Optimal resource usage in ultra-low-power sensor interfaces through context- and resource-cost-aware machine learning

[摘要] This paper introduces an approach that combines machine learning and adaptive hardware to improve the efficiency of ultra-low-power sensor interfaces. Adaptive feature extraction circuits are assisted by hardware embedded training to dynamically activate only the most relevant features. This selection is done in a context- and power cost-aware manner, through modification of the C4.5 algorithm. As proof-of-principle, a Voice Activity Detector illustrates the context-dependent relevance of features, demonstrating average circuit power savings of 70%, without accuracy loss. The RECAS database developed for experimenting with this context- and dynamic resource-cost-aware training is presented and made open-source for the research community. (C) 2015 Elsevier B.V. All rights reserved.

[发布日期] 2015-12-02

[效力级别]  Proceedings Paper

[关键词] Resource cost-aware classifier;Context-aware machine learning;Low-power sensor interface;Adaptive circuits;Power restricted classifier