Development of a Neuromorphic-Friendly Spiking Neural Network for RF Event-based Classification
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2025-01-07
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7090
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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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Cyber Operations, Defense, and Forensics, edge processing, neuromorphic processing, rf fingerprinting, spiking neural network, wirelesshart
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10
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Proceedings of the 58th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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