Ultralow Power Feature Extractor Using Switched-Capacitor-Based Bandpass Filter, Max Operator, and Neural Network Processor for Keyword Spotting

This letter presents an ultralow power keyword spotting (KWS) system using a feature extractor comprising a bandpass filter, a max operator, and a time-delay neural network (TDNN) processor based on a switched capacitor technique. In the proposed KWS system, TDNN is used as a classifier. The first layer of the classifier requires an accurate calculation, whereas the rest of the layers can be binarized. Thus, in this study, feature extraction and the first layer of TDNN are performed in the analog domain, while the remainder is processed in the digital domain. The proposed architecture can remove high-precision ADC, which achieves ultralow power KWS. The proposed feature extractor is fabricated in the 65-nm CMOS process. The measurement results show that the proposed KWS system consumes 270 nW to detect two keywords.