Power adaptive computing system design in energy harvesting environment

Energy harvesting systems provide a promising alternative to battery-powered systems and create an opportunity for architecture and design method innovation for the exploitation of ambient energy source. In this paper, we propose a two-stage optimization approach to develop power adaptive computing systems which can efficiently use energy harvested from solar source. At design time, an SPMD (single process, multiple data) computation structure with multiple parallel processing units is generated, and a convex optimizer runs at run-time to decide how many processing units can operate simultaneously subject to the instant power supplied from the harvester. The approach is evaluated on three embedded applications. The results show that the proposed approach can develop and manage a computing system for each application to adjust its power consumption with respect to the power supply while maximizing speed. Compared to static systems without adaptability, our power adaptive computing system improves the harvested energy utilization efficiency up to 28.8%. These computation systems can be applied to distributed monitor networks to improve computation capability at nodes. In our experiments, the throughput per watt in a node with a ARM9 processor can be improved 19 times by adding the developed adaptive computing system to the node.