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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...
Voice-activity-detectors (VADs) are an efficient way to reduce unimportant audio data and are therefore a crucial step towards energy-efficient ubiquitous sensor networks. Current VADs, however, use computationally expensive feature extraction and model building algorithms with too high power requirements to be integrated in low-power sensor nodes. To drastically reduce the VAD power consumption,...
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