Energy Efficient Heuristic Algorithm for Task Mapping on Shared-Memory Heterogeneous MPSoCs

Existing research mostly reduce mapping time and inter-processors communication energy of the multiprocessor system-on-chips (MPSoCs). Unlike other approaches in this paper we have explored energy efficient task mapping on shared-memory heterogeneous MPSoCs considering the energy performance profile of the processors. We propose mitosis heterogeneous-genetic algorithm (MH-GA) for energy aware task mapping on DVFS-enabled processors in order to maximally exploit the inherent heterogeneity in the MPSoC platform while satisfying the application deadline restriction. The proposed heuristic mapping approach has an integrated list scheduler that assigns priority to the tasks with lower deadlines. The experiments are conducted on 4 synthetic and 4 real-world task graphs (TGs) acquired from embedded systems synthesis benchmarks (E3S). The experimental results are compared with the greedy algorithm and our proposed heuristic algorithm achieves maximum energy efficiency of ~53.2% while reduces the average energy consumption ~21.5%.