An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

Huang, Xiaoyu, Jones, Edward, Zhang, Siru, Xie, Shouyu, Furber, Steve, Goulermas, Yannis, Marsden, Edward, Baistow, Ian, Mitra, Srinjoy and Hamilton, Alister (2021) An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification. In: 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021-5-22 - 2021-5-28.

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Official URL: http://dx.doi.org/10.1109/iscas51556.2021.9401412

Abstract

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.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	event-based signal processing, low power, radioisotope identification, convolutional spiking neural networks, FPGA, SpiNNaker
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	23 Dec 2021 15:20
Last Modified:	17 Mar 2024 12:16
DOI:	10.1109/ISCAS51556.2021.9401412
Open Access URL:	https://arxiv.org/ftp/arxiv/papers/2102/2102.12565...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3145973