Spiking Neural Networks (SNNs) have been an attractive option for deployment
on devices with limited computing resources and lower power consumption because
of the event-driven computing characteristic. As such devices have limited
computing and storage resources, pruning for SNNs has been widely focused
recently. However, the binary and non-differentiable property of spike signals
make pruning deep SNNs challenging, so existing methods require high time
overhead to make pruning decisions. In this paper, inspired by critical brain
hypothesis in neuroscience, we design a regeneration mechanism based on
criticality to efficiently obtain the critical pruned networks. Firstly, we
propose a low-cost metric for the criticality of pruning structures. Then we
re-rank the pruned structures after pruning and regenerate those with higher
criticality. We evaluate our method using VGG-16 and ResNet-19 for both
unstructured pruning and structured pruning. Our method achieves higher
performance compared to current state-of-the-art (SOTA) method with the same
time overhead. We also achieve comparable performances (even better on VGG-16)
compared to the SOTA method with 11.3x and 15.5x acceleration. Moreover, we
investigate underlying mechanism of our method and find that it efficiently
selects potential structures, learns the consistent feature representations and
reduces the overfitting during the recovery phase.

本文提出了一种采用重建机制来高效获取关键修剪网络的方法，通过基于关键修剪结构的低成本度量，重新排名修剪结构并重建那些具有更高关键性的结构，实现了比现有方法更高的性能和加速度，同时研究了机制潜在机理并发现了其在恢复阶段能够高效选择潜在结构、学习一致的特征表示和减少过拟合。