Abstract
This paper presents an energy-adaptive performance management technique for the design of embedded signal processing systems powered by renewable energy sources. By jointly considering the non-deterministic characteristics of renewable energy and the unique relationship between signal processing performance and the required energy consumption, a progressive performance tuning approach is developed to dynamically determine an acceptable signal processing performance in accordance with the changing energy level at runtime. Several practical issues such as energy prediction errors and battery capacity are investigated, and their impacts on the proposed technique are evaluated. The proposed technique is applied to a DCT-based image sensing system. Simulation results demonstrate that by adaptively tuning signal processing kernels with renewable energy, significant improvements in time coverage and energy efficiency can be achieved in the presence of unstable harvested energy.
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