Spiking Neural Network Based Low-Power Radioisotope Identification using FPGA

TL;DR

This paper introduces a low-power FPGA implementation of a Spiking Neural Network for radioisotope identification, achieving high accuracy at various distances with detailed design and validation methods.

Contribution

It presents a novel low-power FPGA-based SNN design optimized for radioisotope identification with comprehensive verification and validation procedures.

Findings

Power consumption of 72 mW achieved

100% inference accuracy at 10 cm

97% accuracy at 25 cm

Abstract

this paper presents a detailed methodology of a Spiking Neural Network (SNN) based low-power design for radioisotope identification. A low power cost of 72 mW has been achieved on FPGA with the inference accuracy of 100% at 10 cm test distance and 97% at 25 cm. 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.