Spiking Token Mixer: An event-driven friendly Former structure for spiking neural networks

Shikuang Deng, Yuhang Wu, Kangrui Du, Shi Gu

Keywords: spiking neural network; event-driven friendly; low energy consumption

TL;DR: We proposed the STMixer architecture, consisting exclusively of convolutional, fully connected layers, and residual path, is more advantageous for deployment in asynchronous scenarios.

Abstract: Spiking neural networks (SNNs), inspired by biological processes, use spike signals for inter-layer communication, presenting an energy-efficient alternative to traditional neural networks. To realize the theoretical advantages of SNNs in energy efficiency, it is essential to deploy them onto neuromorphic chips. On clock-driven synchronous chips, employing shorter time steps can enhance energy efficiency but reduce SNN performance. Compared to the clock-driven synchronous chip, the event-driven asynchronous chip achieves much lower energy consumption but only supports some specific network operations. Recently, a series of SNN projects have achieved tremendous success, significantly improving the SNN's performance. However, event-driven asynchronous chips do not support some of the proposed structures, making it impossible to integrate these SNNs into asynchronous hardware. In response to these problems, we propose the Spiking Token Mixer (STMixer) architecture, which consists exclusively of operations supported by asynchronous scenarios, including convolutional, fully connected layers and residual paths. Our series of experiments also demonstrates that STMixer achieves performance on par with spiking transformers in synchronous scenarios with very low timesteps. This indicates its ability to achieve the same level of performance with lower power consumption in synchronous scenarios. The codes are available at \url{https://github.com/brain-intelligence-lab/STMixer_demo}.

Primary Area: Deep learning architectures

Submission Number: 8144