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Title: Context- and cost-aware feature selection in ultra-low-power sensor interfaces
Authors: Lauwereins, Steven * ×
Badami, Komail *
Meert, Wannes
Verhelst, Marian #
Issue Date: 2014
Host Document: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning pages:93-98
Conference: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning edition:22 location:Bruges, Belgium date:23 - 25 April 2014
Abstract: This paper introduces the use of machine learning to improve efficiency of ultra-low-power sensor interfaces. Adaptive feature extraction circuits are assisted by hardware embedded learning to dynamically activate only most relevant features. This selection is done in a context and power cost-aware way, through modification of the C4.5 algorithm. Furthermore, context dependence of different feature sets is explained. As proof-of-principle, a Voice Activity Detector is expanded with the proposed context- and cost-dependent voice/noise classifier, resulting in an average circuit power savings of 75%, with negligible accuracy loss.
ISBN: 978 287 419 095 7
Publication status: published
KU Leuven publication type: IC
Appears in Collections:ESAT - MICAS, Microelectronics and Sensors
Informatics Section
* (joint) first author
× corresponding author
# (joint) last author

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