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.
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>
<details><summary><b>What's New in November?</b></summary>
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<ahref="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.
- 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).*
