Battery-aware design exploration of scheduling policies for multi-sensor devices

Lifetime maximization is a key challenge in battery-powered multi-sensor devices. Battery-aware power management strategies combine task scheduling with dynamic voltage scaling (DVS), accounting for the fact that the power drawn by the device is different from that provided by the battery due to its many non-idealities. However, state-of-the-art techniques in this field do not take into account several important aspects, such as the impact of sensing tasks on the overall power demand, the (operating point dependent) losses due to multiple DC-DC conversions, and the dynamic modifications in battery efficiency caused by different distributions of the currents in the temporal and in the frequency domains. In this work, we propose a novel approach to identify optimal power management solutions, that addresses all these limitations. Specifically, using advanced battery and DC-DC converter models, we propose methods to explore the scheduling space both statically (at design time) and dynamically (at run-time), accounting not only for computation tasks, but also for communication and sensing. With this method, we show that the battery lifetime can be increased by as much as 23.36% if an optimal power management strategy is adopted.