Smith, MichaelTemple, MichaelDean, James2024-12-262024-12-262025-01-07978-0-9981331-8-833dc2062-6067-4625-9eb0-9690a94bc7f6https://hdl.handle.net/10125/109699This 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.10Attribution-NonCommercial-NoDerivatives 4.0 InternationalCyber Operations, Defense, and Forensicsedge processing, neuromorphic processing, rf fingerprinting, spiking neural network, wirelesshartConference Paper