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Neural Network Distiller: A PyTorch environment for neural network compression

Neta Zmora (Intel AI Lab)
11:05am-11:45am Thursday, September 6, 2018
Location: Continental 7-9
Secondary topics:  Deep Learning tools, Edge computing and Hardware

What you'll learn

  • Explore Distiller, an open source Python package for neural network compression research


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:

  • A framework for integrating pruning, regularization, and quantization algorithms
  • A set of tools for analyzing and evaluating compression performance
  • Example implementations of state-of-the-art compression algorithms

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.

Photo of Neta Zmora

Neta Zmora

Intel AI Lab

Neta Zmora is a deep learning research engineer at the Intel AI Lab, where he wrote Distiller, an open source Python package for neural network compression research. Previously, Neta was the lead software architect of Intel’s Computer Vision Group DL software stack.