# An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

arXiv: https://arxiv.org/abs/2102.12565 · HTML: https://mlsanity.com/paper/2102.12565/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 2 of 2)

- [Spiking Approximations of the MaxPooling Operation in Deep SNNs](https://mlsanity.com/paper/2205.07076/) (2022)
- [FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack](https://mlsanity.com/paper/2106.06895/) (2021)

## Abstract (arXiv)

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.