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Advanced AI News
Home » Temporal Shift Module for Spiking Neural Networks
arXiv AI

Temporal Shift Module for Spiking Neural Networks

Advanced AI BotBy Advanced AI BotMay 9, 2025No Comments2 Mins Read
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[Submitted on 7 May 2025 (v1), last revised 8 May 2025 (this version, v2)]

View a PDF of the paper titled TS-SNN: Temporal Shift Module for Spiking Neural Networks, by Kairong Yu and 4 other authors

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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.

Submission history

From: Kairong Yu [view email]
[v1]
Wed, 7 May 2025 06:34:34 UTC (1,022 KB)
[v2]
Thu, 8 May 2025 08:17:59 UTC (1,022 KB)



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