  Spiking Neural Networks (SNNs) have gained considerable attention due to the
energy-efficient and multiplication-free characteristics. The continuous growth
in scale of deep SNNs poses challenges for model deployment. Network pruning
reduces hardware resource requirements of model deployment by compressing the
network scale. However, existing SNN pruning methods cause high pruning costs
and performance loss because the pruning iterations amplify the training
difficulty of SNNs. In this paper, inspired by the critical brain hypothesis in
neuroscience, we propose a regeneration mechanism based on the neuron
criticality for SNN pruning to enhance feature extraction and accelerate the
pruning process. Firstly, we propose a low-cost metric for the criticality in
SNNs. Then, we re-rank the pruned structures after pruning and regenerate those
with higher criticality to obtain the critical network. Our method achieves
higher performance than the current state-of-the-art (SOTA) method with up to
95.26% reduction of pruning cost. Moreover, we investigate the underlying
mechanism of our method and find that it efficiently selects potential
structures and learns the consistent feature representation.
