C-DPSS: Channel dual-phase sparsity pruning framework for spiking neural networks

Spiking Neural Networks (SNNs) have emerged as an essential paradigm for brain-inspired computing, achieving superior energy efficiency on neuromorphic hardware. However, as network scale increases, SNNs encounter growing challenges in deployment efficiency. While structured pruning provides a practical approach, existing methods typically rely on a dense-to-sparse training pattern, which incurs high computational costs during training and fails to exploit the efficiency of sparse computation from the outset. There is a lack of effective frameworks that enforce structured sparsity within a sparse-to-sparse training regime for SNNs. To bridge this gap, we propose the Channel Dual-phase Sparsity (C-DPSS) framework. This approach leverages Dynamic Sparse Training (DST) to enable efficient topology exploration while progressively enforcing hardware-friendly structured sparsity. Our methodology is grounded in the empirical observation that channel-level neuronal activity patterns stabilize earlier than synaptic weights under certain training regimes. Motivated by this temporal discrepancy, C-DPSS employs a dual-phase saliency metric during training. It defines an early exploration phase dominated by neuronal dynamics, and smoothly transitions to a late refinement phase driven by synaptic efficacy. Extensive experiments on both static and neuromorphic benchmarks demonstrate that C-DPSS provides a robust accuracy-efficiency trade-off. Notably, for VGG-16 on CIFAR-10, C-DPSS achieves 50% channel sparsity and reduces SOPs by 42.1% under S W = 0.90 and S C = 0.50 , with no observable accuracy degradation, showing a favorable accuracy–efficiency trade-off under structured channel pruning. We further examine the large-scale behavior of our approach through an ImageNet-1K feasibility study of sparse-to-sparse structured pruning. Our code is available at: https://github.com/JunLi0514/C-DPSS .