Merge branch 'release_note' into 'master'

feat: add release note for v1.0.0

See merge request applied-machine-learning/sysml/mace!1319

RELEASE.md

+# v1.0.0 (2020-11-04)
+## Support Quantization For MACE Micro
+At the beginning of this year, we released MACE Micro to fully support ultra-low-power inference scenarios of mobile phones and IoT devices. In this version, we support quantization for MACE Micro and integrate CMSIS5 to support Cortex-M chips better.
+
+## Support More Model Formats
+We find more and more R&D engineers are using the PyTorch framework to train their models. In previous versions, MACE transformed the PyTorch model by using ONNX format as a bridge. In order to serve PyTorch developers better, we support direct transformation for PyTorch models in this version, which improves the performance of the model inference.
+At the same time, we cooperated with [MEGVII](URL 'https://www.megvii.com/') company and support its [MegEngine](URL 'https://github.com/MegEngine/MegEngine') model format. If you trained your models by [MegEngine](URL 'https://github.com/MegEngine/MegEngine') framework, now you can use MACE to deploy the models on mobile phones or IoT devices.
+
+## Support More Data Precision
+Armv8.2 provides support for half-precision floating-point data processing instructions, in this version we support the fp16 precision computation by Armv8.2 fp16 instructions, which increases inference speed by roughly 40% for models such as mobilenet-v1 model.
+The bfloat16 (Brain Floating Point) floating-point format is a computer number format occupying 16 bits in computer memory, we also support bfloat16 precision in this version, which increases inference speed by roughly 40% for models such as mobilenet-v1/2 model on some low-end chips.
+
+## Others
+In this version, we also add the following features:
+1. Support more operators, such as `GroupNorm`, `ExtractImagePatches`, `Elu`, etc.
+2. Optimize the performance of the framework and operators, such as the `Reduce` operator.
+3. Support dynamic filter of conv2d/deconv2d.
+4. Integrate MediaTek APU support on mt6873, mt6885, and mt6853.
+
+## Acknowledgement
+Thanks to the following guys who contribute code which makes MACE better.
+
+@ZhangZhijing1, who contributed the bf16 code which was then committed by someone else.
+@yungchienhsu, @Yi-Kai-Chen, @Eric-YK-Chen, @yzchen, @gasgallo, @lq, @huahang, @elswork, @LovelyBuggies, @freewym.
+
+# v0.13.0 (2020-04-03)
+## Support for MACE Micro
+Compared with mobile devices such as mobile phones, micro-controllers are small, low-energy computing devices, which are often embedded in hardware that only needs basic computing, including household appliances and IoT devices. Billions of microcontrollers are produced every year. MACE adds micro-controller support to fully support ultra-low-power inference scenarios of mobile phones and IoT devices. MACE's micro-controller engine does not rely on any OS, heap memory allocation, C++ library or other third-party libraries except the math library. 
+
+## Further Support For Quantization
+MACE supports two kinds of quantization mechanisms: quantization-aware training and post-training quantization. In this version, we add a mixed-use of them. Furthermore, we support Armv8.2 dot product instruction for CPU quantization.
+
+## Performance Optimization
+MACE is continuously optimizing the performance. This time, we add ION buffer support for Qualcomm socs, which greatly improves the inference performance of models that need to switch between GPU and CPU. Moreover, we optimize the operators' performance such as `ResizeNearestNeighbor`, `Deconv`.
+
+## Others
+In this version, We support many new operators, `BatchMatMulV2` and `Select` operators for TensorFlow, `Deconv2d`, `Strided-Slice`, `Sigmoid` for Hexagon DSP and fix some bugs on validation and tuning.
+
+## Acknowledgement
+Thanks for the following guys who contribute code which makes MACE better.
+gasgallo
+
+# v0.12.0 (2019-11-17)
+------
+## Performance Optimization
+We found that the lack of OP implementations on devices(GPU, Hexagon DSP, etc.) would lead to inefficient model execution, for  the memory synchronization between the device and the CPU consumed much time, so we added and enhanced some operators on the GPU( reshape, lpnorm, mvnorm, etc.) and Hexagon DSP (s2d, d2s, sub, etc.) to improve the efficiency of model execution.
+
+## Further Support For Speech Recognition
+In the last version, we supported the Kaldi framework. In Xiaomi we did a lot of work to support the speech recognition model,  including the support of flatten, unsample and other operators in onnx, as well as some bug fixes.
+
+## CMake Support
+MACE is continuously optimizing our compilation tools. This time, we support cmake compilation. Because of the use of ccache for acceleration, the compilation speed of cmake is much faster than the original bazel.
+Related Docs: https://mace.readthedocs.io/en/latest/user_guide/basic_usage_cmake.html
+
+## Others
+In this version, We supported detection of perfomance regression by dana , and  “ gpu_queue_window” parameter is added to yml file,  to solve the UI jam problem caused by GPU task execution.
+Related Docs: https://mace.readthedocs.io/en/latest/faq.html
+
+## Acknowledgement
+Thanks for the following guys who contribute code which make MACE better.
+
+yungchienhsu, gasgallo, albu, yunikkk
+
 # v0.10.0 (2019-01-03)
 ------
 ## Improvements