European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning pages:93-98
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning edition:22 location:Bruges, Belgium date:23 - 25 April 2014
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.