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.
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