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A case study on minimum energy operation for dynamic time warping signal processing in wearable computers

Miniaturization and form factor reduction in wearable computers leads to enhanced wearability. Power optimization typically translates to form factor reduction, hence of paramount importance. This paper demonstrates power consumption analysis obtained for various operating modes in circuits suitable for wearable computers which are typically equipped with sensors that provide time series data (e.g., acceleration, ECG). Dynamic time warping (DTW) is considered a suitable signal processing technique for wearable computers, particularly due to its lower computational complexity requirement and the robustness to speed variations (acceleration and de-acceleration) in time series data.

Wearable computers usually have very low computational performance requirements, which is explored in this work to minimize the system level energy consumption. We provide a comparison among three modes of operations, namely minimum energy operating point (MEOP), minimum voltage operation point (MVOP) and nominal voltage operating point (NVOP) all leveraging sleep transistors when circuits are inactive. The results show that the MVOP, in conjunction with sleep transistors, provides the least energy budget and leads to a reduction in energy consumption compared to the MEO, which is known as a suitable operating mode for ultra-low power circuits.