HUS-SNN: Homeostatic Unbiased Sparsity for N:M Sparse Spiking Neural Networks

Spiking neural networks offer high energy efficiency, but their real-world deployment is limited by the mismatch between theoretical sparsity and dense weight implementations. N:M structured sparsity can improve hardware efficiency, but applying it to SNNs leads to performance issues: gradient expectation bias from the non-commutativity of pruning masks and surrogate gradients, and irreversible neuronal silencing caused by reduced synaptic in-degree. We propose the Homeostatic Unbiased Sparse (HUS) framework to address these issues via a two-path optimization design. It combines gradient-aware mask updates with shadow gradient accumulation and multiplicative updates to enable unbiased topological search under N:M constraints. At the same time, a homeostatic weight update path regulates neuron excitability to prevent silent neurons. Experiments on multiple datasets show that our method maintains competitive accuracy while significantly reducing FLOPs, latency time and energy. Codes are available at https://anonymous.4open.science/r/HUS-SNN-FDE0/.