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

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dc.contributor.authorSmith, Michael
dc.contributor.authorTemple, Michael
dc.contributor.authorDean, James
dc.date.accessioned2024-12-26T21:11:00Z
dc.date.available2024-12-26T21:11:00Z
dc.date.issued2025-01-07
dc.description.abstractThis 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.848
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other33dc2062-6067-4625-9eb0-9690a94bc7f6
dc.identifier.urihttps://hdl.handle.net/10125/109699
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCyber Operations, Defense, and Forensics
dc.subjectedge processing, neuromorphic processing, rf fingerprinting, spiking neural network, wirelesshart
dc.titleDevelopment of a Neuromorphic-Friendly Spiking Neural Network for RF Event-based Classification
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage7090

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