  The emergence of deep and large-scale spiking neural networks (SNNs)
exhibiting high performance across diverse complex datasets has led to a need
for compressing network models due to the presence of a significant number of
redundant structural units, aiming to more effectively leverage their low-power
consumption and biological interpretability advantages. Currently, most model
compression techniques for SNNs are based on unstructured pruning of individual
connections, which requires specific hardware support. Hence, we propose a
structured pruning approach based on the activity levels of convolutional
kernels named Spiking Channel Activity-based (SCA) network pruning framework.
Inspired by synaptic plasticity mechanisms, our method dynamically adjusts the
network's structure by pruning and regenerating convolutional kernels during
training, enhancing the model's adaptation to the current target task. While
maintaining model performance, this approach refines the network architecture,
ultimately reducing computational load and accelerating the inference process.
This indicates that structured dynamic sparse learning methods can better
facilitate the application of deep SNNs in low-power and high-efficiency
scenarios.
