Combining aggregated attention and transformer architecture for accurate and efficient performance of Spiking Neural Networks.

Spiking Neural Networks (SNNs), which simulate the spiking behavior of biological neurons, have attracted significant attention in recent years due to their distinctive low-power characteristics. Meanwhile, Transformer models, known for their powerful self-attention mechanisms and parallel processing capabilities, have demonstrated exceptional performance across various domains, including natural language processing and computer vision. Despite the significant advantages of both SNNs and Transformers, directly combining the low-power benefits of SNNs with the high performance of Transformers remains challenging. Specifically, while the sparse computing mode of SNNs contributes to reduced energy consumption, traditional attention mechanisms depend on dense matrix computations and complex softmax operations. This reliance poses significant challenges for effective execution in low-power scenarios. Traditional methods often struggle to maintain or enhance model performance while striving to reduce energy consumption. Given the tremendous success of Transformers in deep learning, it is a necessary step to explore the integration of SNNs and Transformers to harness the strengths of both. In this paper, we propose a novel model architecture, Spike Aggregation Transformer (SAFormer), that integrates the low-power characteristics of SNNs with the high-performance advantages of Transformer models. The core contribution of SAFormer lies in the design of the Spike Aggregated Self-Attention (SASA) mechanism, which significantly simplifies the computation process by calculating attention weights using only the spike matrices query and key, thereby effectively reducing energy consumption. Additionally, we introduce a Depthwise Convolution Module (DWC) to enhance the feature extraction capabilities, further improving overall accuracy. We evaluated SAFormer on the CIFAR-10, CIFAR-100, Tiny-ImageNet, DVS128-Gesture, and CIFAR10-DVS datasets and demonstrated that SAFormer outperforms state-of-the-art SNNs in both accuracy and energy consumption, highlighting its significant advantages in low-power and high-performance computing.