Spiking Transformer with Spatial-Temporal Attention

• URL: http://arxiv.org/abs/2409.19764v2
• Date: Mon, 25 Nov 2024 21:22:40 GMT
• Title: Spiking Transformer with Spatial-Temporal Attention
• Authors: Donghyun Lee, Yuhang Li, Youngeun Kim, Shiting Xiao, Priyadarshini Panda,
• Abstract summary: Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers. We propose Spiking Transformer with Spatial-Temporal Attention (STAtten), a simple and straightforward architecture that efficiently integrates both spatial and temporal information in the self-attention mechanism. Our method can be seamlessly integrated into existing spike-based transformers without architectural overhaul.
• Score: 26.7175155847563
• License:
• Abstract: Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results through sparse binary computations. However, existing spike-based transformers predominantly focus on spatial attention while neglecting crucial temporal dependencies inherent in spike-based processing, leading to suboptimal feature representation and limited performance. To address this limitation, we propose Spiking Transformer with Spatial-Temporal Attention (STAtten), a simple and straightforward architecture that efficiently integrates both spatial and temporal information in the self-attention mechanism. STAtten introduces a block-wise computation strategy that processes information in spatial-temporal chunks, enabling comprehensive feature capture while maintaining the same computational complexity as previous spatial-only approaches. Our method can be seamlessly integrated into existing spike-based transformers without architectural overhaul. Extensive experiments demonstrate that STAtten significantly improves the performance of existing spike-based transformers across both static and neuromorphic datasets, including CIFAR10/100, ImageNet, CIFAR10-DVS, and N-Caltech101.

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