Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks

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.