Ultra-Low Power Gesture Recognition with pMUT Arrays and Spike-based Beamforming
Sensor arrays constrain the power budget of battery-powered smart sensors as the analog front-end, analog-to-digital conversion (ADC), and digital signal processing is duplicated for each channel. By converting and processing the relevant information in the spiking domain, energy consumption can be reduced by several orders of magnitude. We propose the first end-to-end ultra-low power Gesture Recognition system. It comprises an array of emitting and receiving piezoelectric micromachined ultrasonic transducers (pMUT), driving/sensing electronics, and a novel spike-based beamforming strategy that extracts the distance and angle information from incoming echoes without conventional ADCs. A Spiking Recurrent Neural Network performs Gesture Recognition. We experimentally demonstrate a classification accuracy of 86.0% on a dataset of five 3D gestures collected on our experimental setup.