Low-cost Deployment and Acceleration of Event-based Spiking Convolutional Neural Networks

Qingyang Tian, Faquan Chen, Lisheng Xie, Yifan Zhou, Ziren Wu, Liangshun Wu, Rendong Ying, Peilin Liu

Abstract: By simulating the neurodynamics of biological brains, Spiking Neural Networks (SNNs) leverage sparse spike signal, eliminating the continuous multiply-accumulate operations of traditional Artificial Neural Networks (ANNs). Event-driven SNN processing offers significant advantages in energy efficiency and latency, making it ideal to be deployed on low-end processors. Spiking Convolutional Neural Networks (SC-NNs), which incorporate event-based processing, are increasingly employed for their power efficiency and ability to process spatio-temporal information. Unlike fully-connected networks, which rely on regular vector calculations, convolution operations present challenges for event-driven computation due to their sliding window nature.In this work, we deployed a compact 7-layer SCNN on the ARM Cortex-A9 core of PYNQ Z2 development board, using event-based acceleration. By optimizing data storage and processing sequences, we achieved efficient low-cost deployment. Offline training on the DVS128-Gesture dataset for an object recognition task yielded an accuracy of 93.40%. Following low-precision quantization and deployment, the model maintained a considerable accuracy of 92.36%. Compared to traditional periodic computation, event-based convolution processing achieved a 12.87× speedup. Furthermore, by exploiting the parallelism of feature map data storage along the channel dimension and ARM NEON instruction set, we gained an additional 2.97× speedup.

External IDs:dblp:conf/iscas/TianCXZWWYL25