Spiking neural networks (SNNs) offer a promising energy-efficient alternative
to artificial neural networks, due to their event-driven spiking computation.
However, state-of-the-art deep SNNs (including Spikformer and SEW ResNet)
suffer from non-spike computations (integer-float multiplications) caused by
the structure of their residual connection. These non-spike computations
increase SNNs' power consumption and make them unsuitable for deployment on
mainstream neuromorphic hardware, which only supports spike operations. In this
paper, we propose a hardware-friendly spike-driven residual learning
architecture for SNNs to avoid non-spike computations. Based on this residual
design, we develop Spikingformer, a pure transformer-based spiking neural
network. We evaluate Spikingformer on ImageNet, CIFAR10, CIFAR100, CIFAR10-DVS
and DVS128 Gesture datasets, and demonstrated that Spikingformer outperforms
the state-of-the-art in directly trained pure SNNs as a novel advanced backbone
(74.79$\%$ top-1 accuracy on ImageNet, + 1.41$\%$ compared with Spikformer).
Furthermore, our experiments verify that Spikingformer effectively avoids
non-spike computations and reduces energy consumption by 60.34$\%$ compared
with Spikformer on ImageNet. To our best knowledge, this is the first time that
a pure event-driven transformer-based SNN has been developed.

本研究提出了一种硬件友好的、基于残差设计的、全新的、纯变压器型脉冲神经网络 ——Spikingformer，它能够避免非脉冲计算并使能耗降低 60.34％。Spikingformer 在图像分类任务的表现优于之前的纯 SNN，并且是首次开发出全脉冲驱动的变压器型 SNN。