This paper presents a framework for reducing the energy consumption of embedded real-time systems. We implemented the presented framework as both an optimization toolchain and an energy-aware real-time operating system. The framework consists of the integration of multiple techniques to optimize the energy consumption. The main idea behind our approach is to utilize trade-offs between the energy consumption and the performance of different processor configurations during task checkpoints, and to maintain memory allocation during task context switches. In our framework, a target application is statically analyzed at both intra-task and inter-task levels. Based on these analyzed results, runtime optimization is performed in response to the behavior of the application. A case study shows that our toolchain and real-time operating systems have achieved energy reduction while satisfying the real-time performance. The toolchain has also been successfully applied to a practical application.

Energy minimization has become one of the primary goals in the embedded real-time domains. Consequently, scratch-pad memory has been employed as partial or entire replacement for cache memory due to its better energy efficiency. However, most previous approaches were not applicable to a preemptive multi-task environment. We propose three methods of partitioning and allocation of scratch-pad memory for fixed-priority-based preemptive multi-task systems. The three methods, i.e., spatial, temporal, and hybrid methods, achieve energy reduction in the instruction memory subsystems. With the spatial method, each task occupies its exclusive space in scratch-pad memory. With the temporal method, the running task uses entire scratch-pad space. The content of scratch-pad memory is swapped out as a task executes or gets preempted. The hybrid method is based on the spatial one but a higher priority task can temporarily use the space of lower priority task. The amount of space is prioritized for higher priority tasks. We formulate each method as an integer programming problem that simultaneously determines (1) partitioning of scratch-pad memory space for the tasks, and (2) allocation of program code to scratch-pad memory space for each task. Our methods not only support the real-time task scheduling but also consider aggressively the periods and priorities of tasks for the energy minimization. Additionally, we implement an RTOS-hardware cooperative support mechanism for runtime code allocation to the scratch-pad memory space. We have made the experiments with the fully functional real-time operating system. The experimental results have demonstrated the effectiveness of our techniques. Up to 73% energy reduction compared to a conventional method was achieved.

We propose a synthesis method of nested loops into parallelized circuits by integrating the polyhedral optimization, which is a state-of-the-art technique in the field of software, into high-level synthesis. Our method constructs circuits equipped with multiple processing elements (PEs), using information generated by the polyhedral optimizing compiler. Since multiple PEs cannot concurrently access the off-chip RAM, a method for constructing on-chip buffers is also proposed. Our buffering method reduces the off-chip RAM access conflicts and further enables burst accesses and data reuses. In our experimental result, the buffered circuits generated by our method are 8.2 times on average and 26.5 times at maximum faster than the sequential non-buffered ones, when each of the parallelized circuits is configured with eight PEs.