January 11, 2020

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Neural Network Distiller: A PyTorch environment for neural network compression

Neural Network Distiller: A PyTorch environment for neural network compression

Neta Zmora offers an overview of Distiller, an open source Python package for neural network compression research. Neta discusses the motivation for compressing DNNs, outlines compression approaches, and explores Distiller's design and tools, supported algorithms, and code and documentation. Neta concludes with an example implementation of a compression research paper.

Talk Title Neural Network Distiller: A PyTorch environment for neural network compression
Speakers Neta Zmora (Intel AI Lab)
Conference Artificial Intelligence Conference
Conf Tag Put AI to Work
Location San Francisco, California
Date September 5-7, 2018
URL Talk Page
Slides Talk Slides
Video

Deep learning (DL) and artificial intelligence (AI) are quickly becoming ubiquitous. DL applications employ deep neural networks (DNNs), which are notoriously time, compute, energy, and memory intensive. Intel’s AI Lab has recently open-sourced Neural Network Distiller, a Python package for neural network compression research. Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic. Intel AI thinks that DNN compression can be another catalyst that will help bring deep learning innovation to more industries and application domains, making our lives easier, healthier, and more productive. Distiller is built with the following features and tools, keeping both DL researchers and engineers in mind: Neta Zmora discusses the motivation for compressing DNNs, outlines compression approaches, and explores Distiller’s design and tools, supported algorithms, and code and documentation. Neta concludes with an example implementation of a compression research paper. For more information on Distiller, check out Intel AI’s introductory blog post.

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