Rethinking the Mechanism of the Pattern Pruning and the Circle Importance Hypothesis

Abstract

Network pruning is an effective and widely-used model compression technique. Pattern pruning is a new sparsity dimension pruning approach whose compression ability has been proven in some prior works. However, a detailed study on "pattern" and pattern pruning is still lacking. In this paper, we analyze the mechanism behind pattern pruning. Our analysis reveals that the effectiveness of pattern pruning should be attributed to finding the less important weights even before training. Then, motivated by the fact that the retinal ganglion cells in the biological visual system have approximately concentric receptive fields, we further investigate and propose the Circle Importance Hypothesis to guide the design of efficient patterns. We also design two series of special efficient patterns - circle patterns and semicircle patterns. Moreover, inspired by the neural architecture search technique, we propose a novel one-shot gradient-based pattern pruning algorithm. Besides, we also expand depthwise convolutions with our circle patterns, which improves the accuracy of networks with little extra memory cost. Extensive experiments are performed to validate our hypotheses and the effectiveness of the proposed methods. For example, we reduce the 44.0% FLOPS of ResNet-56 while improving its accuracy to 94.38% on CIFAR-10. And we reduce the 41.0% FLOPS of ResNet-18 with only a 1.11% accuracy drop on ImageNet.