A 23 Μw Keyword Spotting IC with Ring-Oscillator-Based Time-Domain Feature Extraction
IEEE Journal of Solid-State Circuits(2022)
摘要
This article presents the first keyword spotting (KWS) IC that uses a ring-oscillator-based time-domain processing technique for its analog feature extractor (FEx). Its extensive usage of time-encoding schemes allows the analog audio signal to be processed in a fully time-domain manner except for the voltage-to-time conversion stage of the analog front end. Benefiting from fundamental building blocks based on digital logic gates, it offers better technology scalability compared to conventional voltage-domain designs. Fabricated in a 65-nm CMOS process, the prototyped KWS IC occupies 2.03 mm 2 and dissipates 23- $\mu \text{W}$ power consumption, including analog FEx and digital neural network classifier. The 16-channel time-domain FEx achieves a 54.89-dB dynamic range for 16-ms frame shift size while consuming 9.3 $\mu \text{W}$ . The measurement result verifies that the proposed IC performs a 12-class KWS task on the Google Speech Command dataset (GSCD) with >86% accuracy and 12.4-ms latency.
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关键词
Analog,bandpass filter (BPF),classifier,feature extractor (FEx),Google Speech Command dataset (GSCD),keyword spotting (KWS),rectifier,recurrent neural network (RNN),ring oscillator,time domain
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