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An 800 nW Switched-Capacitor Feature Extraction Filterbank for Sound Classification.
- Source :
-
IEEE Transactions on Circuits & Systems. Part I: Regular Papers . Apr2021, Vol. 68 Issue 4, p1578-1588. 11p. - Publication Year :
- 2021
-
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\times $ and $10\times $ , respectively. The 1.44 mm2 chip consumes 800 nW, which corresponds to the lowest normalized power per simultaneously sampled channel in recent literature. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 68
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 149121955
- Full Text :
- https://doi.org/10.1109/TCSI.2020.3047035