AAD-KWS A Sub-- W Keyword Spotting Chip With an Acoustic Activity Detector Embed

Abstract

As a widely used speech-triggered interface, deep- learning-based keyword spotting (KWS) chips require both ultra-low power and high detection accuracy. We propose a sub-microwatt KWS chip with an acoustic activity detection (AAD) to achieve the above two requirements, including the following techniques: first, an optimized feature extractor circuit using nonoverlapping-framed serial Mel frequency cepstral coef- ficient (MFCC) to save half of the computations and data storage; second, a zero-cost