An Area-Efficient Ultra-Low-Power Time-Domain Feature Extractor for Edge Keyword Spotting

Qinyu Chen, Yaoxing Chang, Kwantae Kim, Chang Gao, Shih Chii Liu*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Downloads (Pure)

Abstract

Keyword spotting (KWS) is an important task on edge low-power audio devices. A typical edge KWS system consists of a front-end feature extractor which outputs mel-scale frequency cepstral coefficients (MFCC) features followed by a back-end neural network classifier. KWS edge designs aim for the best power-performance-area metrics. This work proposes an area-efficient ultra-low-power time-domain infinite impulse response (IIR) filter-based feature extractor for a KWS system. It uses a serial architecture, and the architecture is further optimized for a low-cost computing structure and mixed-precision bit selection of the IIR coefficients while maintaining good KWS accuracy. Using a 65 nm process technology and a back-end neural network classifier, this simulated feature extractor has an area of 0.02 mm2 and achieves 3.3 μW @ 1.2 V, and achieves 92.5% accuracy on a 10-keyword, 12-class KWS task using the GSCD dataset.

Original languageEnglish
Title of host publicationISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781665451093
DOIs
Publication statusPublished - 2023
Event56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States
Duration: 21 May 202325 May 2023

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2023-May
ISSN (Print)0271-4310

Conference

Conference56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
Country/TerritoryUnited States
CityMonterey
Period21/05/2325/05/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • hardware acceleration
  • infinite impulse response (IIR)
  • Keyword spotting (KWS)
  • long short-term memory

Fingerprint

Dive into the research topics of 'An Area-Efficient Ultra-Low-Power Time-Domain Feature Extractor for Edge Keyword Spotting'. Together they form a unique fingerprint.

Cite this