diff --git a/README.md b/README.md index f4c0d809ee35e8c7f482bae3a6e1bd807ff4fe27..e01a111cc436e42b5e317f334c81b9cd59ede025 100755 --- a/README.md +++ b/README.md @@ -36,7 +36,36 @@ Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides a [PyTorch](http://pytorch.org/) environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic. <details><summary><b>What's New in November?</b></summary> <p> - <a href="https://bizwebcast.intel.cn/aidc/index_en.aspx?utm_source=other">Come see us in AIDC 2018 Beijing!</a> + + - Quantization: + - To improve quantization results: Added averaging-based activations clipping in SymmetricLinearQuantizer. + - For better control over quantization configuration: Added command line arguments for post-training quantization settings in image classification sample. + - Asymmetric post-training quantization (only symmetric supported so until now) + - Quantization aware training for range-based (min-max) symmetric and asymmetric quantization + - Per-channel quantization support in all of the above scenarios + - Added an implementation of [Dynamic Network Surgery for Efficient DNNs](https://arxiv.org/abs/1608.04493) with: + - A sample implementation on ResNet50 which achieves 82.5% compression 75.5% Top1 (-0.6% from TorchVision baseline). + - A new SplicingPruner pruning algorithm. + - New features for PruningPolicy: + 1. The pruning policy can use two copies of the weights: one is used during the forward-pass, the other during the backward pass. You can control when the mask is frozen and always applied. + 2. Scheduling at the training-iterationgranularity (i.e. at the mini-batch granularity). Until now we could schedule + pruning at the epoch-granularity. + - A bunch of new schedules showing AGP in action; including hybrid schedules combining structured-pruning and element-wise pruning. + - Filter and channel pruning + - Fixed problems arising in non-trivial data dependencies. + - Added [documentation](https://github.com/NervanaSystems/distiller/wiki/Pruning-Filters-&-Channels) + - Changed the YAML API to express complex dependencies when pruning channels and filters. + - Fixed a bunch of bugs + - Image classifier compression sample: + - Added a new command-line argument to report the top N best accuracy scores, instead of just the highest score. + - Added an option to load a model in serialized mode. + - We've fixed a couple of Early Exit bugs, and improved the [documentation](https://nervanasystems.github.io/distiller/algo_earlyexit/index.html) + - We presented Distiller at [AIDC 2018 Beijing](https://bizwebcast.intel.cn/aidc/index_en.aspx?utm_source=other) and @haim-barad presented his Early Exit research implemented using Distiller. + - We've looked up our star-gazers (that might be you ;-) and where they are located:<br> + *The map was generated by [this utility](https://github.com/netaz/github_stars_analytics).* + <center> <img src="imgs/wiki/distiller_star_gazer_Nov11_2018.png"></center> + + </p> </details> <details><summary><b>What's New in October?</b></summary>