Energy-efficient mapping of real-time streaming applications on cluster heterogeneous MPSoCs

In this paper, we propose a novel polynomial time algorithm, called Frequency Driven Mapping, to map real-time streaming applications specified as cyclo-static dataflow (CSDF) graphs onto a cluster heterogeneous MPSoC. The objective of our mapping approach is to reduce the energy consumption and guarantee latency and throughput constraints. The main novelty in our mapping algorithm is twofold: (1) By using hard-realtime scheduling of CSDF graphs, we propose an efficient way to determine a suitable processor type for each task in a CSDF graph, where the energy consumption is minimized and throughput and latency constraints are met; (2) According to an initial mapping derived by a first-fit-decreasing heuristic, we propose a remapping approach, where some tasks are remapped to unused clusters in order to further reduce the energy consumption of the system by cluster dynamic voltage/frequency scaling (DVFS). The experimental results show that the proposed algorithm finds more energy efficient mapping compared to existing approaches. The energy savings due to our proposed algorithm are up to 34%.