From 1af62f87d296e34b2355b4749c2637761084d7b0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=C3=A5ns=20Nilsson?= <mans.nilsson@arm.com>
Date: Tue, 2 Apr 2024 19:46:09 +0200
Subject: [PATCH] Add missing MVE support for int16 convolution (#124)

- Removes arm_convolve_s16
- Updates arm_nn_mat_mul_kernel_s16.c to be used by scalar and DSP
- Adds arm_nn_mat_mult_nt_t_s16.c with MVE support
- Refactors and renames arm_convolve_fast_s16 to arm_convolve_s16 so
that all variants use the previously named fast s16 version.
- Updates unit tests to increase test coverage for convolution s16
- Keras based unit test uses null bias as opposed to array with zeroes
- Correct PDSC file

BUGS=https://github.com/ARM-software/CMSIS-NN/issues/101
https://github.com/ARM-software/CMSIS-NN/issues/119
---
 ARM.CMSIS-NN.pdsc                             |   7 +-
 Include/arm_nnfunctions.h                     |  68 +-
 Include/arm_nnsupportfunctions.h              |  54 +-
 .../arm_convolve_fast_s16.c                   | 232 ------
 .../arm_convolve_get_buffer_sizes_s16.c       |  58 +-
 .../arm_convolve_get_buffer_sizes_s8.c        |  12 +-
 .../ConvolutionFunctions/arm_convolve_s16.c   | 213 ++++-
 .../arm_convolve_wrapper_s16.c                |  38 +-
 .../arm_depthwise_conv_fast_s16.c             |   8 +-
 .../arm_nn_mat_mult_kernel_s16.c}             | 169 ++--
 .../arm_nn_mat_mult_nt_t_s16.c                | 265 ++++++
 .../arm_nn_vec_mat_mult_t_s16.c               |   8 +-
 Tests/UnitTest/CMakeLists.txt                 |   1 -
 .../TestData/int16xint8/biases_data.h         |   6 +-
 .../TestData/int16xint8/config_data.h         |  26 +-
 .../TestData/int16xint8/input_data.h          | 333 +++++++-
 .../TestData/int16xint8/output_mult_data.h    |   6 +-
 .../TestData/int16xint8/output_ref_data.h     |  13 +-
 .../TestData/int16xint8/output_shift_data.h   |   6 +-
 .../TestCases/TestData/int16xint8/test_data.h |   4 +-
 .../TestData/int16xint8/weights_data.h        | 356 +++++++-
 .../TestData/int16xint8_spill/biases_data.h   |   6 +
 .../TestData/int16xint8_spill/config_data.h   |  24 +
 .../TestData/int16xint8_spill/input_data.h    | 281 +++++++
 .../int16xint8_spill/output_mult_data.h       |   7 +
 .../int16xint8_spill/output_ref_data.h        |  12 +
 .../int16xint8_spill/output_shift_data.h      |   6 +
 .../TestData/int16xint8_spill/test_data.h     |   9 +
 .../TestData/int16xint8_spill/weights_data.h  | 781 ++++++++++++++++++
 .../TestData/int16xint8_spill2/biases_data.h  |   6 +
 .../TestData/int16xint8_spill2/config_data.h  |  24 +
 .../TestData/int16xint8_spill2/input_data.h   |  66 ++
 .../int16xint8_spill2/output_mult_data.h      |   7 +
 .../int16xint8_spill2/output_ref_data.h       |  12 +
 .../int16xint8_spill2/output_shift_data.h     |   6 +
 .../TestData/int16xint8_spill2/test_data.h    |   9 +
 .../TestData/int16xint8_spill2/weights_data.h | 176 ++++
 .../TestData/requantize_s64/biases_data.h     |   6 +-
 .../TestData/requantize_s64/config_data.h     |  20 +-
 .../TestData/requantize_s64/input_data.h      |  13 +-
 .../requantize_s64/output_mult_data.h         |   7 +-
 .../TestData/requantize_s64/output_ref_data.h |  10 +-
 .../requantize_s64/output_shift_data.h        |   6 +-
 .../TestData/requantize_s64/test_data.h       |   4 +-
 .../TestData/requantize_s64/weights_data.h    |  20 +-
 .../test_arm_convolve_fast_s16/CMakeLists.txt |  23 -
 .../Unity/unity_test_arm_convolve_fast_s16.c  |  51 --
 .../test_arm_convolve_fast_s16.c              | 328 --------
 .../Unity/unity_test_arm_convolve_s16.c       |   4 +-
 .../test_arm_convolve_s16.c                   | 207 ++++-
 Tests/UnitTest/conv_settings.py               |  30 +-
 Tests/UnitTest/generate_test_data.py          |  80 +-
 52 files changed, 3109 insertions(+), 1015 deletions(-)
 delete mode 100644 Source/ConvolutionFunctions/arm_convolve_fast_s16.c
 rename Source/{NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c => ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c} (62%)
 create mode 100644 Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/biases_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/config_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/input_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_mult_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_ref_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_shift_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/test_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill/weights_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/biases_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/config_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/input_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_mult_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_ref_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_shift_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/test_data.h
 create mode 100644 Tests/UnitTest/TestCases/TestData/int16xint8_spill2/weights_data.h
 delete mode 100644 Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/CMakeLists.txt
 delete mode 100644 Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/Unity/unity_test_arm_convolve_fast_s16.c
 delete mode 100644 Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/test_arm_convolve_fast_s16.c

diff --git a/ARM.CMSIS-NN.pdsc b/ARM.CMSIS-NN.pdsc
index d5240368..5dbf5495 100644
--- a/ARM.CMSIS-NN.pdsc
+++ b/ARM.CMSIS-NN.pdsc
@@ -38,6 +38,7 @@
         <file category="source" name="Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s4_s16.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s8_s16.c"/>
+        <file category="source" name="Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_convolve_1x1_s4_fast.c"/>
@@ -53,7 +54,6 @@
         <file category="source" name="Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s4.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_depthwise_conv_s4_opt.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_depthwise_conv_s4.c"/>
-        <file category="source" name="Source/ConvolutionFunctions/arm_convolve_fast_s16.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_convolve_s4.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_convolve_s8.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_convolve_s16.c"/>
@@ -69,13 +69,13 @@
         <file category="source" name="Source/ConvolutionFunctions/arm_nn_depthwise_conv_s8_core.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_transpose_conv_get_buffer_sizes_s8.c"/>
         <file category="source" name="Source/ConvolutionFunctions/arm_transpose_conv_s8.c"/>
-        <file category="source" name="Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c"/>
         <file category="source" name="Source/ConcatenationFunctions/arm_concatenation_s8_x.c"/>
         <file category="source" name="Source/ConcatenationFunctions/arm_concatenation_s8_w.c"/>
         <file category="source" name="Source/ConcatenationFunctions/arm_concatenation_s8_y.c"/>
         <file category="source" name="Source/ConcatenationFunctions/arm_concatenation_s8_z.c"/>
         <file category="source" name="Source/SVDFunctions/arm_svdf_s8.c"/>
         <file category="source" name="Source/SVDFunctions/arm_svdf_state_s16_s8.c"/>
+        <file category="source" name="Source/SVDFunctions/arm_svdf_get_buffer_sizes_s8.c"/>
         <file category="source" name="Source/PoolingFunctions/arm_max_pool_s8.c"/>
         <file category="source" name="Source/PoolingFunctions/arm_max_pool_s16.c"/>
         <file category="source" name="Source/PoolingFunctions/arm_avgpool_s8.c"/>
@@ -101,9 +101,9 @@
         <file category="source" name="Source/NNSupportFunctions/arm_s8_to_s16_unordered_with_offset.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s4.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8.c"/>
+        <file category="source" name="Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s8_s32.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s16.c"/>
-        <file category="source" name="Source/NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_mat_mul_core_4x_s8.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nntables.c"/>
@@ -112,6 +112,7 @@
         <file category="source" name="Source/NNSupportFunctions/arm_nn_lstm_step_s8.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_lstm_calculate_gate_s8_s16.c"/>
         <file category="source" name="Source/NNSupportFunctions/arm_nn_vec_mat_mul_result_acc_s8_s16.c"/>
+        <file category="source" name="Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_row_offset_s8_s16.c"/>
         <file category="source" name="Source/FullyConnectedFunctions/arm_fully_connected_s4.c"/>
         <file category="source" name="Source/FullyConnectedFunctions/arm_fully_connected_s8.c"/>
         <file category="source" name="Source/FullyConnectedFunctions/arm_fully_connected_s16.c"/>
diff --git a/Include/arm_nnfunctions.h b/Include/arm_nnfunctions.h
index ab4c0eca..e4604c17 100644
--- a/Include/arm_nnfunctions.h
+++ b/Include/arm_nnfunctions.h
@@ -21,8 +21,8 @@
  * Title:        arm_nnfunctions.h
  * Description:  Public header file for CMSIS NN Library
  *
- * $Date:        20 February 2024
- * $Revision:    V.14.0.0
+ * $Date:        11 March 2024
+ * $Revision:    V.15.0.0
 
  *
  * Target :  Arm(R) M-Profile Architecture
@@ -375,8 +375,10 @@ arm_cmsis_nn_status arm_convolve_s4(const cmsis_nn_context *ctx,
  * @param[in]      bias_data      Optional bias data pointer. Data type: int32
  * @param[in]      output_dims    Output tensor dimensions. Format: [N, H, W, C_OUT]
  * @param[out]     output_data    Output data pointer. Data type: int8
-
- * @return     The function returns <code>ARM_CMSIS_NN_SUCCESS</code>
+ *
+ * @return     The function returns <code>ARM_CMSIS_NN_SUCCESS</code> if successful or
+ *                                  <code>ARM_CMSIS_NN_ARG_ERROR</code> if incorrect arguments or
+ *                                  <code>ARM_CMSIS_NN_NO_IMPL_ERROR</code>
  *
  * @details
  *    1. Supported framework: TensorFlow Lite micro
@@ -522,8 +524,10 @@ int32_t arm_transpose_conv_s8_get_buffer_size_mve(const cmsis_nn_dims *input_dim
  * @param[in]      bias_data      Optional bias data pointer. Data type: int64
  * @param[in]      output_dims    Output tensor dimensions. Format: [N, H, W, C_OUT]
  * @param[out]     output_data    Output data pointer. Data type: int16
-
- * @return     The function returns <code>ARM_CMSIS_NN_SUCCESS</code>
+ *
+ * @return     The function returns <code>ARM_CMSIS_NN_SUCCESS</code> if successful or
+ *                                  <code>ARM_CMSIS_NN_ARG_ERROR</code> if incorrect arguments or
+ *                                  <code>ARM_CMSIS_NN_NO_IMPL_ERROR</code>
  *
  * @details
  *    1. Supported framework: TensorFlow Lite micro
@@ -541,47 +545,6 @@ arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
                                      const int64_t *bias_data,
                                      const cmsis_nn_dims *output_dims,
                                      int16_t *output_data);
-/**
- * @brief Optimized s16 convolution function
- * @param[in, out] ctx            Function context that contains the additional buffer if required by the function.
- *                                arm_convolve_fast_s16_get_buffer_size will return the buffer_size if required.
- *                                The caller is expected to clear the buffer, if applicable, for security reasons.
- * @param[in]      conv_params    Convolution parameters (e.g. strides, dilations, pads,...).
- *                                conv_params->input_offset  : Not used
- *                                conv_params->output_offset : Not used
- * @param[in]      quant_params   Per-channel quantization info.
- *                                It contains the multiplier and shift values to be applied to each output channel
- * @param[in]      input_dims     Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
- * @param[in]      input_data     Input (activation) data pointer. Data type: int16
- * @param[in]      filter_dims    Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK are the
- *                                spatial filter dimensions. (filter_dims->w * filter_dims->h * input_dims->c) must not
- exceed 512
- * @param[in]      filter_data    Filter data pointer. Data type: int8
- * @param[in]      bias_dims      Bias tensor dimensions. Format: [C_OUT]
- * @param[in]      bias_data      Optional bias data pointer. Data type: int64
- * @param[in]      output_dims    Output tensor dimensions. Format: [N, H, W, C_OUT]
- * @param[out]     output_data    Output data pointer. Data type: int16
-
- * @return     The function returns <code>ARM_CMSIS_NN_SUCCESS</code>
- *
- * @details
- *    1. Supported framework: TensorFlow Lite micro
- *    2. Additional memory is required for optimization. Refer to argument 'ctx' for details.
- *    3. Implementation supports kernel volumes (filter width * filter height * input channels) < 512.
- *
- */
-
-arm_cmsis_nn_status arm_convolve_fast_s16(const cmsis_nn_context *ctx,
-                                          const cmsis_nn_conv_params *conv_params,
-                                          const cmsis_nn_per_channel_quant_params *quant_params,
-                                          const cmsis_nn_dims *input_dims,
-                                          const int16_t *input_data,
-                                          const cmsis_nn_dims *filter_dims,
-                                          const int8_t *filter_data,
-                                          const cmsis_nn_dims *bias_dims,
-                                          const int64_t *bias_data,
-                                          const cmsis_nn_dims *output_dims,
-                                          int16_t *output_data);
 
 /**
  * @brief Get the required buffer size for s16 convolution function
@@ -594,17 +557,6 @@ arm_cmsis_nn_status arm_convolve_fast_s16(const cmsis_nn_context *ctx,
  */
 int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
 
-/**
- * @brief Get the required buffer size for fast s16 convolution function
- *
- * @param[in]       input_dims    Input (activation) tensor dimensions. Format: [N, H, W, C_IN]
- * @param[in]       filter_dims   Filter tensor dimensions. Format: [C_OUT, HK, WK, C_IN] where HK and WK
- *                                are the spatial filter dimensions
- * @return          The function returns required buffer size(bytes)
- *
- */
-int32_t arm_convolve_fast_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims);
-
 /**
  * @brief Fast s4 version for 1x1 convolution (non-square shape)
  *
diff --git a/Include/arm_nnsupportfunctions.h b/Include/arm_nnsupportfunctions.h
index f2b7f29b..70c57022 100644
--- a/Include/arm_nnsupportfunctions.h
+++ b/Include/arm_nnsupportfunctions.h
@@ -21,8 +21,8 @@
  * Title:        arm_nnsupportfunctions.h
  * Description:  Public header file of support functions for CMSIS NN Library
  *
- * $Date:        14 February 2024
- * $Revision:    V.19.0.0
+ * $Date:        22 March 2024
+ * $Revision:    V.20.0.0
  *
  * Target :  Arm(R) M-Profile Architecture
  * -------------------------------------------------------------------- */
@@ -63,6 +63,10 @@ extern "C" {
 // will result in lower scratch buffer usage.
 #define CH_IN_BLOCK_MVE (124)
 
+// For input of int16 when number of columns are above this limit int64 accumulation is needed
+// to not loose precision.
+#define MAX_COL_COUNT (512)
+
 /**
  * @brief definition to pack four 8 bit values.
  */
@@ -281,8 +285,8 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
                                     const int32_t output_ch,
                                     const int32_t *out_shift,
                                     const int32_t *out_mult,
-                                    const int16_t activation_min,
-                                    const int16_t activation_max,
+                                    const int32_t activation_min,
+                                    const int32_t activation_max,
                                     const int32_t num_col_a,
                                     const int64_t *const output_bias,
                                     int16_t *out_0);
@@ -445,6 +449,48 @@ arm_cmsis_nn_status arm_nn_mat_mult_nt_t_s8(const int8_t *lhs,
                                             const int32_t row_address_offset,
                                             const int32_t lhs_cols_offset);
 
+/**
+ * @brief General Matrix-multiplication function with per-channel requantization and int16 input (LHS) and output.
+ *        This function assumes:
+ *        - LHS input matrix NOT transposed (nt)
+ *        - RHS input matrix transposed (t)
+ *
+ *  @note This operation also performs the broadcast bias addition before the requantization
+ *
+ * @param[in]  lhs                Pointer to the LHS input matrix
+ * @param[in]  rhs                Pointer to the RHS input matrix
+ * @param[in]  bias               Pointer to the bias vector. The length of this vector is equal to the number of
+ *                                output columns (or RHS input rows)
+ * @param[out] dst                Pointer to the output matrix with "m" rows and "n" columns
+ * @param[in]  dst_multipliers    Pointer to the multipliers vector needed for the per-channel requantization.
+ *                                The length of this vector is equal to the number of output columns (or RHS input
+ *                                rows)
+ * @param[in]  dst_shifts         Pointer to the shifts vector needed for the per-channel requantization. The length
+ *                                of this vector is equal to the number of output columns (or RHS input rows)
+ * @param[in]  lhs_rows           Number of LHS input rows
+ * @param[in]  rhs_rows           Number of RHS input rows
+ * @param[in]  rhs_cols           Number of LHS/RHS input columns
+ * @param[in]  activation_min     Minimum value to clamp down the output. Range : int16
+ * @param[in]  activation_max     Maximum value to clamp up the output. Range : int16
+ *
+ * @details MVE implementation only.
+ *
+ * @return     The function returns <code>ARM_CMSIS_NN_SUCCESS</code> or
+ *                                  <code>ARM_CMSIS_NN_NO_IMPL_ERROR</code> if not for MVE
+ *
+ */
+arm_cmsis_nn_status arm_nn_mat_mult_nt_t_s16(const int16_t *lhs,
+                                             const int8_t *rhs,
+                                             const int64_t *bias,
+                                             int16_t *dst,
+                                             const int32_t *dst_multipliers,
+                                             const int32_t *dst_shifts,
+                                             const int32_t lhs_rows,
+                                             const int32_t rhs_rows,
+                                             const int32_t rhs_cols,
+                                             const int32_t activation_min,
+                                             const int32_t activation_max);
+
 /**
  * @brief General Matrix-multiplication function with int8 input and int32 output.
  *        This function assumes:
diff --git a/Source/ConvolutionFunctions/arm_convolve_fast_s16.c b/Source/ConvolutionFunctions/arm_convolve_fast_s16.c
deleted file mode 100644
index 5348c3f7..00000000
--- a/Source/ConvolutionFunctions/arm_convolve_fast_s16.c
+++ /dev/null
@@ -1,232 +0,0 @@
-/*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * 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
- *
- * 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.
- */
-
-/* ----------------------------------------------------------------------
- * Project:      CMSIS NN Library
- * Title:        arm_convolve_fast_s16.c
- * Description:  Optimized s16 version of convolution.
- *
- * $Date:        23 March 2023
- * $Revision:    V.2.3.0
- *
- * Target :  Arm(R) M-Profile Architecture
- *
- * -------------------------------------------------------------------- */
-
-#include "arm_nnfunctions.h"
-#include "arm_nnsupportfunctions.h"
-
-/**
- *  @ingroup Public
- */
-
-/**
- * @addtogroup NNConv
- * @{
- */
-
-/*
- * Basic s16 convolution function.
- *
- * Refer header file for details. Optimal use case for the DSP/MVE implementation is when input and output channels
- * are multiples of 4 or atleast greater than 4.
- *
- */
-
-arm_cmsis_nn_status arm_convolve_fast_s16(const cmsis_nn_context *ctx,
-                                          const cmsis_nn_conv_params *conv_params,
-                                          const cmsis_nn_per_channel_quant_params *quant_params,
-                                          const cmsis_nn_dims *input_dims,
-                                          const int16_t *input_data,
-                                          const cmsis_nn_dims *filter_dims,
-                                          const int8_t *filter_data,
-                                          const cmsis_nn_dims *bias_dims,
-                                          const int64_t *bias_data,
-                                          const cmsis_nn_dims *output_dims,
-                                          int16_t *output_data)
-{
-    (void)bias_dims;
-    if (filter_dims->w * filter_dims->h * input_dims->c >= 512)
-    {
-        return ARM_CMSIS_NN_ARG_ERROR;
-    }
-
-    if (ctx->buf == NULL && arm_convolve_s8_get_buffer_size(input_dims, filter_dims) > 0)
-    {
-        return ARM_CMSIS_NN_ARG_ERROR;
-    }
-    int16_t *buffer_a = (int16_t *)ctx->buf;
-
-    const int32_t input_batches = input_dims->n;
-    const int32_t input_x = input_dims->w;
-    const int32_t input_y = input_dims->h;
-    const int32_t input_ch = input_dims->c;
-    const int32_t kernel_x = filter_dims->w;
-    const int32_t kernel_y = filter_dims->h;
-    const int32_t output_x = output_dims->w;
-    const int32_t output_y = output_dims->h;
-    const int32_t output_ch = output_dims->c;
-    const int32_t rhs_cols = input_ch * kernel_y * kernel_x;
-
-    const int32_t pad_x = conv_params->padding.w;
-    const int32_t pad_y = conv_params->padding.h;
-    const int32_t stride_x = conv_params->stride.w;
-    const int32_t stride_y = conv_params->stride.h;
-
-    const int16_t out_activation_min = conv_params->activation.min;
-    const int16_t out_activation_max = conv_params->activation.max;
-    int32_t *output_mult = quant_params->multiplier;
-    int32_t *output_shift = quant_params->shift;
-
-    for (int i_batch = 0; i_batch < input_batches; i_batch++)
-    {
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-        /* Generate two columns from the input tensor a GEMM computation */
-        int16_t *two_column_buf = buffer_a;
-        int16_t *out = output_data;
-        /* This part implements the im2col function */
-        for (int32_t i_out_y = 0; i_out_y < output_y; i_out_y++)
-        {
-            for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x++)
-            {
-                for (int32_t i_ker_y = i_out_y * stride_y - pad_y; i_ker_y < i_out_y * stride_y - pad_y + kernel_y;
-                     i_ker_y++)
-                {
-                    for (int32_t i_ker_x = i_out_x * stride_x - pad_x; i_ker_x < i_out_x * stride_x - pad_x + kernel_x;
-                         i_ker_x++)
-                    {
-                        if (i_ker_y < 0 || i_ker_y >= input_y || i_ker_x < 0 || i_ker_x >= input_x)
-                        {
-                            /* Filling 0 for out-of-bound paddings */
-                            arm_memset_s8((int8_t *)two_column_buf, 0, sizeof(int16_t) * input_ch);
-                        }
-                        else
-                        {
-                            arm_memcpy_s8((int8_t *)two_column_buf,
-                                          (const int8_t *)(input_data + (i_ker_y * input_x + i_ker_x) * input_ch),
-                                          input_ch * sizeof(int16_t));
-                        }
-                        two_column_buf += input_ch;
-                    }
-                }
-                /* Computation is filed for every 2 columns */
-                if (two_column_buf == buffer_a + 2 * rhs_cols)
-                {
-                    out = arm_nn_mat_mult_kernel_s16(filter_data,
-                                                     buffer_a,
-                                                     output_ch,
-                                                     output_shift,
-                                                     output_mult,
-                                                     out_activation_min,
-                                                     out_activation_max,
-                                                     rhs_cols,
-                                                     bias_data,
-                                                     out);
-
-                    /* Counter reset */
-                    two_column_buf = buffer_a;
-                }
-            }
-        }
-
-        /* Left-over because odd number of output pixels */
-        if (two_column_buf != buffer_a)
-        {
-            const int8_t *ker_a = filter_data;
-            int i;
-
-            for (i = 0; i < output_ch; i++)
-            {
-                /* Init the accumulator*/
-                int32_t sum = 0;
-
-                /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */
-                const int16_t *ip_as_col = buffer_a;
-
-                /* 4 multiply and accumulates are done in one loop. */
-                int32_t col_count = rhs_cols >> 2;
-
-                while (col_count)
-                {
-                    int32_t ker_a1, ker_a2;
-                    int32_t ip_b1, ip_b2;
-
-                    ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2);
-
-                    ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col);
-                    sum = SMLAD(ker_a1, ip_b1, sum);
-                    ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col);
-                    sum = SMLAD(ker_a2, ip_b2, sum);
-
-                    col_count--;
-                }
-                /* Handle left over mac */
-                col_count = rhs_cols & 0x3;
-                while (col_count)
-                {
-                    int8_t ker_a1 = *ker_a++;
-                    int16_t ip_b1 = *ip_as_col++;
-                    sum += ker_a1 * ip_b1;
-                    col_count--;
-                }
-                if (bias_data)
-                {
-                    int32_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i]);
-                    int64_t acc_64 = sum + bias_data[i];
-                    sum = arm_nn_requantize_s64(acc_64, reduced_multiplier, output_shift[i]);
-                }
-                else
-                {
-                    sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]);
-                }
-                sum = MAX(sum, out_activation_min);
-                sum = MIN(sum, out_activation_max);
-                *out++ = (int16_t)sum;
-            }
-        }
-#else
-        (void)input_data;
-        (void)output_data;
-        (void)bias_data;
-        (void)filter_data;
-        (void)buffer_a;
-        (void)kernel_x;
-        (void)kernel_y;
-        (void)pad_x;
-        (void)pad_y;
-        (void)stride_x;
-        (void)stride_y;
-        (void)out_activation_min;
-        (void)out_activation_max;
-        (void)output_mult;
-        (void)output_shift;
-        (void)rhs_cols;
-        return ARM_CMSIS_NN_ARG_ERROR;
-#endif
-        /* Advance to the next batch */
-        input_data += (input_x * input_y * input_ch);
-        output_data += (output_x * output_y * output_ch);
-    }
-
-    /* Return to application */
-    return ARM_CMSIS_NN_SUCCESS;
-}
-
-/**
- * @} end of NNConv group
- */
diff --git a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c
index 56af0e35..ad3291cd 100644
--- a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c
+++ b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s16.c
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+ * SPDX-FileCopyrightText: Copyright 2023-2024 Arm Limited and/or its affiliates <open-source-office@arm.com>
  *
  * SPDX-License-Identifier: Apache-2.0
  *
@@ -21,8 +21,8 @@
  * Title:        arm_convolve_get_buffer_sizes_s16.c
  * Description:  Collection of get buffer size functions for the various s16 convolution layer functions.
  *
- * $Date:        30 January 2023
- * $Revision:    V.1.0.0
+ * $Date:        20 March 2024
+ * $Revision:    V.2.0.0
  *
  * Target :  Arm(R) M-Profile Architecture
  *
@@ -40,30 +40,26 @@
  * @{
  */
 
-__STATIC_INLINE int32_t arm_convolve_fast_s16_get_buffer_size_dsp(const cmsis_nn_dims *input_dims,
-                                                                  const cmsis_nn_dims *filter_dims)
+__STATIC_INLINE int32_t arm_convolve_s16_get_buffer_size_mve(const cmsis_nn_dims *input_dims,
+                                                             const cmsis_nn_dims *filter_dims)
 {
-    return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t);
+    int32_t col_length = input_dims->c * filter_dims->w * filter_dims->h;
+    // Get number of complete lanes with int16 elements (multiple of 8) for given col_length. This is dependent on
+    // implementation of arm_nn_mat_mult_nt_t_s16
+    col_length = (col_length + 7) / 8;
+    // 4 -> number of im2col buffers, 8 -> 8 elements per Q register
+    return 4 * col_length * 8 * (int32_t)sizeof(int16_t);
 }
 
-int32_t arm_convolve_fast_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims)
+int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims)
 {
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-    return arm_convolve_fast_s16_get_buffer_size_dsp(input_dims, filter_dims);
+#if defined(ARM_MATH_MVEI)
+    return arm_convolve_s16_get_buffer_size_mve(input_dims, filter_dims);
 #else
-    (void)input_dims;
-    (void)filter_dims;
-    return 0;
+    return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t);
 #endif
 }
 
-int32_t arm_convolve_s16_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims)
-{
-    (void)input_dims;
-    (void)filter_dims;
-    return 0;
-}
-
 /*
  * Get the required buffer size for arm_convolve_wrapper_s16. This is the recommended function convolve wrapper s16
  * function.
@@ -76,16 +72,10 @@ int32_t arm_convolve_wrapper_s16_get_buffer_size(const cmsis_nn_conv_params *con
                                                  const cmsis_nn_dims *filter_dims,
                                                  const cmsis_nn_dims *output_dims)
 {
-
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-    return arm_convolve_wrapper_s16_get_buffer_size_dsp(conv_params, input_dims, filter_dims, output_dims);
-#else
     (void)conv_params;
     (void)output_dims;
 
-    // MVE and scalar implementation have same buffer requirements
     return arm_convolve_s16_get_buffer_size(input_dims, filter_dims);
-#endif
 }
 
 int32_t arm_convolve_wrapper_s16_get_buffer_size_dsp(const cmsis_nn_conv_params *conv_params,
@@ -93,18 +83,7 @@ int32_t arm_convolve_wrapper_s16_get_buffer_size_dsp(const cmsis_nn_conv_params
                                                      const cmsis_nn_dims *filter_dims,
                                                      const cmsis_nn_dims *output_dims)
 {
-    (void)output_dims;
-
-    if (filter_dims->w * filter_dims->h * input_dims->c < 512 &&
-        (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
-    {
-        return arm_convolve_fast_s16_get_buffer_size_dsp(input_dims, filter_dims);
-    }
-    else
-    {
-
-        return arm_convolve_s16_get_buffer_size(input_dims, filter_dims);
-    }
+    return arm_convolve_wrapper_s16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims);
 }
 
 int32_t arm_convolve_wrapper_s16_get_buffer_size_mve(const cmsis_nn_conv_params *conv_params,
@@ -112,7 +91,10 @@ int32_t arm_convolve_wrapper_s16_get_buffer_size_mve(const cmsis_nn_conv_params
                                                      const cmsis_nn_dims *filter_dims,
                                                      const cmsis_nn_dims *output_dims)
 {
-    return arm_convolve_wrapper_s16_get_buffer_size(conv_params, input_dims, filter_dims, output_dims);
+    (void)conv_params;
+    (void)output_dims;
+
+    return arm_convolve_s16_get_buffer_size_mve(input_dims, filter_dims);
 }
 
 /**
diff --git a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c
index fc48198d..dd09b4ed 100644
--- a/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c
+++ b/Source/ConvolutionFunctions/arm_convolve_get_buffer_sizes_s8.c
@@ -21,8 +21,8 @@
  * Title:        arm_convolve_get_buffer_sizes_s8.c
  * Description:  Collection of get buffer size functions for the various s8 convolution layer functions.
  *
- * $Date:        14 March 2024
- * $Revision:    V.2.1.0
+ * $Date:        28 March 2024
+ * $Revision:    V.2.1.1
  *
  * Target :  Arm(R) M-Profile Architecture
  *
@@ -45,11 +45,11 @@ __STATIC_INLINE int32_t arm_convolve_s8_get_buffer_size_mve(const cmsis_nn_dims
                                                             const cmsis_nn_dims *filter_dims)
 {
     int32_t col_length = input_dims->c * filter_dims->w * filter_dims->h;
-    // Get number of complete int16 lanes(multiple of 8) for given col_length. This is dependent on
+    // Get number of complete lanes with int8 elements (multiple of 16) for given col_length. This is dependent on
     // implementation of arm_nn_mat_mult_nt_t_s8
-    col_length = (col_length + 7) / 8;
-    // 4 -> number of im2col buffers, 8 -> 8 elements per Q register
-    return 4 * col_length * 8 * (int32_t)sizeof(int8_t);
+    col_length = (col_length + 15) / 16;
+    // 4 -> number of im2col buffers, 16 -> 16 elements per Q register
+    return 4 * col_length * 16 * (int32_t)sizeof(int8_t);
 }
 
 __STATIC_INLINE int32_t arm_convolve_1_x_n_s8_get_buffer_size_mve(const cmsis_nn_conv_params *conv_params,
diff --git a/Source/ConvolutionFunctions/arm_convolve_s16.c b/Source/ConvolutionFunctions/arm_convolve_s16.c
index 75d30f16..08e21663 100644
--- a/Source/ConvolutionFunctions/arm_convolve_s16.c
+++ b/Source/ConvolutionFunctions/arm_convolve_s16.c
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates <open-source-office@arm.com>
  *
  * SPDX-License-Identifier: Apache-2.0
  *
@@ -19,10 +19,10 @@
 /* ----------------------------------------------------------------------
  * Project:      CMSIS NN Library
  * Title:        arm_convolve_s16.c
- * Description:  s16 version of convolution using symmetric quantization.
+ * Description:  s16 version of convolution.
  *
- * $Date:        30 January 2023
- * $Revision:    V.2.1.0
+ * $Date:        22 March 2024
+ * $Revision:    V.3.0.0
  *
  * Target :  Arm(R) M-Profile Architecture
  *
@@ -47,7 +47,6 @@
  * are multiples of 4 or atleast greater than 4.
  *
  */
-
 arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
                                      const cmsis_nn_conv_params *conv_params,
                                      const cmsis_nn_per_channel_quant_params *quant_params,
@@ -61,7 +60,12 @@ arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
                                      int16_t *output_data)
 {
     (void)bias_dims;
-    (void)ctx;
+
+    if (ctx->buf == NULL)
+    {
+        return ARM_CMSIS_NN_ARG_ERROR;
+    }
+    int16_t *buffer_a = (int16_t *)ctx->buf;
 
     const int32_t input_batches = input_dims->n;
     const int32_t input_x = input_dims->w;
@@ -72,69 +76,190 @@ arm_cmsis_nn_status arm_convolve_s16(const cmsis_nn_context *ctx,
     const int32_t output_x = output_dims->w;
     const int32_t output_y = output_dims->h;
     const int32_t output_ch = output_dims->c;
+    const int32_t rhs_cols = input_ch * kernel_y * kernel_x;
 
+    const int32_t dilation_x = conv_params->dilation.w;
+    const int32_t dilation_y = conv_params->dilation.h;
     const int32_t pad_x = conv_params->padding.w;
     const int32_t pad_y = conv_params->padding.h;
     const int32_t stride_x = conv_params->stride.w;
     const int32_t stride_y = conv_params->stride.h;
-    const int32_t dilation_x = conv_params->dilation.w;
-    const int32_t dilation_y = conv_params->dilation.h;
 
     const int32_t out_activation_min = conv_params->activation.min;
     const int32_t out_activation_max = conv_params->activation.max;
     int32_t *output_mult = quant_params->multiplier;
     int32_t *output_shift = quant_params->shift;
 
+#if defined(ARM_MATH_MVEI)
+    const int32_t rhs_rows = output_dims->c;
+#endif
+
     for (int i_batch = 0; i_batch < input_batches; i_batch++)
     {
-        /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
-        for (int32_t i_out_ch = 0; i_out_ch < output_ch; i_out_ch++)
-        {
-            const int32_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i_out_ch]);
+        int16_t *im2col = buffer_a;
+        int16_t *out = output_data;
 
-            for (int32_t base_idx_y = -pad_y, i_out_y = 0; i_out_y < output_y; base_idx_y += stride_y, i_out_y++)
+        int32_t lhs_rows = 0;
+
+        /* This part implements the im2col function */
+        for (int32_t i_out_y = 0; i_out_y < output_y; i_out_y++)
+        {
+            for (int32_t i_out_x = 0; i_out_x < output_x; i_out_x++)
             {
-                for (int32_t base_idx_x = -pad_x, i_out_x = 0; i_out_x < output_x; base_idx_x += stride_x, i_out_x++)
+                const int32_t base_idx_x = stride_x * i_out_x - pad_x;
+                const int32_t base_idx_y = stride_y * i_out_y - pad_y;
+
+                for (int32_t i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++)
                 {
-                    int64_t conv_out_acc = 0;
-
-                    const int32_t start_y_max = (-base_idx_y + dilation_y - 1) / dilation_y;
-                    const int32_t ker_y_start = MAX(0, start_y_max);
-                    const int32_t start_x_max = (-base_idx_x + dilation_x - 1) / dilation_x;
-                    const int32_t ker_x_start = MAX(0, start_x_max);
-                    const int32_t end_min_y = (input_y - base_idx_y + dilation_y - 1) / dilation_y;
-                    const int32_t ker_y_end = MIN(kernel_y, end_min_y);
-                    const int32_t end_min_x = (input_x - base_idx_x + dilation_x - 1) / dilation_x;
-                    const int32_t ker_x_end = MIN(kernel_x, end_min_x);
-
-                    for (int32_t i_ker_y = ker_y_start; i_ker_y < ker_y_end; i_ker_y++)
+                    for (int32_t i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++)
                     {
-                        for (int32_t i_ker_x = ker_x_start; i_ker_x < ker_x_end; i_ker_x++)
+                        const int32_t k_y = base_idx_y + dilation_y * i_ker_y;
+                        const int32_t k_x = base_idx_x + dilation_x * i_ker_x;
+
+                        if (k_y < 0 || k_y >= input_y || k_x < 0 || k_x >= input_x)
+                        {
+                            /* Filling 0 for out-of-bound paddings */
+                            arm_memset_s8((int8_t *)im2col, 0, sizeof(int16_t) * (uint32_t)input_ch);
+                        }
+                        else
                         {
-                            const int32_t in_row = base_idx_y + dilation_y * i_ker_y;
-                            const int32_t in_col = base_idx_x + dilation_x * i_ker_x;
-
-                            for (int32_t i_input_ch = 0; i_input_ch < input_ch; i_input_ch++)
-                            {
-                                conv_out_acc += input_data[(in_row * input_x + in_col) * input_ch + i_input_ch] *
-                                    filter_data[i_out_ch * input_ch * kernel_y * kernel_x +
-                                                (i_ker_y * kernel_x + i_ker_x) * input_ch + i_input_ch];
-                            }
+                            arm_memcpy_s8((int8_t *)im2col,
+                                          (const int8_t *)(input_data + (k_y * input_x + k_x) * input_ch),
+                                          (uint32_t)input_ch * sizeof(int16_t));
                         }
+                        im2col += input_ch;
                     }
+                }
 
-                    if (bias_data)
-                    {
-                        conv_out_acc += bias_data[i_out_ch];
-                    }
+                lhs_rows++;
+#if defined(ARM_MATH_MVEI)
+                /* Computation is filed for every 4 columns */
+                if (lhs_rows == 4)
+                {
+                    arm_nn_mat_mult_nt_t_s16(buffer_a,
+                                             filter_data,
+                                             bias_data,
+                                             out,
+                                             output_mult,
+                                             output_shift,
+                                             lhs_rows,
+                                             rhs_rows,
+                                             rhs_cols,
+                                             out_activation_min,
+                                             out_activation_max);
+                    out += lhs_rows * output_ch;
+
+                    lhs_rows = 0;
+                    im2col = buffer_a;
+                }
+#else
+                /* Computation is filed for every 2 columns */
+                if (lhs_rows == 2)
+                {
+                    out = arm_nn_mat_mult_kernel_s16(filter_data,
+                                                     buffer_a,
+                                                     output_ch,
+                                                     output_shift,
+                                                     output_mult,
+                                                     out_activation_min,
+                                                     out_activation_max,
+                                                     rhs_cols,
+                                                     bias_data,
+                                                     out);
 
-                    int32_t conv_out = arm_nn_requantize_s64(conv_out_acc, reduced_multiplier, output_shift[i_out_ch]);
-                    conv_out = MAX(conv_out, out_activation_min);
-                    conv_out = MIN(conv_out, out_activation_max);
-                    output_data[i_out_ch + (i_out_y * output_x + i_out_x) * output_ch] = (int16_t)conv_out;
+                    /* Counter reset */
+                    im2col = buffer_a;
+                    lhs_rows = 0;
                 }
+#endif
             }
+
+            if (out == NULL)
+            {
+                return ARM_CMSIS_NN_NO_IMPL_ERROR;
+            }
+        }
+
+        /* Handle left over columns */
+        if (lhs_rows != 0)
+        {
+#if defined(ARM_MATH_MVEI)
+            arm_nn_mat_mult_nt_t_s16(buffer_a,
+                                     filter_data,
+                                     bias_data,
+                                     out,
+                                     output_mult,
+                                     output_shift,
+                                     lhs_rows,
+                                     rhs_rows,
+                                     rhs_cols,
+                                     out_activation_min,
+                                     out_activation_max);
+            out += lhs_rows * rhs_rows;
+            lhs_rows = 0;
+            im2col = buffer_a;
+#else // #if defined(ARM_MATH_MVEI)
+
+            const int8_t *ker_a = filter_data;
+            int i;
+
+            for (i = 0; i < output_ch; i++)
+            {
+                /* Init the accumulator*/
+                int32_t sum = 0;
+
+                /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */
+                const int16_t *ip_as_col = buffer_a;
+
+    #if defined(ARM_MATH_DSP)
+                /* 4 multiply and accumulates are done in one loop. */
+                int32_t col_count = rhs_cols >> 2;
+
+                while (col_count)
+                {
+                    int32_t ker_a1, ker_a2;
+                    int32_t ip_b1, ip_b2;
+
+                    ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2);
+
+                    ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col);
+                    sum = SMLAD(ker_a1, ip_b1, sum);
+                    ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col);
+                    sum = SMLAD(ker_a2, ip_b2, sum);
+
+                    col_count--;
+                }
+                /* Handle left over mac */
+                col_count = rhs_cols & 0x3;
+    #else
+                uint16_t col_count = rhs_cols;
+
+    #endif
+                while (col_count)
+                {
+                    int8_t ker_a1 = *ker_a++;
+                    int16_t ip_b1 = *ip_as_col++;
+                    sum += ker_a1 * ip_b1;
+                    col_count--;
+                }
+                if (bias_data)
+                {
+                    int32_t reduced_multiplier = REDUCE_MULTIPLIER(output_mult[i]);
+                    int64_t acc_64 = sum + bias_data[i];
+                    sum = arm_nn_requantize_s64(acc_64, reduced_multiplier, output_shift[i]);
+                }
+                else
+                {
+                    sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]);
+                }
+                sum = MAX(sum, out_activation_min);
+                sum = MIN(sum, out_activation_max);
+                *out++ = (int16_t)sum;
+            }
+            lhs_rows = 0;
+#endif // #if defined(ARM_MATH_MVEI)
         }
+
         /* Advance to the next batch */
         input_data += (input_x * input_y * input_ch);
         output_data += (output_x * output_y * output_ch);
diff --git a/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c b/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c
index b05588ed..0d6d8832 100644
--- a/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c
+++ b/Source/ConvolutionFunctions/arm_convolve_wrapper_s16.c
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+ * SPDX-FileCopyrightText: Copyright 2021-2024 Arm Limited and/or its affiliates <open-source-office@arm.com>
  *
  * SPDX-License-Identifier: Apache-2.0
  *
@@ -22,8 +22,8 @@
  * Description:  s16 convolution layer wrapper function with the main purpose to call the optimal kernel available in
  * cmsis-nn to perform the convolution.
  *
- * $Date:        30 January 2023
- * $Revision:    V.2.1.0
+ * $Date:        12 March 2024
+ * $Revision:    V.2.2.0
  *
  * Target :  Arm(R) M-Profile Architecture
  *
@@ -59,37 +59,6 @@ arm_cmsis_nn_status arm_convolve_wrapper_s16(const cmsis_nn_context *ctx,
                                              const cmsis_nn_dims *output_dims,
                                              int16_t *output_data)
 {
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-    if (filter_dims->w * filter_dims->h * input_dims->c < 512 &&
-        (conv_params->dilation.w == 1 && conv_params->dilation.h == 1))
-    {
-        return arm_convolve_fast_s16(ctx,
-                                     conv_params,
-                                     quant_params,
-                                     input_dims,
-                                     input_data,
-                                     filter_dims,
-                                     filter_data,
-                                     bias_dims,
-                                     bias_data,
-                                     output_dims,
-                                     output_data);
-    }
-    else
-    {
-        return arm_convolve_s16(ctx,
-                                conv_params,
-                                quant_params,
-                                input_dims,
-                                input_data,
-                                filter_dims,
-                                filter_data,
-                                bias_dims,
-                                bias_data,
-                                output_dims,
-                                output_data);
-    }
-#else
     return arm_convolve_s16(ctx,
                             conv_params,
                             quant_params,
@@ -101,7 +70,6 @@ arm_cmsis_nn_status arm_convolve_wrapper_s16(const cmsis_nn_context *ctx,
                             bias_data,
                             output_dims,
                             output_data);
-#endif
 }
 
 /**
diff --git a/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c b/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c
index f15578e3..c8f56a27 100644
--- a/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c
+++ b/Source/ConvolutionFunctions/arm_depthwise_conv_fast_s16.c
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2022-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+ * SPDX-FileCopyrightText: Copyright 2022-2024 Arm Limited and/or its affiliates <open-source-office@arm.com>
  *
  * SPDX-License-Identifier: Apache-2.0
  *
@@ -22,8 +22,8 @@
  * Description:  Optimized s16 depthwise separable convolution function for
  *               channel multiplier of 1.
  *
- * $Date:        30 January 2023
- * $Revision:    V.1.3.0
+ * $Date:        19 March 2024
+ * $Revision:    V.1.4.0
  *
  * Target :  Arm(R) M-Profile Architecture
  *
@@ -69,7 +69,7 @@ arm_cmsis_nn_status arm_depthwise_conv_fast_s16(const cmsis_nn_context *ctx,
         return ARM_CMSIS_NN_ARG_ERROR;
     }
 
-    if (filter_dims->w * filter_dims->h >= 512)
+    if (filter_dims->w * filter_dims->h >= MAX_COL_COUNT)
     {
         return ARM_CMSIS_NN_ARG_ERROR;
     }
diff --git a/Source/NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c b/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c
similarity index 62%
rename from Source/NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c
rename to Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c
index b196eb86..9e9c5337 100644
--- a/Source/NNSupportFunctions/arm_nn_mat_mul_kernel_s16.c
+++ b/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_s16.c
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates
  * <open-source-office@arm.com>
  *
  * SPDX-License-Identifier: Apache-2.0
@@ -20,10 +20,10 @@
 /* ----------------------------------------------------------------------
  * Project:      CMSIS NN Library
  * Title:        arm_nn_mat_mult_kernel_s16.c
- * Description:  Matrix-multiplication function for convolution
+ * Description:  Matrix-multiplication function for 16 bits convolution
  *
- * $Date:        5 Janauray 2023
- * $Revision:    V.1.2.0
+ * $Date:        22 March 2024
+ * $Revision:    V.2.0.0
  *
  * Target :  Arm(R) M-Profile Architecture
  * -------------------------------------------------------------------- */
@@ -46,34 +46,35 @@
  * Refer header file for details.
  *
  */
-
 int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
                                     const int16_t *input_b,
                                     const int32_t output_ch,
                                     const int32_t *out_shift,
                                     const int32_t *out_mult,
-                                    const int16_t activation_min,
-                                    const int16_t activation_max,
+                                    const int32_t activation_min,
+                                    const int32_t activation_max,
                                     const int32_t num_col_a,
                                     const int64_t *const output_bias,
                                     int16_t *out_0)
 {
+#if !defined(ARM_MATH_MVEI)
+    const int32_t num_col_a_fast = num_col_a > MAX_COL_COUNT ? MAX_COL_COUNT : num_col_a;
+    const int32_t num_col_a_slow = num_col_a - MAX_COL_COUNT;
 
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-    /* set up the second output pointers */
     int16_t *out_1 = out_0 + output_ch;
     const int64_t *bias = output_bias;
-    uint16_t row_count = output_ch / 2;
+    int32_t row_count = output_ch / 2;
     const int8_t *ip_a0 = input_a;
+    int32_t reduced_multiplier;
 
-    /* this loop over rows in A */
+    /* This loop over rows in A */
     while (row_count)
     {
-        /* setup pointers for B */
+        /* Setup pointers for B */
         const int16_t *ip_b0 = input_b;
         const int16_t *ip_b1 = ip_b0 + num_col_a;
 
-        /* align the second pointer for A */
+        /* Align the second pointer for A */
         const int8_t *ip_a1 = ip_a0 + num_col_a;
 
         /* Init accumulator for channel N and N + 1 */
@@ -82,8 +83,15 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
         int32_t ch_1_out_0 = 0;
         int32_t ch_1_out_1 = 0;
 
-        uint16_t col_count = num_col_a / 4;
-        /* accumulate over the vector */
+        int64_t ch_0_out_0_s64 = 0;
+        int64_t ch_0_out_1_s64 = 0;
+        int64_t ch_1_out_0_s64 = 0;
+        int64_t ch_1_out_1_s64 = 0;
+
+    #if defined(ARM_MATH_DSP)
+        uint16_t col_count = num_col_a_fast / 4;
+
+        /* Accumulate over the vector */
         while (col_count)
         {
             int32_t a01, a02, a11, a12;
@@ -107,8 +115,12 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
             ch_1_out_1 = SMLAD(a12, b1, ch_1_out_1);
 
             col_count--;
-        } /* while over col_count */
-        col_count = num_col_a & 0x3;
+        }
+        col_count = num_col_a_fast & 0x3;
+    #else
+        int32_t col_count = num_col_a_fast;
+    #endif
+
         while (col_count)
         {
             int8_t a0 = *ip_a0++;
@@ -121,22 +133,49 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
             ch_1_out_0 += a1 * b0;
             ch_1_out_1 += a1 * b1;
             col_count--;
-        } /* while over col_count */
-        if (bias)
+        }
+
+        ch_0_out_0_s64 = ch_0_out_0;
+        ch_0_out_1_s64 = ch_0_out_1;
+        ch_1_out_0_s64 = ch_1_out_0;
+        ch_1_out_1_s64 = ch_1_out_1;
+
+        if (num_col_a > MAX_COL_COUNT)
         {
-            int32_t reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
-            int64_t acc_64 = ch_0_out_0 + *bias;
-            ch_0_out_0 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
-            acc_64 = ch_0_out_1 + *bias++;
-            ch_0_out_1 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
-            out_mult++;
+            col_count = num_col_a_slow;
+            while (col_count)
+            {
+                int8_t a0 = *ip_a0++;
+                int16_t b0 = *ip_b0++;
+                int8_t a1 = *ip_a1++;
+                int16_t b1 = *ip_b1++;
+
+                ch_0_out_0_s64 += a0 * b0;
+                ch_0_out_1_s64 += a0 * b1;
+                ch_1_out_0_s64 += a1 * b0;
+                ch_1_out_1_s64 += a1 * b1;
+                col_count--;
+            }
         }
-        else
+
+        if (bias)
         {
-            ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift);
-            ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift);
-            out_mult++;
+            ch_0_out_0_s64 += *bias;
+            ch_0_out_1_s64 += *bias++;
+            ch_1_out_0_s64 += *bias;
+            ch_1_out_1_s64 += *bias++;
         }
+
+        reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
+        ch_0_out_0 = arm_nn_requantize_s64(ch_0_out_0_s64, reduced_multiplier, *out_shift);
+        ch_0_out_1 = arm_nn_requantize_s64(ch_0_out_1_s64, reduced_multiplier, *out_shift);
+        out_mult++;
+        out_shift++;
+
+        reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
+        ch_1_out_0 = arm_nn_requantize_s64(ch_1_out_0_s64, reduced_multiplier, *out_shift);
+        ch_1_out_1 = arm_nn_requantize_s64(ch_1_out_1_s64, reduced_multiplier, *out_shift);
+
         ch_0_out_0 = MAX(ch_0_out_0, activation_min);
         ch_0_out_0 = MIN(ch_0_out_0, activation_max);
         *out_0++ = (int16_t)ch_0_out_0;
@@ -144,23 +183,7 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
         ch_0_out_1 = MAX(ch_0_out_1, activation_min);
         ch_0_out_1 = MIN(ch_0_out_1, activation_max);
         *out_1++ = (int16_t)ch_0_out_1;
-        out_shift++;
 
-        if (bias)
-        {
-            int32_t reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
-            int64_t acc_64 = ch_1_out_0 + *bias;
-            ch_1_out_0 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
-            acc_64 = ch_1_out_1 + *bias++;
-            ch_1_out_1 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
-            out_mult++;
-        }
-        else
-        {
-            ch_1_out_0 = arm_nn_requantize(ch_1_out_0, *out_mult, *out_shift);
-            ch_1_out_1 = arm_nn_requantize(ch_1_out_1, *out_mult, *out_shift);
-            out_mult++;
-        }
         ch_1_out_0 = MAX(ch_1_out_0, activation_min);
         ch_1_out_0 = MIN(ch_1_out_0, activation_max);
         *out_0++ = (int16_t)ch_1_out_0;
@@ -168,24 +191,29 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
         ch_1_out_1 = MAX(ch_1_out_1, activation_min);
         ch_1_out_1 = MIN(ch_1_out_1, activation_max);
         *out_1++ = (int16_t)ch_1_out_1;
+
+        out_mult++;
         out_shift++;
 
-        /* skip row */
+        /* Skip row */
         ip_a0 += num_col_a;
         row_count--;
     }
 
-    /* compute the last odd numbered row if any */
+    /* Compute the last odd numbered row if any */
     if (output_ch & 0x1)
     {
-        /* setup pointers for B */
+        /* Setup pointers for B */
         const int16_t *ip_b0 = input_b;
         const int16_t *ip_b1 = ip_b0 + num_col_a;
 
         int32_t ch_0_out_0 = 0;
         int32_t ch_0_out_1 = 0;
+        int64_t ch_0_out_0_s64 = 0;
+        int64_t ch_0_out_1_s64 = 0;
 
-        uint16_t col_count = num_col_a >> 2;
+    #if defined(ARM_MATH_DSP)
+        uint16_t col_count = num_col_a_fast >> 2;
         while (col_count)
         {
             int32_t a01, a02;
@@ -205,6 +233,9 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
             col_count--;
         }
         col_count = num_col_a & 0x3;
+    #else
+        int32_t col_count = num_col_a_fast;
+    #endif
         while (col_count)
         {
             int8_t a0 = *ip_a0++;
@@ -215,19 +246,35 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
             ch_0_out_1 += a0 * b1;
             col_count--;
         }
-        if (bias)
+
+        ch_0_out_0_s64 = ch_0_out_0;
+        ch_0_out_1_s64 = ch_0_out_1;
+
+        if (num_col_a > MAX_COL_COUNT)
         {
-            int32_t reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
-            int64_t acc_64 = ch_0_out_0 + *bias;
-            ch_0_out_0 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
-            acc_64 = ch_0_out_1 + *bias++;
-            ch_0_out_1 = arm_nn_requantize_s64(acc_64, reduced_multiplier, *out_shift);
+            col_count = num_col_a_slow;
+            while (col_count)
+            {
+                int8_t a0 = *ip_a0++;
+                int16_t b0 = *ip_b0++;
+                int16_t b1 = *ip_b1++;
+
+                ch_0_out_0_s64 += a0 * b0;
+                ch_0_out_1_s64 += a0 * b1;
+                col_count--;
+            }
         }
-        else
+
+        if (bias)
         {
-            ch_0_out_0 = arm_nn_requantize(ch_0_out_0, *out_mult, *out_shift);
-            ch_0_out_1 = arm_nn_requantize(ch_0_out_1, *out_mult, *out_shift);
+            ch_0_out_0_s64 += *bias;
+            ch_0_out_1_s64 += *bias++;
         }
+
+        reduced_multiplier = REDUCE_MULTIPLIER(*out_mult);
+        ch_0_out_0 = arm_nn_requantize_s64(ch_0_out_0_s64, reduced_multiplier, *out_shift);
+        ch_0_out_1 = arm_nn_requantize_s64(ch_0_out_1_s64, reduced_multiplier, *out_shift);
+
         ch_0_out_0 = MAX(ch_0_out_0, activation_min);
         ch_0_out_0 = MIN(ch_0_out_0, activation_max);
         *out_0++ = (int16_t)ch_0_out_0;
@@ -241,7 +288,7 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
 
     out_0 += output_ch;
 
-    /* return the new output pointer with offset */
+    /* Return the new output pointer with offset */
     return out_0;
 #else
     (void)input_a;
@@ -254,11 +301,11 @@ int16_t *arm_nn_mat_mult_kernel_s16(const int8_t *input_a,
     (void)num_col_a;
     (void)output_bias;
     (void)out_0;
-    /* To be completed */
+
     return NULL;
 #endif
 }
 
 /**
  * @} end of Doxygen group
- */
\ No newline at end of file
+ */
diff --git a/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c
new file mode 100644
index 00000000..78972d5b
--- /dev/null
+++ b/Source/NNSupportFunctions/arm_nn_mat_mult_nt_t_s16.c
@@ -0,0 +1,265 @@
+/*
+ * SPDX-FileCopyrightText: Copyright 2020-2024 Arm Limited and/or its affiliates <open-source-office@arm.com>
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * 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
+ *
+ * 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.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mat_mult_nt_t_s16
+ * Description:  Matrix multiplication support function with the right-hand-side (rhs) matrix transposed
+ *
+ * $Date:        2 April 2024
+ * $Revision:    V.1.0.0
+ *
+ * Target :  Arm(R) M-Profile Architecture
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**
+ * @ingroup groupSupport
+ */
+
+/**
+ * @addtogroup supportConvolution
+ * @{
+ */
+
+/*
+ * s16 matrix multiplication with the right-hand-side matrix transposed
+ *
+ * Refer header file for details.
+ *
+ */
+arm_cmsis_nn_status arm_nn_mat_mult_nt_t_s16(const int16_t *lhs,
+                                             const int8_t *rhs,
+                                             const int64_t *bias,
+                                             int16_t *dst,
+                                             const int32_t *dst_multipliers,
+                                             const int32_t *dst_shifts,
+                                             const int32_t lhs_rows,
+                                             const int32_t rhs_rows,
+                                             const int32_t rhs_cols,
+                                             const int32_t activation_min,
+                                             const int32_t activation_max)
+{
+#if defined(ARM_MATH_MVEI)
+
+    const uint32_t rhs_rows_offset = (uint32_t)rhs_rows * sizeof(int16_t);
+    const uint32x4_t scatter_offset = {
+        0, (uint32_t)rhs_rows_offset, (uint32_t)rhs_rows_offset * 2, (uint32_t)rhs_rows_offset * 3};
+
+    const int32_t rhs_cols_fast = rhs_cols > MAX_COL_COUNT ? MAX_COL_COUNT : rhs_cols;
+    const int32_t rhs_cols_slow = rhs_cols - MAX_COL_COUNT;
+
+    int i_items = 0;
+
+    for (; i_items <= (lhs_rows - 4); i_items += 4)
+    {
+        for (int i = 0; i < rhs_rows; i++)
+        {
+            int32_t acc_n0 = 0;
+            int32_t acc_n1 = 0;
+            int32_t acc_n2 = 0;
+            int32_t acc_n3 = 0;
+
+            int64_t acc_n0_s64 = 0;
+            int64_t acc_n1_s64 = 0;
+            int64_t acc_n2_s64 = 0;
+            int64_t acc_n3_s64 = 0;
+
+            const int16_t *ip_row_0 = lhs;
+            const int16_t *ip_row_1 = lhs + rhs_cols;
+            const int16_t *ip_row_2 = lhs + (2 * rhs_cols);
+            const int16_t *ip_row_3 = lhs + (3 * rhs_cols);
+            const int8_t *col_base = rhs + i * rhs_cols;
+
+    #if defined(ARM_MATH_AUTOVECTORIZE)
+            for (int j = 0; j < rhs_cols_fast; j++)
+            {
+                int8_t col = col_base[j];
+                acc_n0 += ip_row_0[j] * col;
+                acc_n1 += ip_row_1[j] * col;
+                acc_n2 += ip_row_2[j] * col;
+                acc_n3 += ip_row_3[j] * col;
+            }
+    #else
+            // Note: If operand initialization is moved around, use '&' constraint to
+            // specify earlyclobber operands.
+            __ASM volatile(" .p2align 2                              \n"
+                           "   wlstp.16        lr, %[cnt], 1f        \n"
+                           "   mov             %[out0], 0            \n"
+                           "   mov             %[out1], 0            \n"
+                           "   mov             %[out2], 0            \n"
+                           "   mov             %[out3], 0            \n"
+                           "   vldrb.s16       q0, [%[col]], #8      \n"
+                           "2:                                       \n"
+                           "   vldrh.u16       q1, [%[row0]], #16     \n"
+                           "   vmlava.s16      %[out0], q0, q1       \n"
+                           "   vldrh.u16       q2, [%[row1]], #16     \n"
+                           "   vmlava.s16      %[out1], q0, q2       \n"
+                           "   vldrh.u16       q3, [%[row2]], #16     \n"
+                           "   vmlava.s16      %[out2], q0, q3       \n"
+                           "   vldrh.u16       q4, [%[row3]], #16     \n"
+                           "   vmlava.s16      %[out3], q0, q4       \n"
+                           "   vldrb.s16       q0, [%[col]], #8      \n"
+                           "   letp            lr, 2b                \n"
+                           "1:                                       \n"
+                           : [col] "+l"(col_base),
+                             [row0] "+l"(ip_row_0),
+                             [row1] "+l"(ip_row_1),
+                             [row2] "+l"(ip_row_2),
+                             [row3] "+l"(ip_row_3),
+                             [out0] "=Te"(acc_n0),
+                             [out1] "=Te"(acc_n1),
+                             [out2] "=Te"(acc_n2),
+                             [out3] "=Te"(acc_n3)
+                           : [cnt] "r"(rhs_cols_fast)
+                           : "q0", "q1", "q2", "q3", "q4", "memory", "r14");
+    #endif
+            acc_n0_s64 = acc_n0;
+            acc_n1_s64 = acc_n1;
+            acc_n2_s64 = acc_n2;
+            acc_n3_s64 = acc_n3;
+
+            if (rhs_cols > MAX_COL_COUNT)
+            {
+                ip_row_0 = lhs + MAX_COL_COUNT;
+                ip_row_1 = lhs + rhs_cols + MAX_COL_COUNT;
+                ip_row_2 = lhs + (2 * rhs_cols) + MAX_COL_COUNT;
+                ip_row_3 = lhs + (3 * rhs_cols) + MAX_COL_COUNT;
+                col_base = rhs + i * rhs_cols + MAX_COL_COUNT;
+
+                for (int j = 0; j < rhs_cols_slow; j++)
+                {
+                    int8_t col = col_base[j];
+                    acc_n0_s64 += ip_row_0[j] * col;
+                    acc_n1_s64 += ip_row_1[j] * col;
+                    acc_n2_s64 += ip_row_2[j] * col;
+                    acc_n3_s64 += ip_row_3[j] * col;
+                }
+            }
+
+            if (bias)
+            {
+                acc_n0_s64 += bias[i];
+                acc_n1_s64 += bias[i];
+                acc_n2_s64 += bias[i];
+                acc_n3_s64 += bias[i];
+            }
+
+            int32_t reduced_multiplier = REDUCE_MULTIPLIER(dst_multipliers[i]);
+            int32_t shift = dst_shifts[i];
+
+            acc_n0 = arm_nn_requantize_s64(acc_n0_s64, reduced_multiplier, shift);
+            acc_n1 = arm_nn_requantize_s64(acc_n1_s64, reduced_multiplier, shift);
+            acc_n2 = arm_nn_requantize_s64(acc_n2_s64, reduced_multiplier, shift);
+            acc_n3 = arm_nn_requantize_s64(acc_n3_s64, reduced_multiplier, shift);
+            int32x4_t res = {acc_n0, acc_n1, acc_n2, acc_n3};
+
+            res = vmaxq_s32(res, vdupq_n_s32(activation_min));
+            res = vminq_s32(res, vdupq_n_s32(activation_max));
+
+            vstrhq_scatter_offset_s32(dst, scatter_offset, res);
+            dst++;
+        }
+
+        lhs += 4 * rhs_cols;
+        dst += (3 * rhs_rows);
+    }
+
+    for (; i_items < lhs_rows; i_items++)
+    {
+        for (int i = 0; i < rhs_rows; i++)
+        {
+            int32_t acc_n0 = 0;
+            int64_t acc_n0_s64 = 0;
+            const int16_t *ip_row_0 = lhs;
+            const int8_t *col_base = rhs + i * rhs_cols;
+
+    #if defined(ARM_MATH_AUTOVECTORIZE)
+            for (int j = 0; j < rhs_cols_fast; j++)
+            {
+                int8_t col = col_base[j];
+                acc_n0 += ip_row_0[j] * col;
+            }
+    #else
+            __ASM volatile(" .p2align 2                              \n"
+                           "   wlstp.32        lr, %[cnt], 1f        \n"
+                           "   mov             %[out0], 0            \n"
+                           "2:                                       \n"
+                           "   vldrb.s32       q0, [%[col]], #4      \n"
+                           "   vldrh.s32       q1, [%[row0]], #8     \n"
+                           "   vmlava.s32      %[out0], q0, q1       \n"
+                           "   letp            lr, 2b                \n"
+                           "1:                                       \n"
+                           : [col] "+l"(col_base), [row0] "+l"(ip_row_0), [out0] "=Te"(acc_n0)
+                           : [cnt] "r"(rhs_cols_fast)
+                           : "q0", "q1", "memory", "r14");
+    #endif
+            acc_n0_s64 = acc_n0;
+
+            if (rhs_cols > MAX_COL_COUNT)
+            {
+                ip_row_0 = lhs + MAX_COL_COUNT;
+                col_base = rhs + i * rhs_cols + MAX_COL_COUNT;
+
+                for (int j = 0; j < rhs_cols_slow; j++)
+                {
+                    int8_t col = col_base[j];
+                    acc_n0_s64 += ip_row_0[j] * col;
+                }
+            }
+
+            if (bias)
+            {
+                acc_n0_s64 += bias[i];
+            }
+
+            int32_t reduced_multiplier = REDUCE_MULTIPLIER(dst_multipliers[i]);
+            int32_t shift = dst_shifts[i];
+
+            acc_n0 = arm_nn_requantize_s64(acc_n0_s64, reduced_multiplier, shift);
+            acc_n0 = MAX(acc_n0, activation_min);
+            acc_n0 = MIN(acc_n0, activation_max);
+            *dst++ = (int16_t)acc_n0;
+        }
+        lhs += rhs_cols;
+    }
+
+#else
+    (void)lhs;
+    (void)rhs;
+    (void)dst_multipliers;
+    (void)dst_shifts;
+    (void)dst;
+    (void)activation_min;
+    (void)activation_max;
+    (void)bias;
+    (void)lhs_rows;
+    (void)lhs_rows;
+    (void)rhs_rows;
+    (void)rhs_cols;
+
+    return ARM_CMSIS_NN_NO_IMPL_ERROR;
+#endif
+    return ARM_CMSIS_NN_SUCCESS;
+}
+
+/**
+ * @} end of Doxygen group
+ */
diff --git a/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c b/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c
index b51fbf73..4c7983e2 100644
--- a/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c
+++ b/Source/NNSupportFunctions/arm_nn_vec_mat_mult_t_s16.c
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
+ * SPDX-FileCopyrightText: Copyright 2020-2024 Arm Limited and/or its affiliates <open-source-office@arm.com>
  *
  * SPDX-License-Identifier: Apache-2.0
  *
@@ -21,8 +21,8 @@
  * Title:        arm_nn_vec_mat_mult_t_s16
  * Description:  s16 vector by matrix (transposed) multiplication
  *
- * $Date:        5 January 2023
- * $Revision:    V.2.2.0
+ * $Date:        22 March 2024
+ * $Revision:    V.2.3.0
  *
  * Target :  Arm(R) M-Profile Architecture
  *
@@ -30,8 +30,6 @@
 
 #include "arm_nnsupportfunctions.h"
 
-#define MAX_COL_COUNT (512)
-
 /**
  * @ingroup groupSupport
  */
diff --git a/Tests/UnitTest/CMakeLists.txt b/Tests/UnitTest/CMakeLists.txt
index d8d8ab7a..cb273db5 100644
--- a/Tests/UnitTest/CMakeLists.txt
+++ b/Tests/UnitTest/CMakeLists.txt
@@ -75,7 +75,6 @@ add_subdirectory(TestCases/test_arm_avgpool_s16)
 add_subdirectory(TestCases/test_arm_avgpool_s8)
 add_subdirectory(TestCases/test_arm_convolve_1x1_s8_fast)
 add_subdirectory(TestCases/test_arm_convolve_1x1_s4_fast)
-add_subdirectory(TestCases/test_arm_convolve_fast_s16)
 add_subdirectory(TestCases/test_arm_convolve_s16)
 add_subdirectory(TestCases/test_arm_convolve_s8)
 add_subdirectory(TestCases/test_arm_convolve_s4)
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h
index 0df67b68..87e0e5d9 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/biases_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #pragma once
 #include <stdint.h>
 
-const int64_t int16xint8_biases[4] = {-2693823, 4268473, -1952572, -1471661};
+const int64_t *int16xint8_biases = NULL;
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h
index 274f27fa..5002212f 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/config_data.h
@@ -1,23 +1,23 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #pragma once
 #define INT16XINT8_OUT_CH 4
-#define INT16XINT8_IN_CH 3
-#define INT16XINT8_INPUT_W 7
+#define INT16XINT8_IN_CH 55
+#define INT16XINT8_INPUT_W 10
 #define INT16XINT8_INPUT_H 8
-#define INT16XINT8_DST_SIZE 48
-#define INT16XINT8_INPUT_SIZE 168
-#define INT16XINT8_OUT_ACTIVATION_MIN -13335
+#define INT16XINT8_DST_SIZE 40
+#define INT16XINT8_INPUT_SIZE 4400
+#define INT16XINT8_OUT_ACTIVATION_MIN -32768
 #define INT16XINT8_OUT_ACTIVATION_MAX 32767
 #define INT16XINT8_INPUT_BATCHES 1
-#define INT16XINT8_FILTER_X 2
+#define INT16XINT8_FILTER_X 9
 #define INT16XINT8_FILTER_Y 4
-#define INT16XINT8_STRIDE_X 2
-#define INT16XINT8_STRIDE_Y 3
+#define INT16XINT8_STRIDE_X 1
+#define INT16XINT8_STRIDE_Y 1
 #define INT16XINT8_PAD_X 0
-#define INT16XINT8_PAD_Y 1
-#define INT16XINT8_OUTPUT_W 4
-#define INT16XINT8_OUTPUT_H 3
+#define INT16XINT8_PAD_Y 0
+#define INT16XINT8_OUTPUT_W 2
+#define INT16XINT8_OUTPUT_H 5
 #define INT16XINT8_INPUT_OFFSET 0
 #define INT16XINT8_OUTPUT_OFFSET 0
 #define INT16XINT8_DILATION_X 1
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h
index 332e9a2d..a9857dce 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/input_data.h
@@ -1,18 +1,321 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #pragma once
 #include <stdint.h>
 
-const int16_t int16xint8_input[168] = {
-    10557,  -29412, 16306,  -5806,  -32348, -19960, 2964,   -32596, 20924,  18562,  -20598, -6976,  27849,  12415,
-    -30223, -27312, -7108,  -12970, 29648,  26680,  -8421,  -14587, 18276,  21648,  -2320,  12407,  -17227, 8336,
-    -19571, 21794,  -7472,  26556,  -18272, 4333,   29296,  -8120,  -14915, 28821,  -21516, 516,    3900,   24368,
-    -3683,  -2848,  -1891,  10821,  -26504, 2110,   5717,   -24497, 1898,   -23262, -11098, -21592, -27834, -30928,
-    15710,  -28005, 5185,   -24728, -22215, -21817, 1560,   -28083, -29076, -26205, -28534, -24994, -5517,  32166,
-    25809,  904,    23030,  32059,  -11323, 3203,   21358,  -29406, -18602, -14718, -26485, -31259, -2470,  -1560,
-    1982,   801,    -418,   -20854, -22982, -25937, 30793,  10965,  2980,   23705,  16271,  10279,  -25793, 11816,
-    30853,  3285,   29763,  20687,  -19271, 972,    27703,  -8887,  -32590, 428,    -31855, -24800, -7196,  3695,
-    9322,   -12119, 26056,  28964,  11767,  -25994, 8310,   25111,  2990,   10228,  9369,   8724,   -23360, -21582,
-    -6857,  -5432,  -20932, -18168, 29165,  -4251,  -30703, -29891, -332,   18088,  22134,  23526,  -22889, 18975,
-    -20517, 18490,  -15958, 11060,  -15179, -1603,  28921,  -15049, 29725,  -21223, 5630,   -27275, -8880,  -16761,
-    29354,  -10064, -15754, 15828,  -9302,  -11527, -5767,  2286,   623,    -805,   24164,  26393,  14253,  11532};
+const int16_t int16xint8_input[4400] = {
+    2258,   -12216, 21955,  28148,  24627,  -27487, 10259,  1230,   -22204, -20857, -4155,  -11625, -12677, -16925,
+    20804,  -2514,  30927,  -8927,  31309,  11732,  -15134, 4402,   9163,   -27708, 4394,   -28138, -31148, 12312,
+    15482,  -24145, 30356,  30332,  5787,   32650,  9767,   9098,   9892,   -3355,  -4309,  28572,  11701,  11084,
+    -14139, 16324,  11331,  7507,   14202,  -19123, 5268,   7244,   -12431, -12248, 9727,   -17937, -336,   26839,
+    21250,  21610,  -14691, -8927,  -10025, 12339,  -19596, 4049,   20204,  -12937, -27421, 17454,  -15713, 1844,
+    14225,  -32744, 20568,  4312,   31329,  30059,  161,    -12642, -23241, -2821,  -8344,  18918,  25202,  -1485,
+    -2434,  6275,   -5036,  -15151, 32710,  -17041, -20987, -19723, -17316, 7476,   22161,  24685,  11959,  22263,
+    19819,  12886,  -18595, -16198, 29482,  11992,  6715,   -26602, 12559,  4968,   9046,   -17713, 21326,  -28775,
+    25570,  -7374,  28219,  10499,  -3695,  13442,  -11035, 16418,  24432,  -12198, 19648,  -2747,  -31244, 32200,
+    12354,  -8623,  21864,  4109,   -15097, 13314,  -19738, -6218,  25493,  105,    15966,  -14539, 24697,  23697,
+    -6783,  -22239, 5334,   30959,  -7932,  -30297, -10048, -2983,  15982,  -25128, -30039, 27386,  11554,  -21482,
+    6832,   959,    -17909, -11444, 23960,  -26539, 30792,  -23153, 26795,  29854,  -21395, 23582,  15220,  13625,
+    7170,   3548,   -2081,  16865,  23240,  -17971, 20097,  -18667, 16294,  12263,  -20768, 20227,  12646,  11252,
+    16940,  -32242, -24922, 1394,   -2967,  -14485, 16886,  -21016, 1822,   17047,  -25726, 7505,   -15337, -31020,
+    -16606, 11889,  31023,  13219,  -25931, 5486,   -30148, 22892,  -16340, -29886, -18678, 23338,  -4706,  3020,
+    -14704, 24188,  -26789, -10494, 20445,  -2109,  -26602, -31952, -1866,  -14889, 19340,  27187,  25327,  17627,
+    30428,  -28544, -28249, 6444,   -4327,  -18179, -23642, 19621,  19011,  -31294, 4899,   31259,  -17601, 14607,
+    21903,  6354,   -13802, -18978, -32115, 3732,   29904,  4626,   -23383, 28943,  9113,   -25375, 8677,   2850,
+    8488,   5178,   31903,  358,    -23079, 8366,   -11574, -8258,  9291,   17477,  13631,  10186,  8484,   -31566,
+    266,    1961,   -22533, 21891,  -32375, 4038,   26877,  -29427, -25273, -14620, -13268, 26394,  11802,  -24003,
+    -8076,  -17636, -3618,  -29911, 16936,  -2277,  2268,   -10356, -19998, 29313,  -11621, 29555,  -12342, -7494,
+    28477,  -16251, -4086,  -12346, 30665,  11081,  -20061, 17093,  14015,  7315,   26903,  -11503, -18209, -21609,
+    -27691, -6059,  24874,  15918,  -10697, 5044,   -19347, 17421,  -7187,  -18499, 28595,  -25618, -17000, 2013,
+    14874,  -19802, 9446,   -29981, -21293, 4382,   17021,  -17156, -32605, -3929,  -18561, 8999,   28740,  15901,
+    -22235, -31659, -27817, 236,    -15083, -13559, -8535,  1339,   29264,  -6857,  -15438, -30669, -31874, -10301,
+    8111,   3394,   20311,  -4880,  -30970, -1774,  -30765, 24651,  30600,  -21806, -11209, -13074, -24038, 16325,
+    -455,   -19716, 8164,   26033,  24054,  10296,  -28583, 9374,   -13281, -2565,  -9242,  -2432,  -20762, -21777,
+    1608,   -13256, 23098,  10212,  -24395, 2988,   -6990,  24210,  -24282, -150,   -11756, -15869, -10787, -11019,
+    -32134, 20143,  17901,  -29358, -24804, 7701,   23521,  -21720, 19618,  -19714, 24610,  -24895, 14481,  -27610,
+    6142,   1560,   -7062,  -5597,  25607,  -7721,  -21721, -17403, -15620, 12053,  -16518, 29563,  -25189, -21034,
+    28873,  -4670,  25135,  -19302, 2997,   -24196, 31592,  14693,  28397,  6745,   799,    -32296, -8703,  14152,
+    6855,   15920,  -28550, -24691, -16018, 8106,   -7677,  -17292, 30067,  -8879,  28534,  20210,  11665,  14108,
+    14473,  8439,   -12283, 10164,  -8052,  17653,  -11761, -8598,  -16125, 16388,  -9305,  7727,   21321,  13638,
+    302,    -30699, 31677,  -31183, 29102,  24018,  -17336, -3904,  29213,  -9921,  -10674, 22526,  -19480, 21842,
+    19717,  9219,   11569,  -15563, -10734, -14940, -21183, 1365,   21113,  -32456, -1466,  -2457,  19736,  15724,
+    948,    -1444,  -26011, 30431,  -14554, -31904, -30898, -10747, 29385,  -21282, -15675, -28299, -8648,  -30366,
+    -19700, -25096, 19371,  -14758, 21414,  -465,   -18208, 27451,  19848,  -9505,  25331,  18744,  28264,  -3961,
+    -9361,  28359,  28543,  -8759,  24868,  27561,  -29541, -26832, 17900,  30838,  -31136, -5088,  -30209, 19159,
+    -26372, 25952,  11925,  22729,  -19587, -30222, 23008,  7425,   28778,  -31865, -25734, -11784, -13230, -30949,
+    -26773, 28503,  -4441,  32312,  12806,  17783,  -19642, 26678,  -31252, 19863,  18844,  -16461, 18793,  8004,
+    31078,  -14812, -6860,  -25725, -4822,  15699,  12604,  30601,  13716,  18066,  -28847, -18723, -1573,  -28568,
+    -5366,  -21533, 20642,  20863,  -11481, 32525,  466,    22573,  -3557,  28248,  -20354, -4060,  12506,  8747,
+    -5808,  -19082, -12126, -23129, -8508,  -19059, 30877,  -19800, 7475,   -3068,  10941,  11532,  14460,  24905,
+    32435,  13237,  -8083,  3238,   -6882,  12881,  -19904, 10427,  -25570, 27449,  5774,   -7542,  7205,   3683,
+    10563,  -27928, 25172,  22985,  -4375,  -18831, -11266, 23721,  13614,  14952,  24372,  -16215, -32157, -14984,
+    -20654, 23141,  -31302, 26824,  -7596,  -24282, -18350, 10367,  18182,  -7147,  22107,  -12827, 30699,  1054,
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+    -7321,  11436,  12389,  6300,   20420,  -27898, 8265,   18487,  -23677, 14945,  -23902, -21175, -26155, 23133,
+    -32665, 776,    8163,   27953,  -11349, -5927,  27751,  -4949,  -7998,  -2989,  -23709, 19833,  13638,  -6709,
+    -32280, -5049,  32028,  10369,  -11774, 23317,  12585,  -18338, 26859,  -14993, -15835, 21275,  -5159,  -1726,
+    21602,  1266,   -13424, 3149,   -10442, 1928,   -18287, 13727,  -4019,  19892,  -10922, 14936,  -214,   2110,
+    -31846, 19574,  -17071, 18407,  31292,  25800,  2090,   1672,   27480,  -19629, 11216,  -17006, 9101,   24825,
+    -10441, -23676, 21067,  -13812, -24285, 31068,  -26184, 11522,  16483,  24081,  -848,   -11582, -4332,  -740,
+    27803,  -621,   -2762,  -11778};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h
index c7e50e3d..9fd93e90 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/output_mult_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #pragma once
 #include <stdint.h>
 
-const int32_t int16xint8_output_mult[4] = {1089212438, 2042635001, 2134124060, 1087333954};
+const int32_t int16xint8_output_mult[4] = {1094038263, 1093426216, 1093614140, 1093822383};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h
index 15280764..babc7a6e 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/output_ref_data.h
@@ -1,10 +1,9 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #pragma once
 #include <stdint.h>
 
-const int16_t int16xint8_output_ref[48] = {
-    -13335, -169, -10400, -1211,  -4046,  17113, 6664,  31020,  -11016, 14661, -12617, 4871,
-    -11590, 6034, -12869, -2736,  -13335, 23907, -6623, 2609,   6054,   6400,  -2306,  -13335,
-    -7031,  1919, 13723,  -13335, -13335, 4406,  23009, -13335, -8348,  12887, 1828,   -13335,
-    -13335, 1073, 13718,  617,    -2508,  10951, 5453,  -11033, -4797,  5220,  -9823,  -2385};
+const int16_t int16xint8_output_ref[40] = {180,  471,  855,  475,   -60,  327,  298,  -198, 147,  108,
+                                           535,  -101, -101, -235,  -535, -188, -547, -944, -548, -328,
+                                           -895, -624, -496, -1029, -809, -671, -453, -410, -266, -563,
+                                           -818, -799, -512, -485,  -639, -968, -102, -9,   -414, -314};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h
index 74770b4b..5ca3315b 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/output_shift_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #pragma once
 #include <stdint.h>
 
-const int32_t int16xint8_output_shift[4] = {-8, -9, -9, -8};
+const int32_t int16xint8_output_shift[4] = {-16, -16, -16, -16};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h
index 8102687f..4d61c5f2 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/test_data.h
@@ -1,5 +1,5 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #include "biases_data.h"
 #include "config_data.h"
 #include "input_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h
index ac8c8768..91884235 100644
--- a/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8/weights_data.h
@@ -1,11 +1,351 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
 #pragma once
 #include <stdint.h>
 
-const int8_t int16xint8_weights[96] = {
-    12,   -38,  -25, -95, 18,  -1,   57,   35,  76,   61,   -54, 20,   -19,  -40,  -127, -7,  39,   110, 39, 45,
-    -20,  -41,  48,  -69, 12,  -105, 127,  58,  -103, 108,  36,  -48,  -101, 42,   -48,  87,  79,   -27, 36, 66,
-    -42,  -114, 80,  40,  115, -126, 94,   -9,  -50,  -121, 45,  -29,  118,  -111, -96,  -23, -85,  71,  82, 16,
-    -127, -82,  83,  -88, 74,  -101, 118,  37,  -91,  -71,  82,  -1,   42,   125,  -58,  54,  -113, 119, 94, -23,
-    114,  91,   -21, 75,  91,  -109, -103, -83, 124,  7,    45,  -127, -5,   -20,  -109, -124};
+const int8_t int16xint8_weights[7920] = {
+    118, 98,  35,  46,  18,  96,  107, 92,  59,  73,  100, 7,   94,  43,  112, 33,  55,  111, 5,   20,  68,  58,  82,
+    93,  120, 46,  10,  25,  76,  30,  111, 9,   94,  23,  118, 56,  39,  45,  72,  123, 47,  23,  62,  50,  44,  78,
+    74,  110, 90,  23,  8,   65,  38,  36,  85,  22,  25,  13,  38,  43,  63,  104, 74,  93,  27,  125, 17,  57,  87,
+    95,  71,  52,  90,  71,  54,  5,   93,  94,  55,  89,  48,  12,  103, 117, 78,  74,  20,  84,  10,  100, 19,  17,
+    73,  41,  37,  86,  104, 54,  83,  35,  114, 35,  39,  76,  48,  86,  120, 92,  27,  40,  64,  71,  30,  88,  9,
+    54,  47,  25,  93,  75,  15,  63,  106, 21,  105, 3,   57,  78,  79,  4,   9,   77,  20,  52,  96,  22,  74,  72,
+    85,  95,  19,  26,  57,  13,  10,  124, 50,  19,  52,  116, 30,  102, 35,  11,  60,  35,  77,  110, 78,  119, 119,
+    115, 71,  71,  68,  12,  22,  114, 81,  66,  65,  66,  94,  127, 56,  19,  79,  51,  71,  3,   90,  107, 20,  97,
+    29,  9,   125, 93,  65,  61,  116, 10,  5,   108, 24,  55,  122, 112, 37,  80,  52,  4,   72,  14,  118, 64,  22,
+    102, 52,  55,  93,  6,   42,  115, 57,  104, 123, 77,  116, 17,  81,  41,  124, 97,  7,   120, 92,  121, 53,  42,
+    126, 26,  3,   73,  79,  120, 51,  24,  124, 84,  41,  70,  3,   3,   67,  27,  83,  60,  24,  49,  72,  42,  54,
+    41,  3,   28,  125, 48,  41,  68,  12,  20,  79,  56,  89,  62,  7,   110, 56,  45,  97,  50,  61,  79,  4,   30,
+    121, 115, 110, 52,  25,  92,  1,   122, 28,  63,  125, 14,  61,  109, 15,  8,   94,  118, 106, 13,  29,  105, 34,
+    88,  107, 1,   110, 48,  101, 121, 8,   12,  42,  61,  23,  54,  50,  42,  35,  101, 124, 19,  14,  54,  30,  96,
+    103, 29,  85,  9,   87,  6,   27,  24,  49,  66,  55,  8,   78,  3,   85,  88,  47,  95,  61,  48,  46,  38,  13,
+    23,  106, 44,  123, 114, 70,  120, 15,  16,  123, 79,  78,  36,  86,  59,  49,  11,  124, 127, 102, 70,  64,  35,
+    112, 92,  49,  29,  109, 118, 48,  114, 39,  110, 33,  107, 43,  14,  5,   72,  41,  72,  74,  76,  2,   8,   63,
+    102, 20,  28,  105, 87,  13,  102, 80,  121, 73,  126, 56,  29,  20,  9,   7,   51,  16,  92,  99,  115, 48,  85,
+    53,  41,  119, 19,  3,   117, 74,  98,  118, 53,  33,  65,  64,  49,  7,   62,  44,  60,  41,  55,  24,  53,  93,
+    10,  111, 27,  38,  50,  70,  62,  69,  119, 40,  48,  42,  65,  45,  118, 15,  36,  29,  87,  33,  52,  1,   84,
+    79,  83,  65,  101, 77,  82,  54,  42,  102, 55,  25,  98,  30,  87,  88,  64,  5,   56,  45,  56,  86,  85,  32,
+    74,  104, 98,  14,  33,  86,  102, 85,  30,  25,  71,  76,  98,  103, 82,  82,  116, 12,  1,   82,  116, 108, 121,
+    122, 103, 56,  79,  54,  64,  95,  83,  70,  42,  99,  119, 60,  91,  123, 108, 41,  26,  21,  8,   60,  83,  22,
+    107, 28,  100, 84,  122, 74,  55,  51,  45,  4,   122, 48,  76,  5,   26,  28,  26,  95,  127, 89,  18,  97,  77,
+    110, 70,  115, 19,  84,  91,  1,   97,  1,   36,  92,  115, 16,  74,  42,  122, 124, 52,  59,  66,  11,  37,  101,
+    101, 28,  112, 107, 24,  83,  71,  101, 73,  112, 36,  93,  63,  117, 43,  76,  36,  76,  30,  45,  2,   28,  121,
+    7,   45,  96,  9,   97,  69,  28,  58,  59,  78,  0,   46,  6,   63,  102, 104, 17,  72,  106, 104, 35,  84,  90,
+    116, 124, 19,  37,  106, 3,   95,  127, 94,  15,  65,  103, 49,  12,  62,  72,  77,  29,  60,  103, 65,  43,  61,
+    66,  65,  2,   88,  104, 111, 113, 37,  50,  117, 105, 11,  87,  46,  4,   31,  36,  74,  103, 39,  50,  11,  115,
+    17,  77,  35,  86,  86,  23,  53,  52,  112, 57,  25,  33,  126, 50,  100, 69,  0,   68,  89,  7,   71,  89,  56,
+    86,  89,  33,  61,  67,  11,  114, 124, 47,  63,  68,  22,  65,  94,  68,  69,  120, 46,  48,  99,  81,  104, 83,
+    60,  76,  6,   98,  35,  94,  121, 4,   80,  48,  126, 125, 104, 25,  35,  120, 67,  25,  22,  83,  22,  55,  3,
+    110, 17,  88,  117, 82,  90,  56,  69,  57,  119, 103, 56,  31,  87,  42,  48,  65,  17,  6,   109, 23,  7,   45,
+    48,  107, 28,  66,  83,  121, 36,  100, 61,  32,  117, 64,  117, 18,  86,  89,  60,  8,   29,  42,  3,   72,  2,
+    28,  82,  16,  54,  55,  98,  39,  18,  125, 39,  91,  34,  13,  86,  88,  125, 67,  3,   14,  19,  82,  84,  37,
+    73,  124, 94,  92,  33,  58,  14,  8,   36,  17,  7,   53,  11,  58,  97,  93,  88,  83,  57,  69,  73,  114, 31,
+    16,  74,  9,   97,  16,  74,  20,  89,  14,  73,  6,   82,  11,  63,  10,  42,  70,  88,  8,   55,  53,  37,  112,
+    68,  62,  11,  74,  11,  51,  110, 41,  44,  107, 4,   5,   80,  31,  17,  122, 10,  19,  67,  37,  34,  10,  12,
+    52,  105, 99,  85,  28,  78,  32,  74,  33,  46,  123, 123, 103, 40,  108, 116, 26,  44,  126, 81,  84,  18,  89,
+    57,  99,  115, 26,  47,  75,  78,  117, 6,   76,  89,  109, 66,  38,  80,  15,  44,  69,  64,  121, 127, 84,  46,
+    122, 33,  18,  93,  58,  72,  52,  115, 87,  98,  73,  71,  62,  7,   76,  65,  117, 92,  53,  32,  115, 33,  125,
+    9,   104, 99,  10,  66,  69,  63,  112, 63,  42,  95,  9,   68,  5,   5,   17,  96,  125, 112, 66,  120, 124, 11,
+    93,  127, 34,  14,  111, 102, 15,  1,   81,  8,   48,  25,  77,  116, 50,  20,  37,  32,  11,  62,  59,  116, 79,
+    104, 78,  44,  70,  31,  32,  11,  75,  93,  30,  81,  87,  1,   18,  77,  93,  69,  36,  101, 95,  65,  60,  37,
+    18,  97,  13,  86,  32,  62,  43,  55,  18,  1,   59,  44,  90,  125, 0,   52,  63,  90,  60,  97,  48,  38,  48,
+    1,   75,  120, 87,  82,  61,  78,  32,  39,  46,  82,  15,  89,  69,  71,  21,  46,  78,  1,   51,  35,  30,  8,
+    122, 123, 114, 36,  83,  44,  20,  74,  116, 109, 22,  17,  51,  48,  83,  90,  52,  95,  35,  11,  8,   34,  9,
+    24,  21,  103, 82,  95,  97,  39,  69,  17,  5,   37,  16,  49,  125, 55,  126, 122, 3,   51,  117, 120, 27,  122,
+    41,  116, 95,  89,  43,  125, 108, 56,  79,  60,  38,  94,  123, 13,  38,  77,  13,  117, 81,  8,   37,  117, 22,
+    124, 47,  97,  54,  125, 95,  48,  5,   24,  78,  58,  125, 29,  54,  23,  65,  86,  85,  19,  83,  53,  95,  12,
+    52,  125, 83,  68,  100, 107, 52,  106, 78,  35,  70,  61,  92,  114, 81,  120, 85,  43,  27,  14,  68,  51,  102,
+    1,   79,  125, 125, 62,  67,  67,  39,  77,  83,  29,  56,  57,  100, 53,  6,   102, 101, 119, 115, 40,  117, 92,
+    42,  25,  29,  89,  79,  56,  13,  38,  87,  93,  121, 20,  112, 100, 90,  43,  21,  113, 83,  119, 28,  12,  72,
+    119, 80,  31,  115, 55,  63,  121, 89,  6,   126, 112, 60,  107, 99,  84,  31,  30,  83,  66,  18,  34,  62,  117,
+    40,  89,  107, 41,  121, 18,  21,  72,  103, 49,  52,  11,  46,  66,  53,  22,  1,   91,  39,  69,  70,  32,  50,
+    123, 9,   121, 83,  89,  9,   58,  95,  11,  104, 80,  12,  80,  89,  76,  2,   84,  113, 61,  95,  89,  106, 13,
+    51,  120, 61,  33,  31,  76,  112, 55,  47,  45,  11,  40,  89,  76,  84,  26,  70,  8,   3,   25,  104, 53,  37,
+    36,  112, 83,  42,  69,  111, 125, 35,  8,   20,  85,  112, 123, 73,  5,   103, 15,  90,  10,  53,  113, 69,  116,
+    16,  14,  56,  70,  42,  24,  10,  78,  113, 87,  47,  104, 97,  49,  93,  33,  18,  126, 83,  55,  120, 119, 125,
+    18,  114, 118, 33,  59,  11,  20,  111, 126, 102, 35,  74,  49,  109, 119, 53,  24,  89,  50,  97,  50,  23,  22,
+    15,  111, 49,  103, 17,  103, 29,  66,  108, 25,  89,  47,  6,   94,  44,  63,  37,  97,  89,  87,  100, 93,  101,
+    66,  31,  114, 77,  64,  75,  47,  33,  32,  36,  76,  11,  75,  10,  26,  124, 62,  60,  18,  110, 115, 84,  66,
+    124, 14,  4,   99,  59,  58,  99,  36,  47,  61,  55,  50,  123, 67,  127, 66,  34,  105, 13,  87,  21,  7,   32,
+    60,  5,   19,  96,  77,  78,  77,  121, 32,  106, 116, 59,  67,  53,  103, 65,  10,  126, 30,  95,  70,  123, 76,
+    18,  81,  125, 27,  13,  63,  32,  8,   38,  15,  123, 67,  112, 16,  90,  43,  13,  29,  3,   20,  21,  26,  78,
+    63,  38,  7,   127, 108, 127, 119, 47,  31,  88,  6,   119, 86,  56,  24,  6,   15,  119, 29,  108, 121, 96,  31,
+    24,  118, 16,  81,  25,  103, 55,  21,  22,  58,  9,   116, 42,  91,  19,  120, 65,  25,  103, 24,  38,  62,  35,
+    109, 44,  97,  36,  97,  64,  46,  55,  16,  74,  34,  118, 127, 20,  18,  6,   11,  22,  21,  44,  109, 48,  46,
+    48,  2,   115, 42,  66,  93,  64,  15,  33,  67,  99,  22,  27,  44,  52,  35,  21,  102, 76,  6,   24,  90,  2,
+    93,  94,  23,  19,  42,  57,  69,  111, 9,   92,  40,  58,  60,  36,  40,  17,  119, 57,  79,  48,  65,  90,  22,
+    66,  73,  66,  20,  54,  127, 54,  37,  84,  72,  39,  8,   5,   95,  116, 82,  32,  51,  56,  97,  47,  47,  70,
+    113, 108, 82,  72,  70,  54,  101, 45,  47,  12,  23,  22,  78,  107, 20,  66,  118, 81,  25,  29,  40,  95,  49,
+    55,  123, 18,  19,  28,  8,   44,  122, 13,  104, 19,  73,  2,   15,  95,  65,  82,  73,  16,  110, 49,  98,  109,
+    73,  42,  112, 112, 49,  43,  18,  2,   22,  40,  95,  19,  57,  15,  76,  66,  43,  91,  112, 127, 71,  21,  98,
+    105, 96,  72,  118, 103, 9,   2,   74,  37,  75,  42,  98,  0,   16,  34,  63,  40,  73,  15,  117, 117, 13,  108,
+    60,  55,  40,  106, 4,   19,  42,  77,  1,   40,  97,  74,  50,  13,  26,  100, 113, 40,  84,  98,  3,   112, 29,
+    35,  74,  81,  22,  9,   45,  113, 69,  91,  109, 48,  24,  17,  15,  97,  125, 75,  57,  72,  57,  117, 27,  72,
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+    69,  12,  71,  19,  108, 57,  103, 45,  84,  46,  53,  39,  82,  3,   113, 75,  18,  116, 13,  40,  93,  78,  5,
+    94,  112, 90,  40,  126, 79,  91,  89,  29,  58,  83,  48,  66,  101, 4,   32,  68,  96,  98,  36,  45,  111, 29,
+    60,  95,  123, 38,  111, 123, 38,  35,  98,  12,  57,  126, 43,  19,  21,  108, 77,  71,  40,  109, 112, 122, 83,
+    77,  70,  76,  5,   14,  115, 100, 88,  1,   2,   75,  12,  15,  74,  8,   91,  108, 96,  27,  114, 49,  19,  71,
+    126, 33,  81,  93,  93,  11,  9,   87,  76,  32,  28,  102, 16,  41,  99,  75,  87,  11,  120, 126, 89,  62,  91,
+    123, 89,  44,  44,  22,  2,   127, 101, 122, 90,  27,  74,  45,  107, 119, 103, 123, 68,  12,  7,   79,  104, 102,
+    88,  78,  84,  119, 2,   41,  57,  31,  40,  109, 34,  64,  51,  110, 87,  125, 59,  32,  66,  95,  8,   114, 65,
+    11,  53,  113, 59,  24,  57,  14,  60,  28,  50,  107, 50,  28,  106, 8,   127, 95,  61,  116, 32,  114, 22,  90,
+    72,  106, 6,   49,  83,  97,  84,  3,   70,  75,  124, 88,  112, 23,  77,  2,   114, 84,  64,  103, 118, 85,  4,
+    109, 40,  15,  30,  31,  6,   117, 95,  99,  107, 41,  23,  124, 76,  86,  127, 89,  86,  87,  76,  106, 73,  63,
+    31,  84,  34,  102, 51,  48,  99,  58,  38,  124, 20,  20,  98,  8,   53,  1,   106, 126, 48,  103, 117, 26,  43,
+    106, 115, 35,  73,  50,  48,  78,  3,   108, 66,  119, 39,  109, 53,  58,  6,   87,  103, 54,  98,  83,  56,  76,
+    37,  9,   99,  8,   69,  37,  46,  19,  87,  13,  97,  42,  29,  56,  110, 28,  41,  20,  34,  119, 107, 56,  53,
+    124, 59,  120, 13,  85,  1,   121, 22,  38,  96,  47,  117, 44,  51,  87,  102, 110, 119, 116, 83,  52,  47,  17,
+    113, 3,   83,  95,  15,  72,  119, 127, 28,  41,  20,  36,  23,  34,  90,  40,  13,  124, 80,  0,   51,  122, 20,
+    66,  14,  122, 83,  35,  115, 115, 31,  77,  42,  82,  104, 50,  91,  90,  106, 57,  5,   53,  42,  41,  35,  58,
+    94,  43,  113, 88,  42,  9,   119, 125, 114, 89,  90,  83,  28,  90,  30,  87,  111, 35,  42,  6,   63,  49,  96,
+    33,  61,  13,  54,  110, 113, 12,  54,  93,  117, 122, 105, 89,  34,  120, 117, 6,   97,  111, 72,  96,  71,  1,
+    92,  24,  5,   50,  29,  100, 96,  61,  72,  35,  118, 93,  93,  72,  21,  95,  5,   104, 104, 90,  110, 24,  51,
+    120, 10,  64,  34,  122, 31,  34,  90,  8,   61,  11,  119, 61,  111, 125, 24,  23,  72,  85,  100, 72,  54,  113,
+    18,  6,   56,  9,   27,  26,  97,  127, 110, 49,  106, 76,  11,  83,  98,  123, 92,  14,  68,  121, 26,  3,   120,
+    17,  24,  73,  8,   14,  39,  27,  95,  71,  67,  4,   58,  58,  5,   111, 92,  127, 6,   10,  94,  75,  69,  78,
+    70,  107, 11,  117, 98,  99,  95,  95,  50,  3,   91,  71,  27,  35,  117, 56,  18,  92,  51,  66,  28,  96,  103,
+    109, 111, 10,  95,  109, 110, 127, 27,  46,  67,  43,  79,  82,  31,  19,  8,   45,  9,   50,  30,  79,  97,  78,
+    69,  119, 2,   46,  18,  50,  31,  85,  126, 78,  7,   67,  42,  22,  45,  25,  3,   103, 59,  36,  91,  41,  6,
+    60,  42,  79,  95,  123, 4,   27,  58,  120, 6,   28,  81,  79,  112, 106, 96,  52,  18,  68,  69,  38,  74,  27,
+    22,  50,  74,  103, 34,  120, 93,  122};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/biases_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/biases_data.h
new file mode 100644
index 00000000..e6f7ade2
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/biases_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int64_t *int16xint8_spill_biases = NULL;
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/config_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/config_data.h
new file mode 100644
index 00000000..7aa2ee73
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/config_data.h
@@ -0,0 +1,24 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#define INT16XINT8_SPILL_OUT_CH 9
+#define INT16XINT8_SPILL_IN_CH 55
+#define INT16XINT8_SPILL_INPUT_W 10
+#define INT16XINT8_SPILL_INPUT_H 8
+#define INT16XINT8_SPILL_DST_SIZE 90
+#define INT16XINT8_SPILL_INPUT_SIZE 4400
+#define INT16XINT8_SPILL_OUT_ACTIVATION_MIN 32667
+#define INT16XINT8_SPILL_OUT_ACTIVATION_MAX 32767
+#define INT16XINT8_SPILL_INPUT_BATCHES 1
+#define INT16XINT8_SPILL_FILTER_X 9
+#define INT16XINT8_SPILL_FILTER_Y 4
+#define INT16XINT8_SPILL_STRIDE_X 1
+#define INT16XINT8_SPILL_STRIDE_Y 1
+#define INT16XINT8_SPILL_PAD_X 0
+#define INT16XINT8_SPILL_PAD_Y 0
+#define INT16XINT8_SPILL_OUTPUT_W 2
+#define INT16XINT8_SPILL_OUTPUT_H 5
+#define INT16XINT8_SPILL_INPUT_OFFSET 0
+#define INT16XINT8_SPILL_OUTPUT_OFFSET 0
+#define INT16XINT8_SPILL_DILATION_X 1
+#define INT16XINT8_SPILL_DILATION_Y 1
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/input_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/input_data.h
new file mode 100644
index 00000000..dd1ccfce
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/input_data.h
@@ -0,0 +1,281 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int16_t int16xint8_spill_input[4400] = {
+    32755, 32727, 32721, 32721, 32701, 32721, 32750, 32682, 32669, 32691, 32714, 32670, 32699, 32722, 32730, 32726,
+    32684, 32730, 32734, 32682, 32699, 32743, 32697, 32740, 32677, 32731, 32721, 32667, 32712, 32747, 32674, 32684,
+    32751, 32713, 32737, 32672, 32696, 32728, 32761, 32690, 32740, 32687, 32672, 32757, 32691, 32742, 32719, 32687,
+    32672, 32672, 32717, 32719, 32753, 32722, 32695, 32695, 32741, 32673, 32701, 32721, 32750, 32739, 32731, 32743,
+    32679, 32762, 32678, 32725, 32675, 32716, 32741, 32735, 32757, 32762, 32719, 32709, 32766, 32738, 32694, 32761,
+    32702, 32764, 32694, 32758, 32716, 32744, 32736, 32701, 32720, 32766, 32710, 32685, 32730, 32743, 32676, 32756,
+    32685, 32692, 32693, 32700, 32715, 32708, 32761, 32693, 32732, 32726, 32674, 32766, 32676, 32680, 32705, 32678,
+    32707, 32704, 32713, 32733, 32761, 32753, 32731, 32692, 32731, 32704, 32753, 32667, 32740, 32738, 32755, 32683,
+    32673, 32671, 32681, 32723, 32751, 32671, 32766, 32692, 32724, 32764, 32723, 32673, 32679, 32697, 32678, 32761,
+    32726, 32698, 32677, 32735, 32690, 32754, 32692, 32752, 32737, 32761, 32674, 32744, 32762, 32731, 32720, 32732,
+    32749, 32713, 32691, 32742, 32675, 32707, 32736, 32712, 32681, 32700, 32737, 32752, 32764, 32742, 32725, 32708,
+    32732, 32687, 32681, 32680, 32672, 32680, 32760, 32712, 32719, 32689, 32713, 32757, 32710, 32757, 32681, 32704,
+    32674, 32670, 32714, 32683, 32740, 32757, 32728, 32751, 32720, 32722, 32725, 32676, 32684, 32720, 32688, 32681,
+    32725, 32705, 32679, 32714, 32750, 32758, 32721, 32742, 32760, 32691, 32698, 32761, 32708, 32667, 32670, 32745,
+    32740, 32748, 32733, 32727, 32739, 32763, 32716, 32683, 32754, 32669, 32746, 32746, 32696, 32764, 32736, 32720,
+    32720, 32683, 32714, 32727, 32756, 32706, 32761, 32691, 32718, 32762, 32692, 32736, 32716, 32733, 32761, 32719,
+    32720, 32698, 32710, 32733, 32732, 32722, 32764, 32732, 32690, 32766, 32694, 32667, 32688, 32695, 32689, 32737,
+    32706, 32705, 32711, 32676, 32683, 32730, 32685, 32741, 32762, 32742, 32692, 32696, 32674, 32757, 32721, 32762,
+    32673, 32673, 32758, 32708, 32758, 32722, 32682, 32674, 32748, 32745, 32688, 32760, 32683, 32756, 32724, 32668,
+    32694, 32753, 32687, 32746, 32738, 32680, 32759, 32676, 32688, 32689, 32736, 32702, 32689, 32762, 32713, 32752,
+    32679, 32682, 32682, 32677, 32685, 32737, 32695, 32766, 32667, 32759, 32731, 32714, 32668, 32732, 32715, 32700,
+    32715, 32755, 32737, 32694, 32729, 32766, 32734, 32762, 32701, 32674, 32726, 32682, 32766, 32748, 32749, 32743,
+    32762, 32679, 32713, 32756, 32751, 32677, 32734, 32759, 32727, 32668, 32750, 32695, 32738, 32742, 32702, 32755,
+    32698, 32710, 32733, 32745, 32765, 32713, 32680, 32710, 32681, 32763, 32725, 32697, 32668, 32723, 32689, 32711,
+    32686, 32743, 32685, 32724, 32750, 32722, 32679, 32751, 32726, 32763, 32686, 32751, 32703, 32668, 32745, 32748,
+    32739, 32691, 32673, 32756, 32735, 32727, 32715, 32747, 32734, 32722, 32706, 32716, 32721, 32766, 32702, 32744,
+    32704, 32668, 32726, 32763, 32713, 32720, 32676, 32738, 32684, 32672, 32747, 32735, 32680, 32693, 32739, 32748,
+    32689, 32718, 32704, 32722, 32676, 32676, 32692, 32756, 32766, 32713, 32713, 32690, 32749, 32754, 32707, 32751,
+    32707, 32760, 32699, 32759, 32735, 32696, 32745, 32743, 32736, 32755, 32686, 32766, 32710, 32667, 32712, 32701,
+    32733, 32669, 32755, 32756, 32685, 32762, 32737, 32754, 32733, 32728, 32722, 32700, 32690, 32722, 32751, 32668,
+    32695, 32737, 32676, 32741, 32724, 32758, 32714, 32684, 32706, 32703, 32699, 32743, 32682, 32719, 32759, 32737,
+    32685, 32752, 32757, 32753, 32721, 32748, 32753, 32679, 32760, 32681, 32708, 32691, 32747, 32732, 32708, 32679,
+    32678, 32737, 32716, 32700, 32723, 32694, 32707, 32721, 32728, 32725, 32749, 32723, 32722, 32713, 32688, 32675,
+    32717, 32764, 32709, 32733, 32738, 32755, 32678, 32676, 32714, 32706, 32689, 32727, 32730, 32720, 32681, 32676,
+    32676, 32763, 32692, 32722, 32714, 32694, 32746, 32682, 32723, 32712, 32678, 32742, 32722, 32731, 32761, 32756,
+    32677, 32688, 32741, 32729, 32694, 32730, 32722, 32679, 32754, 32674, 32739, 32739, 32700, 32739, 32669, 32730,
+    32756, 32744, 32689, 32765, 32682, 32689, 32685, 32680, 32758, 32764, 32678, 32668, 32679, 32743, 32697, 32725,
+    32690, 32696, 32763, 32689, 32734, 32737, 32699, 32687, 32675, 32760, 32695, 32692, 32677, 32679, 32706, 32725,
+    32693, 32762, 32763, 32675, 32685, 32672, 32748, 32749, 32701, 32746, 32730, 32753, 32731, 32738, 32681, 32677,
+    32676, 32687, 32747, 32761, 32759, 32719, 32676, 32733, 32753, 32686, 32686, 32758, 32706, 32703, 32759, 32704,
+    32669, 32725, 32744, 32686, 32721, 32764, 32754, 32729, 32731, 32677, 32741, 32728, 32679, 32722, 32678, 32674,
+    32672, 32672, 32676, 32681, 32729, 32706, 32726, 32669, 32694, 32677, 32762, 32702, 32745, 32739, 32729, 32702,
+    32673, 32739, 32715, 32720, 32723, 32715, 32753, 32704, 32683, 32732, 32763, 32766, 32683, 32724, 32733, 32759,
+    32764, 32704, 32724, 32747, 32751, 32719, 32673, 32752, 32765, 32737, 32707, 32677, 32679, 32688, 32681, 32671,
+    32721, 32689, 32720, 32708, 32702, 32722, 32707, 32691, 32716, 32710, 32764, 32672, 32762, 32742, 32749, 32749,
+    32735, 32753, 32718, 32677, 32753, 32672, 32765, 32699, 32694, 32736, 32728, 32743, 32696, 32739, 32740, 32705,
+    32677, 32670, 32758, 32674, 32676, 32766, 32702, 32750, 32696, 32674, 32720, 32725, 32701, 32679, 32760, 32712,
+    32714, 32714, 32698, 32669, 32763, 32755, 32669, 32765, 32754, 32735, 32729, 32676, 32682, 32741, 32704, 32698,
+    32725, 32748, 32757, 32685, 32748, 32680, 32671, 32718, 32733, 32742, 32737, 32693, 32701, 32675, 32700, 32698,
+    32702, 32766, 32685, 32742, 32697, 32728, 32677, 32747, 32678, 32764, 32676, 32731, 32763, 32684, 32720, 32747,
+    32695, 32765, 32753, 32726, 32711, 32686, 32734, 32699, 32765, 32704, 32765, 32719, 32690, 32714, 32739, 32763,
+    32748, 32761, 32760, 32748, 32763, 32758, 32755, 32757, 32746, 32766, 32672, 32711, 32714, 32691, 32744, 32762,
+    32671, 32737, 32752, 32753, 32739, 32687, 32726, 32726, 32750, 32687, 32673, 32754, 32676, 32765, 32690, 32714,
+    32762, 32724, 32766, 32733, 32715, 32674, 32715, 32731, 32699, 32674, 32694, 32746, 32743, 32705, 32689, 32668,
+    32745, 32718, 32675, 32678, 32743, 32706, 32730, 32679, 32680, 32704, 32692, 32717, 32689, 32761, 32724, 32729,
+    32753, 32668, 32762, 32691, 32675, 32702, 32678, 32673, 32711, 32668, 32718, 32675, 32671, 32736, 32729, 32743,
+    32692, 32730, 32721, 32710, 32759, 32750, 32731, 32698, 32688, 32699, 32671, 32763, 32673, 32713, 32671, 32749,
+    32717, 32694, 32745, 32694, 32694, 32707, 32743, 32705, 32762, 32750, 32750, 32753, 32722, 32689, 32687, 32721,
+    32739, 32725, 32738, 32741, 32706, 32702, 32739, 32684, 32727, 32704, 32691, 32682, 32709, 32738, 32760, 32686,
+    32736, 32719, 32727, 32673, 32753, 32707, 32684, 32701, 32741, 32726, 32737, 32761, 32702, 32723, 32729, 32673,
+    32676, 32691, 32687, 32759, 32678, 32765, 32667, 32733, 32757, 32676, 32751, 32720, 32757, 32722, 32720, 32736,
+    32736, 32735, 32704, 32756, 32670, 32710, 32750, 32682, 32765, 32752, 32741, 32746, 32706, 32733, 32741, 32756,
+    32668, 32678, 32726, 32711, 32731, 32721, 32743, 32749, 32711, 32727, 32692, 32721, 32680, 32711, 32759, 32747,
+    32667, 32710, 32762, 32758, 32678, 32685, 32676, 32740, 32722, 32685, 32733, 32763, 32667, 32736, 32701, 32681,
+    32680, 32693, 32690, 32762, 32673, 32713, 32744, 32694, 32747, 32723, 32671, 32709, 32722, 32762, 32694, 32709,
+    32705, 32741, 32757, 32686, 32696, 32706, 32716, 32730, 32708, 32758, 32750, 32691, 32714, 32755, 32709, 32699,
+    32728, 32677, 32761, 32718, 32752, 32699, 32738, 32739, 32724, 32707, 32667, 32679, 32761, 32711, 32685, 32761,
+    32673, 32742, 32679, 32678, 32718, 32752, 32743, 32678, 32727, 32726, 32693, 32707, 32739, 32687, 32667, 32738,
+    32669, 32676, 32684, 32694, 32730, 32723, 32727, 32687, 32689, 32679, 32749, 32690, 32758, 32675, 32721, 32716,
+    32756, 32732, 32721, 32700, 32757, 32684, 32702, 32694, 32679, 32698, 32740, 32758, 32705, 32759, 32698, 32709,
+    32716, 32669, 32751, 32717, 32756, 32681, 32689, 32685, 32724, 32757, 32732, 32705, 32669, 32749, 32709, 32728,
+    32696, 32674, 32754, 32766, 32705, 32722, 32759, 32712, 32674, 32742, 32686, 32673, 32742, 32703, 32751, 32697,
+    32706, 32718, 32763, 32745, 32719, 32717, 32668, 32719, 32744, 32744, 32674, 32717, 32683, 32686, 32671, 32668,
+    32727, 32732, 32731, 32738, 32707, 32713, 32720, 32686, 32669, 32715, 32739, 32748, 32757, 32712, 32765, 32723,
+    32717, 32678, 32741, 32703, 32687, 32692, 32722, 32738, 32669, 32672, 32734, 32765, 32683, 32698, 32680, 32708,
+    32668, 32695, 32704, 32724, 32745, 32675, 32673, 32718, 32738, 32681, 32714, 32744, 32697, 32709, 32680, 32667,
+    32703, 32762, 32741, 32738, 32757, 32723, 32717, 32762, 32725, 32765, 32731, 32700, 32689, 32760, 32672, 32670,
+    32734, 32672, 32738, 32678, 32681, 32686, 32705, 32761, 32742, 32745, 32753, 32751, 32731, 32742, 32766, 32759,
+    32755, 32675, 32688, 32676, 32703, 32760, 32738, 32747, 32690, 32686, 32760, 32724, 32754, 32724, 32676, 32683,
+    32671, 32716, 32725, 32728, 32674, 32719, 32670, 32673, 32720, 32679, 32695, 32729, 32762, 32719, 32717, 32746,
+    32719, 32758, 32668, 32668, 32705, 32763, 32684, 32719, 32674, 32680, 32707, 32719, 32738, 32723, 32738, 32741,
+    32714, 32719, 32691, 32685, 32752, 32746, 32679, 32711, 32673, 32765, 32691, 32698, 32669, 32694, 32700, 32763,
+    32693, 32729, 32704, 32741, 32733, 32721, 32724, 32713, 32719, 32690, 32709, 32705, 32733, 32712, 32733, 32739,
+    32715, 32707, 32739, 32717, 32686, 32718, 32675, 32748, 32683, 32763, 32729, 32744, 32681, 32714, 32766, 32680,
+    32730, 32744, 32697, 32757, 32708, 32715, 32721, 32688, 32755, 32685, 32716, 32668, 32704, 32700, 32738, 32733,
+    32720, 32742, 32702, 32706, 32719, 32675, 32751, 32713, 32682, 32708, 32690, 32759, 32742, 32715, 32749, 32730,
+    32693, 32710, 32766, 32692, 32686, 32694, 32753, 32756, 32691, 32760, 32742, 32747, 32744, 32745, 32733, 32724,
+    32695, 32711, 32707, 32680, 32742, 32722, 32691, 32742, 32763, 32673, 32737, 32711, 32758, 32733, 32723, 32760,
+    32689, 32715, 32676, 32761, 32720, 32708, 32706, 32673, 32670, 32759, 32674, 32678, 32703, 32684, 32737, 32745,
+    32672, 32699, 32670, 32697, 32718, 32692, 32700, 32763, 32725, 32670, 32710, 32762, 32719, 32738, 32764, 32746,
+    32709, 32710, 32682, 32743, 32684, 32668, 32673, 32719, 32685, 32668, 32704, 32669, 32703, 32708, 32674, 32699,
+    32668, 32755, 32745, 32758, 32707, 32764, 32752, 32740, 32715, 32679, 32685, 32672, 32687, 32714, 32693, 32680,
+    32680, 32708, 32735, 32757, 32727, 32685, 32742, 32744, 32745, 32700, 32693, 32702, 32717, 32715, 32712, 32717,
+    32683, 32717, 32698, 32712, 32696, 32719, 32756, 32699, 32738, 32764, 32741, 32693, 32719, 32764, 32741, 32759,
+    32698, 32670, 32682, 32697, 32702, 32697, 32689, 32688, 32672, 32678, 32757, 32694, 32729, 32766, 32737, 32725,
+    32762, 32753, 32689, 32680, 32720, 32673, 32764, 32719, 32701, 32669, 32751, 32685, 32693, 32705, 32762, 32716,
+    32764, 32681, 32725, 32715, 32749, 32757, 32735, 32698, 32738, 32669, 32733, 32727, 32696, 32735, 32712, 32749,
+    32741, 32737, 32714, 32685, 32706, 32751, 32707, 32724, 32751, 32696, 32728, 32747, 32737, 32694, 32695, 32675,
+    32675, 32757, 32699, 32668, 32683, 32733, 32747, 32762, 32718, 32720, 32748, 32715, 32759, 32747, 32673, 32766,
+    32744, 32736, 32719, 32716, 32674, 32735, 32752, 32752, 32689, 32668, 32730, 32722, 32748, 32737, 32733, 32759,
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+    32710, 32678, 32744, 32671, 32686, 32733, 32682, 32672, 32712, 32763, 32727, 32710, 32678, 32712, 32733, 32708,
+    32680, 32736, 32670, 32682, 32691, 32759, 32726, 32693, 32684, 32678, 32720, 32715, 32699, 32698, 32745, 32683,
+    32687, 32714, 32684, 32685, 32687, 32683, 32740, 32742, 32681, 32764, 32714, 32698, 32752, 32734, 32755, 32697,
+    32671, 32682, 32722, 32721, 32726, 32690, 32699, 32696, 32681, 32717, 32670, 32670, 32676, 32674, 32677, 32764,
+    32675, 32739, 32766, 32682, 32697, 32717, 32679, 32725, 32682, 32669, 32667, 32756, 32741, 32688, 32717, 32677,
+    32685, 32759, 32670, 32702, 32687, 32703, 32737, 32714, 32711, 32734, 32737, 32755, 32744, 32719, 32698, 32756,
+    32708, 32686, 32764, 32747, 32677, 32727, 32678, 32697, 32701, 32719, 32677, 32712, 32678, 32743, 32713, 32712,
+    32708, 32668, 32692, 32695, 32688, 32766, 32668, 32745, 32752, 32681, 32704, 32707, 32730, 32708, 32681, 32732,
+    32703, 32705, 32730, 32761, 32741, 32740, 32689, 32752, 32703, 32695, 32697, 32667, 32765, 32706, 32669, 32750,
+    32678, 32731, 32677, 32741, 32669, 32726, 32716, 32670, 32751, 32701, 32720, 32741, 32689, 32724, 32704, 32737,
+    32714, 32704, 32739, 32682, 32760, 32728, 32736, 32710, 32703, 32749, 32751, 32677, 32711, 32748, 32748, 32756,
+    32704, 32727, 32763, 32765, 32683, 32714, 32674, 32764, 32728, 32720, 32736, 32698, 32719, 32749, 32711, 32680,
+    32696, 32714, 32689, 32690, 32708, 32687, 32756, 32747, 32720, 32722, 32736, 32704, 32738, 32729, 32743, 32723,
+    32760, 32727, 32713, 32709, 32736, 32713, 32750, 32670, 32747, 32734, 32754, 32673, 32722, 32670, 32720, 32684,
+    32688, 32669, 32764, 32677, 32693, 32764, 32756, 32713, 32736, 32732, 32737, 32695, 32728, 32760, 32684, 32736,
+    32680, 32724, 32748, 32747, 32721, 32687, 32718, 32672, 32699, 32758, 32700, 32686, 32668, 32723, 32751, 32698,
+    32751, 32684, 32748, 32734, 32766, 32734, 32754, 32721, 32675, 32734, 32727, 32670, 32734, 32685, 32737, 32673,
+    32762, 32732, 32712, 32761, 32757, 32715, 32693, 32754, 32750, 32687, 32696, 32757, 32758, 32737, 32683, 32757,
+    32709, 32696, 32696, 32757, 32752, 32671, 32678, 32729, 32671, 32681, 32686, 32724, 32714, 32668, 32674, 32694,
+    32695, 32763, 32725, 32701, 32750, 32682, 32683, 32754, 32712, 32762, 32716, 32732, 32701, 32749, 32761, 32670,
+    32671, 32716, 32755, 32727, 32696, 32758, 32732, 32715, 32687, 32696, 32694, 32720, 32716, 32727, 32715, 32724,
+    32740, 32739, 32696, 32747, 32758, 32707, 32697, 32731, 32713, 32716, 32672, 32733, 32713, 32753, 32713, 32742};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_mult_data.h
new file mode 100644
index 00000000..9b36b1a9
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_mult_data.h
@@ -0,0 +1,7 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int32_t int16xint8_spill_output_mult[9] =
+    {1100398998, 1100624659, 1100035049, 1099990516, 1100661379, 1100714636, 1100682734, 1100519706, 1100868940};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_ref_data.h
new file mode 100644
index 00000000..ee02443f
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_ref_data.h
@@ -0,0 +1,12 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int16_t int16xint8_spill_output_ref[90] = {
+    32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667,
+    32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667,
+    32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32720,
+    32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667,
+    32667, 32667, 32720, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667,
+    32667, 32667, 32667, 32667, 32667, 32719, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32718};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_shift_data.h
new file mode 100644
index 00000000..b9beffc0
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/output_shift_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int32_t int16xint8_spill_output_shift[9] = {-16, -16, -16, -16, -16, -16, -16, -16, -16};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/test_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/test_data.h
new file mode 100644
index 00000000..4d61c5f2
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/test_data.h
@@ -0,0 +1,9 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#include "biases_data.h"
+#include "config_data.h"
+#include "input_data.h"
+#include "output_mult_data.h"
+#include "output_ref_data.h"
+#include "output_shift_data.h"
+#include "weights_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill/weights_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/weights_data.h
new file mode 100644
index 00000000..35ff86df
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill/weights_data.h
@@ -0,0 +1,781 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int8_t int16xint8_spill_weights[17820] = {
+    79,  101, 58,  81,  42,  71,  35,  44,  102, 13,  8,   58,  12,  100, 15,  59,  85,  94,  86,  9,   50,  53,  63,
+    17,  76,  82,  122, 74,  26,  41,  46,  92,  125, 70,  75,  32,  62,  53,  80,  21,  102, 110, 17,  21,  82,  104,
+    120, 114, 26,  19,  33,  74,  116, 60,  96,  45,  110, 53,  102, 97,  124, 47,  8,   100, 25,  2,   75,  16,  36,
+    115, 33,  86,  59,  8,   62,  108, 101, 35,  62,  106, 51,  87,  94,  102, 87,  68,  107, 60,  75,  24,  79,  50,
+    96,  26,  21,  29,  26,  34,  102, 102, 31,  35,  118, 117, 104, 53,  82,  124, 2,   27,  62,  117, 70,  93,  14,
+    7,   3,   76,  15,  27,  120, 17,  10,  1,   93,  75,  101, 63,  19,  37,  119, 11,  16,  109, 35,  91,  68,  58,
+    45,  84,  25,  39,  18,  55,  118, 64,  45,  112, 95,  67,  121, 109, 75,  26,  62,  37,  82,  19,  46,  20,  108,
+    91,  108, 47,  34,  102, 22,  126, 2,   30,  44,  98,  28,  58,  77,  20,  83,  17,  65,  101, 101, 11,  87,  42,
+    101, 36,  55,  81,  24,  82,  101, 42,  2,   86,  17,  120, 10,  51,  75,  41,  66,  123, 73,  102, 9,   64,  14,
+    57,  14,  3,   75,  91,  37,  92,  11,  35,  44,  28,  21,  124, 36,  13,  25,  3,   122, 24,  17,  46,  18,  113,
+    32,  20,  3,   118, 89,  124, 20,  120, 44,  51,  74,  100, 120, 75,  68,  35,  106, 111, 7,   108, 99,  110, 113,
+    28,  21,  68,  42,  119, 108, 9,   14,  39,  47,  59,  115, 21,  109, 93,  95,  53,  21,  18,  35,  106, 79,  43,
+    54,  52,  118, 77,  63,  65,  105, 67,  12,  27,  12,  126, 71,  103, 23,  100, 72,  22,  7,   113, 1,   71,  66,
+    84,  96,  57,  76,  117, 72,  6,   85,  126, 35,  22,  111, 28,  117, 12,  80,  82,  115, 40,  59,  38,  115, 7,
+    100, 111, 20,  33,  77,  60,  119, 16,  123, 96,  94,  66,  99,  115, 47,  8,   109, 105, 56,  9,   8,   17,  116,
+    97,  112, 47,  40,  100, 48,  16,  44,  61,  24,  107, 34,  67,  26,  92,  2,   78,  107, 89,  11,  8,   123, 74,
+    90,  108, 84,  18,  28,  29,  70,  16,  115, 23,  106, 102, 64,  123, 56,  101, 125, 53,  122, 39,  70,  4,   39,
+    41,  25,  72,  71,  69,  70,  62,  106, 57,  105, 125, 64,  113, 96,  27,  102, 37,  8,   83,  91,  38,  90,  63,
+    118, 10,  35,  40,  121, 89,  50,  106, 30,  110, 120, 45,  18,  10,  85,  119, 23,  8,   15,  33,  34,  100, 84,
+    74,  6,   111, 70,  47,  12,  30,  54,  102, 92,  92,  119, 83,  50,  95,  70,  106, 88,  77,  45,  73,  116, 127,
+    104, 70,  22,  25,  83,  46,  67,  3,   82,  120, 93,  73,  43,  72,  81,  105, 100, 105, 84,  3,   32,  14,  53,
+    117, 52,  106, 45,  31,  30,  123, 67,  95,  66,  53,  16,  58,  18,  19,  50,  109, 123, 25,  61,  13,  17,  91,
+    25,  47,  116, 22,  64,  50,  28,  13,  22,  109, 1,   71,  102, 16,  25,  29,  38,  12,  32,  87,  65,  52,  2,
+    83,  54,  46,  111, 48,  63,  36,  44,  44,  37,  31,  106, 33,  54,  39,  8,   103, 40,  15,  92,  14,  3,   75,
+    26,  27,  107, 99,  93,  124, 22,  94,  38,  68,  25,  67,  108, 9,   25,  2,   14,  108, 2,   41,  36,  24,  125,
+    41,  76,  119, 87,  103, 64,  60,  52,  98,  71,  40,  104, 89,  77,  49,  12,  122, 18,  23,  82,  93,  103, 94,
+    78,  30,  68,  57,  23,  79,  113, 40,  56,  118, 22,  78,  22,  67,  53,  37,  29,  75,  30,  79,  31,  101, 104,
+    68,  117, 19,  72,  5,   89,  39,  102, 4,   9,   36,  29,  106, 57,  98,  19,  28,  126, 10,  52,  9,   16,  29,
+    103, 38,  126, 64,  78,  51,  30,  32,  103, 46,  48,  118, 59,  100, 31,  42,  7,   78,  16,  78,  124, 49,  16,
+    36,  59,  22,  85,  42,  107, 45,  12,  68,  51,  104, 57,  77,  113, 101, 89,  43,  91,  97,  76,  114, 76,  126,
+    29,  15,  102, 35,  65,  88,  46,  98,  103, 15,  64,  89,  18,  47,  77,  22,  59,  23,  29,  74,  105, 16,  33,
+    74,  110, 3,   18,  113, 127, 12,  4,   84,  96,  66,  24,  9,   118, 71,  33,  35,  100, 67,  36,  21,  83,  101,
+    32,  124, 82,  14,  84,  27,  59,  20,  34,  25,  84,  85,  114, 79,  72,  5,   42,  86,  42,  105, 109, 44,  119,
+    29,  110, 1,   98,  43,  94,  77,  43,  117, 51,  29,  12,  125, 23,  26,  75,  32,  43,  58,  11,  108, 82,  53,
+    63,  67,  109, 1,   30,  92,  53,  124, 45,  45,  15,  116, 70,  95,  31,  121, 44,  5,   92,  79,  48,  56,  40,
+    65,  101, 52,  125, 85,  19,  114, 44,  64,  19,  105, 88,  26,  38,  39,  124, 43,  54,  76,  13,  86,  93,  45,
+    3,   33,  105, 53,  108, 61,  118, 59,  125, 93,  113, 22,  97,  96,  4,   79,  113, 39,  123, 93,  114, 87,  28,
+    92,  90,  27,  8,   103, 108, 6,   87,  31,  119, 74,  89,  39,  71,  123, 116, 108, 92,  83,  92,  64,  54,  23,
+    46,  98,  15,  48,  120, 18,  124, 80,  79,  27,  24,  95,  90,  53,  87,  59,  65,  14,  40,  107, 74,  62,  74,
+    104, 77,  36,  104, 86,  0,   73,  103, 18,  93,  12,  87,  102, 62,  110, 34,  20,  17,  69,  3,   101, 31,  37,
+    117, 117, 113, 51,  24,  76,  78,  64,  51,  61,  39,  26,  45,  61,  75,  16,  57,  39,  79,  59,  106, 35,  76,
+    27,  18,  73,  42,  6,   58,  92,  102, 59,  30,  17,  103, 24,  88,  61,  70,  41,  101, 8,   118, 97,  106, 69,
+    10,  0,   97,  8,   11,  89,  63,  13,  65,  81,  55,  51,  10,  11,  17,  105, 13,  113, 123, 113, 93,  54,  123,
+    35,  113, 98,  14,  99,  40,  68,  106, 45,  48,  116, 115, 66,  71,  17,  46,  123, 49,  92,  122, 110, 103, 56,
+    54,  17,  102, 16,  103, 35,  117, 23,  18,  41,  11,  67,  107, 13,  64,  59,  73,  23,  39,  69,  10,  27,  93,
+    61,  2,   26,  45,  80,  8,   85,  64,  60,  110, 74,  77,  72,  67,  50,  38,  6,   52,  113, 55,  84,  62,  6,
+    90,  109, 15,  43,  72,  55,  106, 8,   89,  5,   120, 120, 38,  110, 37,  93,  59,  126, 35,  117, 59,  86,  125,
+    74,  75,  76,  95,  19,  82,  20,  91,  7,   39,  64,  5,   14,  57,  39,  1,   13,  22,  8,   53,  66,  6,   13,
+    83,  96,  32,  83,  82,  99,  121, 104, 65,  109, 79,  35,  3,   44,  1,   90,  1,   116, 72,  43,  5,   109, 59,
+    76,  127, 25,  85,  72,  34,  126, 114, 40,  43,  117, 61,  92,  52,  117, 62,  124, 102, 6,   26,  95,  112, 9,
+    56,  121, 13,  102, 86,  27,  110, 71,  6,   123, 97,  123, 87,  52,  118, 107, 37,  42,  10,  46,  12,  82,  126,
+    8,   71,  16,  40,  84,  18,  50,  124, 67,  68,  65,  18,  117, 58,  108, 67,  66,  55,  87,  60,  102, 34,  77,
+    27,  63,  101, 12,  46,  84,  82,  50,  110, 87,  66,  10,  17,  110, 60,  111, 57,  105, 123, 27,  99,  85,  92,
+    87,  66,  58,  33,  16,  21,  15,  57,  61,  121, 52,  94,  8,   109, 111, 24,  22,  31,  125, 94,  33,  90,  19,
+    64,  89,  124, 20,  60,  126, 39,  101, 69,  81,  42,  18,  64,  100, 50,  62,  108, 66,  58,  80,  38,  72,  40,
+    22,  42,  2,   47,  95,  35,  22,  78,  46,  21,  8,   75,  84,  98,  99,  13,  127, 87,  108, 52,  62,  104, 49,
+    43,  102, 63,  35,  68,  37,  30,  47,  49,  1,   105, 114, 116, 62,  19,  121, 105, 5,   113, 111, 26,  70,  122,
+    4,   119, 95,  122, 75,  52,  107, 45,  1,   42,  27,  40,  108, 97,  117, 103, 13,  118, 15,  65,  90,  25,  116,
+    33,  20,  9,   116, 80,  37,  11,  39,  92,  33,  81,  100, 19,  60,  20,  11,  95,  114, 85,  116, 23,  52,  52,
+    7,   39,  120, 43,  34,  25,  116, 39,  101, 106, 10,  65,  84,  54,  87,  85,  55,  41,  44,  123, 97,  49,  16,
+    76,  91,  24,  114, 92,  51,  79,  34,  5,   40,  21,  72,  82,  120, 68,  20,  113, 101, 50,  51,  27,  56,  22,
+    100, 48,  21,  113, 48,  90,  30,  8,   52,  76,  18,  113, 54,  51,  22,  120, 29,  105, 58,  51,  98,  99,  40,
+    56,  45,  47,  125, 42,  91,  112, 72,  124, 81,  70,  99,  87,  1,   97,  40,  20,  8,   57,  112, 31,  59,  124,
+    101, 44,  0,   40,  93,  36,  6,   106, 70,  48,  60,  100, 112, 38,  91,  22,  103, 31,  19,  10,  46,  70,  75,
+    29,  10,  120, 96,  5,   37,  101, 124, 0,   46,  89,  60,  2,   95,  27,  58,  7,   46,  63,  3,   14,  61,  10,
+    123, 7,   51,  22,  18,  39,  38,  9,   125, 9,   75,  47,  46,  24,  53,  4,   80,  35,  98,  38,  114, 36,  86,
+    68,  89,  11,  73,  16,  117, 75,  81,  25,  98,  127, 97,  79,  92,  22,  16,  25,  32,  74,  9,   52,  54,  95,
+    33,  60,  66,  101, 23,  57,  7,   77,  33,  121, 37,  6,   68,  29,  71,  79,  33,  106, 117, 85,  46,  49,  3,
+    84,  68,  126, 49,  119, 8,   79,  57,  16,  40,  82,  91,  60,  94,  3,   49,  4,   40,  125, 27,  7,   14,  78,
+    19,  83,  19,  117, 23,  83,  0,   39,  107, 65,  63,  44,  61,  112, 65,  68,  55,  19,  52,  11,  79,  82,  119,
+    59,  8,   114, 92,  32,  120, 27,  119, 48,  57,  102, 46,  94,  38,  46,  62,  26,  81,  106, 34,  103, 25,  108,
+    51,  38,  20,  8,   78,  76,  33,  76,  72,  92,  74,  114, 112, 55,  104, 16,  89,  60,  89,  39,  124, 65,  49,
+    118, 15,  21,  81,  21,  73,  97,  111, 66,  89,  122, 104, 82,  9,   25,  17,  81,  56,  27,  117, 58,  101, 41,
+    92,  72,  18,  2,   16,  40,  42,  59,  46,  106, 59,  91,  75,  117, 7,   82,  95,  69,  45,  54,  22,  41,  94,
+    55,  0,   36,  73,  75,  14,  0,   17,  81,  74,  47,  127, 13,  60,  56,  34,  92,  20,  53,  41,  64,  20,  55,
+    102, 118, 15,  105, 7,   22,  70,  93,  53,  48,  84,  107, 16,  89,  86,  52,  39,  26,  114, 51,  67,  71,  87,
+    6,   71,  49,  70,  59,  9,   50,  105, 121, 77,  100, 47,  119, 8,   61,  12,  2,   50,  121, 89,  46,  109, 56,
+    114, 69,  96,  49,  104, 58,  80,  35,  125, 25,  114, 106, 36,  111, 34,  118, 93,  16,  48,  9,   85,  61,  39,
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+    122, 32,  83,  2,   57,  104, 120, 123, 7,   92,  5,   62,  18,  49,  26,  118, 17,  124, 13,  101, 125, 114, 118,
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+    40,  114, 70,  71,  23,  59,  119, 12,  91,  53,  30,  102, 125, 46,  47,  1,   59,  126, 87,  32,  32,  115, 45,
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+    42,  1,   117, 16,  60,  21,  82,  93,  39,  83,  120, 52,  10,  111, 46,  36,  49,  122, 30,  58,  116, 74,  19,
+    15,  55,  44,  118, 11,  50,  28,  102, 6,   5,   37,  35,  45,  100, 117, 98,  115, 6,   63,  65,  65,  108, 73,
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+    60,  49,  117, 67,  120, 7,   114, 43,  119, 69,  107, 110, 99,  5,   95,  15,  86,  35,  22,  47,  113, 104, 32,
+    78,  24,  69,  6,   107, 75,  3,   22,  126, 63,  125, 41,  75,  110, 87,  69,  21,  63,  92,  21,  56,  106, 18,
+    65,  47,  75,  95,  14,  114, 97,  126, 22,  17,  77,  55,  65,  32,  38,  126, 83,  106, 49,  69,  7,   15,  67,
+    97,  19,  29,  106, 25,  64,  111, 19,  44,  39,  7,   43,  81,  51,  115, 40,  119, 21,  93,  65,  122, 59,  7,
+    30,  94,  33,  67,  51,  70,  56,  18,  73,  122, 86,  32,  21,  98,  83,  65,  126, 71,  35,  8,   2,   125, 24,
+    7,   63,  56,  3,   44,  44,  61,  97,  29,  78,  97,  5,   117, 94,  33,  34,  51,  75,  42,  16,  63,  95,  16,
+    69,  80,  58,  90,  83,  19,  72,  77,  64,  32,  115, 39,  59,  32,  65,  40,  12,  22,  32,  40,  47,  42,  102,
+    3,   30,  90,  26,  15,  98,  53,  92,  15,  126, 73,  97,  45,  68,  83,  2,   33,  122, 74,  56,  47,  98,  84,
+    72,  118, 37,  59,  76,  40,  90,  33,  62,  76,  51,  91,  11,  111, 116, 120, 59,  79,  46,  114, 70,  21,  116,
+    7,   41,  40,  25,  106, 72,  118, 6,   14,  109, 93,  102, 8,   77,  33,  111, 16,  85,  112, 99,  82,  106, 108,
+    28,  46,  84,  65,  105, 27,  30,  77,  55,  65,  109, 57,  44,  96,  26,  12,  42,  99,  23,  101, 9,   96,  113,
+    59,  24,  90,  14,  21,  57,  55,  81,  67,  112, 104, 124, 3,   57,  116, 121, 97,  120, 10,  4,   38,  49,  40,
+    23,  5,   78,  20,  35,  114, 93,  61,  80,  61,  66,  27,  120, 9,   30,  59,  107, 39,  54,  125, 114, 46,  61,
+    39,  82,  116, 103, 78,  32,  32,  120, 116, 38,  29,  27,  12,  3,   40,  53,  18,  32,  22,  75,  86,  125, 12,
+    28,  90,  28,  97,  31,  67,  55,  42,  112, 113, 42,  49,  59,  76,  109, 20,  52,  57,  113, 115, 17,  24,  116,
+    65,  107, 115, 119, 105, 59,  86,  83,  59,  75,  123, 107, 72,  39,  104, 22,  85,  88,  99,  52,  70,  46,  106,
+    60,  26,  4,   3,   2,   80,  48,  121, 33,  74,  0,   99,  94,  40,  71,  29,  82,  29,  59,  74,  72,  68,  42,
+    112, 42,  37,  77,  20,  85,  23,  30,  87,  102, 123, 48,  50,  21,  51,  16,  121, 45,  86,  99,  65,  63,  40,
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+    73,  11,  81,  23,  82,  106, 27,  19,  41,  83,  56,  60,  3,   81,  42,  109, 63,  68,  45,  10,  77,  41,  91,
+    93,  119, 94,  7,   12,  64,  81,  113, 97,  32,  36,  41,  69,  24,  73,  83,  106, 29,  63,  81,  51,  64,  48,
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+    120, 109, 11,  42,  58,  96,  95,  83,  124, 19,  107, 118, 41,  21,  124, 125, 33,  114, 29,  22,  40,  85,  50,
+    70,  118, 45,  109, 27,  24,  28,  94,  118, 86,  95,  105, 55,  15,  16,  16,  11,  48,  98,  65,  75,  87,  82,
+    118, 104, 105, 98,  122, 78,  31,  75,  47,  111, 89,  66,  97,  51,  68,  32,  6,   31,  117, 90,  70,  107, 85,
+    72,  1,   35,  21,  82,  75,  118, 42,  81,  10,  36,  0,   8,   126, 54,  112, 118, 67,  126, 108, 16,  117, 103,
+    82,  34,  124, 4,   20,  89,  112, 43,  46,  106, 96,  123, 44,  56,  41,  24,  44,  90,  80,  116, 33,  36,  6,
+    120, 70,  81,  61,  72,  47,  47,  79,  126, 56,  105, 68,  4,   34,  95,  61,  121, 11,  40,  11,  77,  28,  9,
+    41,  98,  38,  82,  14,  90,  25,  125, 44,  12,  105, 92,  123, 22,  78,  87,  6,   42,  113, 31,  81,  17,  68,
+    52,  81,  20,  119, 32,  5,   51,  11,  74,  81,  119, 45,  117, 7,   33,  126, 10,  92,  69,  74,  76,  106, 28,
+    11,  76,  92,  74,  8,   111, 70,  35,  71,  77,  127, 106, 45,  125, 21,  108, 87,  85,  48,  80,  71,  113, 107,
+    114, 52,  115, 66,  48,  61,  65,  125, 41,  71,  25,  67,  59,  112, 86,  78,  18,  105, 68,  2,   95,  30,  97,
+    105, 52,  91,  40,  117, 102, 84,  68,  108, 9,   120, 73,  119, 12,  56,  6,   1,   115, 43,  58,  58,  114, 117,
+    54,  83,  113, 125, 23,  50,  109, 5,   51,  62,  90,  22,  109, 69,  92,  77,  17,  43,  67,  114, 26,  104, 21,
+    15,  1,   42,  75,  30,  124, 8,   29,  70,  67,  73,  28,  34,  38,  64,  118, 64,  32,  70,  111, 12,  88,  109,
+    35,  113, 81,  17,  43,  97,  16,  83,  46,  71,  67,  93,  31,  15,  8,   20,  7,   12,  65,  89,  3,   54,  64,
+    56,  79,  23,  54,  108, 48,  89,  13,  35,  35,  112, 35,  53,  46,  1,   32,  78,  32,  29,  89,  16,  2,   41,
+    14,  97,  75,  82,  61,  114, 37,  53,  123, 61,  97,  10,  120, 108, 98,  39,  1,   83,  78,  116, 82,  127, 81,
+    83,  90,  104, 123, 16,  120, 116, 49,  32,  58,  97,  24,  35,  81,  98,  101, 52,  95,  90,  34,  103, 79,  101,
+    55,  58,  68,  95,  3,   63,  116, 20,  108, 109, 99,  9,   72,  80,  40,  17,  7,   115, 54,  26,  84,  12,  42,
+    110, 52,  82,  41,  78,  118, 115, 24,  36,  60,  48,  62,  119, 30,  13,  38,  85,  108, 35,  36,  121, 58,  43,
+    92,  127, 16,  55,  42,  75,  35,  50,  101, 2,   119, 57,  92,  109, 79,  94,  99,  3,   95,  60,  15,  36,  19,
+    78,  8,   2,   0,   83,  69,  100, 98,  53,  59,  107, 121, 47,  40,  109, 49,  43,  81,  22,  14,  27,  38,  64,
+    121, 50,  62,  11,  30,  50,  77,  50,  86,  96,  4,   110, 34,  18,  76,  20,  115, 121, 95,  116, 106, 1,   17,
+    108, 115, 84,  75,  106, 8,   75,  95,  126, 78,  53,  42,  122, 89,  116, 56,  26,  113, 70,  86,  58,  95,  65,
+    48,  96,  116, 113, 117, 17,  123, 26,  26,  117, 33,  91,  2,   115, 36,  66,  92,  6,   66,  118, 105, 117, 54,
+    60,  104, 93,  83,  34,  93,  47,  114, 111, 30,  64,  70,  19,  46,  20,  49,  17,  53,  35,  110, 78,  111, 40,
+    72,  58,  115, 119, 26,  86,  82,  19,  51,  116, 60,  45,  120, 81,  57,  46,  124, 7,   18,  15,  119, 22,  107,
+    80,  110, 78,  27,  47,  13,  64,  62,  91,  102, 104, 59,  47,  109, 82,  81,  103, 80,  115, 103, 55,  109, 71,
+    108, 124, 44,  85,  117, 79,  123, 119, 125, 124, 70,  47,  42,  123, 54,  30,  108, 54,  59,  101, 13,  127, 67,
+    48,  8,   45,  87,  42,  28,  49,  56,  98,  107, 111, 63,  55,  73,  28,  43,  96,  120, 62,  77,  25,  47,  32,
+    40,  48,  101, 106, 115, 32,  5,   92,  86,  27,  115, 127, 107, 9,   14,  49,  121, 24,  60,  61,  26,  57,  10,
+    21,  51,  56,  69,  69,  35,  79,  5,   109, 25,  79,  56,  52,  88,  88,  4,   38,  22,  54,  102, 85,  32,  80,
+    72,  36,  58,  67,  80,  4,   6,   104, 82,  7,   53,  37,  48,  93,  42,  49,  49,  27,  61,  56,  7,   38,  83,
+    113, 99,  83,  30,  82,  110, 25,  63,  88,  58,  70,  97,  50,  34,  20,  45,  71,  77,  89,  76,  12,  59,  107,
+    79,  80,  125, 47,  85,  86,  43,  70,  80,  22,  36,  124, 44,  23,  7,   37,  50,  51,  46,  122, 5,   89,  1,
+    47,  72,  30,  89,  10,  102, 103, 72,  75,  116, 121, 13,  104, 75,  43,  79,  106, 50,  56,  35,  25,  27,  10,
+    73,  17,  58,  112, 102, 49,  72,  63,  84,  36,  126, 26,  76,  118, 50,  90,  113, 90,  118, 16,  10,  85,  127,
+    79,  30,  50,  85,  29,  45,  103, 99,  52,  111, 57,  110, 85,  25,  16,  36,  118, 64,  41,  48,  26,  29,  62,
+    40,  60,  37,  3,   73,  87,  112, 57,  64,  4,   97,  90,  84,  57,  18,  94,  92,  62,  97,  101, 50,  86,  34,
+    29,  116, 59,  44,  124, 49,  9,   27,  71,  118, 53,  18,  76,  55,  11,  100, 61,  108, 115, 120, 49,  73,  80,
+    42,  23,  74,  113, 35,  31,  38,  35,  121, 3,   96,  122, 28,  26,  20,  12,  67,  46,  84,  117, 30,  81,  58,
+    125, 14,  39,  19,  95,  58,  44,  96,  122, 60,  39,  77,  109, 58,  10,  35,  73,  120, 101, 24,  87,  126, 72,
+    90,  9,   1,   18,  12,  16,  87,  32,  71,  12,  10,  5,   123, 127, 64,  98,  81,  27,  68,  89,  57,  126, 31,
+    94,  126, 60,  31,  109, 71,  120, 34,  1,   115, 75,  119, 26,  122, 104, 9,   101, 106, 52,  75,  46,  46,  54,
+    37,  69,  38,  38,  65,  67,  68,  31,  40,  60,  110, 117, 8,   42,  45,  117, 89,  114, 11,  86,  15,  58,  27,
+    113, 47,  71,  15,  49,  79,  50,  106, 10,  38,  80,  39,  86,  87,  74,  49,  126, 42,  99,  127, 80,  26,  110,
+    31,  83,  110, 67,  32,  48,  66,  48,  110, 121, 120, 90,  36,  68,  67,  124, 68,  115, 26,  60,  112, 51,  25,
+    83,  17,  84,  73,  125, 44,  42,  48,  75,  24,  33,  98,  88,  102, 110, 124, 6,   119, 118, 65,  47,  68,  88,
+    19,  32,  22,  53,  51,  64,  11,  8,   25,  16,  8,   102, 18,  118, 97,  110, 52,  19,  70,  5,   111, 61,  103,
+    17,  91,  11,  16,  39,  11,  43,  107, 115, 47,  95,  8,   8,   72,  31,  9,   62,  7,   50,  103, 67,  86,  121,
+    83,  57,  123, 59,  67,  71,  40,  6,   119, 11,  98,  51,  103, 26,  54,  107, 100, 89};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/biases_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/biases_data.h
new file mode 100644
index 00000000..ace28d6c
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/biases_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int64_t *int16xint8_spill2_biases = NULL;
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/config_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/config_data.h
new file mode 100644
index 00000000..b79e38d8
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/config_data.h
@@ -0,0 +1,24 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#define INT16XINT8_SPILL2_OUT_CH 9
+#define INT16XINT8_SPILL2_IN_CH 12
+#define INT16XINT8_SPILL2_INPUT_W 10
+#define INT16XINT8_SPILL2_INPUT_H 8
+#define INT16XINT8_SPILL2_DST_SIZE 90
+#define INT16XINT8_SPILL2_INPUT_SIZE 960
+#define INT16XINT8_SPILL2_OUT_ACTIVATION_MIN 32667
+#define INT16XINT8_SPILL2_OUT_ACTIVATION_MAX 32767
+#define INT16XINT8_SPILL2_INPUT_BATCHES 1
+#define INT16XINT8_SPILL2_FILTER_X 9
+#define INT16XINT8_SPILL2_FILTER_Y 4
+#define INT16XINT8_SPILL2_STRIDE_X 1
+#define INT16XINT8_SPILL2_STRIDE_Y 1
+#define INT16XINT8_SPILL2_PAD_X 0
+#define INT16XINT8_SPILL2_PAD_Y 0
+#define INT16XINT8_SPILL2_OUTPUT_W 2
+#define INT16XINT8_SPILL2_OUTPUT_H 5
+#define INT16XINT8_SPILL2_INPUT_OFFSET 0
+#define INT16XINT8_SPILL2_OUTPUT_OFFSET 0
+#define INT16XINT8_SPILL2_DILATION_X 1
+#define INT16XINT8_SPILL2_DILATION_Y 1
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/input_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/input_data.h
new file mode 100644
index 00000000..d81c3483
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/input_data.h
@@ -0,0 +1,66 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int16_t int16xint8_spill2_input[960] = {
+    32732, 32696, 32712, 32689, 32702, 32755, 32747, 32756, 32690, 32671, 32733, 32703, 32714, 32668, 32687, 32730,
+    32742, 32735, 32699, 32674, 32711, 32675, 32719, 32718, 32737, 32694, 32748, 32669, 32735, 32676, 32743, 32755,
+    32740, 32719, 32746, 32688, 32719, 32733, 32739, 32689, 32766, 32703, 32674, 32687, 32728, 32704, 32708, 32691,
+    32730, 32684, 32701, 32683, 32731, 32712, 32723, 32722, 32724, 32700, 32733, 32703, 32745, 32732, 32714, 32694,
+    32732, 32754, 32695, 32741, 32750, 32739, 32688, 32722, 32756, 32674, 32681, 32759, 32741, 32697, 32706, 32747,
+    32687, 32730, 32741, 32667, 32682, 32684, 32726, 32691, 32721, 32755, 32735, 32715, 32732, 32689, 32716, 32749,
+    32677, 32692, 32694, 32739, 32715, 32717, 32723, 32717, 32716, 32736, 32678, 32701, 32694, 32705, 32688, 32718,
+    32701, 32758, 32700, 32709, 32688, 32737, 32687, 32736, 32676, 32727, 32731, 32688, 32713, 32730, 32698, 32686,
+    32762, 32722, 32685, 32720, 32754, 32753, 32711, 32726, 32760, 32759, 32680, 32702, 32683, 32761, 32713, 32699,
+    32674, 32679, 32727, 32757, 32672, 32691, 32720, 32728, 32704, 32700, 32680, 32704, 32749, 32765, 32719, 32730,
+    32683, 32751, 32736, 32718, 32696, 32674, 32674, 32690, 32747, 32690, 32763, 32706, 32758, 32720, 32710, 32735,
+    32721, 32698, 32738, 32668, 32718, 32694, 32681, 32707, 32706, 32761, 32756, 32687, 32700, 32686, 32686, 32680,
+    32751, 32761, 32715, 32744, 32697, 32712, 32686, 32725, 32764, 32746, 32739, 32739, 32733, 32763, 32718, 32720,
+    32714, 32681, 32696, 32711, 32734, 32761, 32746, 32697, 32729, 32711, 32677, 32740, 32741, 32715, 32729, 32685,
+    32685, 32726, 32728, 32707, 32763, 32731, 32676, 32729, 32711, 32714, 32739, 32753, 32729, 32716, 32735, 32712,
+    32724, 32722, 32731, 32699, 32765, 32745, 32695, 32739, 32755, 32726, 32750, 32756, 32693, 32700, 32697, 32752,
+    32726, 32732, 32722, 32684, 32724, 32706, 32737, 32755, 32684, 32764, 32703, 32687, 32733, 32671, 32699, 32726,
+    32703, 32747, 32670, 32667, 32724, 32697, 32719, 32739, 32760, 32740, 32700, 32756, 32716, 32754, 32696, 32744,
+    32750, 32732, 32761, 32760, 32751, 32668, 32753, 32698, 32748, 32667, 32760, 32746, 32725, 32708, 32711, 32706,
+    32715, 32752, 32667, 32734, 32724, 32745, 32743, 32723, 32682, 32706, 32718, 32762, 32733, 32676, 32708, 32680,
+    32761, 32673, 32710, 32749, 32704, 32738, 32684, 32681, 32756, 32669, 32691, 32726, 32760, 32742, 32725, 32667,
+    32760, 32704, 32728, 32706, 32719, 32709, 32671, 32759, 32720, 32669, 32742, 32695, 32694, 32765, 32717, 32761,
+    32730, 32712, 32714, 32687, 32750, 32734, 32730, 32720, 32745, 32688, 32752, 32679, 32749, 32723, 32708, 32688,
+    32683, 32712, 32668, 32690, 32669, 32749, 32734, 32678, 32678, 32698, 32713, 32727, 32702, 32694, 32697, 32759,
+    32730, 32749, 32745, 32696, 32733, 32701, 32672, 32730, 32748, 32707, 32747, 32758, 32711, 32691, 32688, 32681,
+    32755, 32748, 32748, 32735, 32727, 32707, 32677, 32712, 32751, 32741, 32724, 32674, 32689, 32752, 32740, 32710,
+    32764, 32676, 32729, 32674, 32688, 32757, 32709, 32724, 32710, 32683, 32764, 32762, 32713, 32765, 32669, 32667,
+    32704, 32736, 32749, 32732, 32712, 32748, 32733, 32695, 32743, 32693, 32761, 32740, 32675, 32682, 32744, 32714,
+    32765, 32668, 32757, 32711, 32756, 32701, 32721, 32706, 32733, 32715, 32720, 32744, 32739, 32740, 32670, 32757,
+    32675, 32687, 32722, 32753, 32683, 32752, 32685, 32685, 32688, 32747, 32727, 32701, 32734, 32677, 32669, 32674,
+    32675, 32706, 32753, 32695, 32759, 32731, 32687, 32754, 32738, 32680, 32702, 32752, 32714, 32731, 32730, 32749,
+    32721, 32699, 32760, 32684, 32719, 32687, 32694, 32720, 32671, 32723, 32679, 32741, 32690, 32737, 32737, 32689,
+    32684, 32685, 32687, 32734, 32729, 32755, 32736, 32742, 32756, 32710, 32734, 32766, 32725, 32701, 32756, 32682,
+    32683, 32749, 32741, 32736, 32695, 32723, 32726, 32758, 32678, 32689, 32681, 32765, 32708, 32718, 32693, 32737,
+    32724, 32677, 32720, 32721, 32680, 32683, 32752, 32765, 32696, 32765, 32745, 32684, 32699, 32681, 32731, 32729,
+    32678, 32711, 32667, 32668, 32756, 32713, 32745, 32737, 32741, 32673, 32719, 32678, 32759, 32673, 32714, 32698,
+    32670, 32759, 32702, 32691, 32710, 32744, 32686, 32715, 32739, 32726, 32721, 32758, 32701, 32682, 32727, 32737,
+    32743, 32704, 32762, 32743, 32701, 32685, 32720, 32763, 32729, 32752, 32763, 32696, 32735, 32697, 32700, 32707,
+    32695, 32700, 32697, 32744, 32714, 32704, 32708, 32741, 32739, 32669, 32719, 32743, 32674, 32712, 32715, 32701,
+    32704, 32708, 32684, 32707, 32736, 32731, 32697, 32729, 32689, 32741, 32730, 32760, 32689, 32674, 32766, 32693,
+    32689, 32736, 32678, 32685, 32672, 32753, 32683, 32761, 32691, 32700, 32709, 32708, 32672, 32672, 32763, 32742,
+    32696, 32714, 32722, 32752, 32735, 32697, 32724, 32678, 32712, 32668, 32707, 32678, 32679, 32696, 32759, 32760,
+    32709, 32741, 32724, 32717, 32689, 32705, 32725, 32722, 32681, 32736, 32684, 32695, 32721, 32669, 32706, 32740,
+    32693, 32704, 32737, 32694, 32738, 32720, 32690, 32763, 32689, 32675, 32725, 32765, 32669, 32676, 32688, 32723,
+    32750, 32669, 32763, 32707, 32755, 32720, 32740, 32688, 32766, 32721, 32734, 32723, 32685, 32700, 32756, 32736,
+    32702, 32721, 32698, 32722, 32745, 32718, 32675, 32752, 32702, 32726, 32668, 32675, 32714, 32694, 32745, 32676,
+    32743, 32731, 32745, 32759, 32758, 32708, 32700, 32703, 32752, 32725, 32708, 32683, 32720, 32732, 32686, 32760,
+    32766, 32733, 32750, 32737, 32746, 32702, 32684, 32709, 32731, 32733, 32689, 32713, 32730, 32762, 32688, 32710,
+    32716, 32674, 32722, 32667, 32678, 32710, 32747, 32688, 32715, 32708, 32738, 32679, 32720, 32709, 32705, 32713,
+    32703, 32681, 32732, 32684, 32717, 32715, 32711, 32736, 32685, 32695, 32669, 32758, 32725, 32680, 32758, 32678,
+    32670, 32720, 32705, 32715, 32743, 32703, 32711, 32717, 32683, 32736, 32732, 32700, 32728, 32726, 32717, 32716,
+    32713, 32763, 32697, 32711, 32736, 32704, 32728, 32761, 32677, 32679, 32731, 32720, 32712, 32723, 32762, 32718,
+    32699, 32751, 32755, 32694, 32736, 32670, 32679, 32676, 32701, 32760, 32735, 32716, 32726, 32714, 32703, 32667,
+    32671, 32668, 32729, 32738, 32727, 32704, 32748, 32717, 32716, 32717, 32701, 32748, 32721, 32679, 32747, 32707,
+    32757, 32696, 32712, 32757, 32693, 32711, 32741, 32687, 32763, 32747, 32730, 32727, 32698, 32702, 32678, 32699,
+    32756, 32733, 32697, 32691, 32732, 32732, 32680, 32690, 32739, 32740, 32750, 32717, 32674, 32686, 32703, 32671,
+    32726, 32672, 32738, 32724, 32765, 32690, 32749, 32714, 32694, 32691, 32725, 32676, 32699, 32725, 32713, 32762,
+    32724, 32690, 32676, 32710, 32697, 32765, 32670, 32758, 32728, 32681, 32744, 32712, 32741, 32765, 32756, 32766,
+    32713, 32706, 32737, 32758, 32733, 32763, 32670, 32667, 32674, 32726, 32718, 32732, 32735, 32734, 32703, 32673,
+    32709, 32723, 32717, 32759, 32754, 32671, 32723, 32719, 32740, 32677, 32697, 32668, 32671, 32739, 32704, 32744};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_mult_data.h
new file mode 100644
index 00000000..0fd80c3c
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_mult_data.h
@@ -0,0 +1,7 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int32_t int16xint8_spill2_output_mult[9] =
+    {1231320420, 1228194292, 1228950954, 1231269879, 1231112138, 1229528901, 1230557932, 1230167876, 1229640034};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_ref_data.h
new file mode 100644
index 00000000..ba5623b5
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_ref_data.h
@@ -0,0 +1,12 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int16_t int16xint8_spill2_output_ref[90] = {
+    32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32718, 32667, 32667, 32667, 32667, 32667,
+    32667, 32667, 32667, 32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32714, 32667, 32667,
+    32667, 32667, 32667, 32667, 32667, 32667, 32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667,
+    32716, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32713, 32667, 32667, 32667, 32667, 32667,
+    32667, 32667, 32667, 32714, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32713, 32667, 32667,
+    32667, 32667, 32667, 32667, 32667, 32667, 32714, 32667, 32667, 32667, 32667, 32667, 32667, 32667, 32667};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_shift_data.h
new file mode 100644
index 00000000..074e382c
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/output_shift_data.h
@@ -0,0 +1,6 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int32_t int16xint8_spill2_output_shift[9] = {-14, -14, -14, -14, -14, -14, -14, -14, -14};
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/test_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/test_data.h
new file mode 100644
index 00000000..4d61c5f2
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/test_data.h
@@ -0,0 +1,9 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#include "biases_data.h"
+#include "config_data.h"
+#include "input_data.h"
+#include "output_mult_data.h"
+#include "output_ref_data.h"
+#include "output_shift_data.h"
+#include "weights_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/weights_data.h b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/weights_data.h
new file mode 100644
index 00000000..4aee9323
--- /dev/null
+++ b/Tests/UnitTest/TestCases/TestData/int16xint8_spill2/weights_data.h
@@ -0,0 +1,176 @@
+// Generated by test_settings.py using tensorflow version 2.14.0 (Keras version 2.14.0).
+// Interpreter from tensorflow version 2.14.0 and revision v2.14.0-rc1-21-g4dacf3f368e.
+#pragma once
+#include <stdint.h>
+
+const int8_t int16xint8_spill2_weights[3888] = {
+    41,  91,  43,  84,  71,  88,  125, 36,  81,  15,  18,  116, 85,  94,  126, 54,  95,  93,  126, 68,  45,  123, 116,
+    107, 119, 74,  90,  36,  77,  114, 21,  70,  67,  112, 103, 34,  32,  85,  37,  115, 67,  55,  97,  113, 86,  103,
+    0,   82,  21,  101, 37,  67,  124, 6,   50,  83,  119, 75,  66,  83,  114, 75,  56,  105, 119, 33,  31,  75,  124,
+    40,  40,  12,  28,  115, 37,  11,  121, 87,  95,  68,  18,  67,  88,  42,  53,  38,  127, 14,  5,   93,  17,  25,
+    29,  22,  78,  106, 19,  106, 7,   50,  28,  120, 73,  5,   76,  17,  19,  102, 126, 36,  99,  95,  40,  79,  34,
+    98,  0,   107, 123, 41,  122, 88,  53,  94,  38,  40,  41,  42,  117, 75,  8,   115, 107, 104, 35,  46,  1,   105,
+    88,  15,  30,  124, 121, 41,  60,  6,   48,  1,   93,  111, 34,  125, 113, 121, 47,  11,  10,  78,  55,  60,  114,
+    66,  69,  72,  80,  47,  68,  13,  81,  75,  41,  53,  119, 50,  103, 123, 55,  125, 10,  91,  113, 5,   125, 6,
+    24,  11,  37,  5,   124, 2,   89,  89,  43,  53,  13,  2,   54,  40,  125, 70,  117, 74,  60,  69,  22,  99,  96,
+    103, 126, 98,  39,  99,  10,  86,  31,  36,  30,  2,   34,  71,  91,  105, 10,  39,  126, 15,  103, 91,  83,  13,
+    46,  60,  54,  9,   124, 40,  19,  65,  50,  100, 117, 116, 46,  103, 118, 24,  104, 109, 57,  28,  93,  49,  96,
+    85,  127, 104, 8,   105, 127, 118, 58,  8,   13,  92,  104, 60,  71,  113, 20,  55,  76,  82,  30,  59,  48,  48,
+    19,  105, 80,  117, 100, 58,  64,  121, 72,  10,  56,  88,  26,  49,  60,  5,   90,  99,  51,  108, 77,  35,  121,
+    24,  126, 80,  123, 89,  56,  49,  3,   13,  26,  104, 55,  19,  105, 95,  24,  102, 111, 24,  84,  33,  55,  68,
+    106, 88,  3,   76,  98,  57,  67,  22,  49,  40,  42,  108, 115, 7,   123, 54,  125, 5,   5,   27,  74,  111, 108,
+    22,  109, 88,  69,  36,  26,  8,   17,  39,  81,  63,  91,  78,  70,  123, 110, 122, 88,  63,  7,   111, 32,  26,
+    111, 109, 92,  76,  63,  13,  35,  37,  119, 91,  31,  59,  99,  115, 44,  5,   127, 98,  10,  51,  5,   97,  89,
+    126, 73,  10,  24,  65,  15,  21,  28,  93,  46,  87,  5,   66,  57,  5,   79,  43,  64,  101, 44,  91,  118, 39,
+    94,  59,  55,  87,  42,  99,  58,  11,  38,  10,  17,  43,  85,  61,  45,  37,  89,  106, 85,  80,  70,  124, 13,
+    29,  85,  3,   104, 13,  106, 117, 123, 17,  76,  5,   14,  37,  58,  44,  88,  74,  63,  62,  73,  115, 23,  47,
+    126, 62,  4,   9,   34,  41,  74,  40,  97,  3,   35,  91,  26,  102, 86,  29,  32,  78,  122, 59,  13,  5,   43,
+    89,  84,  19,  72,  5,   104, 53,  71,  47,  3,   67,  20,  4,   33,  46,  17,  52,  56,  28,  124, 13,  22,  107,
+    127, 70,  38,  20,  65,  98,  2,   4,   17,  41,  12,  54,  108, 71,  3,   7,   54,  89,  122, 3,   58,  88,  100,
+    0,   37,  54,  41,  17,  2,   83,  89,  74,  103, 78,  54,  93,  10,  71,  2,   73,  113, 89,  112, 44,  1,   14,
+    42,  40,  27,  12,  13,  85,  126, 61,  106, 27,  92,  43,  77,  88,  36,  124, 102, 28,  106, 127, 50,  26,  81,
+    59,  78,  70,  84,  39,  105, 90,  66,  56,  97,  119, 86,  65,  40,  12,  62,  116, 70,  108, 118, 82,  1,   111,
+    124, 90,  0,   72,  91,  117, 111, 19,  71,  80,  42,  36,  115, 46,  44,  83,  40,  32,  59,  85,  84,  19,  3,
+    53,  125, 65,  17,  63,  31,  14,  111, 48,  102, 73,  17,  11,  80,  73,  126, 105, 16,  95,  98,  41,  122, 15,
+    37,  50,  15,  121, 67,  36,  37,  52,  23,  3,   114, 109, 76,  25,  37,  101, 76,  87,  25,  88,  105, 42,  91,
+    104, 101, 89,  111, 39,  118, 19,  126, 5,   32,  68,  45,  33,  12,  6,   76,  69,  50,  10,  124, 114, 25,  1,
+    40,  10,  105, 105, 103, 33,  119, 103, 89,  122, 86,  126, 18,  64,  3,   82,  104, 32,  26,  30,  82,  57,  47,
+    57,  105, 119, 125, 101, 58,  27,  110, 46,  19,  109, 111, 117, 2,   3,   64,  73,  83,  21,  71,  29,  61,  123,
+    94,  4,   15,  59,  60,  39,  100, 101, 80,  21,  69,  97,  84,  76,  114, 113, 36,  107, 84,  22,  26,  18,  87,
+    107, 24,  73,  59,  121, 117, 78,  27,  56,  73,  57,  74,  47,  11,  37,  124, 53,  78,  11,  2,   61,  75,  107,
+    43,  22,  14,  15,  113, 120, 126, 104, 35,  87,  119, 78,  19,  79,  1,   96,  100, 48,  36,  93,  54,  76,  96,
+    12,  29,  127, 80,  126, 41,  86,  117, 40,  20,  81,  93,  0,   53,  13,  124, 113, 55,  107, 118, 21,  9,   12,
+    28,  2,   92,  17,  10,  41,  60,  121, 59,  43,  92,  116, 30,  110, 11,  98,  51,  71,  61,  116, 32,  29,  119,
+    107, 106, 58,  114, 117, 88,  55,  120, 42,  90,  32,  87,  104, 109, 79,  103, 122, 56,  67,  46,  107, 42,  25,
+    122, 80,  23,  1,   121, 76,  82,  102, 92,  84,  6,   126, 125, 45,  83,  85,  3,   125, 124, 14,  89,  71,  56,
+    115, 64,  69,  83,  55,  124, 118, 11,  38,  94,  73,  97,  3,   112, 45,  5,   51,  77,  56,  1,   101, 46,  10,
+    53,  77,  46,  28,  26,  104, 105, 45,  86,  81,  3,   7,   104, 95,  5,   15,  51,  11,  95,  9,   87,  30,  100,
+    90,  20,  66,  85,  57,  100, 0,   110, 24,  76,  53,  24,  107, 98,  101, 112, 57,  99,  20,  62,  49,  125, 43,
+    12,  123, 102, 14,  64,  68,  83,  94,  127, 84,  124, 64,  36,  46,  13,  39,  67,  71,  9,   44,  122, 36,  125,
+    16,  45,  90,  98,  35,  125, 20,  25,  81,  21,  8,   76,  45,  76,  26,  115, 52,  66,  53,  120, 107, 54,  115,
+    63,  9,   2,   2,   27,  38,  43,  90,  67,  91,  56,  82,  109, 17,  30,  46,  21,  49,  60,  19,  6,   10,  122,
+    33,  68,  86,  23,  87,  92,  62,  105, 110, 7,   8,   77,  120, 67,  90,  18,  57,  44,  3,   61,  68,  92,  42,
+    116, 106, 92,  71,  5,   22,  105, 69,  6,   112, 32,  44,  91,  66,  111, 53,  67,  66,  110, 25,  123, 71,  125,
+    113, 35,  75,  96,  32,  1,   88,  43,  115, 93,  59,  78,  116, 39,  16,  46,  29,  75,  58,  99,  69,  106, 31,
+    102, 104, 94,  29,  81,  113, 59,  91,  43,  100, 74,  83,  85,  3,   26,  6,   56,  66,  69,  87,  83,  18,  88,
+    107, 15,  117, 35,  97,  69,  52,  40,  69,  86,  82,  114, 84,  97,  76,  11,  49,  18,  62,  53,  114, 88,  71,
+    112, 46,  40,  91,  17,  5,   34,  5,   5,   102, 4,   15,  109, 57,  16,  38,  26,  61,  43,  13,  111, 112, 55,
+    15,  35,  9,   67,  62,  26,  35,  105, 103, 13,  55,  84,  48,  60,  70,  102, 123, 121, 28,  105, 73,  5,   89,
+    44,  3,   64,  106, 95,  68,  41,  40,  117, 4,   51,  25,  100, 4,   9,   115, 21,  51,  36,  81,  21,  90,  75,
+    20,  25,  125, 122, 88,  16,  52,  107, 3,   54,  7,   7,   39,  102, 91,  76,  104, 118, 64,  127, 72,  35,  54,
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+    40};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h
index 42d1ae86..bbd481c9 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/biases_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #pragma once
 #include <stdint.h>
 
-const int64_t requantize_s64_biases[2] = {47, 37};
+const int64_t requantize_s64_biases[8] = {2718092, 2556575, 646371, 1427241, 2010306, 3766647, 1886211, 1482898};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h
index e014cca9..359f3f95 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/config_data.h
@@ -1,12 +1,12 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #pragma once
-#define REQUANTIZE_S64_OUT_CH 2
-#define REQUANTIZE_S64_IN_CH 2
-#define REQUANTIZE_S64_INPUT_W 3
-#define REQUANTIZE_S64_INPUT_H 2
-#define REQUANTIZE_S64_DST_SIZE 4
-#define REQUANTIZE_S64_INPUT_SIZE 12
+#define REQUANTIZE_S64_OUT_CH 8
+#define REQUANTIZE_S64_IN_CH 8
+#define REQUANTIZE_S64_INPUT_W 4
+#define REQUANTIZE_S64_INPUT_H 4
+#define REQUANTIZE_S64_DST_SIZE 72
+#define REQUANTIZE_S64_INPUT_SIZE 128
 #define REQUANTIZE_S64_OUT_ACTIVATION_MIN -32768
 #define REQUANTIZE_S64_OUT_ACTIVATION_MAX 32767
 #define REQUANTIZE_S64_INPUT_BATCHES 1
@@ -16,8 +16,8 @@
 #define REQUANTIZE_S64_STRIDE_Y 1
 #define REQUANTIZE_S64_PAD_X 0
 #define REQUANTIZE_S64_PAD_Y 0
-#define REQUANTIZE_S64_OUTPUT_W 2
-#define REQUANTIZE_S64_OUTPUT_H 1
+#define REQUANTIZE_S64_OUTPUT_W 3
+#define REQUANTIZE_S64_OUTPUT_H 3
 #define REQUANTIZE_S64_INPUT_OFFSET 0
 #define REQUANTIZE_S64_OUTPUT_OFFSET 0
 #define REQUANTIZE_S64_DILATION_X 1
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h
index 1a058e6d..16b016d2 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/input_data.h
@@ -1,6 +1,13 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #pragma once
 #include <stdint.h>
 
-const int16_t requantize_s64_input[12] = {97, 100, 51, 39, 29, 117, -111, -104, 41, 38, 115, -113};
+const int16_t requantize_s64_input[128] = {
+    12,   97,  80,   37,   -72,  16,   -110, -46, 109,  -28, -21, 20,   24,  66,  -110, -71, -31, -109, 90,
+    11,   64,  77,   83,   -38,  -26,  44,   86,  -109, -88, -39, 43,   -36, 29,  -95,  -90, -36, -30,  -41,
+    8,    62,  25,   -124, -107, -104, -113, -7,  18,   6,   44,  -18,  5,   -81, -18,  64,  -49, -29,  82,
+    124,  66,  -63,  -111, -103, 99,   109,  -15, 101,  -2,  39,  21,   -42, -51, -115, -59, 25,  -121, -69,
+    -46,  104, -20,  -8,   -91,  31,   -114, 25,  -27,  39,  79,  -117, -28, -93, -31,  19,  107, 61,   -8,
+    -125, -66, 97,   -87,  -98,  -113, -111, 51,  65,   104, 76,  90,   110, -39, -98,  -74, -56, -56,  -48,
+    34,   -46, -107, 12,   -74,  14,   96,   77,  85,   -61, -17, 40,   -18, 97};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h
index a2fc73d1..1f5cffeb 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_mult_data.h
@@ -1,6 +1,7 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #pragma once
 #include <stdint.h>
 
-const int32_t requantize_s64_output_mult[2] = {1853463365, 1838451297};
+const int32_t requantize_s64_output_mult[8] =
+    {1414739540, 1453951389, 1391394644, 1429974075, 1417631388, 1463499963, 1461229183, 1369934873};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h
index 75a7fa4c..8037cbdd 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_ref_data.h
@@ -1,6 +1,10 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #pragma once
 #include <stdint.h>
 
-const int16_t requantize_s64_output_ref[4] = {-68, 186, -134, -116};
+const int16_t requantize_s64_output_ref[72] = {
+    3491, 3462, 833,  1853, 2589, 5061, 2542, 1794, 3482, 3408, 813,  1895, 2613, 4994, 2499, 1816, 3508, 3371,
+    766,  1820, 2610, 4960, 2545, 1878, 3523, 3346, 811,  1887, 2611, 5067, 2517, 1843, 3481, 3363, 787,  1903,
+    2590, 4992, 2493, 1863, 3501, 3384, 896,  1931, 2619, 4995, 2398, 1810, 3497, 3405, 835,  1929, 2571, 5008,
+    2538, 1832, 3566, 3349, 806,  1854, 2573, 4989, 2502, 1870, 3500, 3393, 754,  1804, 2560, 4972, 2570, 1898};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h
index c73834a4..de069ed5 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/output_shift_data.h
@@ -1,6 +1,6 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #pragma once
 #include <stdint.h>
 
-const int32_t requantize_s64_output_shift[2] = {-7, -7};
+const int32_t requantize_s64_output_shift[8] = {-9, -9, -9, -9, -9, -9, -9, -9};
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h
index 8102687f..7930b388 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/test_data.h
@@ -1,5 +1,5 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #include "biases_data.h"
 #include "config_data.h"
 #include "input_data.h"
diff --git a/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h b/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h
index 816357f5..f263b88a 100644
--- a/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h
+++ b/Tests/UnitTest/TestCases/TestData/requantize_s64/weights_data.h
@@ -1,7 +1,19 @@
-// Generated by generate_test_data.py using tensorflow version 2.10.0 (Keras version 2.10.0).
-// Interpreter from tensorflow version 2.10.0 and revision upstream/v2.10.0-0-g359c3cdfc5f.
+// Generated by test_settings.py using tensorflow version 2.15.0 (Keras version 2.15.0).
+// Interpreter from tensorflow version 2.15.0 and revision v2.15.0-2-g0b15fdfcb3f.
 #pragma once
 #include <stdint.h>
 
-const int8_t requantize_s64_weights[16] =
-    {-95, -110, -39, 99, -127, 73, -58, 108, 101, 127, -17, -89, 24, -99, -34, 87};
+const int8_t requantize_s64_weights[256] = {
+    74,   87,   -103, 86,   32,   107,  -13,  -53,  -118, 113,  -41,  -13,  -45, -91, -26,  -106, 89,   86,   49,  -127,
+    51,   -58,  -80,  -117, 16,   -69,  -115, 83,   14,   86,   79,   76,   -82, 41,  36,   77,   -120, 40,   -97, -75,
+    -14,  -81,  16,   22,   -60,  -55,  -100, -22,  -56,  -127, -77,  -30,  75,  -13, 49,   -37,  33,   -52,  19,  -8,
+    -6,   -79,  -48,  2,    -54,  80,   39,   -14,  -45,  -126, -52,  -86,  86,  114, 28,   107,  -53,  -109, 107, 121,
+    72,   -10,  -84,  -7,   -43,  -104, 69,   -13,  -83,  69,   -103, -4,   22,  26,  -127, 12,   116,  94,   -50, 54,
+    -55,  6,    -45,  90,   -103, 8,    28,   -114, -44,  127,  96,   25,   -20, 107, -102, -52,  -96,  -39,  61,  -100,
+    8,    -88,  -79,  76,   80,   -53,  65,   -22,  60,   18,   112,  -48,  2,   127, -19,  3,    -54,  1,    -43, -93,
+    -51,  84,   -1,   88,   44,   -44,  -29,  -40,  117,  68,   -17,  -15,  0,   37,  -29,  -5,   60,   112,  -68, 64,
+    -17,  54,   -81,  122,  -49,  -8,   -121, 68,   80,   -127, -104, -117, -56, -49, -53,  122,  -6,   -93,  115, -46,
+    117,  -82,  48,   44,   -38,  -23,  -34,  -84,  29,   -70,  8,    -48,  -66, -47, 104,  61,   -8,   -122, 109, -70,
+    -28,  -101, 38,   -119, 23,   114,  -98,  -33,  73,   58,   -14,  96,   -67, -87, -120, 127,  56,   -23,  37,  -56,
+    5,    39,   61,   47,   -71,  -88,  -49,  -111, -54,  110,  -68,  56,   -2,  -50, -62,  87,   -58,  -15,  -12, -56,
+    -114, -28,  50,   -22,  48,   39,   -56,  63,   -38,  115,  127,  61,   52,  -22, 58,   118};
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/CMakeLists.txt b/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/CMakeLists.txt
deleted file mode 100644
index 1f1a3093..00000000
--- a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/CMakeLists.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-#
-# Copyright (C) 2010-2021 Arm Limited or its affiliates. All rights reserved.
-#
-# SPDX-License-Identifier: Apache-2.0
-#
-# 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
-#
-# 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.
-#
-
-add_cmsis_nn_unit_test_executable(test_arm_convolve_fast_s16)
-
-target_sources(test_arm_convolve_fast_s16 PRIVATE
-    Unity/unity_test_arm_convolve_fast_s16.c
-    Unity/TestRunner/unity_test_arm_convolve_fast_s16_runner.c)
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/Unity/unity_test_arm_convolve_fast_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/Unity/unity_test_arm_convolve_fast_s16.c
deleted file mode 100644
index cff35bc8..00000000
--- a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/Unity/unity_test_arm_convolve_fast_s16.c
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (C) 2010-2021, 2023 Arm Limited or its affiliates. All rights reserved.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * 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
- *
- * 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 <stdbool.h>
-#include <stdint.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-#include "../test_arm_convolve_fast_s16.c"
-#include "unity.h"
-
-#ifdef USING_FVP_CORSTONE_300
-extern void uart_init(void);
-#endif
-
-/* This function is called from the autogenerated file.
- * The name must be exactly like this
- */
-void setUp(void)
-{ /* This is run before EACH TEST */
-#ifdef USING_FVP_CORSTONE_300
-    uart_init();
-#endif
-}
-
-/* This function is called from the autogenerated file.
- * The name must be exactly like this
- */
-void tearDown(void) {}
-
-void test_int16xint8_arm_convolve_fast_s16(void) { int16xint8_arm_convolve_fast_s16(); }
-void test_requantize_s64_arm_convolve_fast_s16(void) { requantize_s64_arm_convolve_fast_s16(); }
-void test_buffer_size_arm_convolve_fast_s16(void) { buffer_size_arm_convolve_fast_s16(); }
-void test_buffer_size_mve_arm_convolve_fast_s16(void) { buffer_size_mve_arm_convolve_fast_s16(); }
-void test_buffer_size_dsp_arm_convolve_fast_s16(void) { buffer_size_dsp_arm_convolve_fast_s16(); }
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/test_arm_convolve_fast_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/test_arm_convolve_fast_s16.c
deleted file mode 100644
index 97854c58..00000000
--- a/Tests/UnitTest/TestCases/test_arm_convolve_fast_s16/test_arm_convolve_fast_s16.c
+++ /dev/null
@@ -1,328 +0,0 @@
-/*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> All rights
- * reserved.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * 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
- *
- * 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 <stdlib.h>
-
-#include <arm_nnfunctions.h>
-#include <unity.h>
-
-#include "../TestData/int16xint8/test_data.h"
-#include "../TestData/requantize_s64/test_data.h"
-#include "../Utils/validate.h"
-
-void int16xint8_arm_convolve_fast_s16(void)
-{
-    int16_t output[INT16XINT8_DST_SIZE] = {0};
-
-    cmsis_nn_context ctx;
-    cmsis_nn_conv_params conv_params;
-    cmsis_nn_per_channel_quant_params quant_params;
-    cmsis_nn_dims input_dims;
-    cmsis_nn_dims filter_dims;
-    cmsis_nn_dims bias_dims;
-    cmsis_nn_dims output_dims;
-
-    const int64_t *bias_data = int16xint8_biases;
-    const int8_t *kernel_data = int16xint8_weights;
-    const int16_t *input_data = int16xint8_input;
-    const int16_t *output_ref = int16xint8_output_ref;
-    const int32_t output_ref_size = INT16XINT8_DST_SIZE;
-
-    input_dims.n = INT16XINT8_INPUT_BATCHES;
-    input_dims.w = INT16XINT8_INPUT_W;
-    input_dims.h = INT16XINT8_INPUT_H;
-    input_dims.c = INT16XINT8_IN_CH;
-    filter_dims.w = INT16XINT8_FILTER_X;
-    filter_dims.h = INT16XINT8_FILTER_Y;
-    output_dims.w = INT16XINT8_OUTPUT_W;
-    output_dims.h = INT16XINT8_OUTPUT_H;
-    output_dims.c = INT16XINT8_OUT_CH;
-
-    conv_params.padding.w = INT16XINT8_PAD_X;
-    conv_params.padding.h = INT16XINT8_PAD_Y;
-    conv_params.stride.w = INT16XINT8_STRIDE_X;
-    conv_params.stride.h = INT16XINT8_STRIDE_Y;
-    conv_params.dilation.w = INT16XINT8_DILATION_X;
-    conv_params.dilation.h = INT16XINT8_DILATION_X;
-
-    conv_params.input_offset = 0;
-    conv_params.output_offset = 0;
-    conv_params.activation.min = INT16XINT8_OUT_ACTIVATION_MIN;
-    conv_params.activation.max = INT16XINT8_OUT_ACTIVATION_MAX;
-    quant_params.multiplier = (int32_t *)int16xint8_output_mult;
-    quant_params.shift = (int32_t *)int16xint8_output_shift;
-
-    int buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
-    ctx.buf = malloc(buf_size);
-
-    arm_cmsis_nn_status result = arm_convolve_wrapper_s16(&ctx,
-                                                          &conv_params,
-                                                          &quant_params,
-                                                          &input_dims,
-                                                          input_data,
-                                                          &filter_dims,
-                                                          kernel_data,
-                                                          &bias_dims,
-                                                          bias_data,
-                                                          &output_dims,
-                                                          output);
-    if (ctx.buf)
-    {
-        // The caller is responsible to clear the scratch buffers for security reasons if applicable.
-        memset(ctx.buf, 0, buf_size);
-        free(ctx.buf);
-    }
-
-    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
-    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
-    memset(output, 0, sizeof(output));
-
-    buf_size = arm_convolve_fast_s16_get_buffer_size(&input_dims, &filter_dims);
-    ctx.buf = malloc(buf_size);
-
-    result = arm_convolve_fast_s16(&ctx,
-                                   &conv_params,
-                                   &quant_params,
-                                   &input_dims,
-                                   input_data,
-                                   &filter_dims,
-                                   kernel_data,
-                                   &bias_dims,
-                                   bias_data,
-                                   &output_dims,
-                                   output);
-    if (ctx.buf)
-    {
-        memset(ctx.buf, 0, buf_size);
-        free(ctx.buf);
-    }
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
-    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
-#else
-    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_ARG_ERROR, result);
-#endif
-}
-
-void requantize_s64_arm_convolve_fast_s16(void)
-{
-    int16_t output[REQUANTIZE_S64_DST_SIZE] = {0};
-
-    cmsis_nn_context ctx;
-    cmsis_nn_conv_params conv_params;
-    cmsis_nn_per_channel_quant_params quant_params;
-    cmsis_nn_dims input_dims;
-    cmsis_nn_dims filter_dims;
-    cmsis_nn_dims bias_dims;
-    cmsis_nn_dims output_dims;
-
-    const int64_t *bias_data = requantize_s64_biases;
-    const int8_t *kernel_data = requantize_s64_weights;
-    const int16_t *input_data = requantize_s64_input;
-    const int16_t *output_ref = requantize_s64_output_ref;
-    const int32_t output_ref_size = REQUANTIZE_S64_DST_SIZE;
-
-    input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
-    input_dims.w = REQUANTIZE_S64_INPUT_W;
-    input_dims.h = REQUANTIZE_S64_INPUT_H;
-    input_dims.c = REQUANTIZE_S64_IN_CH;
-    filter_dims.w = REQUANTIZE_S64_FILTER_X;
-    filter_dims.h = REQUANTIZE_S64_FILTER_Y;
-    output_dims.w = REQUANTIZE_S64_OUTPUT_W;
-    output_dims.h = REQUANTIZE_S64_OUTPUT_H;
-    output_dims.c = REQUANTIZE_S64_OUT_CH;
-
-    conv_params.padding.w = REQUANTIZE_S64_PAD_X;
-    conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
-    conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
-    conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
-    conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
-    conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
-    conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
-    conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
-    conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
-    conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
-    quant_params.multiplier = (int32_t *)requantize_s64_output_mult;
-    quant_params.shift = (int32_t *)requantize_s64_output_shift;
-
-    int buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
-    ctx.buf = malloc(buf_size);
-
-    arm_cmsis_nn_status result = arm_convolve_wrapper_s16(&ctx,
-                                                          &conv_params,
-                                                          &quant_params,
-                                                          &input_dims,
-                                                          input_data,
-                                                          &filter_dims,
-                                                          kernel_data,
-                                                          &bias_dims,
-                                                          bias_data,
-                                                          &output_dims,
-                                                          output);
-
-    if (ctx.buf)
-    {
-        memset(ctx.buf, 0, buf_size);
-        free(ctx.buf);
-    }
-    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
-    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
-    memset(output, 0, sizeof(output));
-
-    buf_size = arm_convolve_fast_s16_get_buffer_size(&input_dims, &filter_dims);
-    ctx.buf = malloc(buf_size);
-
-    result = arm_convolve_fast_s16(&ctx,
-                                   &conv_params,
-                                   &quant_params,
-                                   &input_dims,
-                                   input_data,
-                                   &filter_dims,
-                                   kernel_data,
-                                   &bias_dims,
-                                   bias_data,
-                                   &output_dims,
-                                   output);
-    if (ctx.buf)
-    {
-        memset(ctx.buf, 0, buf_size);
-        free(ctx.buf);
-    }
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
-    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
-#else
-    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_ARG_ERROR, result);
-#endif
-}
-
-void buffer_size_arm_convolve_fast_s16(void)
-{
-    cmsis_nn_conv_params conv_params;
-    cmsis_nn_dims input_dims;
-    cmsis_nn_dims filter_dims;
-    cmsis_nn_dims output_dims;
-
-    input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
-    input_dims.w = REQUANTIZE_S64_INPUT_W;
-    input_dims.h = REQUANTIZE_S64_INPUT_H;
-    input_dims.c = REQUANTIZE_S64_IN_CH;
-    filter_dims.w = REQUANTIZE_S64_FILTER_X;
-    filter_dims.h = REQUANTIZE_S64_FILTER_Y;
-    output_dims.w = REQUANTIZE_S64_OUTPUT_W;
-    output_dims.h = REQUANTIZE_S64_OUTPUT_H;
-    output_dims.c = REQUANTIZE_S64_OUT_CH;
-
-    conv_params.padding.w = REQUANTIZE_S64_PAD_X;
-    conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
-    conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
-    conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
-    conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
-    conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
-    conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
-    conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
-    conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
-    conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
-
-    const int32_t buf_size = arm_convolve_fast_s16_get_buffer_size(&input_dims, &filter_dims);
-    const int32_t wrapper_buf_size =
-        arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
-
-    TEST_ASSERT_EQUAL(wrapper_buf_size, buf_size);
-}
-
-void buffer_size_mve_arm_convolve_fast_s16(void)
-{
-#if defined(ARM_MATH_MVEI)
-    cmsis_nn_conv_params conv_params;
-    cmsis_nn_dims input_dims;
-    cmsis_nn_dims filter_dims;
-    cmsis_nn_dims output_dims;
-
-    input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
-    input_dims.w = REQUANTIZE_S64_INPUT_W;
-    input_dims.h = REQUANTIZE_S64_INPUT_H;
-    input_dims.c = REQUANTIZE_S64_IN_CH;
-    filter_dims.w = REQUANTIZE_S64_FILTER_X;
-    filter_dims.h = REQUANTIZE_S64_FILTER_Y;
-    output_dims.w = REQUANTIZE_S64_OUTPUT_W;
-    output_dims.h = REQUANTIZE_S64_OUTPUT_H;
-    output_dims.c = REQUANTIZE_S64_OUT_CH;
-
-    conv_params.padding.w = REQUANTIZE_S64_PAD_X;
-    conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
-    conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
-    conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
-    conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
-    conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
-    conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
-    conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
-    conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
-    conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
-
-    const int32_t wrapper_buf_size =
-        arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
-    const int32_t mve_wrapper_buf_size =
-        arm_convolve_wrapper_s16_get_buffer_size_mve(&conv_params, &input_dims, &filter_dims, &output_dims);
-
-    TEST_ASSERT_EQUAL(wrapper_buf_size, mve_wrapper_buf_size);
-#endif
-}
-
-void buffer_size_dsp_arm_convolve_fast_s16(void)
-{
-#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
-    cmsis_nn_conv_params conv_params;
-    cmsis_nn_dims input_dims;
-    cmsis_nn_dims filter_dims;
-    cmsis_nn_dims output_dims;
-
-    input_dims.n = REQUANTIZE_S64_INPUT_BATCHES;
-    input_dims.w = REQUANTIZE_S64_INPUT_W;
-    input_dims.h = REQUANTIZE_S64_INPUT_H;
-    input_dims.c = REQUANTIZE_S64_IN_CH;
-    filter_dims.w = REQUANTIZE_S64_FILTER_X;
-    filter_dims.h = REQUANTIZE_S64_FILTER_Y;
-    output_dims.w = REQUANTIZE_S64_OUTPUT_W;
-    output_dims.h = REQUANTIZE_S64_OUTPUT_H;
-    output_dims.c = REQUANTIZE_S64_OUT_CH;
-
-    conv_params.padding.w = REQUANTIZE_S64_PAD_X;
-    conv_params.padding.h = REQUANTIZE_S64_PAD_Y;
-    conv_params.stride.w = REQUANTIZE_S64_STRIDE_X;
-    conv_params.stride.h = REQUANTIZE_S64_STRIDE_Y;
-    conv_params.dilation.w = REQUANTIZE_S64_DILATION_X;
-    conv_params.dilation.h = REQUANTIZE_S64_DILATION_Y;
-
-    conv_params.input_offset = REQUANTIZE_S64_INPUT_OFFSET;
-    conv_params.output_offset = REQUANTIZE_S64_OUTPUT_OFFSET;
-    conv_params.activation.min = REQUANTIZE_S64_OUT_ACTIVATION_MIN;
-    conv_params.activation.max = REQUANTIZE_S64_OUT_ACTIVATION_MAX;
-
-    const int32_t wrapper_buf_size =
-        arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
-    const int32_t dsp_wrapper_buf_size =
-        arm_convolve_wrapper_s16_get_buffer_size_dsp(&conv_params, &input_dims, &filter_dims, &output_dims);
-
-    TEST_ASSERT_EQUAL(wrapper_buf_size, dsp_wrapper_buf_size);
-#endif
-}
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c
index b3df59b7..96232496 100644
--- a/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c
+++ b/Tests/UnitTest/TestCases/test_arm_convolve_s16/Unity/unity_test_arm_convolve_s16.c
@@ -1,5 +1,5 @@
 /*
- * Copyright (C) 2010-2023 Arm Limited or its affiliates. All rights reserved.
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates <open-source-office@arm.com> All rights
  *
  * SPDX-License-Identifier: Apache-2.0
  *
@@ -52,3 +52,5 @@ void test_int16xint8_dilation_3_arm_convolve_s16(void) { int16xint8_dilation_3_a
 void test_buffer_size_arm_convolve_s16(void) { buffer_size_arm_convolve_s16(); }
 void test_buffer_size_mve_arm_convolve_s16(void) { buffer_size_mve_arm_convolve_s16(); }
 void test_buffer_size_dsp_arm_convolve_s16(void) { buffer_size_dsp_arm_convolve_s16(); }
+void test_int16xint8_spill_arm_convolve_s16(void) { int16xint8_spill_arm_convolve_s16(); }
+void test_int16xint8_spill2_arm_convolve_s16(void) { int16xint8_spill2_arm_convolve_s16(); }
diff --git a/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c b/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c
index 00b74342..25194e8b 100644
--- a/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c
+++ b/Tests/UnitTest/TestCases/test_arm_convolve_s16/test_arm_convolve_s16.c
@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> All rights
+ * SPDX-FileCopyrightText: Copyright 2010-2024 Arm Limited and/or its affiliates <open-source-office@arm.com> All rights
  * reserved.
  *
  * SPDX-License-Identifier: Apache-2.0
@@ -26,6 +26,8 @@
 #include "../TestData/int16xint8_dilation_1/test_data.h"
 #include "../TestData/int16xint8_dilation_2/test_data.h"
 #include "../TestData/int16xint8_dilation_3/test_data.h"
+#include "../TestData/int16xint8_spill/test_data.h"
+#include "../TestData/int16xint8_spill2/test_data.h"
 #include "../TestData/requantize_s64/test_data.h"
 #include "../Utils/validate.h"
 
@@ -73,18 +75,18 @@ void int16xint8_arm_convolve_s16(void)
 
     int buf_size = arm_convolve_s16_get_buffer_size(&input_dims, &filter_dims);
     ctx.buf = malloc(buf_size);
-
-    arm_cmsis_nn_status result = arm_convolve_s16(&ctx,
-                                                  &conv_params,
-                                                  &quant_params,
-                                                  &input_dims,
-                                                  input_data,
-                                                  &filter_dims,
-                                                  kernel_data,
-                                                  &bias_dims,
-                                                  bias_data,
-                                                  &output_dims,
-                                                  output);
+    arm_cmsis_nn_status result;
+    result = arm_convolve_s16(&ctx,
+                              &conv_params,
+                              &quant_params,
+                              &input_dims,
+                              input_data,
+                              &filter_dims,
+                              kernel_data,
+                              &bias_dims,
+                              bias_data,
+                              &output_dims,
+                              output);
     if (ctx.buf)
     {
         // The caller is responsible to clear the scratch buffers for security reasons if applicable.
@@ -109,7 +111,6 @@ void int16xint8_arm_convolve_s16(void)
                                       bias_data,
                                       &output_dims,
                                       output);
-
     if (ctx.buf)
     {
         memset(ctx.buf, 0, buf_size);
@@ -588,3 +589,181 @@ void buffer_size_dsp_arm_convolve_s16(void)
     TEST_ASSERT_EQUAL(wrapper_buf_size, dsp_wrapper_buf_size);
 #endif
 }
+
+void int16xint8_spill_arm_convolve_s16(void)
+{
+    int16_t output[INT16XINT8_SPILL_DST_SIZE] = {0};
+
+    cmsis_nn_context ctx;
+    cmsis_nn_conv_params conv_params;
+    cmsis_nn_per_channel_quant_params quant_params;
+    cmsis_nn_dims input_dims;
+    cmsis_nn_dims filter_dims;
+    cmsis_nn_dims bias_dims;
+    cmsis_nn_dims output_dims;
+
+    const int64_t *bias_data = int16xint8_spill_biases;
+    const int8_t *kernel_data = int16xint8_spill_weights;
+    const int16_t *input_data = int16xint8_spill_input;
+    const int16_t *output_ref = int16xint8_spill_output_ref;
+    const int32_t output_ref_size = INT16XINT8_SPILL_DST_SIZE;
+
+    input_dims.n = INT16XINT8_SPILL_INPUT_BATCHES;
+    input_dims.w = INT16XINT8_SPILL_INPUT_W;
+    input_dims.h = INT16XINT8_SPILL_INPUT_H;
+    input_dims.c = INT16XINT8_SPILL_IN_CH;
+    filter_dims.w = INT16XINT8_SPILL_FILTER_X;
+    filter_dims.h = INT16XINT8_SPILL_FILTER_Y;
+    output_dims.w = INT16XINT8_SPILL_OUTPUT_W;
+    output_dims.h = INT16XINT8_SPILL_OUTPUT_H;
+    output_dims.c = INT16XINT8_SPILL_OUT_CH;
+
+    conv_params.padding.w = INT16XINT8_SPILL_PAD_X;
+    conv_params.padding.h = INT16XINT8_SPILL_PAD_Y;
+    conv_params.stride.w = INT16XINT8_SPILL_STRIDE_X;
+    conv_params.stride.h = INT16XINT8_SPILL_STRIDE_Y;
+    conv_params.dilation.w = INT16XINT8_SPILL_DILATION_X;
+    conv_params.dilation.h = INT16XINT8_SPILL_DILATION_Y;
+
+    conv_params.input_offset = 0;
+    conv_params.output_offset = 0;
+    conv_params.activation.min = INT16XINT8_SPILL_OUT_ACTIVATION_MIN;
+    conv_params.activation.max = INT16XINT8_SPILL_OUT_ACTIVATION_MAX;
+    quant_params.multiplier = (int32_t *)int16xint8_spill_output_mult;
+    quant_params.shift = (int32_t *)int16xint8_spill_output_shift;
+
+    int buf_size = arm_convolve_s16_get_buffer_size(&input_dims, &filter_dims);
+    ctx.buf = malloc(buf_size);
+    arm_cmsis_nn_status result;
+    result = arm_convolve_s16(&ctx,
+                              &conv_params,
+                              &quant_params,
+                              &input_dims,
+                              input_data,
+                              &filter_dims,
+                              kernel_data,
+                              &bias_dims,
+                              bias_data,
+                              &output_dims,
+                              output);
+    if (ctx.buf)
+    {
+        // The caller is responsible to clear the scratch buffers for security reasons if applicable.
+        memset(ctx.buf, 0, buf_size);
+        free(ctx.buf);
+    }
+    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+    memset(output, 0, sizeof(output));
+
+    buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
+    ctx.buf = malloc(buf_size);
+
+    result = arm_convolve_wrapper_s16(&ctx,
+                                      &conv_params,
+                                      &quant_params,
+                                      &input_dims,
+                                      input_data,
+                                      &filter_dims,
+                                      kernel_data,
+                                      &bias_dims,
+                                      bias_data,
+                                      &output_dims,
+                                      output);
+    if (ctx.buf)
+    {
+        memset(ctx.buf, 0, buf_size);
+        free(ctx.buf);
+    }
+    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+}
+
+void int16xint8_spill2_arm_convolve_s16(void)
+{
+    int16_t output[INT16XINT8_SPILL2_DST_SIZE] = {0};
+
+    cmsis_nn_context ctx;
+    cmsis_nn_conv_params conv_params;
+    cmsis_nn_per_channel_quant_params quant_params;
+    cmsis_nn_dims input_dims;
+    cmsis_nn_dims filter_dims;
+    cmsis_nn_dims bias_dims;
+    cmsis_nn_dims output_dims;
+
+    const int64_t *bias_data = int16xint8_spill2_biases;
+    const int8_t *kernel_data = int16xint8_spill2_weights;
+    const int16_t *input_data = int16xint8_spill2_input;
+    const int16_t *output_ref = int16xint8_spill2_output_ref;
+    const int32_t output_ref_size = INT16XINT8_SPILL2_DST_SIZE;
+
+    input_dims.n = INT16XINT8_SPILL2_INPUT_BATCHES;
+    input_dims.w = INT16XINT8_SPILL2_INPUT_W;
+    input_dims.h = INT16XINT8_SPILL2_INPUT_H;
+    input_dims.c = INT16XINT8_SPILL2_IN_CH;
+    filter_dims.w = INT16XINT8_SPILL2_FILTER_X;
+    filter_dims.h = INT16XINT8_SPILL2_FILTER_Y;
+    output_dims.w = INT16XINT8_SPILL2_OUTPUT_W;
+    output_dims.h = INT16XINT8_SPILL2_OUTPUT_H;
+    output_dims.c = INT16XINT8_SPILL2_OUT_CH;
+
+    conv_params.padding.w = INT16XINT8_SPILL2_PAD_X;
+    conv_params.padding.h = INT16XINT8_SPILL2_PAD_Y;
+    conv_params.stride.w = INT16XINT8_SPILL2_STRIDE_X;
+    conv_params.stride.h = INT16XINT8_SPILL2_STRIDE_Y;
+    conv_params.dilation.w = INT16XINT8_SPILL2_DILATION_X;
+    conv_params.dilation.h = INT16XINT8_SPILL2_DILATION_Y;
+
+    conv_params.input_offset = 0;
+    conv_params.output_offset = 0;
+    conv_params.activation.min = INT16XINT8_SPILL2_OUT_ACTIVATION_MIN;
+    conv_params.activation.max = INT16XINT8_SPILL2_OUT_ACTIVATION_MAX;
+    quant_params.multiplier = (int32_t *)int16xint8_spill2_output_mult;
+    quant_params.shift = (int32_t *)int16xint8_spill2_output_shift;
+
+    int buf_size = arm_convolve_s16_get_buffer_size(&input_dims, &filter_dims);
+    ctx.buf = malloc(buf_size);
+    arm_cmsis_nn_status result;
+    result = arm_convolve_s16(&ctx,
+                              &conv_params,
+                              &quant_params,
+                              &input_dims,
+                              input_data,
+                              &filter_dims,
+                              kernel_data,
+                              &bias_dims,
+                              bias_data,
+                              &output_dims,
+                              output);
+    if (ctx.buf)
+    {
+        // The caller is responsible to clear the scratch buffers for security reasons if applicable.
+        memset(ctx.buf, 0, buf_size);
+        free(ctx.buf);
+    }
+    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+    memset(output, 0, sizeof(output));
+
+    buf_size = arm_convolve_wrapper_s16_get_buffer_size(&conv_params, &input_dims, &filter_dims, &output_dims);
+    ctx.buf = malloc(buf_size);
+
+    result = arm_convolve_wrapper_s16(&ctx,
+                                      &conv_params,
+                                      &quant_params,
+                                      &input_dims,
+                                      input_data,
+                                      &filter_dims,
+                                      kernel_data,
+                                      &bias_dims,
+                                      bias_data,
+                                      &output_dims,
+                                      output);
+    if (ctx.buf)
+    {
+        memset(ctx.buf, 0, buf_size);
+        free(ctx.buf);
+    }
+    TEST_ASSERT_EQUAL(ARM_CMSIS_NN_SUCCESS, result);
+    TEST_ASSERT_TRUE(validate_s16(output, output_ref, output_ref_size));
+}
diff --git a/Tests/UnitTest/conv_settings.py b/Tests/UnitTest/conv_settings.py
index c18bdd3d..5b7831d7 100644
--- a/Tests/UnitTest/conv_settings.py
+++ b/Tests/UnitTest/conv_settings.py
@@ -53,7 +53,9 @@ def __init__(self,
                  dilation_x=1,
                  dilation_y=1,
                  interpreter="tensorflow",
-                 int4_weights=False):
+                 int4_weights=False,
+                 weights_min=TestSettings.INT32_MIN,
+                 weights_max=TestSettings.INT32_MAX):
         super().__init__(dataset,
                          testtype,
                          regenerate_weights,
@@ -87,6 +89,9 @@ def __init__(self,
         self.scaling_factors = []
         self.groups = groups
 
+        self.weights_min = weights_min
+        self.weights_max = weights_max
+
         if self.test_type == 'depthwise_conv':
             self.channel_multiplier = self.output_ch // self.input_ch
             if self.output_ch % self.input_ch != 0:
@@ -311,8 +316,8 @@ def generate_data(self, input_data=None, weights=None, biases=None) -> None:
             else:
                 weights = self.get_randomized_data(weight_shape,
                                                    self.kernel_table_file,
-                                                   minrange=TestSettings.INT32_MIN,
-                                                   maxrange=TestSettings.INT32_MAX,
+                                                   minrange=self.weights_min,
+                                                   maxrange=self.weights_max,
                                                    decimals=1,
                                                    regenerate=self.regenerate_new_weights)
 
@@ -327,18 +332,26 @@ def generate_data(self, input_data=None, weights=None, biases=None) -> None:
                                                     padding=self.padding,
                                                     input_shape=input_shape[1:],
                                                     dilation_rate=(self.dilation_y, self.dilation_x),
-                                                    groups=self.groups)
+                                                    groups=self.groups,
+                                                    use_bias=self.generate_bias)
                 model.add(conv_layer)
-                conv_layer.set_weights([weights, biases])
+                if self.generate_bias:
+                    conv_layer.set_weights([weights, biases])
+                else:
+                    conv_layer.set_weights([weights])
             elif self.test_type == 'depthwise_conv':
                 depthwise_layer = keras.layers.DepthwiseConv2D(kernel_size=(self.filter_y, self.filter_x),
                                                                   strides=(self.stride_y, self.stride_x),
                                                                   padding=self.padding,
                                                                   depth_multiplier=self.channel_multiplier,
                                                                   input_shape=input_shape[1:],
-                                                                  dilation_rate=(self.dilation_y, self.dilation_x))
+                                                                  dilation_rate=(self.dilation_y, self.dilation_x),
+                                                                  use_bias=self.generate_bias)
                 model.add(depthwise_layer)
-                depthwise_layer.set_weights([weights, biases])
+                if self.generate_bias:
+                    depthwise_layer.set_weights([weights, biases])
+                else:
+                    depthwise_layer.set_weights([weights])
             elif self.test_type == 'transpose_conv':
                 transposed_conv_layer = keras.layers.Conv2DTranspose(self.output_ch,
                                                                         kernel_size=(self.filter_y, self.filter_x),
@@ -371,8 +384,7 @@ def generate_data(self, input_data=None, weights=None, biases=None) -> None:
         all_layers_details = interpreter.get_tensor_details()
         filter_layer = all_layers_details[filter_index]
 
-        if self.test_type == 'transpose_conv' and not self.generate_bias:
-            # TODO: real null bias for all operators and not only transpose conv.
+        if not self.int4_weights and not self.generate_bias:
             bias_layer = None
             biases = []
         else:
diff --git a/Tests/UnitTest/generate_test_data.py b/Tests/UnitTest/generate_test_data.py
index 6d8b375c..a51ebe60 100755
--- a/Tests/UnitTest/generate_test_data.py
+++ b/Tests/UnitTest/generate_test_data.py
@@ -595,21 +595,74 @@ def load_testdata_sets(regenerate_input, regenerate_weights, regenerate_biases,
                                           regenerate_input,
                                           regenerate_biases,
                                           schema_file,
-                                          in_ch=3,
+                                          in_ch=55,
                                           out_ch=4,
-                                          x_in=7,
+                                          x_in=10,
                                           y_in=8,
-                                          w_x=2,
+                                          w_x=9,
                                           w_y=4,
-                                          stride_x=2,
-                                          stride_y=3,
-                                          pad=True,
+                                          stride_x=1,
+                                          stride_y=1,
+                                          pad=False,
+                                          generate_bias=False,
                                           randmin=TestSettings.INT16_MIN,
                                           randmax=TestSettings.INT16_MAX,
-                                          out_activation_min=-13335,
-                                          out_activation_max=32767,
+                                          out_activation_min=TestSettings.INT16_MIN,
+                                          out_activation_max=TestSettings.INT16_MAX,
                                           int16xint8=True,
                                           interpreter=interpreter)
+    dataset = 'int16xint8_spill'
+    testdata_sets[dataset] = ConvSettings(dataset,
+                                          type_of_test,
+                                          regenerate_weights,
+                                          regenerate_input,
+                                          regenerate_biases,
+                                          schema_file,
+                                          in_ch=55,
+                                          out_ch=9,
+                                          x_in=10,
+                                          y_in=8,
+                                          w_x=9,
+                                          w_y=4,
+                                          stride_x=1,
+                                          stride_y=1,
+                                          pad=False,
+                                          generate_bias=False,
+                                          randmin=TestSettings.INT16_MAX - 100,
+                                          randmax=TestSettings.INT16_MAX,
+                                          out_activation_min=TestSettings.INT16_MAX - 100,
+                                          out_activation_max=TestSettings.INT16_MAX,
+                                          bias_min=TestSettings.INT64_MIN,
+                                          bias_max=TestSettings.INT64_MAX,
+                                          int16xint8=True,
+                                          interpreter=interpreter,
+                                          weights_min=100)
+    dataset = 'int16xint8_spill2'
+    testdata_sets[dataset] = ConvSettings(dataset,
+                                          type_of_test,
+                                          regenerate_weights,
+                                          regenerate_input,
+                                          regenerate_biases,
+                                          schema_file,
+                                          in_ch=12,
+                                          out_ch=9,
+                                          x_in=10,
+                                          y_in=8,
+                                          w_x=9,
+                                          w_y=4,
+                                          stride_x=1,
+                                          stride_y=1,
+                                          pad=False,
+                                          generate_bias=False,
+                                          randmin=TestSettings.INT16_MAX - 100,
+                                          randmax=TestSettings.INT16_MAX,
+                                          out_activation_min=TestSettings.INT16_MAX - 100,
+                                          out_activation_max=TestSettings.INT16_MAX,
+                                          bias_min=TestSettings.INT64_MIN,
+                                          bias_max=TestSettings.INT64_MAX,
+                                          int16xint8=True,
+                                          interpreter=interpreter,
+                                          weights_min=100)
     dataset = 'requantize_s64'
     testdata_sets[dataset] = ConvSettings(dataset,
                                           type_of_test,
@@ -617,20 +670,21 @@ def load_testdata_sets(regenerate_input, regenerate_weights, regenerate_biases,
                                           regenerate_input,
                                           regenerate_biases,
                                           schema_file,
-                                          in_ch=2,
-                                          out_ch=2,
-                                          x_in=3,
-                                          y_in=2,
+                                          in_ch=8,
+                                          out_ch=8,
+                                          x_in=4,
+                                          y_in=4,
                                           w_x=2,
                                           w_y=2,
                                           stride_x=1,
                                           stride_y=1,
                                           pad=False,
+                                          generate_bias=True,
                                           out_activation_min=TestSettings.INT16_MIN,
                                           out_activation_max=TestSettings.INT16_MAX,
                                           int16xint8=True,
                                           bias_min=-0x300,
-                                          bias_max=0x9fff,
+                                          bias_max=TestSettings.INT32_MAX + 1000,
                                           interpreter=interpreter)
     dataset = 'int16xint8_dilation_1'
     testdata_sets[dataset] = ConvSettings(dataset,