Edge Device CNN Classification Using Eventized RF Fingerprints

First-step demonstration activity is presented here for an envisioned "event radio" capability that mimics neuromorphic event-based camera concepts. Radio Frequency (RF) eventization is introduced and its impact on Convolutional Neural Network (CNN) classification is assessed. Classification of eight commercial WirelessHART adapters is performed using sparsely populated event-based fingerprints containing 200-of-896 possible event detections-this is an important first-step toward realizing an envisioned neuromorphic-friendly Spiking Neural Network (SNN) capability supporting edge RF sensing. Superiority of Gabor Transform (GTX) features from collected bursts is evident in CNN classification results that include 1) classification accuracy greater than 90 percent using an average of 200 detected events per burst, versus 300 events per burst required for GTX-Direct eventization, and 2) a non-eventized versus RF eventized classification loss in classification accuracy of 4.11 percent for GTX-Derivative eventization, versus a loss of 5.42 percent in accuracy for GTX-Direct eventization. The CNN performance here motivates next-step event radio research aimed at demonstrating a neuromorphic-friendly SNN RF sensing capability using RF eventized fingerprints. Future demonstration objectives include completing the CNN-to-SNN transition, characterizing SNN classification performance, and performing hardware demonstrations. These objectives support achievement of an envisioned 1000X performance improvement that includes a 10X reduction in required power and 100X improvement in overall processing speed.