PCNN: Pattern-based Fine-Grained Regular Pruning Towards Optimizing CNN Accelerators

Zhanhong Tan, Jiebo Song, Xiaolong Ma, Sia Huat Tan, Hongyang Chen, Yuanqing Miao, Yifu Wu, Shaokai Ye, Yanzhi Wang, Dehui Li, Kaisheng Ma

Abstract: Weight pruning is a powerful technique to realize model compression. We propose PCNN, a fine-grained regular 1D pruning method. A novel index format called Sparsity Pattern Mask (SPM) is presented to encode the sparsity in PCNN. Leveraging SPM with limited pruning patterns and non-zero sequences with equal length, PCNN can be efficiently employed in hardware. Evaluated on VGG-16 and ResNet-18, our PCNN achieves the compression rate up to 8.4× with only 0.2% accuracy loss. We also implement a pattern-aware architecture in 55nm process, achieving up to 9.0× speedup and 28.39 TOPS/W efficiency with only 3.1% on-chip memory overhead of indices.