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

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