@@ -9,6 +9,77 @@ 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-differentiable programming IR -- 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 optimizations for x86
- Initial release of the differentiable programming IR, Relay
- 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
- Backend support #2477
- TOPI
- Initial support for sparse tensor computation
- Improve ARM CPU depthwise convolution performance #2345
- Port winograd ops to relay #2356
- Add faster-rcnn proposal op #2420
- 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.