TDAP: Temporal and Dependency Aware Pruning for Spiking Neural Networks

Spiking Neural Networks (SNNs) provide an energy-efficient alternative to tradi tional Artificial Neural Networks for event-based vision tasks. However, due to substantial parameter redundancy, their deployment on resource-constrained neu romorphic hardware remains challenging. Existing post-training pruning meth ods for SNNs primarily treat each channel as an independent feature extractor, overlooking two critical characteristics of spike dynamics: (1) the temporal infor mativeness of information propagation across time steps, and (2) the predictive dependencies reflecting inter-channel relationships. To address these limitations, we propose TDAP (Temporal Dependency-Aware Pruning), a post-training pruning framework that discovers channel dependencies directly from calibration data without requiring fine-tuning. TDAP comprises two modules: (i) a Temporal Firing Rate module that encodes information arrival through learnable temporal weightings, and (ii) a Channel Cross-Correlation Dependency module that captures predictive relationships between channels via lagged correlation analysis with spike-specific statistics. We further introduce a geometric mean fusion strategy to aggregate the temporal and dependency mod ules. On the challenging CIFAR-100 dataset, TDAP attains 54.23% at 60% spar sity, surpassing pure temporal methods by up to 8.63pp. For event-based DVS CIFAR10, TDAP reaches 37.00% at 70% sparsity, exceeding Wanda by 13.30pp. These results validate that modeling temporal dependency and channel depen dency is essential for aggressive SNN compression.