SpikingDynamicMaskFormer: Enhancing Efficiency in Spiking Neural Networks with Dynamic Masking

Spiking Neural Networks (SNNs) offer promising low-power alternatives to conventional neural models but often incur considerable redundancy in parameters and computations. To address these inefficiencies, we propose SpikingDynamicMaskFormer (SDMFormer), a novel framework that integrates dynamic masking and lightweight position encoding into a spike-based Transformer backbone. Specifically, our Dynamic Mask Encoder Block adaptively suppresses ineffective spike channels by learning mask parameters, reducing parameter count to 37.93–42.69% of the original Spikformer. Simultaneously, a redesigned lightweight position embedding replaces resource-intensive relative position convolutions, further lowering complexity. Experiments on three neuromorphic vision datasets—DVS128, CIFAR10-DVS and N-Caltech101—demonstrate that SDMFormer cuts energy consumption by 42.79–50.13% relative to Spikformer while maintaining or slightly surpassing accuracy. Moreover, compared with recent leading works, SDMFormer achieves competitive accuracy with substantially fewer parameters and delivers higher inference efficiency, reaching up to 196.20 img/s on CIFAR10-DVS. These results highlight the efficacy of combining event-driven attention with structured pruning and parameter-efficient position encoding, indicating the potential of SDMFormer for resource-efficient SNN deployment in low-power applications.