Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformer

• URL: http://arxiv.org/abs/2503.00226v2
• Date: Mon, 17 Mar 2025 03:17:00 GMT
• Title: Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformer
• Authors: Yufei Guo, Xiaode Liu, Yuanpei Chen, Weihang Peng, Yuhan Zhang, Zhe Ma,
• Abstract summary: Spiking Neural Networks have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks.<n>This paper introduces Accurate Addition-Only Spiking Self-Attention (A$2$OS$2$A)
• Score: 15.93436166506258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers have demonstrated outstanding performance across a wide range of tasks, owing to their self-attention mechanism, but they are highly energy-consuming. Spiking Neural Networks have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks, leveraging event-driven computation and binary spikes for information transfer. The combination of Transformers' capabilities with the energy efficiency of SNNs offers a compelling opportunity. This paper addresses the challenge of adapting the self-attention mechanism of Transformers to the spiking paradigm by introducing a novel approach: Accurate Addition-Only Spiking Self-Attention (A$^2$OS$^2$A). Unlike existing methods that rely solely on binary spiking neurons for all components of the self-attention mechanism, our approach integrates binary, ReLU, and ternary spiking neurons. This hybrid strategy significantly improves accuracy while preserving non-multiplicative computations. Moreover, our method eliminates the need for softmax and scaling operations. Extensive experiments show that the A$^2$OS$^2$A-based Spiking Transformer outperforms existing SNN-based Transformers on several datasets, even achieving an accuracy of 78.66\% on ImageNet-1K. Our work represents a significant advancement in SNN-based Transformer models, offering a more accurate and efficient solution for real-world applications.

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