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arXiv · 2102.12565

An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

Xiaoyu Huang·Edward Jones·Siru Zhang·Shouyu Xie·Steve Furber·Yannis Goulermas·Edward Marsden·Ian Baistow·Srinjoy Mitra·Alister Hamilton
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Abstract

This paper details the FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high-resolution data. Power consumption of 75 mW has been achieved on an FPGA implementation of a CSNN, with an 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.

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Xiaoyu Huang, Edward Jones, Siru Zhang, Shouyu Xie, Steve Furber, Yannis Goulermas, Edward Marsden, Ian Baistow, Srinjoy Mitra, Alister Hamilton. 2021-02-24. An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification. https://arxiv.org/abs/2102.12565

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