Energy-efficient mapping of real-time applications on heterogeneous MPSoCs using task replication

In this paper, we study the problem of exploiting parallelism in a hard real-time streaming application modeled as a Synchronous Data Flow (SDF) graph and scheduled on a cluster heterogeneous Multi-Processor System-on-Chip (MPSoC) platform such that energy consumption is minimized and a throughput requirement is satisfied. We propose a polynomial-time solution approach which: 1) determines a processor type for each task in an SDF graph such that the throughput constraint is met and energy consumption is minimized; 2) determines a replication factor for each task in an SDF graph such that the distribution of the workload on the same type of processors is balanced, which enables processors to run at a lower frequency, hence reducing the energy consumption. Experiments on a set of real-life streaming applications demonstrate that our approach reduces energy consumption by 66% on average while meeting the same throughput requirement when compared to related energy minimization approaches.