An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

Mike Huang, Edward Jones, Siru Zhang, Shouyu Xie, Steve Furber, Yannis Goulermas, Edward Marsden, Ian Baistow, Srinjoy Mitra, Alister Hamilton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper presents a detailed FPGA implementation methodology of Convolutional Spiking Neural Network based low-power and high-resolution radioisotope identification. A power budget of 74 mW has been achieved on an FPGA with the inference accuracy of 90.62% at a synthetic dataset. The design verification and chip validation methods are presented. It also discusses SNN simulation on SpiNNaker for rapid prototyping and various considerations specific to the application such as test distance, integration time, and SNN hyperparameter selections.
Original languageEnglish
Title of host publication2021 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
ISBN (Print)978-1-7281-9201-7
DOIs
Publication statusPublished - 27 Apr 2021

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