A 67.5μJ/Prediction Accelerator for Spiking Neural Networks in Image Segmentation

作者：Qinyu Chen, Guoqiang He, Xinyuan Wang, Jin Xu, Sirui Shen, Hui Chen, Yuxiang Fu, Li Li · 发表于：IEEE Transactions on Circuits & Systems II Express Briefs · 年份：2021 · DOI：10.1109/tcsii.2021.3098633 · 被引用次数：28 · 研究领域：Advanced Memory and Neural Computing、Neural dynamics and brain function、Ferroelectric and Negative Capacitance Devices

Spiking Neural Networks (SNNs) is promising to enable low power and high performance edge computing hardware design and have recently attracted attentions of researchers. Compared to Artificial Neural Networks (ANNs), SNNs, which present more realistic brain-inspired computing models, are developed as an alternative to ANNs. However, the temporal primitive of SNNs causes irregular and repeated data accesses, leading to high latency and extra power consumption. In this work, we propose an efficient architecture for SNNs by exploiting event-based characteristics. A reconfigurable spiking neuron processing unit is proposed to support a variety of spike-layers. Furthermore, to reduce the cycles needed per frame, an efficient dataflow with fast-filtering mechanism is introduced to leverage the sparsity of discrete spikes. The results show that this design achieves 67.5$\mu \text{J}$/image prediction energy with a throughput of 2.2K FPS. The core size is 0.89 mm2under 28-nm technology, with 90.98% computing hardware utilization and a competitive accuracy 97.10% on a driving dataset.