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An 800 nW Switched-Capacitor Feature Extraction Filterbank for Sound Classification | IEEE Journals & Magazine | IEEE Xplore

An 800 nW Switched-Capacitor Feature Extraction Filterbank for Sound Classification


Abstract:

This paper presents a 32-channel analog filterbank for front-end signal processing in sound classification systems. It employs a passive N-path switched capacitor topolog...Show More

Abstract:

This paper presents a 32-channel analog filterbank for front-end signal processing in sound classification systems. It employs a passive N-path switched capacitor topology to achieve high power efficiency and reconfigurability. The circuit's unwanted harmonic mixing products are absorbed by the machine learning model during training. To enable a systematic pre-silicon study of this effect, we develop a computationally efficient circuit model that can process large machine learning datasets on practical time scales. Measured results using a 130 nm CMOS prototype IC indicate competitive classification accuracy on datasets for baby cry detection (93.7% AUC) and voice commands (92.4% average precision), while lowering the feature extraction energy compared to digital realizations by approximately 2× and 10×, respectively. The 1.44 mm2 chip consumes 800 nW, which corresponds to the lowest normalized power per simultaneously sampled channel in recent literature.
Page(s): 1578 - 1588
Date of Publication: 12 January 2021

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