Energy-adaptive scheduling of imprecise computation tasks for QoS optimization in real-Time MPSoC systems

The key issue of renewable generations such as solar and wind in energy harvesting system is the uncertainty of energy availability. The characteristic of imprecise computation that accepts an approximate result when energy is limited and executes more computations yielding better results if more energy is available, can be exploited to intelligently handle the uncertainty. In this paper, we first propose a task allocation scheme that adaptively assigns real-time imprecise computation tasks to individual processors considering uncertainties in renewable energy sources. The proposed task allocation scheme enhances energy efficiency by minimizing system energy consumption followed by adapting the execution of imprecise computation tasks to the energy availability. We then present a QoS-aware task scheduling scheme that determines the optional execution cycles of tasks allocated to processors. The proposed task scheduling scheme maximizes system QoS under the energy budget constraint.