Neural Network Compression Via Sparse Optimization

TL;DR

This paper introduces a neural network compression method based on sparse stochastic optimization, achieving significant FLOPs reduction with minimal accuracy loss, and is adaptable to various applications.

Contribution

It presents a novel model compression framework leveraging sparse stochastic optimization, which is easier to implement across different scenarios than existing heuristic methods.

Findings

Achieved up to 7.2x FLOPs reduction on VGG16 for CIFAR10.

Achieved up to 2.9x FLOPs reduction on ResNet50 for ImageNet.

Demonstrated effectiveness on benchmark compression tasks.

Abstract

The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have been a significant amount of work regarding network compression, while most of them are heuristic rule-based or typically not friendly to be incorporated into varying scenarios. On the other hand, sparse optimization yielding sparse solutions naturally fits the compression requirement, but due to the limited study of sparse optimization in stochastic learning, its extension and application onto model compression is rarely well explored. In this work, we propose a model compression framework based on the recent progress on sparse stochastic optimization. Compared to existing model compression techniques, our method is effective and requires fewer extra engineering efforts to incorporate with varying applications, and has been numerically demonstrated on benchmark compression tasks. Particularly, we achieve up to 7.2 and 2.9 times FLOPs reduction with the same level of evaluation accuracy on VGG16 for CIFAR10 and ResNet50 for ImageNet compared to the baseline heavy models, respectively.