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

An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

Signal Processing 2021-02-26 v1

Authors: 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.

Keywords

fpga implementation convolutional spiking neural network radioisotope identification low-power spiNNaker inference accuracy

Cite

@article{arxiv.2102.12565,
  title  = {An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification},
  author = {Xiaoyu Huang and Edward Jones and Siru Zhang and Shouyu Xie and Steve Furber and Yannis Goulermas and Edward Marsden and Ian Baistow and Srinjoy Mitra and Alister Hamilton},
  journal= {arXiv preprint arXiv:2102.12565},
  year   = {2021}
}

Comments

5 pages, 10 FIGURES, IEEE ISCAS 2021

An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

Abstract

Keywords

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