"Development of a Neuromorphic-Friendly Spiking Neural Network for RF E" by Michael Smith, Michael A. Temple et al. https://hdl.handle.net/10125/109699">
 

Development of a Neuromorphic-Friendly Spiking Neural Network for RF Event-based Classification

Document Type

Conference Proceeding

Publication Date

1-7-2025

Abstract

This paper provides details for the most recent step taken in RndF-to-CNN-to-SNN classifier transition activity supporting an envisioned RF “event radio” concept. Successful results here include the transition from CNNs to neuromorphic-friendly CNN-derived SNNs and pique sufficient interest for pursuing next-step hardware demonstrations. Consistent with earlier RndF and CNN works that used the same experimentally collected WirelessHART signals, SNN results here show that two-dimensional event-based fingerprinting is best overall using events detected in burst Gabor transform responses. The approximate %CΔ≈−2% decrease in average percent correct classification performance resulting from RF eventization encoding is effectively offset by a complementary %CΔ≈+2% to +3% increase that occurs with the CNN-to-SNN transition. This level of neuromorphic-friendly SNN performance is promising when considering the potential 10X-100X energy efficiencies that remain to be demonstrated.

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This is an Open Access conference paper distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. CC BY-NC-ND 4.0

Presented at HICSS 2025 as part of the Minitrack on Cyber Operations, Defense, and Forensics

Source Publication

58th Hawaii International Conference on System Sciences, HICSS 2025

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