FPGA-based fast bin-ratio spiking ensemble network for radioisotope identification

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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

In this work, we demonstrate the training, conversion, and implementation flow of an FPGA-based bin-ratio ensemble spiking neural network applied for radioisotope identification. The combination of techniques including learned step quantisation (LSQ) and pruning facilitated the implementation by compressing the network’s parameters down to 30% yet retaining the accuracy of 97.04% with an accuracy loss of less than 1%. Meanwhile, the proposed ensemble network of 20 3-layer spiking neural networks (SNNs), which incorporates 1160 spiking neurons, only needs 334 for a single inference with the given clock frequency of 100 MHz. Under such optimisation, this FPGA implementation in an Artix-7 board consumes 157 per inference by estimation.
Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalNeural Networks
Volume176
Early online date24 Apr 2024
DOIs
Publication statusE-pub ahead of print - 24 Apr 2024

Keywords / Materials (for Non-textual outputs)

  • spiking neural networks
  • Bin-ratio ensemble networks
  • Field programmable gate array (FPGA)
  • Radioisotope identification

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