High-Accuracy and Energy-Efficient Acoustic Inference using Hardware-Aware Training and a 0.34nW/Ch Full-Wave Rectifier

Abstract

A full-wave rectifier (FWR) is a necessary component of many analog acoustic feature extractor (FEx) designs targeted at edge audio applications. However, analog circuits that perform close-to-ideal rectification contribute a significant portion of the total power of the FEx. This work presents an energy-efficient FWR design by using a dynamic comparator and scaling the comparator clock frequency with its input signal bandwidth. Simulated in a 65nm CMOS process, the rectifier circuit consumes 0.34nW per channel for a 0.6V supply. Although the FWR does not perform ideal rectification, an acoustic FEx behavioral model in Python is proposed based on our FWR design, and a neural network trained with the output of the proposed behavioral model recovers high classification accuracy in an audio keyword spotting (KWS) task. The behavioral model also included comparator noise and offset extracted from transistor-level simulation. The whole KWS chain using our behavioral model achieves 89.45% accuracy for 12-class KWS on the Google Speech Commands Dataset.