A Sparse Frequency-Aware Spiking Transformer with Hierarchical Wavelet Pruning and Adaptive Dual-Path Routing

Spiking neural networks (SNNs) offer significant energy efficiency advantages, yet existing compression methods perform uniform pruning on temporal feature maps, failing to exploit the substantially differentiated redundancy structures among different frequency components. Inspired by the M/P pathway frequency segregation mechanism in biological visual systems, this paper proposes the Sparse Frequency-Aware Transformer (SFAT), which achieves frequency-aware structured sparsity through hierarchical Haar wavelet decomposition: coefficient-wise soft thresholding distinguishes low/high-frequency subbands, while channel-wise energy gating adaptively prunes the four quadrant subbands. To compensate for high-frequency information loss induced by pruning, we design the Dual-Path MLP Routing mechanism (DPMR), comprising a frequency compensation path with subband-specific experts and global gating, supporting both a Parallel mode for high-precision scenarios and a Lightweight mode for edge deployment. Experiments on CIFAR-100, Tiny-ImageNet, and neuromorphic datasets demonstrate that SFAT(Parallel) achieves 79.70% on CIFAR-100 and 66.44% on Tiny-ImageNet, outperforming existing Spiking Transformer methods while reducing energy consumption by approximately 60%; SFAT(Lightweight) achieves comparable accuracy with nearly half the parameters, validating the synergistic optimization between frequency decomposition and structural compression. Code will be made available upon acceptance.