An Area-Efficient Ultra-Low-Power Time-Domain Feature Extractor for Edge Keyword Spotting
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Date
2023
Publication Type
Conference Paper
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yes
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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 65nm process technology and a back-end neural network classifier, this simulated feature extractor has an area of 0.02mm2 and achieves 3.3 mu W @ 1.2V, and achieves 92.5% accuracy on a 10-keyword, 12-class KWS task using the GSCD dataset.
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published
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IEEE ISCAS 2023 Symposium Proceedings
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Publisher
IEEE
Event
IEEE International Symposium on Circuits and Systems (ISCAS 2023)
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Software
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Subject
Keyword spotting (KWS); infinite impulse response (IIR); hardware acceleration; long short-term memory
Organisational unit
02533 - Institut für Neuroinformatik / Institute of Neuroinformatics