An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

作者：Xiaoyu Huang, Edward Jones, Siru Zhang, Shouyu Xie, Steve Furber, Yannis Goulermas, Edward Marsden, Ian Baistow, Srinjoy Mitra, Alister Hamilton · 年份：2021 · DOI：10.1109/iscas51556.2021.9401412 · 被引用次数：9 · 研究领域：Advanced Memory and Neural Computing、CCD and CMOS Imaging Sensors、Neuroscience and Neural Engineering

This paper details FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high resolution data. A power consumption of 75 mW has been achieved on an FPGA implementation of a CSNN, with the inference accuracy of 90.62% on a synthetic dataset. The chip validation method is presented. Prototyping was accelerated by evaluating SNN parameters using SpiNNaker neuromorphic platform.