Energy and Task-Aware Partitioning on Single-ISA Clustered Heterogeneous Processors

Heterogeneous multi-core processing is increasingly adopted in embedded systems. Heterogeneous platforms can provide energy consumption reduction by employing longstanding techniques like Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM). An effective energy-management strategy simultaneously exploits hardware-and software-level energy-reduction techniques. Energy-efficient partitioning is one software-level method where task allocation to heterogeneous clusters directly impacts the total system energy. In this paper, we couple the problem of energy-efficient partitioning on single-ISA heterogeneous platforms with task-aware scheduling. Tasks differ in their instruction mix, cache, memory and I/O access, execution path, and active processing and SoC circuitry. This affects their power demand. We make further use of underlying hardware frequency scaling to reduce the system energy. We propose four variants of our Task and Cluster Heterogeneity Aware Partitioning (TCHAP) targeting ARM big.LITTLE platforms, and show that our algorithms achieve up to 30 percent energy-reduction on average compared to a state-of-the-art scheme.