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 8.4X 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-chip
memory overhead of indices.

PCNN 是一种细粒度 1D 修剪模型的技术，使用 SPM 编码 PCNN 中的稀疏性，实现在硬件中高效应用，并在 VGG-16 和 ResNet-18 上实现 8.4 倍的压缩率和仅 0.2% 的精度损失，同时在 55nm 工艺中实现了高达 9.0 倍的加速和 28.39 TOPS/W 的效率以及不到 3.1% 的芯片内存开销。