TS-SNN: Temporal Shift Module for Spiking Neural Networks
Journal:
arXiv
Published Date:
May 7, 2025
Abstract
Spiking Neural Networks (SNNs) are increasingly recognized for their
biological plausibility and energy efficiency, positioning them as strong
alternatives to Artificial Neural Networks (ANNs) in neuromorphic computing
applications. SNNs inherently process temporal information by leveraging the
precise timing of spikes, but balancing temporal feature utilization with low
energy consumption remains a challenge. In this work, we introduce Temporal
Shift module for Spiking Neural Networks (TS-SNN), which incorporates a novel
Temporal Shift (TS) module to integrate past, present, and future spike
features within a single timestep via a simple yet effective shift operation. A
residual combination method prevents information loss by integrating shifted
and original features. The TS module is lightweight, requiring only one
additional learnable parameter, and can be seamlessly integrated into existing
architectures with minimal additional computational cost. TS-SNN achieves
state-of-the-art performance on benchmarks like CIFAR-10 (96.72\%), CIFAR-100
(80.28\%), and ImageNet (70.61\%) with fewer timesteps, while maintaining low
energy consumption. This work marks a significant step forward in developing
efficient and accurate SNN architectures.
