An Event-Based Time-Incremented SNN Architecture Supporting Energy-Efficient Device Classification

Recent advances in Radio Frequency (RF)-based device classification have shown promise in enabling secure and efficient wireless communications. However, the energy efficiency and low-latency processing capabilities of neuromorphic computing have yet to be fully leveraged in this domain. This paper is a first step toward enabling an end-to-end neuromorphic system for RF device classification, specifically supporting development of a neuromorphic classifier that enforces temporal causality without requiring non-neuromorphic classifier pre-training. This Spiking Neural Network (SNN) classifier streamlines the development of an end-to-end neuromorphic device classification system, further expanding the energy efficiency gains of neuromorphic processing to the realm of RF fingerprinting. Using experimentally collected WirelessHART transmissions, the TI-SNN achieves classification accuracy above 90% while reducing fingerprint density by nearly seven-fold and spike activity by over an order of magnitude compared to a baseline Rate-Encoded SNN (RE-SNN). These reductions translate to significant potential energy savings while maintaining competitive accuracy relative to Random Forest and CNN baselines. The results position the TI-SNN as a step toward a fully neuromorphic “RF Event Radio” capable of low-latency, energy-efficient device discrimination at the edge.