Exploring the Granularity of Sparsity in Convolutional Neural Networks

Abstract

Sparsity helps reducing the computation complexity of DNNs by skipping the multiplication with zeros. The granularity of sparsity affects the efficiency of hardware architecture and the prediction accuracy. In this paper we quantitatively measure the accuracy-sparsity relationship with different granularity. Coarse-grained sparsity brings more regular sparsity pattern, making it easier for hardware acceleration, and our experimental results show that coarsegrained sparsity have very small impact on the sparsity ratio given no loss of accuracy. Moreover, due to the index saving effect, coarse-grained sparsity is able to obtain similar or even better compression rates than fine-grained sparsity at the same accuracy threshold. Our analysis, which is based on the framework of a recent sparse convolutional neural network (SCNN) accelerator, further demonstrates that it saves 30% – 35% of memory references compared with fine-grained sparsity.