_version
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 from unit_tests.utils.graph import build_graph
 
 
 class TestIdentity(CommonTFLayerTest):
-    def create_identity_net(self, shape, ir_version):
+    def create_identity_net(self, shape, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -27,12 +28,11 @@ def create_identity_net(self, shape, ir_version):
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
-            x_shape = shape.copy()
-            # reshaping
-            if len(x_shape) >= 3:
-                x_shape.append(x_shape.pop(1))
+            tf_x_shape = shape.copy()
 
-            x = tf.compat.v1.placeholder(tf.float32, x_shape, 'Input')
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+
+            x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
             id = tf.identity(x, name="Operation")
             tf.nn.relu(id, name='Operation')
 
@@ -47,7 +47,7 @@ def create_identity_net(self, shape, ir_version):
 
         ref_net = None
 
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             nodes_attributes = {
                 'inputX': {'kind': 'op', 'type': 'Parameter'},
                 'inputX_data': {'shape': shape, 'kind': 'data'},
@@ -68,9 +68,9 @@ def create_identity_net(self, shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_precommit)
     @pytest.mark.precommit
-    def test_identity_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_identity_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_identity_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_identity_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data = [dict(shape=[1]),
                  dict(shape=[1, 224]),
@@ -80,6 +80,6 @@ def test_identity_precommit(self, params, ie_device, precision, ir_version, temp
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_identity(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_identity_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_identity(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_identity_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Log.py b/tests/layer_tests/tensorflow_tests/test_tf_Log.py
deleted file mode 100644
index f979071395183e..00000000000000
--- a/tests/layer_tests/tensorflow_tests/test_tf_Log.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import pytest
-
-from common.layer_test_class import check_ir_version
-from common.tf_layer_test_class import CommonTFLayerTest
-from unit_tests.utils.graph import build_graph
-
-
-class TestLog(CommonTFLayerTest):
-    def create_log_net(self, shape, ir_version):
-        """
-            Tensorflow net                 IR net
-
-            Input->Log       =>       Input->Log
-
-        """
-
-        #
-        #   Create Tensorflow model
-        #
-
-        import tensorflow as tf
-
-        tf.compat.v1.reset_default_graph()
-
-        # Create the graph and model
-        with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 3:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
-
-            tf.math.log(input, name='Operation')
-
-            tf.compat.v1.global_variables_initializer()
-            tf_net = sess.graph_def
-
-        ref_net = None
-
-        if check_ir_version(10, None, ir_version):
-            nodes_attributes = {
-                'input': {'kind': 'op', 'type': 'Parameter'},
-                'input_data': {'shape': shape, 'kind': 'data'},
-                'log': {'kind': 'op', 'type': 'Log'},
-                'log_data': {'shape': shape, 'kind': 'data'},
-                'result': {'kind': 'op', 'type': 'Result'}
-            }
-
-            ref_net = build_graph(nodes_attributes,
-                                  [('input', 'input_data'),
-                                   ('input_data', 'log'),
-                                   ('log', 'log_data'),
-                                   ('log_data', 'result')
-                                   ])
-
-        return tf_net, ref_net
-
-    test_data_precommit = [
-        pytest.param(dict(shape=[3, 2, 3, 7, 6]), marks=pytest.mark.skip(reason="Skipped until fixed"))]
-
-    @pytest.mark.parametrize("params", test_data_precommit)
-    @pytest.mark.precommit
-    def test_log_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_log_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
-
-    test_data = [dict(shape=[1]),
-                 dict(shape=[2, 5]),
-                 dict(shape=[5, 3, 7, 4]),
-                 dict(shape=[3, 2, 3, 7, 6])]
-
-    @pytest.mark.parametrize("params", test_data)
-    @pytest.mark.nightly
-    def test_log(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_log_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_LogSoftmax.py b/tests/layer_tests/tensorflow_tests/test_tf_LogSoftmax.py
index 8cb3087cc3e2fc..35219b88f7abd7 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_LogSoftmax.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_LogSoftmax.py
@@ -7,12 +7,13 @@
 import pytest
 from common.layer_test_class import check_ir_version
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 from mo.front.common.partial_infer.utils import int64_array
 from unit_tests.utils.graph import build_graph
 
 
 class TestLogSoftmax(CommonTFLayerTest):
-    def create_log_softmax_net(self, shape, reduction_axis, ir_version):
+    def create_log_softmax_net(self, shape, reduction_axis, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -30,11 +31,10 @@ def create_log_softmax_net(self, shape, reduction_axis, ir_version):
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 3:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
+            tf_x_shape = shape.copy()
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            input = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
             if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
                 tf.nn.log_softmax(input, name='Operation', axis=reduction_axis)
             else:
@@ -57,7 +57,7 @@ def create_log_softmax_net(self, shape, reduction_axis, ir_version):
         reduce_sum_shape[reduction_axis] = 1
 
         converted_shape = shape if rank != 1 else shape[0]
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             ref_nodes_attributes = {
                 'input': {'kind': 'op', 'type': 'Parameter', 'shape': converted_shape},
                 'input_data': {'shape': shape, 'kind': 'data', 'value': None},
@@ -122,9 +122,9 @@ def create_log_softmax_net(self, shape, reduction_axis, ir_version):
 
     @pytest.mark.parametrize("params", test_data_precommit)
     @pytest.mark.precommit
-    def test_log_softmax_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_log_softmax_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_log_softmax_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_log_softmax_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data = [dict(shape=[1], reduction_axis=-1),
                  dict(shape=[2, 5], reduction_axis=-1),
@@ -133,6 +133,6 @@ def test_log_softmax_precommit(self, params, ie_device, precision, ir_version, t
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_log_softmax(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_log_softmax_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_log_softmax(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_log_softmax_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Mul.py b/tests/layer_tests/tensorflow_tests/test_tf_Mul.py
index a877e639d6571a..8fbec72ec7f5dc 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Mul.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Mul.py
@@ -5,10 +5,11 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestMul(CommonTFLayerTest):
-    def create_mul_placeholder_const_net(self, x_shape, y_shape, ir_version):
+    def create_mul_placeholder_const_net(self, x_shape, y_shape, ir_version, use_new_frontend):
         """
             Tensorflow net                  IR net
 
@@ -30,11 +31,9 @@ def create_mul_placeholder_const_net(self, x_shape, y_shape, ir_version):
         with tf.compat.v1.Session() as sess:
             tf_x_shape = x_shape.copy()
             tf_y_shape = y_shape.copy()
-            # reshaping
-            if len(tf_x_shape) >= 3:
-                tf_x_shape.append(tf_x_shape.pop(1))
-            if len(tf_y_shape) >= 3:
-                tf_y_shape.append(tf_y_shape.pop(1))
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            tf_y_shape = permute_nchw_to_nhwc(tf_y_shape, use_new_frontend)
 
             x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
             constant_value = np.random.randint(-255, 255, tf_y_shape).astype(np.float32)
@@ -44,7 +43,6 @@ def create_mul_placeholder_const_net(self, x_shape, y_shape, ir_version):
             y = tf.constant(constant_value)
 
             mul = tf.multiply(x, y, name="Operation")
-            mul_shape = mul.shape.as_list()
 
             tf.compat.v1.global_variables_initializer()
             tf_net = sess.graph_def
@@ -55,23 +53,6 @@ def create_mul_placeholder_const_net(self, x_shape, y_shape, ir_version):
         #   Moreover, do not forget to validate ALL layer attributes!!!
         #
 
-        if len(mul_shape) >= 3:
-            # Permute mul_shape to (N,C,...) format
-            order = [0, len(mul_shape) - 1] + list(range(1, len(mul_shape) - 1))
-            mul_shape = [mul_shape[i] for i in order]
-
-        y_shape_to_compare = tf_y_shape.copy()
-        while len(y_shape_to_compare) < len(x_shape):
-            # Expand shape of constant with 1
-            y_shape_to_compare = [1] + y_shape_to_compare
-            constant_value = np.expand_dims(constant_value, axis=0)
-
-        if len(y_shape_to_compare) >= 3:
-            # Permute constant_value to (N,C,...) format for correct further reshape
-            order = [0, len(y_shape_to_compare) - 1] + list(range(1, len(y_shape_to_compare) - 1))
-            y_shape_to_compare = [y_shape_to_compare[i] for i in order]
-            constant_value = np.transpose(constant_value, order)
-
         ref_net = None
 
         return tf_net, ref_net
@@ -87,9 +68,9 @@ def create_mul_placeholder_const_net(self, x_shape, y_shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [
         # Power
@@ -104,9 +85,9 @@ def test_mul_placeholder_const_1D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         # Power
@@ -122,9 +103,9 @@ def test_mul_placeholder_const_2D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         # Power
@@ -139,9 +120,9 @@ def test_mul_placeholder_const_3D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         # Power
@@ -158,9 +139,9 @@ def test_mul_placeholder_const_4D(self, params, ie_device, precision, ir_version
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     ###############################################################################################
     #                                                                                             #
@@ -174,9 +155,9 @@ def test_mul_placeholder_const_5D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_broadcast_1D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_broadcast_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_broadcast_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_2D = [
         # Power
@@ -193,9 +174,9 @@ def test_mul_placeholder_const_broadcast_1D(self, params, ie_device, precision,
 
     @pytest.mark.parametrize("params", test_data_broadcast_2D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_broadcast_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_broadcast_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_3D = [
         # Power
@@ -217,9 +198,9 @@ def test_mul_placeholder_const_broadcast_2D(self, params, ie_device, precision,
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_broadcast_3D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_broadcast_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_broadcast_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_4D = [
         # Power
@@ -235,9 +216,9 @@ def test_mul_placeholder_const_broadcast_3D(self, params, ie_device, precision,
         # Eltwise
         dict(x_shape=[1, 3, 1, 1], y_shape=[3, 1]),
         # Eltwise
-        dict(x_shape=[1, 3, 1, 2], y_shape=[3, 1, 2]),
+        dict(x_shape=[1, 2, 1, 3], y_shape=[3, 1, 2]),
         # Eltwise
-        dict(x_shape=[1, 3, 1, 2], y_shape=[1, 3, 2]),
+        dict(x_shape=[1, 2, 1, 3], y_shape=[1, 3, 2]),
         # Eltwise
         dict(x_shape=[1, 3, 100, 224], y_shape=[1, 1, 1, 224]),
         # Eltwise
@@ -247,9 +228,9 @@ def test_mul_placeholder_const_broadcast_3D(self, params, ie_device, precision,
     @pytest.mark.parametrize("params", test_data_broadcast_4D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_mul_placeholder_const_broadcast_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_broadcast_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_5D = [
         # Power
@@ -274,6 +255,6 @@ def test_mul_placeholder_const_broadcast_4D(self, params, ie_device, precision,
 
     @pytest.mark.parametrize("params", test_data_broadcast_5D)
     @pytest.mark.nightly
-    def test_mul_placeholder_const_broadcast_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_mul_placeholder_const_broadcast_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_mul_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_NormalizeL2.py b/tests/layer_tests/tensorflow_tests/test_tf_NormalizeL2.py
index 1dc4b914172645..00217ead0d9daf 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_NormalizeL2.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_NormalizeL2.py
@@ -7,7 +7,7 @@
 from common.tf_layer_test_class import CommonTFLayerTest
 from mo.front.common.partial_infer.utils import int64_array
 from unit_tests.utils.graph import build_graph
-from tensorflow_tests.permutation_utils import permute_nchw_to_nhwc, permute_nhwc_to_nchw
+from common.utils.tf_utils import permute_nchw_to_nhwc, permute_nchw_to_nhwc
 
 
 class TestNormalizeL2(CommonTFLayerTest):
@@ -33,7 +33,7 @@ def build_tf_graph(shape, axes):
             return tf_net
 
     @staticmethod
-    def create_normalize_l2_net_fusable(shape, axes, output_axes, ir_version):
+    def create_normalize_l2_net_fusable(shape, axes, output_axes, ir_version, use_new_frontend):
         tf_net = TestNormalizeL2.build_tf_graph(shape, axes)
 
         nodes_attributes = {
@@ -57,16 +57,18 @@ def create_normalize_l2_net_fusable(shape, axes, output_axes, ir_version):
                                ('normalize_l2_data', 'result'),
                                ])
 
+        if use_new_frontend:
+            ref_net = None
         return tf_net, ref_net
 
     @staticmethod
-    def create_normalize_l2_net_non_fusable(shape, axes, output_axes, ir_version):
+    def create_normalize_l2_net_non_fusable(shape, axes, output_axes, ir_version, use_new_frontend):
         tf_net = TestNormalizeL2.build_tf_graph(shape, axes)
 
         reduced_shape = permute_nchw_to_nhwc(shape).copy()
         for axis in axes:
             reduced_shape[axis] = 1
-        reduced_shape = permute_nhwc_to_nchw(reduced_shape)
+        reduced_shape = permute_nchw_to_nhwc(reduced_shape)
 
         eltwise_shapes = int64_array(np.ones(len(shape)))
         nodes_attributes = {
@@ -136,6 +138,8 @@ def create_normalize_l2_net_non_fusable(shape, axes, output_axes, ir_version):
                                ('multiply_data', 'result'),
                                ])
 
+        if use_new_frontend:
+            ref_net = None
         return tf_net, ref_net
 
     test_data_fusable_precommit = [
@@ -147,9 +151,9 @@ def create_normalize_l2_net_non_fusable(shape, axes, output_axes, ir_version):
 
     @pytest.mark.parametrize("params", test_data_fusable_precommit)
     @pytest.mark.precommit
-    def test_NormalizeL2_fusable_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_normalize_l2_net_fusable(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_NormalizeL2_fusable_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_normalize_l2_net_fusable(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_non_fusable_precommit = [
         pytest.param(dict(shape=[2, 3, 5], axes=[0, 1, 2], output_axes=[0, 1, 2]),
@@ -162,10 +166,10 @@ def test_NormalizeL2_fusable_precommit(self, params, ie_device, precision, ir_ve
 
     @pytest.mark.parametrize("params", test_data_non_fusable_precommit)
     @pytest.mark.precommit
-    def test_NormalizeL2_non_fusable_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_normalize_l2_net_non_fusable(**params, ir_version=ir_version),
+    def test_NormalizeL2_non_fusable_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_normalize_l2_net_non_fusable(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_fusable = [
         dict(shape=[5, 6], axes=[1], output_axes=[1]),
@@ -178,9 +182,9 @@ def test_NormalizeL2_non_fusable_precommit(self, params, ie_device, precision, i
 
     @pytest.mark.parametrize("params", test_data_fusable)
     @pytest.mark.nightly
-    def test_NormalizeL2_fusable(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_normalize_l2_net_fusable(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_NormalizeL2_fusable(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_normalize_l2_net_fusable(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_non_fusable = [
         dict(shape=[5], axes=[0], output_axes=[0]),
@@ -201,7 +205,7 @@ def test_NormalizeL2_fusable(self, params, ie_device, precision, ir_version, tem
 
     @pytest.mark.parametrize("params", test_data_non_fusable)
     @pytest.mark.nightly
-    def test_NormalizeL2_non_fusable(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_normalize_l2_net_non_fusable(**params, ir_version=ir_version),
+    def test_NormalizeL2_non_fusable(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_normalize_l2_net_non_fusable(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_OneHot.py b/tests/layer_tests/tensorflow_tests/test_tf_OneHot.py
index 5e2d1d9131bf6b..feff58c11526f5 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_OneHot.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_OneHot.py
@@ -4,12 +4,12 @@
 import numpy as np
 import pytest
 from common.tf_layer_test_class import CommonTFLayerTest
-from tensorflow_tests.permutation_utils import permute_nchw_to_nhwc
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestOneHot(CommonTFLayerTest):
     @staticmethod
-    def create_one_hot_net(shape, depth, on_value, off_value, axis, ir_version):
+    def create_one_hot_net(shape, depth, on_value, off_value, axis, ir_version, use_new_frontend):
         """
             Tensorflow net
 
@@ -53,13 +53,6 @@ def create_one_hot_net(shape, depth, on_value, off_value, axis, ir_version):
         #   Create reference IR net
         #
 
-        if on_value is None:
-            on_value = 1.0
-        if off_value is None:
-            off_value = 0.0
-
-        axis = len(shape) if axis is None else axis
-
         ref_net = None
 
         return tf_net, ref_net
@@ -71,9 +64,9 @@ def create_one_hot_net(shape, depth, on_value, off_value, axis, ir_version):
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_OneHot_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_one_hot_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_OneHot_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_one_hot_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [
         dict(shape=[5, 6], depth=7, on_value=None, off_value=None, axis=None),
@@ -90,9 +83,9 @@ def test_OneHot_1D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_OneHot_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_one_hot_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_OneHot_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_one_hot_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         dict(shape=[5, 6, 7], depth=8, on_value=None, off_value=None, axis=None),
@@ -110,9 +103,9 @@ def test_OneHot_2D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_OneHot_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_one_hot_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_OneHot_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_one_hot_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         dict(shape=[5, 6, 7, 8], depth=9, on_value=None, off_value=None, axis=None),
@@ -132,9 +125,9 @@ def test_OneHot_3D(self, params, ie_device, precision, ir_version, temp_dir):
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_OneHot_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_one_hot_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_OneHot_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_one_hot_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         dict(shape=[4, 5, 6, 7, 8], depth=9, on_value=None, off_value=None, axis=None),
@@ -154,6 +147,6 @@ def test_OneHot_4D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_OneHot_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_one_hot_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_OneHot_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_one_hot_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Pooling.py b/tests/layer_tests/tensorflow_tests/test_tf_Pooling.py
index 3074ccad19d6bb..4b236d868aacb5 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Pooling.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Pooling.py
@@ -8,7 +8,7 @@
 
 
 class TestPooling(CommonTFLayerTest):
-    def create_pooling_net(self, kernel_size, strides, pads, in_shape, out_shape, method, ir_version):
+    def create_pooling_net(self, kernel_size, strides, pads, in_shape, out_shape, method, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -66,7 +66,7 @@ def create_pooling_net(self, kernel_size, strides, pads, in_shape, out_shape, me
 
         ref_net = None
 
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             nodes_attributes = {
                 'input': {'kind': 'op', 'type': 'Parameter'},
                 'input_data': {'shape': in_shape, 'kind': 'data'},
@@ -156,9 +156,9 @@ def create_pooling_net(self, kernel_size, strides, pads, in_shape, out_shape, me
 
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_pool_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_pooling_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_pool_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_pooling_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = []
     for method in ['max', 'avg']:
@@ -229,8 +229,8 @@ def test_pool_4D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_pool_5D(self, params, ie_device, precision, ir_version, temp_dir):
+    def test_pool_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
         if ie_device == 'GPU':
             pytest.skip("5D tensors is not supported on GPU")
-        self._test(*self.create_pooling_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+        self._test(*self.create_pooling_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py b/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py
index 400ea147524cd0..347c092cddfa8f 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py
@@ -9,23 +9,23 @@
 
 from common.layer_test_class import check_ir_version
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestTFRandomUniform(CommonTFLayerTest):
     def create_tf_random_uniform_net(self, global_seed, op_seed, x_shape, min_val, max_val, input_type, precision,
-                                     ir_version):
+                                     ir_version, use_new_frontend):
         tf.compat.v1.reset_default_graph()
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
             tf_x_shape = x_shape.copy()
-            # reshaping
-            if len(tf_x_shape) >= 3:
-                tf_x_shape.append(tf_x_shape.pop(1))
 
-            x = tf.compat.v1.placeholder(input_type, x_shape, 'Input')
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+
+            x = tf.compat.v1.placeholder(input_type, tf_x_shape, 'Input')
             if global_seed is not None:
-                tf.random.set_seed(global_seed)
+                tf.compat.v1.random.set_random_seed(global_seed)
             random_uniform = tf.random.uniform(x_shape, seed=op_seed, dtype=input_type, minval=min_val,
                                                maxval=max_val) + x
 
@@ -33,7 +33,7 @@ def create_tf_random_uniform_net(self, global_seed, op_seed, x_shape, min_val, m
             tf_net = sess.graph_def
 
         ref_net = None
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
 
             const_for_layer_tests = lambda name, value, shape, shape1: {
                 **{name + '_dd': {'kind': 'data', 'value': value, 'shape': shape1}},
@@ -86,8 +86,9 @@ def create_tf_random_uniform_net(self, global_seed, op_seed, x_shape, min_val, m
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_tf_random_uniform(self, params, ie_device, precision, ir_version, temp_dir):
+    def test_tf_random_uniform(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
         if ie_device == 'GPU':
             pytest.skip("RandomUniform is not supported on GPU")
-        self._test(*self.create_tf_random_uniform_net(**params, precision=precision, ir_version=ir_version), ie_device,
-                   precision, temp_dir=temp_dir, ir_version=ir_version, **params)
+        self._test(*self.create_tf_random_uniform_net(**params, precision=precision, ir_version=ir_version,
+                                                      use_new_frontend=use_new_frontend), ie_device,
+                   precision, temp_dir=temp_dir, ir_version=ir_version, use_new_frontend=use_new_frontend, **params)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_ReLU.py b/tests/layer_tests/tensorflow_tests/test_tf_ReLU.py
deleted file mode 100644
index c178e6f8b55aef..00000000000000
--- a/tests/layer_tests/tensorflow_tests/test_tf_ReLU.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import pytest
-
-from common.layer_test_class import check_ir_version
-from common.tf_layer_test_class import CommonTFLayerTest
-from unit_tests.utils.graph import build_graph
-
-
-class TestReLU(CommonTFLayerTest):
-    def create_relu_net(self, shape, ir_version):
-        """
-            Tensorflow net                 IR net
-
-            Input->ReLU       =>       Input->ReLU
-
-        """
-
-        #
-        #   Create Tensorflow model
-        #
-
-        import tensorflow as tf
-
-        tf.compat.v1.reset_default_graph()
-
-        # Create the graph and model
-        with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 3:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
-
-            tf.nn.relu(input, name='Operation')
-
-            tf.compat.v1.global_variables_initializer()
-            tf_net = sess.graph_def
-
-        #
-        #   Create reference IR net
-        #   Please, specify 'type': 'Input' for input node
-        #   Moreover, do not forget to validate ALL layer attributes!!!
-        #
-
-        ref_net = None
-
-        if check_ir_version(10, None, ir_version):
-            nodes_attributes = {
-                'input': {'kind': 'op', 'type': 'Parameter'},
-                'input_data': {'shape': shape, 'kind': 'data'},
-                'ReLU': {'kind': 'op', 'type': 'ReLU'},
-                'ReLU_data': {'shape': shape, 'kind': 'data'},
-                'result': {'kind': 'op', 'type': 'Result'}
-            }
-
-            ref_net = build_graph(nodes_attributes,
-                                  [('input', 'input_data'),
-                                   ('input_data', 'ReLU'),
-                                   ('ReLU', 'ReLU_data'),
-                                   ('ReLU_data', 'result')
-                                   ])
-
-        return tf_net, ref_net
-
-    test_data_precommit = [dict(shape=[1, 3, 50, 100, 224])]
-
-    @pytest.mark.parametrize("params", test_data_precommit)
-    @pytest.mark.precommit
-    def test_relu_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_relu_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
-
-    test_data = [dict(shape=[1]),
-                 dict(shape=[1, 224]),
-                 pytest.param(dict(shape=[1, 3, 224]), marks=pytest.mark.xfail(reason="*-19053")),
-                 dict(shape=[1, 3, 100, 224]),
-                 dict(shape=[1, 3, 50, 100, 224])]
-
-    @pytest.mark.parametrize("params", test_data)
-    @pytest.mark.nightly
-    def test_relu(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_relu_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_ReLU6.py b/tests/layer_tests/tensorflow_tests/test_tf_ReLU6.py
index fd846a82358428..e0db2713f9137b 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_ReLU6.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_ReLU6.py
@@ -5,11 +5,12 @@
 
 from common.layer_test_class import check_ir_version
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 from unit_tests.utils.graph import build_graph
 
 
 class TestReLU6(CommonTFLayerTest):
-    def create_relu6_net(self, shape, ir_version):
+    def create_relu6_net(self, shape, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -27,11 +28,10 @@ def create_relu6_net(self, shape, ir_version):
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 3:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
+            tf_x_shape = shape.copy()
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            input = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
 
             tf.nn.relu6(input, name='Operation')
 
@@ -46,7 +46,7 @@ def create_relu6_net(self, shape, ir_version):
 
         ref_net = None
 
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             nodes_attributes = {
                 'input': {'kind': 'op', 'type': 'Parameter'},
                 'input_data': {'shape': shape, 'kind': 'data'},
@@ -68,9 +68,9 @@ def create_relu6_net(self, shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_precommit)
     @pytest.mark.precommit
-    def test_relu6_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_relu6_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_relu6_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_relu6_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data = [dict(shape=[1]),
                  dict(shape=[1, 224]),
@@ -80,6 +80,6 @@ def test_relu6_precommit(self, params, ie_device, precision, ir_version, temp_di
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_relu6(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_relu6_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_relu6(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_relu6_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Reduce.py b/tests/layer_tests/tensorflow_tests/test_tf_Reduce.py
index 53341943943606..3830c6504b79ba 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Reduce.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Reduce.py
@@ -5,6 +5,7 @@
 import numpy as np
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestReduceOps(CommonTFLayerTest):
@@ -13,7 +14,7 @@ def _prepare_input(self, inputs_dict):
             inputs_dict[input] = np.random.random(inputs_dict[input])
         return inputs_dict
 
-    def create_reduce_net(self, shape, operation, keep_dims, axis, ir_version):
+    def create_reduce_net(self, shape, operation, keep_dims, axis, ir_version, use_new_frontend):
         import tensorflow as tf
         fn_mapping = {'sum': tf.reduce_sum,
                       'max': tf.reduce_max,
@@ -23,11 +24,10 @@ def create_reduce_net(self, shape, operation, keep_dims, axis, ir_version):
                       }
         tf.compat.v1.reset_default_graph()
         with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            if len(shapes) >= 4:
-                shapes.append(shapes.pop(1))
+            tf_x_shape = shape.copy()
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
 
-            x = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
+            x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
             fn_mapping[operation](x, axis=axis, keepdims=keep_dims, name='Operation')
             tf.compat.v1.global_variables_initializer()
             tf_net = sess.graph_def
@@ -46,9 +46,9 @@ def create_reduce_net(self, shape, operation, keep_dims, axis, ir_version):
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.parametrize("keep_dims", [True, False])
     @pytest.mark.nightly
-    def test_reduce(self, params, keep_dims, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_reduce_net(**params, keep_dims=keep_dims, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_reduce(self, params, keep_dims, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_reduce_net(**params, keep_dims=keep_dims, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_pre_commit = []
     for operation in ['sum', 'max', 'prod', 'min', 'mean']:
@@ -58,6 +58,6 @@ def test_reduce(self, params, keep_dims, ie_device, precision, ir_version, temp_
     @pytest.mark.parametrize("params", test_data_pre_commit)
     @pytest.mark.parametrize("keep_dims", [False])
     @pytest.mark.precommit
-    def test_reduce_precommit(self, params, keep_dims, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_reduce_net(**params, keep_dims=keep_dims, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_reduce_precommit(self, params, keep_dims, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_reduce_net(**params, keep_dims=keep_dims, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Resample_pattern_new.py b/tests/layer_tests/tensorflow_tests/test_tf_Resample_pattern_new.py
index e3afdda6972d02..6d2aa2ff24fee7 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Resample_pattern_new.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Resample_pattern_new.py
@@ -14,7 +14,7 @@ def _prepare_input(self, inputs_dict):
             inputs_dict[input] = np.random.randint(1, 256, inputs_dict[input]).astype(np.float32)
         return inputs_dict
 
-    def create_resample_net(self, shape, factor):
+    def create_resample_net(self, shape, factor, use_new_frontend):
         """
             The sub-graph in TF that could be expressed as a single Resample operation.
         """
@@ -29,10 +29,10 @@ def create_resample_net(self, shape, factor):
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
-            tf_shape = shape.copy()
-            tf_shape = np.array(tf_shape)[[0, 2, 3, 1]]
+            tf_x_shape = shape.copy()
+            tf_x_shape = np.array(tf_x_shape)[[0, 2, 3, 1]]
 
-            input = tf.compat.v1.placeholder(tf.float32, tf_shape, 'Input')
+            input = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
 
             transpose_1 = tf.transpose(a=input, perm=[1, 2, 3, 0])
             expand_dims = tf.expand_dims(transpose_1, 0)
@@ -50,22 +50,24 @@ def create_resample_net(self, shape, factor):
         #   Moreover, do not forget to validate ALL layer attributes!!!
         #
 
-        new_shape = shape.copy()
-        new_shape[2] *= factor
-        new_shape[3] *= factor
-        nodes_attributes = {
-            'input': {'kind': 'op', 'type': 'Input'},
-            'input_data': {'shape': shape, 'kind': 'data'},
-            'resample': {'kind': 'op', 'type': 'caffe.ResampleParameter.NEAREST', "factor": factor,
-                         "height": 0, "width": 0, "antialias": 0},
-            'resample_data': {'shape': new_shape, 'kind': 'data'},
-        }
-
-        ref_net = build_graph(nodes_attributes,
-                              [('input', 'input_data'),
-                               ('input_data', 'resample'),
-                               ('resample', 'resample_data')
-                               ])
+        ref_net = None
+        if not use_new_frontend:
+            new_shape = shape.copy()
+            new_shape[2] *= factor
+            new_shape[3] *= factor
+            nodes_attributes = {
+                'input': {'kind': 'op', 'type': 'Input'},
+                'input_data': {'shape': shape, 'kind': 'data'},
+                'resample': {'kind': 'op', 'type': 'caffe.ResampleParameter.NEAREST', "factor": factor,
+                             "height": 0, "width": 0, "antialias": 0},
+                'resample_data': {'shape': new_shape, 'kind': 'data'},
+            }
+
+            ref_net = build_graph(nodes_attributes,
+                                  [('input', 'input_data'),
+                                   ('input_data', 'resample'),
+                                   ('resample', 'resample_data')
+                                   ])
 
         return tf_net, ref_net
 
@@ -76,6 +78,6 @@ def create_resample_net(self, shape, factor):
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
     @pytest.mark.xfail(reason="*-22273")
-    def test_resample(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_resample_net(params['shape'], params['factor']),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_resample(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_resample_net(params['shape'], params['factor'], use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Roll.py b/tests/layer_tests/tensorflow_tests/test_tf_Roll.py
index 340adcc1eca60a..585bf5367ae578 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Roll.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Roll.py
@@ -1,18 +1,18 @@
 import pytest
 import tensorflow as tf
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestTFRoll(CommonTFLayerTest):
-    def create_tf_roll_net(self, shift, axis, x_shape, input_type, ir_version):
+    def create_tf_roll_net(self, shift, axis, x_shape, input_type, ir_version, use_new_frontend):
         tf.compat.v1.reset_default_graph()
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
             tf_x_shape = x_shape.copy()
-            # reshaping
-            if len(tf_x_shape) >= 3:
-                tf_x_shape.append(tf_x_shape.pop(1))
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
 
             x = tf.compat.v1.placeholder(input_type, tf_x_shape, 'Input')
             roll = tf.roll(x, shift=shift, axis=axis)
@@ -35,8 +35,8 @@ def create_tf_roll_net(self, shift, axis, x_shape, input_type, ir_version):
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_tf_roll(self, params, ie_device, precision, ir_version, temp_dir):
+    def test_tf_roll(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
         if ie_device == 'GPU':
             pytest.skip("Roll is not supported on GPU")
-        self._test(*self.create_tf_roll_net(**params, ir_version=ir_version), ie_device, precision,
-                   temp_dir=temp_dir, ir_version=ir_version, **params)
+        self._test(*self.create_tf_roll_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend), ie_device, precision,
+                   temp_dir=temp_dir, ir_version=ir_version, **params, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Rsqrt.py b/tests/layer_tests/tensorflow_tests/test_tf_Rsqrt.py
index 953d73f6809b3a..c8013ae4010d17 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Rsqrt.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Rsqrt.py
@@ -5,6 +5,7 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestRsqrt(CommonTFLayerTest):
@@ -13,7 +14,7 @@ def _prepare_input(self, inputs_dict):
             inputs_dict[input] = np.random.randint(1, 256, inputs_dict[input]).astype(np.float32)
         return inputs_dict
 
-    def create_rsqrt_net(self, shape, ir_version):
+    def create_rsqrt_net(self, shape, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -31,11 +32,10 @@ def create_rsqrt_net(self, shape, ir_version):
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) >= 3:
-                shapes.append(shapes.pop(1))
-            input = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
+            tf_x_shape = shape.copy()
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            input = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
 
             tf.math.rsqrt(input, name='Operation')
 
@@ -56,9 +56,9 @@ def create_rsqrt_net(self, shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_precommit)
     @pytest.mark.precommit
-    def test_rsqrt_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_rsqrt_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_rsqrt_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_rsqrt_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data = [dict(shape=[1]),
                  dict(shape=[1, 224]),
@@ -68,6 +68,6 @@ def test_rsqrt_precommit(self, params, ie_device, precision, ir_version, temp_di
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_rsqrt(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_rsqrt_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_rsqrt(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_rsqrt_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Select.py b/tests/layer_tests/tensorflow_tests/test_tf_Select.py
index ee24964589b494..365ddf471a9142 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Select.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Select.py
@@ -4,11 +4,11 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
-from tensorflow_tests.permutation_utils import permute_nchw_to_nhwc
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestSelect(CommonTFLayerTest):
-    def create_select_net(self, shape_condition, shape_input, ir_version):
+    def create_select_net(self, shape_condition, shape_input, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -56,9 +56,9 @@ def create_select_net(self, shape_condition, shape_input, ir_version):
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_select_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_select_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_select_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_select_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [
         dict(shape_condition=[2], shape_input=[2, 3]),
@@ -67,9 +67,9 @@ def test_select_1D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_select_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_select_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_select_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_select_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         dict(shape_condition=[3], shape_input=[3, 4, 5]),
@@ -78,9 +78,9 @@ def test_select_2D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_select_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_select_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_select_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_select_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         dict(shape_condition=[3], shape_input=[3, 4, 5, 6]),
@@ -90,9 +90,9 @@ def test_select_3D(self, params, ie_device, precision, ir_version, temp_dir):
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
     @pytest.mark.precommit
-    def test_select_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_select_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_select_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_select_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         dict(shape_condition=[3], shape_input=[3, 4, 5, 6, 7]),
@@ -102,6 +102,6 @@ def test_select_4D(self, params, ie_device, precision, ir_version, temp_dir):
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_select_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_select_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_select_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_select_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Softplus.py b/tests/layer_tests/tensorflow_tests/test_tf_Softplus.py
deleted file mode 100644
index a295e4660e9174..00000000000000
--- a/tests/layer_tests/tensorflow_tests/test_tf_Softplus.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import pytest
-
-from common.layer_test_class import check_ir_version
-from common.tf_layer_test_class import CommonTFLayerTest
-from unit_tests.utils.graph import build_graph
-
-
-class TestSoftplus(CommonTFLayerTest):
-    def create_softplus_net(self, shape, ir_version):
-        """
-            Tensorflow net                 IR net
-
-            Input->Softplus       =>       Input->Softplus
-
-        """
-
-        #
-        #   Create Tensorflow model
-        #
-
-        import tensorflow as tf
-
-        tf.reset_default_graph()
-
-        # Create the graph and model
-        with tf.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) > 3:
-                shapes.append(shapes.pop(1))
-            input = tf.placeholder(tf.float32, shapes, 'Input')
-
-            tf.math.softplus(input, name='Operation')
-
-            tf.global_variables_initializer()
-            tf_net = sess.graph_def
-
-        #
-        #   Create reference IR net
-        #   Please, specify 'type': 'Input' for input node
-        #   Moreover, do not forget to validate ALL layer attributes!!!
-        #
-
-        ref_net = None
-
-        if check_ir_version(10, None, ir_version):
-            nodes_attributes = {
-                'input': {'kind': 'op', 'type': 'Parameter'},
-                'input_data': {'shape': shape, 'kind': 'data'},
-                'Softplus': {'kind': 'op', 'type': 'SoftPlus'},
-                'Softplus_data': {'shape': shape, 'kind': 'data'},
-                'result': {'kind': 'op', 'type': 'Result'}
-            }
-
-            ref_net = build_graph(nodes_attributes,
-                                  [('input', 'input_data'),
-                                   ('input_data', 'Softplus'),
-                                   ('Softplus', 'Softplus_data'),
-                                   ('Softplus_data', 'result')
-                                   ])
-
-        return tf_net, ref_net
-
-    test_data_precommit = [
-        pytest.param(dict(shape=[1, 3, 100, 224]),
-                     marks=pytest.mark.skip(reason="Skipped until fixed")),
-        pytest.param(dict(shape=[1, 3, 50, 100, 224]),
-                     marks=pytest.mark.skip(reason="Skipped until fixed"))
-    ]
-
-    @pytest.mark.parametrize("params", test_data_precommit)
-    @pytest.mark.precommit
-    def test_softplus_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_softplus_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
-
-    test_data = [dict(shape=[1]),
-                 dict(shape=[1, 224]),
-                 dict(shape=[1, 3, 224]),
-                 dict(shape=[1, 3, 100, 224]),
-                 dict(shape=[1, 3, 50, 100, 224])]
-
-    @pytest.mark.parametrize("params", test_data)
-    @pytest.mark.nightly
-    def test_softplus(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_softplus_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_SpaceToBatch.py b/tests/layer_tests/tensorflow_tests/test_tf_SpaceToBatch.py
index f86ab7dc2acf21..df22d3ed543d3d 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_SpaceToBatch.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_SpaceToBatch.py
@@ -7,7 +7,7 @@
 
 
 class TestSpaceToBatch(CommonTFLayerTest):
-    def create_space_to_batch_net(self, in_shape, pads_value, block_shape_value, out_shape, ir_version):
+    def create_space_to_batch_net(self, in_shape, pads_value, block_shape_value, out_shape, ir_version, use_new_frontend):
         """
             Tensorflow net               IR net
 
@@ -28,7 +28,7 @@ def create_space_to_batch_net(self, in_shape, pads_value, block_shape_value, out
             x = tf.compat.v1.placeholder(tf.float32, in_shape, 'Input')
             pads = tf.constant(pads_value)
             block_shape = tf.constant(block_shape_value)
-            tf.space_to_batch(x, block_shape, pads, name='Operation')
+            tf.space_to_batch_nd(x, block_shape, pads, name='Operation')
 
             tf.compat.v1.global_variables_initializer()
             tf_net = sess.graph_def
@@ -61,9 +61,9 @@ def create_space_to_batch_net(self, in_shape, pads_value, block_shape_value, out
 
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_space_to_batch_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_space_to_batch_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_space_to_batch_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_space_to_batch_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         dict(in_shape=[3, 3, 4, 5, 2], block_shape_value=[3, 4, 2], pads_value=[[1, 2], [0, 0], [3, 0]],
@@ -75,6 +75,6 @@ def test_space_to_batch_4D(self, params, ie_device, precision, ir_version, temp_
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_space_to_batch_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_space_to_batch_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_space_to_batch_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_space_to_batch_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Squeeze.py b/tests/layer_tests/tensorflow_tests/test_tf_Squeeze.py
index 0ef13751b3463e..84694709dd5b97 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Squeeze.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Squeeze.py
@@ -4,10 +4,11 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestSqueeze(CommonTFLayerTest):
-    def create_squeeze_net(self, shape, axis, ir_version):
+    def create_squeeze_net(self, shape, axis, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -25,12 +26,11 @@ def create_squeeze_net(self, shape, axis, ir_version):
 
         # Create the graph and model
         with tf.compat.v1.Session() as sess:
-            x_shape = shape.copy()
-            # reshaping
-            if len(x_shape) >= 3:
-                x_shape.append(x_shape.pop(1))
+            tf_x_shape = shape.copy()
 
-            x = tf.compat.v1.placeholder(tf.float32, x_shape, 'Input')
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+
+            x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
             squeeze = tf.squeeze(x, axis=axis, name="Operation")
 
             tf.compat.v1.global_variables_initializer()
@@ -42,25 +42,21 @@ def create_squeeze_net(self, shape, axis, ir_version):
         #   Moreover, do not forget to validate ALL layer attributes!!!
         #
 
-        unsigned_axis = [ax if ax > -1 else len(x_shape) + ax for ax in axis]
-        if not unsigned_axis:
-            unsigned_axis = [i for i, dim in enumerate(shape) if dim == 1]
-
         ref_net = None
 
         return tf_net, ref_net
 
     test_data_1D = [
-        pytest.param(dict(shape=[1], axis=[]), marks=pytest.mark.xfail(reason="*-18807")),
-        pytest.param(dict(shape=[1], axis=[0]), marks=pytest.mark.xfail(reason="*-18859")),
-        pytest.param(dict(shape=[1], axis=[-1]), marks=pytest.mark.xfail(reason="*-18859"))
+        pytest.param(dict(shape=[1], axis=[])),
+        pytest.param(dict(shape=[1], axis=[0])),
+        pytest.param(dict(shape=[1], axis=[-1]))
     ]
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_squeeze_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_squeeze_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_squeeze_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_squeeze_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [
         pytest.param(dict(shape=[1, 1], axis=[]), marks=pytest.mark.xfail(reason="*-18807")),
@@ -70,9 +66,9 @@ def test_squeeze_1D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_squeeze_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_squeeze_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_squeeze_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_squeeze_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         pytest.param(dict(shape=[1, 1, 3], axis=[]),
@@ -84,9 +80,9 @@ def test_squeeze_2D(self, params, ie_device, precision, ir_version, temp_dir):
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_squeeze_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_squeeze_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_squeeze_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_squeeze_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         pytest.param(dict(shape=[1, 1, 50, 100], axis=[]), marks=pytest.mark.xfail(reason="*-18807")),
@@ -98,9 +94,9 @@ def test_squeeze_3D(self, params, ie_device, precision, ir_version, temp_dir):
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_squeeze_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_squeeze_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_squeeze_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_squeeze_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         pytest.param(dict(shape=[1, 1, 50, 100, 224], axis=[]), marks=pytest.mark.xfail(reason="*-18807")),
@@ -119,6 +115,6 @@ def test_squeeze_4D(self, params, ie_device, precision, ir_version, temp_dir):
                                reason="*-19394")
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_squeeze_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_squeeze_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_squeeze_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_squeeze_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_StridedSlice.py b/tests/layer_tests/tensorflow_tests/test_tf_StridedSlice.py
index d03994c7052c13..8de592e805de4d 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_StridedSlice.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_StridedSlice.py
@@ -10,7 +10,7 @@ class TestStridedSlice(CommonTFLayerTest):
 
     @staticmethod
     def create_strided_slice_net(input_shape, begin, end, strides, begin_mask, end_mask, ellipsis_mask,
-                                 new_axis_mask, shrink_axis_mask, ir_version):
+                                 new_axis_mask, shrink_axis_mask, ir_version, use_new_frontend):
 
         #
         # Create Tensorflow model
@@ -60,9 +60,9 @@ def create_strided_slice_net(input_shape, begin, end, strides, begin_mask, end_m
 
     @pytest.mark.parametrize('params', test_squeeze_data)
     @pytest.mark.nightly
-    def test_strided_slice_replace_with_squeeze(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_strided_slice_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_strided_slice_replace_with_squeeze(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_strided_slice_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_unsqueeze_data = [
         dict(input_shape=[1, 5], begin=[0, 0], end=[1, 5], strides=[1, 1], begin_mask=0,
@@ -85,6 +85,6 @@ def test_strided_slice_replace_with_squeeze(self, params, ie_device, precision,
 
     @pytest.mark.parametrize('params', test_unsqueeze_data)
     @pytest.mark.nightly
-    def test_strided_slice_replace_with_unsqueeze(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_strided_slice_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_strided_slice_replace_with_unsqueeze(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_strided_slice_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Sub.py b/tests/layer_tests/tensorflow_tests/test_tf_Sub.py
index 00f63494255d5b..7d6532c0b3be5d 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Sub.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Sub.py
@@ -5,10 +5,11 @@
 import pytest
 
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 
 
 class TestSub(CommonTFLayerTest):
-    def create_sub_placeholder_const_net(self, x_shape, y_shape, ir_version):
+    def create_sub_placeholder_const_net(self, x_shape, y_shape, ir_version, use_new_frontend):
         """
             Tensorflow net                  IR net
 
@@ -30,11 +31,9 @@ def create_sub_placeholder_const_net(self, x_shape, y_shape, ir_version):
         with tf.compat.v1.Session() as sess:
             tf_x_shape = x_shape.copy()
             tf_y_shape = y_shape.copy()
-            # reshaping
-            if len(tf_x_shape) >= 3:
-                tf_x_shape.append(tf_x_shape.pop(1))
-            if len(tf_y_shape) >= 3:
-                tf_y_shape.append(tf_y_shape.pop(1))
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            tf_y_shape = permute_nchw_to_nhwc(tf_y_shape, use_new_frontend)
 
             x = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
             constant_value = np.random.randint(-256, 256, tf_y_shape).astype(np.float32)
@@ -55,23 +54,6 @@ def create_sub_placeholder_const_net(self, x_shape, y_shape, ir_version):
         #   Moreover, do not forget to validate ALL layer attributes!!!
         #
 
-        if len(sub_shape) >= 3:
-            # Permute sub_shape to (N,C,...) format
-            order = [0, len(sub_shape) - 1] + list(range(1, len(sub_shape) - 1))
-            sub_shape = [sub_shape[i] for i in order]
-
-        y_shape_to_compare = tf_y_shape.copy()
-        while len(y_shape_to_compare) < len(x_shape):
-            # Expand shape of constant with 1
-            y_shape_to_compare = [1] + y_shape_to_compare
-            constant_value = np.expand_dims(constant_value, axis=0)
-
-        if len(y_shape_to_compare) >= 3:
-            # Permute constant_value to (N,C,...) format for correct further reshape
-            order = [0, len(y_shape_to_compare) - 1] + list(range(1, len(y_shape_to_compare) - 1))
-            y_shape_to_compare = [y_shape_to_compare[i] for i in order]
-            constant_value = np.transpose(constant_value, order)
-
         ref_net = None
 
         return tf_net, ref_net
@@ -87,10 +69,10 @@ def create_sub_placeholder_const_net(self, x_shape, y_shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [
         # Power
@@ -105,10 +87,10 @@ def test_sub_placeholder_const_1D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         # Power
@@ -125,10 +107,10 @@ def test_sub_placeholder_const_2D(self, params, ie_device, precision, ir_version
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         # Power
@@ -144,10 +126,10 @@ def test_sub_placeholder_const_3D(self, params, ie_device, precision, ir_version
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         # Power
@@ -164,10 +146,10 @@ def test_sub_placeholder_const_4D(self, params, ie_device, precision, ir_version
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     ###############################################################################################
     #                                                                                             #
@@ -181,10 +163,10 @@ def test_sub_placeholder_const_5D(self, params, ie_device, precision, ir_version
 
     @pytest.mark.parametrize("params", test_data_broadcast_1D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_broadcast_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_broadcast_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_2D = [
         # Power
@@ -201,10 +183,10 @@ def test_sub_placeholder_const_broadcast_1D(self, params, ie_device, precision,
 
     @pytest.mark.parametrize("params", test_data_broadcast_2D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_broadcast_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_broadcast_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_3D = [
         # Power
@@ -226,10 +208,10 @@ def test_sub_placeholder_const_broadcast_2D(self, params, ie_device, precision,
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_broadcast_3D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_broadcast_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_broadcast_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_4D = [
         # Power
@@ -258,10 +240,10 @@ def test_sub_placeholder_const_broadcast_3D(self, params, ie_device, precision,
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_broadcast_4D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_broadcast_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_broadcast_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_broadcast_5D = [
         # Power
@@ -288,7 +270,7 @@ def test_sub_placeholder_const_broadcast_4D(self, params, ie_device, precision,
     # TODO mark as precommit (after successfully passing in nightly)
     @pytest.mark.parametrize("params", test_data_broadcast_5D)
     @pytest.mark.nightly
-    def test_sub_placeholder_const_broadcast_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version),
+    def test_sub_placeholder_const_broadcast_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_sub_placeholder_const_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
                    ie_device, precision, ir_version,
-                   temp_dir=temp_dir)
+                   temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_Swish.py b/tests/layer_tests/tensorflow_tests/test_tf_Swish.py
index 158a43353ac79c..13a4c1c98ad99c 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_Swish.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_Swish.py
@@ -5,11 +5,12 @@
 
 from common.layer_test_class import check_ir_version
 from common.tf_layer_test_class import CommonTFLayerTest
+from common.utils.tf_utils import permute_nchw_to_nhwc
 from unit_tests.utils.graph import build_graph
 
 
 class TestSwish(CommonTFLayerTest):
-    def create_swish_net(self, shape, ir_version):
+    def create_swish_net(self, shape, ir_version, use_new_frontend):
         """
             Tensorflow net                 IR net
 
@@ -27,11 +28,10 @@ def create_swish_net(self, shape, ir_version):
 
         # Create the graph and model
         with tf.Session() as sess:
-            shapes = shape.copy()
-            # reshaping
-            if len(shapes) > 3:
-                shapes.append(shapes.pop(1))
-            input = tf.placeholder(tf.float32, shapes, 'Input')
+            tf_x_shape = shape.copy()
+
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+            input = tf.placeholder(tf.float32, tf_x_shape, 'Input')
 
             tf.nn.swish(input)
 
@@ -46,7 +46,7 @@ def create_swish_net(self, shape, ir_version):
 
         ref_net = None
 
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             nodes_attributes = {
                 'input': {'kind': 'op', 'type': 'Parameter'},
                 'input_data': {'shape': shape, 'kind': 'data'},
@@ -71,9 +71,9 @@ def create_swish_net(self, shape, ir_version):
 
     @pytest.mark.parametrize("params", test_data_precommit)
     @pytest.mark.precommit
-    def test_swish_precommit(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_swish_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_swish_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_swish_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data = [dict(shape=[1]),
                  dict(shape=[1, 224]),
@@ -83,6 +83,6 @@ def test_swish_precommit(self, params, ie_device, precision, ir_version, temp_di
 
     @pytest.mark.parametrize("params", test_data)
     @pytest.mark.nightly
-    def test_swish(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_swish_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_swish(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_swish_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_TopK.py b/tests/layer_tests/tensorflow_tests/test_tf_TopK.py
index c2d2fe54c07044..fe1b4e1949d51d 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_TopK.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_TopK.py
@@ -7,12 +7,12 @@
 from common.tf_layer_test_class import CommonTFLayerTest
 from mo.ops.op import PermuteAttrs
 from unit_tests.utils.graph import build_graph
-from tensorflow_tests.permutation_utils import permute_nchw_to_nhwc, permute_axis
+from common.utils.tf_utils import permute_nchw_to_nhwc, permute_axis
 
 
 class Test_TopK(CommonTFLayerTest):
     @staticmethod
-    def create_topK_net(shape, k, ir_version):
+    def create_topK_net(shape, k, ir_version, use_new_frontend):
         """
             Tensorflow net:
 
@@ -57,7 +57,7 @@ def create_topK_net(shape, k, ir_version):
 
         ref_net = None
 
-        if check_ir_version(10, None, ir_version):
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
             nodes_attributes = {
                 'input': {'kind': 'op', 'type': 'Parameter'},
                 'input_data': {'shape': shape, 'kind': 'data'},
@@ -94,9 +94,9 @@ def create_topK_net(shape, k, ir_version):
 
     @pytest.mark.parametrize("params", test_data_1D)
     @pytest.mark.nightly
-    def test_TopK_1D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_topK_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_TopK_1D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_topK_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_2D = [
         dict(shape=[14, 15], k=10),
@@ -105,9 +105,9 @@ def test_TopK_1D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_2D)
     @pytest.mark.nightly
-    def test_TopK_2D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_topK_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_TopK_2D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_topK_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_3D = [
         dict(shape=[13, 14, 15], k=10),
@@ -116,9 +116,9 @@ def test_TopK_2D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_3D)
     @pytest.mark.nightly
-    def test_TopK_3D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_topK_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_TopK_3D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_topK_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_4D = [
         dict(shape=[12, 13, 14, 15], k=10),
@@ -127,9 +127,9 @@ def test_TopK_3D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_4D)
     @pytest.mark.nightly
-    def test_TopK_4D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_topK_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_TopK_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_topK_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
 
     test_data_5D = [
         dict(shape=[11, 12, 13, 14, 15], k=10),
@@ -138,6 +138,6 @@ def test_TopK_4D(self, params, ie_device, precision, ir_version, temp_dir):
 
     @pytest.mark.parametrize("params", test_data_5D)
     @pytest.mark.nightly
-    def test_TopK_5D(self, params, ie_device, precision, ir_version, temp_dir):
-        self._test(*self.create_topK_net(**params, ir_version=ir_version),
-                   ie_device, precision, ir_version, temp_dir=temp_dir)
+    def test_TopK_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend):
+        self._test(*self.create_topK_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_UnaryOps.py b/tests/layer_tests/tensorflow_tests/test_tf_UnaryOps.py
new file mode 100644
index 00000000000000..6281db52dce4b0
--- /dev/null
+++ b/tests/layer_tests/tensorflow_tests/test_tf_UnaryOps.py
@@ -0,0 +1,202 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+from common.layer_test_class import check_ir_version
+from common.tf_layer_test_class import CommonTFLayerTest
+from unit_tests.utils.graph import build_graph
+from common.utils.tf_utils import permute_nchw_to_nhwc
+
+import numpy as np
+
+
+class TestUnaryOps(CommonTFLayerTest):
+    current_op_type = None
+
+    def _prepare_input(self, inputs_dict):
+        non_negative = ['Sqrt', 'Log']
+        narrow_borders = ["Sinh", "Cosh", "Tanh", "Exp"]
+        within_one = ['Asin', 'Acos', 'Atanh']
+        from_one = ['Acosh']
+
+        logical_type = ['LogicalNot']
+
+        # usual function domain
+        lower = -256
+        upper = 256
+
+        # specific domains
+        if self.current_op_type in non_negative:
+            lower = 0
+        elif self.current_op_type in narrow_borders:
+            lower = -16
+            upper = 16
+        elif self.current_op_type in from_one:
+            lower = 1
+        elif self.current_op_type in within_one:
+            lower = -1
+            upper = 1
+
+        for input in inputs_dict.keys():
+            if self.current_op_type in logical_type:
+                inputs_dict[input] = np.random.randint(0, 1, inputs_dict[input]).astype(np.bool)
+            else:
+                inputs_dict[input] = np.random.uniform(lower, upper, inputs_dict[input]).astype(np.float32)
+
+        return inputs_dict
+
+    def create_net_with_unary_op(self, shape, ir_version, op_type, use_new_frontend):
+        """
+            Tensorflow net                 IR net
+
+            Input->UnaryOp       =>       Input->UnaryOp
+
+        """
+        import tensorflow as tf
+        self.current_op_type = op_type
+        op_type_to_tf = {
+            'Abs': tf.math.abs,
+            'Acos': tf.math.acos,
+            'Acosh': tf.math.acosh,
+            'Asin': tf.math.asin,
+            'Asinh': tf.math.asinh,
+            'Atan': tf.math.atan,
+            'Atanh': tf.math.atanh,
+            'Ceiling': tf.math.ceil,
+            'Cos': tf.math.cos,
+            'Cosh': tf.math.cosh,
+            'Elu': tf.nn.elu,
+            'Exp': tf.math.exp,
+            'Floor': tf.math.floor,
+            'Log': tf.math.log,
+            'LogicalNot': tf.math.logical_not,
+            'Negative': tf.math.negative,
+            'Sigmoid': tf.nn.sigmoid,
+            'Sign': tf.math.sign,
+            'Sin': tf.math.sin,
+            'Sinh': tf.math.sinh,
+            'SoftPlus': tf.nn.softplus,
+            'Tan': tf.math.tan,
+            'Tanh': tf.math.tanh,
+            'ReLU': tf.nn.relu,
+        }
+
+        #
+        #   Create Tensorflow model
+        #
+
+        tf.compat.v1.reset_default_graph()
+
+        type = tf.float32
+        if op_type == "LogicalNot":
+            type = tf.bool
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            tf_x_shape = shape.copy()
+            tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
+
+            input = tf.compat.v1.placeholder(type, tf_x_shape, 'Input')
+            op_type_to_tf[self.current_op_type](input, name='Operation')
+
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        #
+        #   Create reference IR net
+        #   Please, specify 'type': 'Input' for input node
+        #   Moreover, do not forget to validate ALL layer attributes!!!
+        #
+
+        ref_net = None
+
+        if check_ir_version(10, None, ir_version) and not use_new_frontend:
+            nodes_attributes = {
+                'input': {'kind': 'op', 'type': 'Parameter'},
+                'input_data': {'shape': shape, 'kind': 'data'},
+                'testing_op': {'kind': 'op', 'type': self.current_op_type},
+                'testing_data': {'shape': shape, 'kind': 'data'},
+                'result': {'kind': 'op', 'type': 'Result'}
+            }
+
+            ref_net = build_graph(nodes_attributes,
+                                  [('input', 'input_data'),
+                                   ('input_data', 'testing_op'),
+                                   ('testing_op', 'testing_data'),
+                                   ('testing_data', 'result')
+                                   ])
+
+        return tf_net, ref_net
+
+    test_data_precommit = [dict(shape=[4, 6, 8, 10, 12])]
+
+    @pytest.mark.parametrize("params", test_data_precommit)
+    @pytest.mark.parametrize("op_type", ['Elu',
+                                         'Sigmoid',
+                                         'Sin',
+                                         'Sinh',
+                                         'Cos',
+                                         'Cosh',
+                                         'Abs',
+                                         'Negative',
+                                         'Exp',
+                                         'Tan',
+                                         'Tanh',
+                                         'Floor',
+                                         'ReLU',
+                                         'Ceiling',
+                                         'Asin',
+                                         'Acos',
+                                         'Atan',
+                                         'Log',
+                                         'Sign',
+                                         'SoftPlus',
+                                         'Atanh',
+                                         'Acosh',
+                                         'Asinh',
+                                         'LogicalNot',
+                                         ])
+    @pytest.mark.precommit
+    def test_unary_op_precommit(self, params, ie_device, precision, ir_version, temp_dir, op_type, use_new_frontend):
+        if ie_device == 'GPU':
+            pytest.skip("5D tensors is not supported on GPU")
+        self._test(*self.create_net_with_unary_op(**params, ir_version=ir_version, op_type=op_type,
+                                                  use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
+
+    test_data = [dict(shape=[10, 12]),
+                 dict(shape=[8, 10, 12]),
+                 dict(shape=[6, 8, 10, 12]),
+                 dict(shape=[4, 6, 8, 10, 12])]
+
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("op_type", ['Elu',
+                                         'Sigmoid',
+                                         'Sin',
+                                         'Sinh',
+                                         'Cos',
+                                         'Cosh',
+                                         'Abs',
+                                         'Negative',
+                                         'Exp',
+                                         'Tan',
+                                         'Tanh',
+                                         'Floor',
+                                         'ReLU',
+                                         'Ceiling',
+                                         'Asin',
+                                         'Acos',
+                                         'Atan',
+                                         'Log',
+                                         'LogicalNot',
+                                         'Sign',
+                                         'SoftPlus',
+                                         'Atanh',
+                                         'Acosh',
+                                         'Asinh'])
+    @pytest.mark.nightly
+    def test_unary_op(self, params, ie_device, precision, ir_version, temp_dir, op_type, use_new_frontend):
+        if ie_device == 'GPU':
+            pytest.skip("5D tensors is not supported on GPU")
+        self._test(*self.create_net_with_unary_op(**params, ir_version=ir_version, op_type=op_type,
+                                                  use_new_frontend=use_new_frontend),
+                   ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend)
diff --git a/tests/memory_tests/test_runner/conftest.py b/tests/memory_tests/test_runner/conftest.py
index a863eda8533222..1886249e99f9ed 100644
--- a/tests/memory_tests/test_runner/conftest.py
+++ b/tests/memory_tests/test_runner/conftest.py
@@ -430,8 +430,7 @@ def prepare_timeline_report(pytestconfig):
         import jinja2  # pylint: disable=import-outside-toplevel
 
         env = jinja2.Environment(
-            loader=jinja2.FileSystemLoader(
-                searchpath=Path().absolute() / 'memory_template'),
+            loader=jinja2.FileSystemLoader(searchpath=os.path.join(abs_path('.'), 'memory_template')),
             autoescape=False)
         template = env.get_template('timeline_report.html')
         template.stream(records=records, timelines=timelines).dump(report_path)
diff --git a/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml b/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml
index 042e7745a1260c..05d67549066b7d 100644
--- a/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml
+++ b/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml
@@ -10,12 +10,12 @@
         <model path="public/mobilenet-ssd/FP32/mobilenet-ssd.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="820887" vmpeak="820887" vmrss="114363" vmhwm="114363" />
         <model path="public/mobilenet-ssd/FP32/mobilenet-ssd.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="1667380" vmpeak="1667380" vmrss="472123" vmhwm="753719" />
 
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="714792" vmpeak="714792" vmrss="32432" vmhwm="32432" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="1516876" vmpeak="1537109" vmrss="265798" vmhwm="340506" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="815084" vmpeak="815084" vmrss="35053" vmhwm="35053" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="1697930" vmpeak="1783126" vmrss="266214" vmhwm="340667" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="715192" vmpeak="715192" vmrss="33404" vmhwm="33404" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="1602166" vmpeak="1687363" vmrss="265257" vmhwm="340563" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="908018" vmpeak="908018" vmrss="41142" vmhwm="41142" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="2108095" vmpeak="2230602" vmrss="215618" vmhwm="281486" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1008342" vmpeak="1093008" vmrss="43602" vmhwm="43602" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2203952" vmpeak="2241272" vmrss="214328" vmhwm="280103" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="908549" vmpeak="908549" vmrss="43492" vmhwm="43492" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2108100" vmpeak="2230612" vmrss="193330" vmhwm="280550" />
 
         <model path="public/ssd300/FP32/ssd300.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="1054892" vmpeak="1054892" vmrss="292962" vmhwm="292962" />
         <model path="public/ssd300/FP32/ssd300.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="1887948" vmpeak="1925471" vmrss="792849" vmhwm="1081917" />
@@ -39,12 +39,12 @@
         <model path="public/mobilenet-ssd/FP16/mobilenet-ssd.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="834516" vmpeak="834516" vmrss="128102" vmhwm="128102" />
         <model path="public/mobilenet-ssd/FP16/mobilenet-ssd.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="1623206" vmpeak="1708402" vmrss="459144" vmhwm="762455" />
 
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="714942" vmpeak="714942" vmrss="33004" vmhwm="33004" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="1524187" vmpeak="1549865" vmrss="244857" vmhwm="363906" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="inference_with_streams" device="CPU" vmsize="815235" vmpeak="892377" vmrss="36332" vmhwm="36332" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="1691196" vmpeak="1776392" vmrss="265616" vmhwm="362377" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="912386" vmpeak="912386" vmrss="34309" vmhwm="34309" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="1595469" vmpeak="1680666" vmrss="268387" vmhwm="385330" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="908169" vmpeak="908169" vmrss="42021" vmhwm="42021" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2107846" vmpeak="2145161" vmrss="192961" vmhwm="280217" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="inference_with_streams" device="CPU" vmsize="1008113" vmpeak="1088786" vmrss="44028" vmhwm="44028" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="2203697" vmpeak="2222500" vmrss="193143" vmhwm="279931" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="908700" vmpeak="908700" vmrss="43227" vmhwm="43227" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="2107846" vmpeak="2145161" vmrss="194516" vmhwm="281372" />
 
         <model path="public/ssd300/FP16/ssd300.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1120184" vmpeak="1120184" vmrss="359200" vmhwm="359200" />
         <model path="public/ssd300/FP16/ssd300.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="1630569" vmpeak="1752530" vmrss="546364" vmhwm="874426" />
diff --git a/tests/time_tests/src/timetests/timetest_infer.cpp b/tests/time_tests/src/timetests/timetest_infer.cpp
index 83f47f837e1690..e4870ec500c4be 100644
--- a/tests/time_tests/src/timetests/timetest_infer.cpp
+++ b/tests/time_tests/src/timetests/timetest_infer.cpp
@@ -3,6 +3,7 @@
 //
 
 #include <inference_engine.hpp>
+#include <ie_plugin_config.hpp>
 #include <iostream>
 
 #include "common_utils.h"
@@ -29,6 +30,8 @@ int runPipeline(const std::string &model, const std::string &device) {
       {
         SCOPED_TIMER(load_plugin);
         ie.GetVersions(device);
+        // enables performance hint for specified device
+        ie.SetConfig({{CONFIG_KEY(PERFORMANCE_HINT), CONFIG_VALUE(LATENCY)}}, device);
       }
       {
         SCOPED_TIMER(create_exenetwork);
diff --git a/tests/time_tests/src/timetests/timetest_infer_cache.cpp b/tests/time_tests/src/timetests/timetest_infer_cache.cpp
index f1c657f6aa463f..0b0ee0c28e1c7c 100644
--- a/tests/time_tests/src/timetests/timetest_infer_cache.cpp
+++ b/tests/time_tests/src/timetests/timetest_infer_cache.cpp
@@ -3,6 +3,7 @@
 //
 
 #include <inference_engine.hpp>
+#include <ie_plugin_config.hpp>
 #include <iostream>
 
 #include "common_utils.h"
@@ -31,7 +32,8 @@ int runPipeline(const std::string &model, const std::string &device) {
       }
       {
         SCOPED_TIMER(load_network);
-        ie.SetConfig({{"CACHE_DIR", "models_cache"}});
+        // enables cache
+        ie.SetConfig({{CONFIG_KEY(CACHE_DIR), "models_cache"}});
         exeNetwork = ie.LoadNetwork(model, device);
       }
       {
diff --git a/thirdparty/CMakeLists.txt b/thirdparty/CMakeLists.txt
index 781e75a34ece54..35f327ca8eab1d 100644
--- a/thirdparty/CMakeLists.txt
+++ b/thirdparty/CMakeLists.txt
@@ -10,16 +10,17 @@ if(ENABLE_LTO)
     set(CMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE ON)
 endif()
 
-add_subdirectory(ittapi EXCLUDE_FROM_ALL)
+add_subdirectory(ittapi)
 add_subdirectory(itt_collector EXCLUDE_FROM_ALL)
 add_subdirectory(zlib EXCLUDE_FROM_ALL)
 add_subdirectory(cnpy EXCLUDE_FROM_ALL)
 if(ENABLE_CLDNN)
-    add_subdirectory(ocl EXCLUDE_FROM_ALL)
+    add_subdirectory(ocl)
 endif()
 
 add_subdirectory(xbyak EXCLUDE_FROM_ALL)
 openvino_developer_export_targets(COMPONENT openvino_common TARGETS xbyak)
+ov_install_static_lib(xbyak openvino_common)
 
 #
 # Pugixml
@@ -31,10 +32,14 @@ if(ENABLE_SYSTEM_PUGIXML)
     add_library(pugixml::static ALIAS pugixml)
 else()
     function(ie_build_pugixml)
-        set(BUILD_SHARED_LIBS OFF)
-        add_subdirectory(pugixml EXCLUDE_FROM_ALL)
+        function(ie_build_pugixml_static)
+            set(BUILD_SHARED_LIBS OFF)
+            add_subdirectory(pugixml EXCLUDE_FROM_ALL)
+        endfunction()
+        ie_build_pugixml_static()
         set_property(TARGET pugixml-static PROPERTY EXPORT_NAME pugixml)
         openvino_developer_export_targets(COMPONENT openvino_common TARGETS pugixml::static)
+        ov_install_static_lib(pugixml-static openvino_common)
     endfunction()
 
     ie_build_pugixml()
@@ -52,6 +57,9 @@ add_subdirectory(fluid/modules/gapi EXCLUDE_FROM_ALL)
 set_target_properties(ade fluid PROPERTIES FOLDER thirdparty)
 openvino_developer_export_targets(COMPONENT openvino_common TARGETS ade fluid)
 
+ov_install_static_lib(ade openvino_common)
+ov_install_static_lib(fluid openvino_common)
+
 #
 # Gflags
 #
@@ -121,12 +129,6 @@ endif()
 #
 # oneDNN for GPU plugin
 #
-if (ANDROID OR (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 7.0))
-    # oneDNN doesn't support old compilers and android builds for now, so we'll build GPU plugin without oneDNN
-    set(ENABLE_ONEDNN_FOR_GPU OFF CACHE BOOL "")
-else()
-    set(ENABLE_ONEDNN_FOR_GPU ON CACHE BOOL "")
-endif()
 
 if(ENABLE_ONEDNN_FOR_GPU)
     function(build_onednn_gpu)
@@ -136,6 +138,10 @@ if(ENABLE_ONEDNN_FOR_GPU)
         set(ONEDNN_LIBRARY "${ONEDNN_BUILD_DIR}/src/${CMAKE_STATIC_LIBRARY_PREFIX}onednn_gpu${CMAKE_STATIC_LIBRARY_SUFFIX}")
         set(ONEDNN_OUTPUT_LIBRARY "${ONEDNN_INSTALL_DIR}/${CMAKE_STATIC_LIBRARY_PREFIX}onednn_gpu${CMAKE_STATIC_LIBRARY_SUFFIX}")
 
+        if(CMAKE_COMPILER_IS_GNUCXX)
+            ie_add_compiler_flags(-Wno-undef)
+        endif()
+
         # Get processors count to limit number of threads spawned by make
         include(ProcessorCount)
         ProcessorCount(CORES_COUNT)
@@ -150,15 +156,22 @@ if(ENABLE_ONEDNN_FOR_GPU)
             EXCLUDE_FROM_ALL ON
             CMAKE_ARGS "-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}"
                        "-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}"
+                       "-DCMAKE_CXX_COMPILER_LAUNCHER=${CMAKE_CXX_COMPILER_LAUNCHER}"
+                       "-DCMAKE_C_COMPILER_LAUNCHER=${CMAKE_C_COMPILER_LAUNCHER}"
+                       "-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}"
+                       "-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}"
+                       "-DCMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE=${ENABLE_LTO}"
+                       "-DCMAKE_POLICY_DEFAULT_CMP0069=NEW"
                        "-DDNNL_CPU_RUNTIME=NONE"
                        "-DDNNL_GPU_RUNTIME=OCL"
-                       "-DDNNL_GPU_RUNTIME=OCL"
                        "-DDNNL_LIBRARY_NAME=onednn_gpu"
                        "-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}"
                        "-DCMAKE_RUNTIME_OUTPUT_DIRECTORY=${CMAKE_RUNTIME_OUTPUT_DIRECTORY}"
                        "-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_LIBRARY_OUTPUT_DIRECTORY}"
                        "-DDNNL_ENABLE_CONCURRENT_EXEC=ON"
                        "-DDNNL_ENABLE_PRIMITIVE_CACHE=ON"
+                       "-DDNNL_ENABLE_JIT_PROFILING=${BUILD_SHARED_LIBS}"
+                       "-DDNNL_ENABLE_ITT_TASKS=${BUILD_SHARED_LIBS}"
                        "-DDNNL_BUILD_TESTS=OFF"
                        "-DDNNL_BUILD_EXAMPLES=OFF"
                        "-DDNNL_BLAS_VENDOR=NONE"
@@ -171,6 +184,23 @@ if(ENABLE_ONEDNN_FOR_GPU)
             COMMAND ${CMAKE_COMMAND} -E echo "OneDNN $<CONFIG> build for GPU complete"
             BUILD_BYPRODUCTS ${ONEDNN_OUTPUT_LIBRARY}
         )
+
+        ExternalProject_Get_property(onednn_gpu_build SOURCE_DIR)
+        ExternalProject_Get_property(onednn_gpu_build BINARY_DIR)
+
+        # WA: create temp 'include' directory
+        file(MAKE_DIRECTORY "${BINARY_DIR}/include")
+
+        add_library(onednn_gpu STATIC IMPORTED GLOBAL)
+        set_target_properties(onednn_gpu PROPERTIES
+            IMPORTED_LOCATION ${ONEDNN_OUTPUT_LIBRARY}
+            INTERFACE_INCLUDE_DIRECTORIES "$<BUILD_INTERFACE:${BINARY_DIR}/include>;$<BUILD_INTERFACE:${SOURCE_DIR}/include>"
+            INTERFACE_COMPILE_DEFINITIONS ENABLE_ONEDNN_FOR_GPU)
+        add_dependencies(onednn_gpu onednn_gpu_build)
+
+        # TODO: does not work for imported targets; need to import this target in
+        # OpenVINOConfig.cmake explicitly
+        # ov_install_static_lib(onednn_gpu gpu)
     endfunction()
 
     build_onednn_gpu()
diff --git a/thirdparty/cnpy/cnpy.cpp b/thirdparty/cnpy/cnpy.cpp
index ea89bea9c088b2..859f898210a39e 100644
--- a/thirdparty/cnpy/cnpy.cpp
+++ b/thirdparty/cnpy/cnpy.cpp
@@ -170,7 +170,8 @@ void cnpy::parse_npy_header(FILE* fp, size_t& word_size, std::vector<size_t>& sh
 void cnpy::parse_zip_footer(FILE* fp, uint16_t& nrecs, size_t& global_header_size, size_t& global_header_offset)
 {
     std::vector<char> footer(22);
-    fseek(fp,-22,SEEK_END);
+    if(fseek(fp,-22,SEEK_END) != 0)
+        throw std::runtime_error("parse_zip_footer: failed fseek");
     size_t res = fread(&footer[0],sizeof(char),22,fp);
     if(res != 22)
         throw std::runtime_error("parse_zip_footer: failed fread");
@@ -195,7 +196,7 @@ cnpy::NpyArray load_the_npy_file(FILE* fp) {
     size_t word_size = 0;
     bool fortran_order = false;
     cnpy::parse_npy_header(fp,word_size,shape,fortran_order);
-    if (word_size >= 0 && word_size < ULLONG_MAX) {
+    if (word_size > 0 && word_size < ULLONG_MAX) {
         cnpy::NpyArray arr(shape, word_size, fortran_order);
         size_t nread = fread(arr.data<char>(), 1, arr.num_bytes(), fp);
         if (nread != arr.num_bytes())
@@ -223,6 +224,7 @@ cnpy::NpyArray load_the_npz_array(FILE* fp, uint32_t compr_bytes, uint32_t uncom
     d_stream.opaque = Z_NULL;
     d_stream.avail_in = 0;
     d_stream.next_in = Z_NULL;
+    d_stream.total_out = 0;
     err = inflateInit2(&d_stream, -MAX_WBITS);
 
     d_stream.avail_in = compr_bytes;
@@ -237,7 +239,7 @@ cnpy::NpyArray load_the_npz_array(FILE* fp, uint32_t compr_bytes, uint32_t uncom
     size_t word_size = 0;
     bool fortran_order = false;
     cnpy::parse_npy_header(&buffer_uncompr[0],word_size,shape,fortran_order);
-    if (word_size >= 0 && word_size < ULLONG_MAX) {
+    if (word_size > 0 && word_size < ULLONG_MAX) {
         cnpy::NpyArray array(shape, word_size, fortran_order);
 
         size_t offset = uncompr_bytes - array.num_bytes();
@@ -344,7 +346,8 @@ cnpy::NpyArray cnpy::npz_load(std::string fname, std::string varname) {
         else {
             //skip past the data
             uint32_t size = *(uint32_t*) &local_header[22];
-            fseek(fp,size,SEEK_CUR);
+            if (fseek(fp,size,SEEK_CUR) !=0)
+                throw std::runtime_error("npz_load: failed fseek");
         }
     }
 
@@ -370,5 +373,3 @@ cnpy::NpyArray cnpy::npy_load(std::string fname) {
     }
 
 }
-
-
diff --git a/thirdparty/cnpy/cnpy.h b/thirdparty/cnpy/cnpy.h
index e8935e8937e89c..40b959a6a8a0cd 100644
--- a/thirdparty/cnpy/cnpy.h
+++ b/thirdparty/cnpy/cnpy.h
@@ -155,7 +155,8 @@ namespace cnpy {
             //below, we will write the the new data at the start of the global header then append the global header and footer below it
             size_t global_header_size;
             parse_zip_footer(fp,nrecs,global_header_size,global_header_offset);
-            fseek(fp,global_header_offset,SEEK_SET);
+            if(fseek(fp,global_header_offset,SEEK_SET)!=0 )
+                throw std::runtime_error("npz_save: failed fseek");
             global_header.resize(global_header_size);
             size_t res = fread(&global_header[0],sizeof(char),global_header_size,fp);
             if(res != global_header_size){
diff --git a/thirdparty/ittapi/CMakeLists.txt b/thirdparty/ittapi/CMakeLists.txt
index c50ff92fa272b3..87cf02a851be5d 100644
--- a/thirdparty/ittapi/CMakeLists.txt
+++ b/thirdparty/ittapi/CMakeLists.txt
@@ -11,13 +11,19 @@ if(ENABLE_PROFILING_ITT)
             message(WARNING "Profiling option enabled, but no ITT library was found under INTEL_VTUNE_DIR")
         endif()
     else()
-        add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ittapi ${CMAKE_BINARY_DIR}/_deps/ittapi)
+        add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ittapi ${CMAKE_BINARY_DIR}/_deps/ittapi EXCLUDE_FROM_ALL)
 
         target_compile_definitions(ittnotify INTERFACE ENABLE_PROFILING_ITT)
-        if (UNIX)
+        if(CMAKE_COMPILER_IS_GNUCXX)
             target_compile_options(ittnotify PRIVATE -Wno-undef)
         endif()
 
+        # override INTERFACE_INCLUDE_DIRECTORIES
+        set_property(TARGET ittnotify PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+                     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/ittapi/src/ittnotify>
+                     $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/ittapi/include>)
+
         openvino_developer_export_targets(COMPONENT openvino_common TARGETS ittnotify)
+        ov_install_static_lib(ittnotify openvino_common)
     endif()
 endif()
diff --git a/thirdparty/ocl/CMakeLists.txt b/thirdparty/ocl/CMakeLists.txt
index a0648080ea13a6..7a54e9558c75bb 100644
--- a/thirdparty/ocl/CMakeLists.txt
+++ b/thirdparty/ocl/CMakeLists.txt
@@ -43,10 +43,20 @@ set(CMAKE_C_FLAGS "")
 set(CMAKE_CXX_FLAGS "")
 set(CMAKE_C_FLAGS_RELEASE "")
 set(CMAKE_CXX_FLAGS_RELEASE "")
+# OpenCL has absolute paths to include directories
+set(CMAKE_WARN_ON_ABSOLUTE_INSTALL_DESTINATION OFF)
+if(NOT DEFINED USE_DYNAMIC_VCXX_RUNTIME)
+    set(USE_DYNAMIC_VCXX_RUNTIME ON)
+endif()
 
-add_subdirectory(icd_loader)
+add_subdirectory(icd_loader EXCLUDE_FROM_ALL)
 
-target_include_directories(OpenCL SYSTEM PUBLIC ${OPENCL_HEADERS_DIR})
+# override INTERFACE_INCLUDE_DIRECTORIES
+set_property(TARGET OpenCL PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+    $<BUILD_INTERFACE:${OPENCL_ICD_LOADER_HEADERS_DIR}>)
+foreach(dir IN LISTS OPENCL_HEADERS_DIR)
+    target_include_directories(OpenCL SYSTEM PUBLIC $<BUILD_INTERFACE:${dir}>)
+endforeach()
 
 # The following varables are needed to make find_package(OpenCL) work
 set(OPENCL_VERSION_2_2 ON CACHE BOOL "" FORCE)
@@ -58,3 +68,5 @@ get_lib_path("${OUTPUT_ROOT}/${BIN_FOLDER}" OPENCL_LIB_DIR)
 get_lib_name("OpenCL" OPENCL_LIB_NAME)
 
 set(OpenCL_LIBRARY "${OPENCL_LIB_DIR}/${OPENCL_LIB_NAME}" CACHE PATH "" FORCE)
+
+ov_install_static_lib(OpenCL gpu)
diff --git a/thirdparty/onednn_gpu b/thirdparty/onednn_gpu
index 1ab3f50381ae85..75d978369d0c5b 160000
--- a/thirdparty/onednn_gpu
+++ b/thirdparty/onednn_gpu
@@ -1 +1 @@
-Subproject commit 1ab3f50381ae85ab5ab0a29d5565ebe72ce77030
+Subproject commit 75d978369d0c5be04ec36c3cea2e00a14da1ec83
diff --git a/thirdparty/onnx/CMakeLists.txt b/thirdparty/onnx/CMakeLists.txt
index 8c9d8b6e8c60bb..263802dd42fc69 100644
--- a/thirdparty/onnx/CMakeLists.txt
+++ b/thirdparty/onnx/CMakeLists.txt
@@ -23,7 +23,7 @@ endmacro()
 
 set(ONNX_USE_PROTOBUF_SHARED_LIBS ${BUILD_SHARED_LIBS} CACHE BOOL "Use dynamic protobuf by ONNX library" FORCE)
 set(ONNX_NAMESPACE ${NGRAPH_ONNX_NAMESPACE})
-set(ONNX_USE_LITE_PROTO ${NGRAPH_USE_PROTOBUF_LITE} CACHE BOOL "Use protobuf lite for ONNX library" FORCE)
+set(ONNX_USE_LITE_PROTO ON CACHE BOOL "Use protobuf lite for ONNX library" FORCE)
 set(ONNX_ML ON CACHE BOOL "Use ONNX ML" FORCE)
 if(CMAKE_CROSSCOMPILING)
     set(ONNX_CUSTOM_PROTOC_EXECUTABLE ${SYSTEM_PROTOC})
diff --git a/thirdparty/onnx/onnx b/thirdparty/onnx/onnx
index 1089b9e8045a3a..da889e6b957503 160000
--- a/thirdparty/onnx/onnx
+++ b/thirdparty/onnx/onnx
@@ -1 +1 @@
-Subproject commit 1089b9e8045a3a2882d7bb6a1dbaeaf9cae131da
+Subproject commit da889e6b95750350726d149bf447bf0cd1245964
diff --git a/thirdparty/protobuf/CMakeLists.txt b/thirdparty/protobuf/CMakeLists.txt
index 821c6764666ea2..97cb66b5462484 100644
--- a/thirdparty/protobuf/CMakeLists.txt
+++ b/thirdparty/protobuf/CMakeLists.txt
@@ -69,7 +69,7 @@ if(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG OR
         INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 endif()
 
-if(protobuf_VERSION VERSION_LESS "3.9" AND NGRAPH_USE_PROTOBUF_LITE)
+if(protobuf_VERSION VERSION_LESS "3.9")
     message(FATAL_ERROR "Minimum supported version of protobuf-lite library is 3.9.0 (provided ${protobuf_VERSION})")
 endif()
 
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 9798bde8bb1767..fb4dd45ca856e7 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -63,4 +63,9 @@ if(ENABLE_PYTHON)
             COMPONENT python_tools_${PYTHON_VERSION})
 
     ie_cpack(python_tools python_tools_${PYTHON_VERSION})
+
+    if(ENABLE_WHEEL)
+        add_subdirectory(openvino_dev)
+    endif()
+
 endif()
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/main.py b/tools/benchmark_tool/openvino/tools/benchmark/main.py
index 47fabb34c48fd7..422dc0d3991dc7 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/main.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/main.py
@@ -334,7 +334,10 @@ def set_throughput_streams():
         paths_to_input = list()
         if args.paths_to_input:
             for path in args.paths_to_input:
-                paths_to_input.append(os.path.abspath(*path) if args.paths_to_input else None)
+                if ":" in next(iter(path), ""):
+                    paths_to_input.extend(path)
+                else:
+                    paths_to_input.append(os.path.abspath(*path))
         set_inputs(paths_to_input, batch_size, app_inputs_info, infer_requests)
 
         if statistics:
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/parameters.py b/tools/benchmark_tool/openvino/tools/benchmark/parameters.py
index 89a3c6283dc5fa..f7f42b1c16c4d3 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/parameters.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/parameters.py
@@ -33,7 +33,9 @@ def parse_args():
                       help='Show this help message and exit.')
     args.add_argument('-i', '--paths_to_input', action='append', nargs='+', type=str, required=False,
                       help='Optional. '
-                           'Path to a folder with images and/or binaries or to specific image or binary file.')
+                           'Path to a folder with images and/or binaries or to specific image or binary file.'
+                           'It is also allowed to map files to network inputs: '
+                           'input_1:file_1,file_2,input_4:file_4 input_2:file_3')
     args.add_argument('-m', '--path_to_model', type=str, required=True,
                       help='Required. Path to an .xml/.onnx file with a trained model or '
                            'to a .blob file with a trained compiled model.')
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
index 0566b9b6ea8c93..dcae3f0aefa4fb 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
@@ -3,12 +3,17 @@
 
 import os
 import cv2
+import re
 import numpy as np
 from glob import glob
+from collections import defaultdict
+from pathlib import Path
+from itertools import chain
 
 from .constants import IMAGE_EXTENSIONS, BINARY_EXTENSIONS
 from .logging import logger
 
+
 def set_inputs(paths_to_input, batch_size, app_input_info, requests):
   requests_input_data = get_inputs(paths_to_input, batch_size, app_input_info, requests)
   for i in range(len(requests)):
@@ -18,7 +23,11 @@ def set_inputs(paths_to_input, batch_size, app_input_info, requests):
             raise Exception(f"No input with name {k} found!")
         inputs[k].buffer[:] = v
 
+
 def get_inputs(paths_to_input, batch_size, app_input_info, requests):
+    input_file_mapping = parse_paths_to_input(paths_to_input)
+    check_input_file_mapping(input_file_mapping, app_input_info)
+
     input_image_sizes = {}
     for key in sorted(app_input_info.keys()):
         info = app_input_info[key]
@@ -31,13 +40,15 @@ def get_inputs(paths_to_input, batch_size, app_input_info, requests):
     image_files = list()
     binary_files = list()
 
-    if paths_to_input:
+    if paths_to_input and not input_file_mapping:
         image_files = get_files_by_extensions(paths_to_input, IMAGE_EXTENSIONS)
-        image_files.sort()
         binary_files = get_files_by_extensions(paths_to_input, BINARY_EXTENSIONS)
-        binary_files.sort()
 
-    if (len(image_files) == 0) and (len(binary_files) == 0):
+    if input_file_mapping and len(input_file_mapping) < len(app_input_info):
+        not_provided_inputs = set(app_input_info) - set(input_file_mapping)
+        logger.warning("No input files were given for the inputs: "
+                       f"{', '.join(not_provided_inputs)}. This inputs will be filled with random values!")
+    elif (len(image_files) == 0) and (len(binary_files) == 0):
         logger.warning("No input files were given: all inputs will be filled with random values!")
     else:
         binary_to_be_used = binaries_count * batch_size * len(requests)
@@ -76,13 +87,23 @@ def get_inputs(paths_to_input, batch_size, app_input_info, requests):
             info = app_input_info[key]
             if info.is_image:
                 # input is image
+                if key in input_file_mapping:
+                    input_data[key] = fill_blob_with_image(input_file_mapping[key], request_id, batch_size,
+                                                           keys.index(key), len(keys), info, from_map=True)
+                    continue
+
                 if len(image_files) > 0:
                     input_data[key] = fill_blob_with_image(image_files, request_id, batch_size, keys.index(key),
                                                            len(keys), info)
                     continue
 
             # input is binary
-            if len(binary_files):
+            if len(binary_files) or key in input_file_mapping:
+                if key in input_file_mapping:
+                    input_data[key] = fill_blob_with_binary(input_file_mapping[key], request_id, batch_size,
+                                                           keys.index(key), len(keys), info, from_map=True)
+                    continue
+
                 input_data[key] = fill_blob_with_binary(binary_files, request_id, batch_size, keys.index(key),
                                                         len(keys), info)
                 continue
@@ -106,9 +127,19 @@ def get_inputs(paths_to_input, batch_size, app_input_info, requests):
 
 
 def get_files_by_extensions(paths_to_input, extensions):
-    get_extension = lambda file_path: file_path.split(".")[-1].upper()
+    if len(paths_to_input) == 1:
+        files = [file for file in paths_to_input[0].split(",") if file]
 
+        if all(get_extension(file) in extensions for file in files):
+            check_files_exist(files)
+            return files
+
+    return get_files_by_extensions_for_not_list_of_files(paths_to_input, extensions)
+
+
+def get_files_by_extensions_for_not_list_of_files(paths_to_input, extensions):
     input_files = list()
+
     for path_to_input in paths_to_input:
         if os.path.isfile(path_to_input):
             files = [os.path.normpath(path_to_input)]
@@ -119,13 +150,21 @@ def get_files_by_extensions(paths_to_input, extensions):
             file_extension = get_extension(file)
             if file_extension in extensions:
                 input_files.append(file)
+        input_files.sort()
+        return input_files
+
+
+def get_extension(file_path):
+    return file_path.split(".")[-1].upper()
 
-    return input_files
 
-def fill_blob_with_image(image_paths, request_id, batch_size, input_id, input_size, info):
+def fill_blob_with_image(image_paths, request_id, batch_size, input_id, input_size, info, from_map=False):
     shape = info.shape
     images = np.ndarray(shape)
-    image_index = request_id * batch_size * input_size + input_id
+    if from_map:
+        image_index = request_id * batch_size
+    else:
+        image_index = request_id * batch_size * input_size + input_id
 
     scale_mean = (not np.array_equal(info.scale, (1.0, 1.0, 1.0)) or not np.array_equal(info.mean, (0.0, 0.0, 0.0)))
 
@@ -154,7 +193,10 @@ def fill_blob_with_image(image_paths, request_id, batch_size, input_id, input_si
 
         images[b] = image
 
-        image_index += input_size
+        if from_map:
+            image_index += 1
+        else:
+            image_index += input_size
     return images
 
 def get_dtype(precision):
@@ -173,12 +215,15 @@ def get_dtype(precision):
         return format_map[precision]
     raise Exception("Can't find data type for precision: " + precision)
 
-def fill_blob_with_binary(binary_paths, request_id, batch_size, input_id, input_size, info):
+def fill_blob_with_binary(binary_paths, request_id, batch_size, input_id, input_size, info, from_map=False):
     binaries = np.ndarray(info.shape)
     shape = info.shape.copy()
     if 'N' in info.layout:
         shape[info.layout.index('N')] = 1
-    binary_index = request_id * batch_size * input_size + input_id
+    if from_map:
+        binary_index = request_id * batch_size
+    else:
+        binary_index = request_id * batch_size * input_size + input_id
     dtype = get_dtype(info.precision)[0]
     for b in range(batch_size):
         binary_index %= len(binary_paths)
@@ -191,7 +236,11 @@ def fill_blob_with_binary(binary_paths, request_id, batch_size, input_id, input_
             raise Exception(
                 f"File {binary_filename} contains {binary_file_size} bytes but network expects {blob_size}")
         binaries[b] = np.reshape(np.fromfile(binary_filename, dtype), shape)
-        binary_index += input_size
+
+        if from_map:
+            binary_index += 1
+        else:
+            binary_index += input_size
 
     return binaries
 
@@ -214,3 +263,75 @@ def fill_blob_with_random(layer):
     if layer.shape:
         return rs.uniform(rand_min, rand_max, layer.shape).astype(dtype)
     return (dtype)(rs.uniform(rand_min, rand_max))
+
+
+def parse_paths_to_input(paths_to_inputs):
+    input_dicts_list = [parse_path(path) for path in paths_to_inputs]
+    inputs = defaultdict(list)
+    for input_dict in input_dicts_list:
+        for input_name, input_files in input_dict.items():
+            inputs[input_name] += input_files
+    return {input_: files for input_, files in inputs.items() if files}
+
+
+def parse_path(path):
+    """
+    Parse "input_1:file1,file2,input_2:file3" into a dict
+    """
+    inputs = re.findall(r"([^,]\w+):", path)
+    input_files = [file for file in re.split(r"[^,]\w+:", path) if file]
+    return {
+        input_: files.strip(",").split(",") for input_, files in zip(inputs, input_files)
+    }
+
+
+def check_input_file_mapping(input_file_mapping, app_input_info):
+    check_inputs(app_input_info, input_file_mapping)
+    check_input_file_mapping_files_exists(input_file_mapping)
+    check_files_extensions(app_input_info, input_file_mapping)
+
+
+def check_inputs(app_input_info, input_file_mapping):
+    wrong_inputs = [
+        input_ for input_ in input_file_mapping if input_ not in app_input_info
+    ]
+    if wrong_inputs:
+        raise Exception(
+            f"Wrong input mapping! Cannot find inputs: {wrong_inputs}. "
+            f"Available inputs: {list(app_input_info)}. "
+            "Please check `-i` input data"
+        )
+
+
+def check_input_file_mapping_files_exists(input_file_mapping):
+    check_files_exist(chain.from_iterable(input_file_mapping.values()))
+
+
+def check_files_exist(input_files_list):
+    not_files = [
+        file for file in input_files_list if not Path(file).is_file()
+    ]
+    if not_files:
+        not_files = ",\n".join(not_files)
+        raise Exception(
+            f"Inputs are not files or does not exist!\n {not_files}"
+        )
+
+
+def check_files_extensions(app_input_info, input_file_mapping):
+    unsupported_files = []
+    for input_, files in input_file_mapping.items():
+        info = app_input_info[input_]
+
+        proper_extentions = IMAGE_EXTENSIONS if info.is_image else BINARY_EXTENSIONS
+        unsupported = "\n".join(
+                [file for file in files if Path(file).suffix.upper().strip(".") not in proper_extentions]
+            )
+        if unsupported:
+            unsupported_files.append(unsupported)
+    if unsupported_files:
+        unsupported_files = "\n".join(unsupported_files)
+        raise Exception(
+            f"This files has unsupported extensions: {unsupported_files}.\n"
+            f"Supported extentions:\nImages: {IMAGE_EXTENSIONS}\nBinary: {BINARY_EXTENSIONS}"
+        )
diff --git a/tools/openvino_dev/CMakeLists.txt b/tools/openvino_dev/CMakeLists.txt
new file mode 100644
index 00000000000000..0418784dd52271
--- /dev/null
+++ b/tools/openvino_dev/CMakeLists.txt
@@ -0,0 +1,22 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(WHEEL_VERSION "${IE_VERSION}" CACHE STRING "Version of this release" FORCE)
+set(WHEEL_BUILD "${IE_VERSION_BUILD}" CACHE STRING "Build number of this release" FORCE)
+set(SETUP_PY "${CMAKE_CURRENT_SOURCE_DIR}/setup.py")
+
+set(openvino_wheel_name "openvino_dev-${WHEEL_VERSION}-${WHEEL_BUILD}-py3-none-any.whl")
+set(openvino_wheels_output_dir "${CMAKE_BINARY_DIR}/wheels")
+set(openvino_wheel_path "${openvino_wheels_output_dir}/${openvino_wheel_name}")
+
+add_custom_command(OUTPUT ${openvino_wheel_path}
+    COMMAND ${CMAKE_COMMAND} -E env OPENVINO_VERSION=${WHEEL_VERSION}
+    ${PYTHON_EXECUTABLE} ${SETUP_PY} clean bdist_wheel
+        --dist-dir ${openvino_wheels_output_dir}
+        --build=${WHEEL_BUILD}
+    WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
+    COMMENT "Building Python wheel ${openvino_wheel_name}"
+    VERBATIM)
+
+add_custom_target(openvino_dev_wheel ALL DEPENDS ${openvino_wheel_path})
diff --git a/tools/openvino_dev/requirements.txt b/tools/openvino_dev/requirements.txt
new file mode 100644
index 00000000000000..ccc569a0194b30
--- /dev/null
+++ b/tools/openvino_dev/requirements.txt
@@ -0,0 +1,28 @@
+defusedxml>=0.7.1
+scipy~=1.5.4
+jstyleson~=0.0.2
+numpy>=1.16.6,<1.20
+addict>=2.4.0
+pandas~=1.1.5
+hyperopt~=0.1.2
+networkx~=2.5
+tqdm>=4.54.1
+texttable~=1.6.3
+py-cpuinfo>=7.0.0
+PyYAML>=5.4.1
+pillow>=8.1.2
+scikit-image>=0.17.2
+scikit-learn>=0.24.1
+yamlloader>=0.5
+shapely>=1.7.1
+nibabel>=3.2.1
+pydicom>=2.1.2
+sentencepiece>=0.1.95
+tokenizers>=0.10.1
+editdistance>=0.5.3
+parasail>=1.2.4
+fast-ctc-decode>=0.2.5
+rawpy>=0.16.0
+nltk>=3.5
+opencv-python==4.5.*
+progress>=1.5
diff --git a/tools/openvino_dev/setup.cfg b/tools/openvino_dev/setup.cfg
new file mode 100644
index 00000000000000..0c1c44b4ca4a4d
--- /dev/null
+++ b/tools/openvino_dev/setup.cfg
@@ -0,0 +1,21 @@
+[options]
+setup_requires = 
+    wheel
+
+[options.extras_require]
+
+[options.package_data]
+    * = *
+
+[options.entry_points]
+console_scripts =
+
+[metadata]
+license_files =
+    readme*
+    *LICENSE*
+    *license*
+    *third-party-programs*
+    *EULA*
+    ../../licensing/*
+    ../../LICENSE
diff --git a/tools/openvino_dev/setup.py b/tools/openvino_dev/setup.py
new file mode 100644
index 00000000000000..ffd8da71647e6b
--- /dev/null
+++ b/tools/openvino_dev/setup.py
@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+
+# Copyright (C) 2018-2021 Intel Corporation SPDX-License-Identifier: Apache-2.0
+
+""" Use this script to create a openvino-dev wheel package:
+    $ python3 setup.py bdist_wheel
+"""
+# pylint: disable-msg=line-too-long
+
+import os
+import sys
+import platform
+import subprocess  # nosec
+import shutil
+from distutils import log
+from distutils.command.build import build
+from distutils.command.clean import clean
+from pathlib import Path
+from fnmatch import fnmatchcase
+import pkg_resources
+from setuptools.command.install import install
+from setuptools import setup, find_namespace_packages
+
+PYTHON_VERSION = f'python{sys.version_info.major}.{sys.version_info.minor}'
+SCRIPT_DIR = Path(__file__).resolve().parents[0]
+OPENVINO_DIR = Path(__file__).resolve().parents[2]
+SRC_DIR = SCRIPT_DIR / 'src'
+
+PKG_INSTALL_CFG = {
+    'openvino-mo': {
+        'src_dir': OPENVINO_DIR / 'model-optimizer',
+        'black_list': ['*unit_tests*'],
+        'prefix': 'mo',
+        'extract_entry_points': True,
+        'extract_requirements': True,
+        'extract_extras': True,
+    },
+    'benchmark_tool': {
+        'src_dir': OPENVINO_DIR / 'tools' / 'benchmark_tool',
+        'black_list': [],
+        'prefix': 'benchmark_tool',
+        'extract_entry_points': True,
+    },
+    "accuracy_checker": {
+        'src_dir': OPENVINO_DIR / 'tools' / 'pot' / 'thirdparty' / 'open_model_zoo' / 'tools' / 'accuracy_checker',  # noqa:E501
+        'black_list': ['*tests*'],
+        'prefix': 'accuracy_checker',
+        'extract_entry_points': True,
+    },
+    "omz_tools": {
+        'src_dir': OPENVINO_DIR / 'tools' / 'pot' / 'thirdparty' / 'open_model_zoo' / 'tools' / 'model_tools',  # noqa:E501
+        'black_list': [],
+        'prefix': 'omz_tools',
+        'extract_requirements': True,
+        'extract_entry_points': True,
+        'extract_extras': True,
+    },
+    "pot": {
+        'src_dir': OPENVINO_DIR / 'tools' / 'pot',
+        'black_list': ['*tests*'],
+        'prefix': 'pot',
+        'extract_entry_points': True,
+    },
+}
+
+
+def ignore_patterns(*patterns):
+    """
+    Filter names by given patterns
+    """
+    return lambda name: any(fnmatchcase(name, pat=pat) for pat in patterns)
+
+
+class CustomBuild(build):
+    """Custom implementation of build"""
+
+    def run(self):
+
+        # pylint: disable-msg=too-many-locals
+        self.announce('Installing packages', level=log.INFO)
+        for cmp, cmp_data in PKG_INSTALL_CFG.items():
+            self.announce(f'Processing package: {cmp}', level=log.INFO)
+            subprocess.call([sys.executable, 'setup.py', 'install',
+                            '--root', str(SCRIPT_DIR),
+                             '--prefix', str(cmp_data.get("prefix"))],
+                            cwd=str(cmp_data.get('src_dir')))
+
+            # grab installed modules
+            lib_dir = 'lib/site-packages' if platform.system() == 'Windows' else f'lib/{PYTHON_VERSION}/site-packages'
+            src = SCRIPT_DIR / cmp_data.get('prefix') / lib_dir
+
+            egg_info = list(src.glob('**/*.egg-info'))
+            if egg_info:
+
+                def raw_req(req):
+                    req.marker = None
+                    return str(req)
+
+                distributions = pkg_resources.find_distributions(str(Path(egg_info[0]).parent))
+                for dist in distributions:
+                    self.announce(f'Distribution: {dist.egg_name()}', level=log.INFO)
+
+                    # load install_requires list
+                    install_requires = list(sorted(map(raw_req, dist.requires())))
+                    self.announce(f'Install requires: {install_requires}', level=log.INFO)
+                    if cmp_data.get("extract_requirements"):
+                        self.distribution.install_requires.extend(install_requires)
+
+                    # load extras_require
+                    if cmp_data.get("extract_extras"):
+                        for extra in dist.extras:
+                            if extra not in self.distribution.extras_require:
+                                self.distribution.extras_require[extra] = []
+                            extras_require = set(map(raw_req, dist.requires((extra,))))
+                            self.announce(f'Extras: {extra}:{extras_require}', level=log.INFO)
+                            self.distribution.extras_require[extra].extend(extras_require)
+
+                    # extract console scripts
+                    if cmp_data.get("extract_entry_points"):
+                        for console_scripts in dist.get_entry_map('console_scripts'):
+                            self.announce(f'Entry point: {console_scripts}', level=log.INFO)
+                            entry = dist.get_entry_info('console_scripts', console_scripts)
+                            self.distribution.entry_points['console_scripts'].append(str(entry))
+
+            # copy modules to the build directory
+            dst = Path(self.build_lib)
+            black_list = cmp_data.get('black_list')
+            exclude = ignore_patterns('*ez_setup*', '*__pycache__*', '*.egg-info*', *black_list)
+            for path in src.glob('**/*'):
+                if path.is_dir() or exclude(str(path)):
+                    continue
+                path_rel = path.relative_to(src)
+                (dst / path_rel.parent).mkdir(exist_ok=True, parents=True)
+                shutil.copyfile(path, dst / path_rel)
+
+        # add dependecy on runtime package
+        runtime_req = [f'openvino=={self.distribution.get_version()}']
+        self.distribution.install_requires.extend(runtime_req)
+
+        self.announce(f'{self.distribution.install_requires}', level=log.DEBUG)
+        self.announce(f'{self.distribution.extras_require}', level=log.DEBUG)
+        self.announce(f'{self.distribution.entry_points}', level=log.DEBUG)
+
+
+class CustomInstall(install):
+    """Enable build_clib during the installation"""
+
+    def run(self):
+        self.run_command('build')
+        install.run(self)
+
+
+class CustomClean(clean):
+    """Clean up staging directories"""
+
+    def clean(self, install_cfg):
+        """Clean components staging directories"""
+        for comp, comp_data in install_cfg.items():
+            install_prefix = comp_data.get('prefix')
+            self.announce(f'Cleaning {comp}: {install_prefix}', level=log.INFO)
+            if os.path.exists(install_prefix):
+                shutil.rmtree(install_prefix)
+
+    def run(self):
+        self.clean(PKG_INSTALL_CFG)
+        for pattern in './build ./dist **/*.pyc **/*.tgz **/*.egg-info'.split(' '):
+            paths = SCRIPT_DIR.glob(pattern)
+            for path in paths:
+                if path.is_file() and path.exists():
+                    path = path.parent
+                self.announce(f'Cleaning: {path}', level=log.INFO)
+                shutil.rmtree(path)
+        clean.run(self)
+
+
+def get_description(desc_file_path):
+    """read description from README.md"""
+    with open(desc_file_path, 'r', encoding='utf-8') as fstream:
+        description = fstream.read()
+    return description
+
+with (SCRIPT_DIR / 'requirements.txt').open() as requirements:
+    install_reqs = [
+        str(requirement)
+        for requirement
+        in pkg_resources.parse_requirements(requirements)
+    ]
+
+
+setup(
+    name='openvino-dev',
+    version=os.getenv('OPENVINO_VERSION', '0.0.0'),
+    author='Intel® Corporation',
+    license='OSI Approved :: Apache Software License',
+    author_email='openvino_pushbot@intel.com',
+    url='https://docs.openvinotoolkit.org/latest/index.html',
+    download_url='https://github.com/openvinotoolkit/openvino/tags',
+    description='OpenVINO™ Developer Package',
+    long_description=get_description(SCRIPT_DIR.parents[1] / 'docs/install_guides/pypi-openvino-dev.md'),
+    long_description_content_type='text/markdown',
+    classifiers=[
+        'Programming Language :: Python :: 3',
+        'OSI Approved :: Apache Software License',
+        'Operating System :: OS Independent',
+    ],
+    cmdclass={
+        'build': CustomBuild,
+        'install': CustomInstall,
+        'clean': CustomClean,
+    },
+    entry_points = {
+        'console_scripts': [],
+    },
+    install_requires=install_reqs,
+    packages=find_namespace_packages(where=str(SRC_DIR)),
+    package_dir={'': str(SRC_DIR)},
+)
diff --git a/tools/openvino_dev/src/openvino/__init__.py b/tools/openvino_dev/src/openvino/__init__.py
new file mode 100644
index 00000000000000..855c54922a0cda
--- /dev/null
+++ b/tools/openvino_dev/src/openvino/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (C) 2020-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+__path__ = __import__('pkgutil').extend_path(__path__, __name__)
diff --git a/tools/openvino_dev/src/openvino/tools/__init__.py b/tools/openvino_dev/src/openvino/tools/__init__.py
new file mode 100644
index 00000000000000..855c54922a0cda
--- /dev/null
+++ b/tools/openvino_dev/src/openvino/tools/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (C) 2020-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+__path__ = __import__('pkgutil').extend_path(__path__, __name__)
diff --git a/tools/pot/openvino/tools/pot/algorithms/finetuning/layers.py b/tools/pot/openvino/tools/pot/algorithms/finetuning/layers.py
index 79e89edd0a40e0..7d716db991e68b 100644
--- a/tools/pot/openvino/tools/pot/algorithms/finetuning/layers.py
+++ b/tools/pot/openvino/tools/pot/algorithms/finetuning/layers.py
@@ -93,8 +93,8 @@ def forward(self, x):
         scale = self.scale.exp()
         s = self.val_h * scale.reciprocal()
         x = x - self.min
-        x = x.clamp(max=self.val_h, min=self.val_l)
         x = x * s
+        x = x.clamp(max=self.val_h, min=self.val_l)
         x = STERound.apply(x, self.val_l, self.val_h)
         x = x * s.reciprocal() + self.min
         return x
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
index 68f09a59b24460..ddf74a68a879b8 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
@@ -409,7 +409,7 @@ def register_statistics(self, model, stats_collector):
                 input_name = biased_after_param_nodes[node.name]
                 statistics_layout[input_name] = {'batch_mean_param_in': agf.batch_mean}
                 self._collected_stat_inputs.append(input_name)
-            statistics_layout[add_node.name] =\
+            statistics_layout[add_node_name] =\
                 {'mean_per_channel': TensorStatisticAxis(granularity='perchannel',
                                                          type='mean',
                                                          inplace_statistics=self.config['inplace_statistics'],
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py
index 8a1b4545ea1196..306def42cb6909 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py
@@ -441,7 +441,33 @@ def _custom_broadcast(arrays_list):
 def create_renamed_layers_mapping(model, stats_layout):
     changed_names_map = {}
     for layer_name in stats_layout:
-        node = get_node_by_name(model, layer_name)
+        node_name = layer_name
+        port_id = None
+        if isinstance(layer_name, tuple):
+            node_name, port_id = layer_name
+        node = get_node_by_name(model, node_name)
         if node is not None and 'orig_node_name' in node:
-            changed_names_map[node.name] = node['orig_node_name']
+            name_change_to = node['orig_node_name'] if port_id is None else (node['orig_node_name'], port_id)
+            changed_names_map[layer_name] = name_change_to
     return changed_names_map
+
+
+def get_num_levels(x: np.ndarray) -> int:
+    """
+        Calculates the number of discret levels of the values
+        in the input NumPy tensor x
+        :param x: the input tensor
+        :return the number of discret value levels in the input tensor x
+    """
+    NUM_BINS = 256
+    x = x.flatten()
+    hist, _ = np.histogram(x, NUM_BINS)
+    non_empty_bins = [i for i, v in enumerate(hist) if v > 0]
+    deltas = [non_empty_bins[i]-non_empty_bins[i-1] for i in range(1, len(non_empty_bins))]
+    if deltas == []:
+        return 0
+    d = min(deltas)
+    if d == 1:
+        return -1
+
+    return round(NUM_BINS / d)
diff --git a/tools/pot/openvino/tools/pot/configs/hardware/IgnoredPatterns.md b/tools/pot/openvino/tools/pot/configs/hardware/IgnoredPatterns.md
index d0d9b17cf96d68..1580f677bcf3cb 100755
--- a/tools/pot/openvino/tools/pot/configs/hardware/IgnoredPatterns.md
+++ b/tools/pot/openvino/tools/pot/configs/hardware/IgnoredPatterns.md
@@ -292,6 +292,46 @@ graph TB
 
 ---
 
+**Name:** se_block<br/>
+**Pattern:** <br/>
+
+```mermaid
+graph TB
+    input(ReduceMean) --> conv_fc(Convolution, MatMul)
+    conv_fc(Convolution, MatMul) --> bias(Add)
+    bias_const(Const) --> bias(Add)
+    bias(Add) --> act(ReLU, PReLU)
+    act(ReLU, PReLU) --> conv_fc_1(Convolution, MatMul)
+    conv_fc_1(Convolution, MatMul) --> bias_1(Add)
+    bias_const_1(Const) --> bias_1(Add)
+    bias_1(Add) --> act_1(Sigmoid)
+    act_1(Sigmoid) --> output(Multiply)
+    style input fill:#73C2FB
+    style output fill:#73C2FB
+```
+
+---
+
+**Name:** se_block_swish_activation<br/>
+**Pattern:** <br/>
+
+```mermaid
+graph TB
+    input(ReduceMean) --> conv_fc(Convolution, MatMul)
+    conv_fc(Convolution, MatMul) --> bias(Add)
+    bias_const(Const) --> bias(Add)
+    bias(Add) --> swish(Swish)
+    swish(Swish) --> conv_fc_1(Convolution, MatMul)
+    conv_fc_1(Convolution, MatMul) --> bias_1(Add)
+    bias_const_1(Const) --> bias_1(Add)
+    bias_1(Add) --> act_1(Sigmoid)
+    act_1(Sigmoid) --> output(Multiply)
+    style input fill:#73C2FB
+    style output fill:#73C2FB
+```
+
+---
+
 **Name:** softmax<br/>
 **Pattern:** <br/>
 
diff --git a/tools/pot/openvino/tools/pot/graph/passes.py b/tools/pot/openvino/tools/pot/graph/passes.py
index 980d14f8972d9c..4705fbfa039d63 100644
--- a/tools/pot/openvino/tools/pot/graph/passes.py
+++ b/tools/pot/openvino/tools/pot/graph/passes.py
@@ -216,7 +216,7 @@ def check_split_concat(self, node):
         jump_split_concat_ops: jump_over_split_concat
     }
 
-    def find_nodes_fq_int(self, graph):
+    def delete_fq_non_quantizable_node_precision(self, graph):
         type_infer(graph)
         fq_removal = RemoveFakeQuantize()
         fq_removal.quantize_agnostic_operations = self.quantize_agnostic_operations
@@ -227,7 +227,7 @@ def find_nodes_fq_int(self, graph):
             fq = fq_queue.popleft()
             if fq.in_port(0).get_source() is not None and fq.in_port(0).get_source().is_data_type_defined():
                 type_node = fq.in_port(0).get_source().get_data_type()
-                if type_node in (np.int32, np.int64):
+                if type_node in (np.int32, np.int64, bool):
                     node_int_fq.append(fq.name)
                     fq_removal.find_and_remove_node(graph, fq.name)
 
@@ -649,12 +649,20 @@ def rename_fqs_in_the_end(self, graph: Graph):
         def change_names(_, match):
             fq_node = match['fq']
             input_node = get_node_input(fq_node, 0)
+            new_fq_name = copy(input_node.name)
+            if 'orig_node_name' in input_node:
+                new_fq_name = copy(input_node['orig_node_name'])
+
+            input_node_outputs = get_all_node_outputs(input_node)
+            if all([op.type == 'FakeQuantize' for op in input_node_outputs]):
+                new_fq_name += '.{}'.format(fq_node.in_port(0).get_source().idx)
 
             fq_node['orig_fq_name'] = copy(fq_node.name)
-            fq_node.name = copy(input_node.name)
+            fq_node.name = copy(new_fq_name)
 
-            input_node['orig_node_name'] = copy(input_node.name)
-            input_node.name = '{original_name}/pre_fq_input'.format(original_name=input_node.name)
+            if 'orig_node_name' not in input_node:
+                input_node['orig_node_name'] = copy(input_node.name)
+                input_node.name = '{original_name}/pre_fq_input'.format(original_name=input_node.name)
 
         pattern = get_fq_result_pattern()
         apply_pattern(
diff --git a/tools/pot/openvino/tools/pot/graph/special_patterns.py b/tools/pot/openvino/tools/pot/graph/special_patterns.py
index a4dfe24008d35c..2f32378aadaab4 100644
--- a/tools/pot/openvino/tools/pot/graph/special_patterns.py
+++ b/tools/pot/openvino/tools/pot/graph/special_patterns.py
@@ -28,6 +28,26 @@ def create_swish_pattern():
     return pattern.set_name('swish_activation').pattern
 
 
+@registry_ignore_patterns('blocks')
+def create_se_pattern():
+    """
+    Removing this pattern can drop accuracy after quantization of model w/ SE-blocks
+    """
+    pattern = PatternBuilder()
+    pattern.insert_se(start_name='input', end_name='output')
+    return pattern.set_name('se_block').pattern
+
+
+@registry_ignore_patterns('blocks')
+def create_se_swish_pattern():
+    """
+    Removing this pattern can drop accuracy after quantization of model w/ SE-blocks
+    """
+    pattern = PatternBuilder()
+    pattern.insert_se(start_name='input', end_name='output', is_swish=True)
+    return pattern.set_name('se_block_swish_activation').pattern
+
+
 @registry_ignore_patterns('blocks')
 def create_biased_op_pattern():
     pattern = PatternBuilder()
diff --git a/tools/pot/openvino/tools/pot/graph/transformer.py b/tools/pot/openvino/tools/pot/graph/transformer.py
index 77194c19455ca9..44e1fb4e8e830e 100644
--- a/tools/pot/openvino/tools/pot/graph/transformer.py
+++ b/tools/pot/openvino/tools/pot/graph/transformer.py
@@ -54,7 +54,7 @@ def _insert_fake_quantize(self, graph):
         self.fq_optimization.find_and_replace_pattern(graph)
         graph.clean_up()
 
-        self.fq_propagation.find_nodes_fq_int(graph)
+        self.fq_propagation.delete_fq_non_quantizable_node_precision(graph)
         graph.clean_up()
 
         self.fq_name_swapper.rename_fqs_in_the_end(graph)
diff --git a/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py b/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py
index 634243604905c9..6e537a29604dd2 100644
--- a/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py
+++ b/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py
@@ -80,7 +80,7 @@ def insert_abs_max(self, node, type_stat, node_name, **kwargs):
         abs_node = Abs(node.graph, {"name": type_stat + node_name}).create_node()
         abs_node.in_port(0).connect(node.out_port(0))
         max_op = create_op_node_with_second_input(node.graph, ReduceMax, int64_array(axis_const),
-                                                  dict(name='abs_max_' + node.name))
+                                                  dict(name='abs_max_' + node_name))
         abs_node.out_port(0).connect(max_op.in_port(0))
         self.insert_result(node, max_op, type_stat)
         return None
diff --git a/tools/pot/openvino/tools/pot/utils/telemetry.py b/tools/pot/openvino/tools/pot/utils/telemetry.py
index 688e54c33b9951..6268cfa3b9d214 100644
--- a/tools/pot/openvino/tools/pot/utils/telemetry.py
+++ b/tools/pot/openvino/tools/pot/utils/telemetry.py
@@ -13,7 +13,11 @@
 logger = get_logger(__name__)
 
 
-def send_event(action, label, telemetry=tm.Telemetry(app_name='pot', app_version=pot_version)):
+def get_tid_telemetry():
+    return 'UA-17808594-29'
+
+
+def send_event(action, label, telemetry=tm.Telemetry(tid=get_tid_telemetry(), app_name='pot', app_version=pot_version)):
     try:
         telemetry.send_event('pot', action, label)
     except Exception as e: # pylint: disable=broad-except
@@ -22,7 +26,7 @@ def send_event(action, label, telemetry=tm.Telemetry(app_name='pot', app_version
 
 def send_configuration(algo_config, engine, interface='API'):
     try:
-        telemetry = tm.Telemetry(app_name='pot', app_version=pot_version)
+        telemetry = tm.Telemetry(tid=get_tid_telemetry(), app_name='pot', app_version=pot_version)
 
         target_device = ','.join(set(algorithm['params'].get('target_device', 'ANY') for algorithm in algo_config))
         algorithms = {f'algorithm_{i}': algorithm['name'] for i, algorithm in enumerate(algo_config)}
@@ -54,7 +58,7 @@ def send_configuration(algo_config, engine, interface='API'):
 
 def start_session_telemetry():
     try:
-        telemetry = tm.Telemetry(app_name='pot', app_version=pot_version)
+        telemetry = tm.Telemetry(tid=get_tid_telemetry(), app_name='pot', app_version=pot_version)
         telemetry.start_session('pot')
         return telemetry
     except Exception as e: # pylint: disable=broad-except
diff --git a/tools/pot/tests/data/models/lstm_example/lstm_example.json b/tools/pot/tests/data/models/lstm_example/lstm_example.json
new file mode 100644
index 00000000000000..de45bbb1b86724
--- /dev/null
+++ b/tools/pot/tests/data/models/lstm_example/lstm_example.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba5a652c26a0ff2d72d0a20fa4ea89801432e1a07f130d434bbac51f87704987
+size 186
diff --git a/tools/pot/tests/data/models/lstm_example/lstm_example.onnx b/tools/pot/tests/data/models/lstm_example/lstm_example.onnx
new file mode 100644
index 00000000000000..30507001ee9d51
--- /dev/null
+++ b/tools/pot/tests/data/models/lstm_example/lstm_example.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43ebd524dc790f0cdf08f4693f74d9b5be550c503c67aa1819bd13496fbee0be
+size 341880
diff --git a/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.bin b/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.bin
new file mode 100644
index 00000000000000..6610aea14ea82b
--- /dev/null
+++ b/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0af1173b44db9f52b9878032b87679cc16521ec0c21fc413228e0854da4efed0
+size 526864
diff --git a/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.json b/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.json
new file mode 100644
index 00000000000000..4fc2ca9cd5e4f0
--- /dev/null
+++ b/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de67711db3e1fb404a8b61be29954ab5b336d76b8cf7d3c5c98f7b9faab7c6e4
+size 283
diff --git a/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.xml b/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.xml
new file mode 100644
index 00000000000000..d7035fe9d8705a
--- /dev/null
+++ b/tools/pot/tests/data/models/multiple_outputs_net_example/multiple_outputs_net_example.xml
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eafca25515ecb19d073a96fbaa234c5c9004bf9646419bb1d3f9c0faa25c19c2
+size 12739
diff --git a/tools/pot/tests/data/reference_models/efficientnet_b0_example_ig_pt_pytorch.xml b/tools/pot/tests/data/reference_models/efficientnet_b0_example_ig_pt_pytorch.xml
index 90adca11bf106c..a3087ffbf1b913 100644
--- a/tools/pot/tests/data/reference_models/efficientnet_b0_example_ig_pt_pytorch.xml
+++ b/tools/pot/tests/data/reference_models/efficientnet_b0_example_ig_pt_pytorch.xml
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:df841d3e81aa6476e54787bcfec5e334bee27b80bbad7c8446b0f7f120ca5e7e
-size 570777
+oid sha256:181402cfe46282cf96d82f1e0b68f2ba5ccbcaccbb1ce3e712fd1b4cb5883917
+size 463708
diff --git a/tools/pot/tests/data/reference_models/lstm_example_pytorch.xml b/tools/pot/tests/data/reference_models/lstm_example_pytorch.xml
new file mode 100644
index 00000000000000..c49048bca97158
--- /dev/null
+++ b/tools/pot/tests/data/reference_models/lstm_example_pytorch.xml
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d0db9b3aa632378aea94c872a9d4d4b3798b72432cab942aea14893872d0f56
+size 126939
diff --git a/tools/pot/tests/data/reference_models/multiple_outputs_net_example_dldt.xml b/tools/pot/tests/data/reference_models/multiple_outputs_net_example_dldt.xml
new file mode 100644
index 00000000000000..1d8619041a5f45
--- /dev/null
+++ b/tools/pot/tests/data/reference_models/multiple_outputs_net_example_dldt.xml
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76959e68b61e35bca1e4d0a815a2fdd2a50fbfa1ca6d4cb6218d6d57f76603b2
+size 23366
diff --git a/tools/pot/tests/test_graph.py b/tools/pot/tests/test_graph.py
index febe190f2c707e..5ee601eb5653aa 100755
--- a/tools/pot/tests/test_graph.py
+++ b/tools/pot/tests/test_graph.py
@@ -18,13 +18,14 @@
 GNA_CONFIG_PATH = HARDWARE_CONFIG_PATH / 'gna.json'
 
 TEST_MODELS = [
-    ('mobilenetv2_example', 'pytorch'),
-    ('resnet_example', 'pytorch'),
-    ('googlenet_example', 'pytorch'),
-    ('mobilenetv2_ssd_example', 'pytorch'),
-    ('densenet121_example', 'pytorch'),
-    ('multiple_out_ports_net', 'tf'),  # multiple output ports in node case check,
-    # ('rm_nnet4a', 'kaldi')
+    ('mobilenetv2_example', 'pytorch', 'ANY'),
+    ('resnet_example', 'pytorch', 'ANY'),
+    ('googlenet_example', 'pytorch', 'ANY'),
+    ('mobilenetv2_ssd_example', 'pytorch', 'ANY'),
+    ('densenet121_example', 'pytorch', 'ANY'),
+    ('multiple_out_ports_net', 'tf', 'ANY'),
+    ('lstm_example', 'pytorch', 'GNA'),
+    ('multiple_outputs_net_example', 'dldt', 'GNA')
 ]
 
 CASCADE_MAP = Dict({
@@ -37,15 +38,16 @@
 
 
 @pytest.mark.parametrize(
-    'model_name, model_framework', TEST_MODELS,
+    'model_name, model_framework, target_device', TEST_MODELS,
     ids=['{}_{}'.format(m[0], m[1]) for m in TEST_MODELS])
-def test_build_quantization_graph(tmp_path, models, model_name, model_framework):
+def test_build_quantization_graph(tmp_path, models, model_name, model_framework, target_device):
     model = models.get(model_name, model_framework, tmp_path)
-    model = load_model(model.model_params)
+    model = load_model(model.model_params, target_device=target_device)
 
-    hardware_config = HardwareConfig.from_json(CPU_CONFIG_PATH.as_posix())
-    if model_framework == 'kaldi':
+    if target_device == 'GNA':
         hardware_config = HardwareConfig.from_json(GNA_CONFIG_PATH.as_posix())
+    else:
+        hardware_config = HardwareConfig.from_json(CPU_CONFIG_PATH.as_posix())
 
     quantization_model = GraphTransformer(hardware_config).insert_fake_quantize(model)
 
@@ -246,27 +248,21 @@ def test_multibranch_propagation_without_fq_moving():
 
 
 MODELS_WITH_LSTM = [
-    # ('rm_lstm4f', 'kaldi', {
-    #     'prev_memory_output69':
-    #         ['next_lstm_output108', 'lstmprojectedstreams/Shape', 'input_fullyconnected/WithoutBiases'],
-    #     'prev_memory_state82':
-    #         ['state_filtered_tahn100', 'clamp_scaleshift101/Mul_', 'next_lstm_state98'],
-    #     'prev_memory_output':
-    #         ['next_lstm_output', 'affinetransform/WithoutBiases'],
-    #     'prev_memory_state':
-    #         ['state_filtered_tahn', 'clamp_scaleshift/Mul_', 'next_lstm_state']
-    # })
+    ('lstm_example', 'pytorch', {
+        'LSTM_15/TensorIterator/22/variable_1':
+            ['Assign_298'],
+        'LSTM_15/TensorIterator/24/variable_2':
+            ['Assign_305'],
+        'LSTM_19/TensorIterator/22/variable_1':
+            ['Assign_327'],
+        'LSTM_19/TensorIterator/24/variable_2':
+            ['Assign_334']
+    })
 ]
 
 
-@pytest.fixture(scope='module', params=MODELS_WITH_LSTM,
-                ids=['{}_{}'.format(m[0], m[1]) for m in MODELS_WITH_LSTM])
-def _params(request):
-    return request.param
-
-
-def test_lstm_ends(_params, tmp_path, models):
-    model_name, model_framework, lstm_ends_ref = _params
+def test_lstm_ends(tmp_path, models):
+    model_name, model_framework, lstm_ends_ref = MODELS_WITH_LSTM[0]
     model = models.get(model_name, model_framework, tmp_path)
     model = load_model(model.model_params)
     read_values = get_nodes_by_type(model, ['ReadValue'])
diff --git a/tools/pot/tests/test_tensor_statistics.py b/tools/pot/tests/test_tensor_statistics.py
index 99489dadb452e4..8998c803b357c2 100644
--- a/tools/pot/tests/test_tensor_statistics.py
+++ b/tools/pot/tests/test_tensor_statistics.py
@@ -7,6 +7,8 @@
 from openvino.tools.pot.statistics.function_selector import AGGREGATION_FN, ACTIVATIONS_STATS_FN, WEIGHTS_STATS_FN, \
     get_aggregation_function, get_stats_function_for_activations, get_stats_function_for_weights, PERCHANNEL, PERTENSOR
 
+from openvino.tools.pot.algorithms.quantization.fake_quantize import get_num_levels
+
 INPUT_SHAPES = [(2, 2, 1), (2, 2, 2)]
 AGG_INPUTS = [np.reshape(np.array(range(np.prod(shape)), dtype=np.float32), shape) for shape in INPUT_SHAPES]
 
@@ -137,14 +139,17 @@ def test_weights_function(name, scale):
                               WEIGHTS_STATS_FN[PERCHANNEL].registry_dict.keys()]
 
 
-@pytest.mark.parametrize(
-    'name, transpose', WEIGHTS_CH_STATS_FUNCTIONS,
-    ids=['{}_{}'.format(fn[0], fn[1]) for fn in WEIGHTS_CH_STATS_FUNCTIONS])
-def test_weights_transpose_function(name, transpose):
-    fn = get_stats_function_for_weights(name, PERCHANNEL)
-    if name in ['quantile', 'abs_quantile']:
-        result = fn(INPUT, q=1e-2, transpose=transpose)
-    else:
-        result = fn(INPUT, transpose=transpose)
-    expected = GOLD_VALUES_CH_TRANS_WEIGHT_FUNCTIONS[name]
-    np.testing.assert_almost_equal(result, expected)
+NUM_LEVELS_PARAMS = [
+    (np.random.randint, (0, 2, (3, 100, 100)), 1, 1),
+    (np.random.randint, (-32, 32, (3, 100, 100)), 64, 1),
+    (np.random.randint, (-32, 32, (3, 100, 100)), 64, 1/512),
+    (np.random.rand, (3, 100, 100), -1, 1),
+    (np.random.randint, (0, 1, (3, 100, 100)), 0, 1)
+]
+
+
+@pytest.mark.parametrize('gen_func,params,expected,coef', NUM_LEVELS_PARAMS)
+def test_get_num_levels_function(gen_func, params, expected, coef):
+    test_1 = gen_func(*params) * coef
+    result = get_num_levels(test_1)
+    assert result == expected