AAD-KWS: A Sub- μW Keyword Spotting Chip With an Acoustic Activity Detector Embedded in MFCC and a Tunable Detection Window in 28-nm CMOS

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 AAD by using MFCC’s 1st-order output to clock gate neural network (NN) and postprocessing (PP) unit, with 0 miss rate; third, a tunable detection window to adapt to different keyword lengths for better accuracy; and finally, a true form computation method to decrease data transitions and optimized PP. Implemented in a 28-nm CMOS process, this AAD-KWS chip has a 0.4-V supply, an 8-kHz frequency for MFCC, and a 200-kHz frequency for other parts. It consumes 0.36 µW in quiet scenarios when AAD is enabled and 0.8 µW in normal scenarios, where the MFCC circuit consumes only 170 nW. Its accuracy reaches 97.8% for two