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Version 0.5 #2604

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Feb 16, 2019
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70 changes: 70 additions & 0 deletions NEWS.md
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,76 @@ Refer to the Roadmap issue for complete list on on-going version features.
If you check in something that is not reflected in Roadmap issue, please reply
to that issue so it can get added.


## 0.5
This release features several major improvements. Some of the highlights are: Arbitrary bits quantization algorithm; High-level auto-differentiation language -- Relay.

- Fully featured 8-bit network support
- 8bit quantizer
- Arbitrary bits quantization algorithm
- Intel cpu support
- ARM cpu support
- NVidia GPU 8-bit kernel
- int8 gemm recipe
- int8 conv2d
- Autotvm integration
- Automated tuning and scheduling
- AutoTVM optimizations for mobile GPUs
- AutoTVM optimizations for CUDA
- AutoTVM for x86
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- Introduction of Relay, NNVMv2 IR
- Support for first-order automatic differentiation #2321
- Generic & informative Relay error reporting #2408
- Relay IR text format support #1781
- Support control flows
- A Normal Form Canonicalization #2251
- Type system support
- End to end compilation
* Frontend support: Caffe2 #2507 , CoreML #2476 , Keras #2376 , MXNet #2163 , ONNX, TFLite #2365
* Operator coverage #1799 #2051
- FoldScaleAxis #2020
- SimplifyInference #2033
- CombineParallelConv2D #2089
- InstrumentBoundCheckers pass #2079
- Bind & FoldConstant #2100
- Alter Op Layout #2150
- General OpFusion #2090
- CodeGen
- Gcc / g++ compatible C code generator for TVM #2161
- Device type annotation for heterogeneous compilation #2361
- Cache packed func ptr, lift alloca #2070
- Generalize compute to tensor region #1476
- Runtime
- Relay interpreter and compiler #1954
- Heterogeneous runtime #1695
- Language bindings: Golang runtime #1470 , Rust runtime #1597
- Add min_repeat_ms to time_evaluator #2200
- Bundled interpreter demonstration #2297
- Enable PlanMemory in the graph runtime #2120
- Language Binding
- Rust frontend #2292
- VTA
- Improved RPC for VTA #2043
- Hybrid python programming model
- Support for scheduling #2416
- Support for Inter-function call #2287
- TOP
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- Initial support for sparse tensor computation
- Improve ARM CPU depthwise convolution performance #2345
- Port winograd ops to relay #2356
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- Tutorials and docs
- Relay language docs #2232
- Tutorials on how to use SGX backend
- How to write a pass in python
- General lowering flow of TVM
- How to do tensorize
- TFLite frontend tutorial #2508
- Keras seq2seq model for translation tutorial #1815
- Committer guide and tips #2468
- Code review guideline on API designs #2459



## 0.4

This release features several major improvements. The high-level graph optimizer is now part of TVM repo. Some of the highlights are: Initial support of AutoTVM for automated optimization; customized accelerator backend VTA.
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2 changes: 1 addition & 1 deletion version.py
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Expand Up @@ -16,7 +16,7 @@
# current version
# We use the version of the incoming release for code
# that is under development
__version__ = "0.5.dev"
__version__ = "0.5"

# Implementations
def update(file_name, pattern, repl):
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