Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning

TL;DR

This paper introduces a unified, efficient framework for post-training compression of deep neural networks that combines pruning and quantization, significantly improving accuracy and compression trade-offs without retraining.

Contribution

A novel compression framework extending the Optimal Brain Surgeon method to include quantization, enabling effective one-shot model compression for DNNs.

Findings

Significantly better compression-accuracy trade-offs than existing methods

Enables accurate combined pruning and quantization post-training

Efficient in time and space for practical deployment

Abstract

We consider the problem of model compression for deep neural networks (DNNs) in the challenging one-shot/post-training setting, in which we are given an accurate trained model, and must compress it without any retraining, based only on a small amount of calibration input data. This problem has become popular in view of the emerging software and hardware support for executing models compressed via pruning and/or quantization with speedup, and well-performing solutions have been proposed independently for both compression approaches. In this paper, we introduce a new compression framework which covers both weight pruning and quantization in a unified setting, is time- and space-efficient, and considerably improves upon the practical performance of existing post-training methods. At the technical level, our approach is based on an exact and efficient realization of the classical Optimal Brain Surgeon (OBS) framework of [LeCun, Denker, and Solla, 1990] extended to also cover weight quantization at the scale of modern DNNs. From the practical perspective, our experimental results show that it can improve significantly upon the compression-accuracy trade-offs of existing post-training methods, and that it can enable the accurate compound application of both pruning and quantization in a post-training setting.