A 23-mu W Keyword Spotting IC With Ring-Oscillator-Based Time-Domain Feature Extraction
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Date
2022
Publication Type
Journal Article
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yes
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Abstract
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-SW 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 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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published
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Journal / series
Volume
57 (11)
Pages / Article No.
3298 - 3311
Publisher
IEEE
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Edition / version
Methods
Software
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Date collected
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Subject
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
Organisational unit
08836 - Delbrück, Tobias (Tit.-Prof.)
02533 - Institut für Neuroinformatik / Institute of Neuroinformatics