An integrated optimization framework for reducing the energy consumption of embedded real-time applications

This paper presents a framework for the purpose of energy optimization of embedded real-time systems. We implemented the presented framework as an optimization toolchain and an energy-aware real-time operating system. Our framework is synthetic, that is, multiple techniques optimize the target application together. The main idea of our approach is to utilize a trade-off between energy and performance of the processor configuration. The optimal processor configuration is selected at each appropriate point in the task. Additionally, an optimization technique about the memory allocation is employed in our framework. Our framework is also gradual, that is, the target application is optimized in a step-by-step manner. The characteristic and the behavior of target applications are analyzed and optimized for both intra-task and inter-task levels by our toolchain at the static time. Based on the results of static time optimization, the runtime energy optimization is performed by a real-time operating system according to the behavior of the application. A case study shows that energy minimization is achieved on average while keeping the real-time performance.