Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
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 %��Δ≈−2% decrease in average percent correct classification performance resulting from RF eventization encoding is effectively offset by a complementary %��Δ≈+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.
Recommended Citation
Smith, Michael; Temple, Michael; and Dean, James, "Development of a Neuromorphic-Friendly Spiking Neural Network for RF Event-based Classification" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/st/digital_forensics/2
Development of a Neuromorphic-Friendly Spiking Neural Network for RF Event-based Classification
Hilton Waikoloa Village, Hawaii
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 %��Δ≈−2% decrease in average percent correct classification performance resulting from RF eventization encoding is effectively offset by a complementary %��Δ≈+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.
https://aisel.aisnet.org/hicss-58/st/digital_forensics/2