Ultra low power granular decision making using cross correlation: Optimizing bit resolution for template matching

Advances in technology have led to development of wearable sensing, computing and communication devices that can be woven into the physical environment of our daily lives, enabling a large variety of new applications in several domains including wellness and health care. Despite their tremendous potential to impact our lives, wearable health monitoring systems face a number of hurdles to become a reality. The enabling processors and architectures demand a large amount of energy, requiring sizable batteries. In this paper, we propose a granular decision making architecture that can be viewed as a tiered wake up circuitry. This module, in combination with a low-power microcontroller, enables an ultra low-power architecture. The significant power saving is achieved by performing a preliminary ultra low-power signal processing and hence, keeping the microcontroller off when the incoming signal is not of interest. The preliminary signal processing is performed by a set of special purpose functional units, also called screening blocks, that implements template matching functions. We formulate and solve an optimization problem to select screening blocks such that the accuracy requirements of the signal processing are accommodated while the total power is minimized. Our experimental results on real data from wearable motion sensors show that the proposed algorithm achieves 65.2% energy saving while maintaining 92.7% sensitivity in recognizing human movements.