Spiking Neural Network based Real-time Radar Gesture Recognition Live Demonstration

This live demo aims at continuously real-time classifying radar gesture signals from the real world with the neuromorphic hardware SpiNNaker 2 prototype to play the game. With the 10 MHz operation frequency on SpiNNaker 2 FPGA, the closed-loop setup realizes around 35 ms delay from PC sending input data to receiving classification output, and there is nearly no feeling of apparent delay when testers are playing the game. The energy cost per frame is 3.29 mu J, and the operation cycle is less than 8 k. Even if our current middleware has not considered balanced work loading among different processing cores, the tightly couple memory usage on the heaviest loaded processing element is less than half of the total 128 kB available memory space based on the directly trained gesture recognition spiking neural network (SNN) model with 2048 input neurons, 5 hidden neurons, and 4 classification outputs.