To solve computationally expensive problems, multiple processor SoCs (MPSoCs) are frequently used. Mapping of applications to MPSoC architectures and scheduling of tasks are key problems in system level design of embe...To solve computationally expensive problems, multiple processor SoCs (MPSoCs) are frequently used. Mapping of applications to MPSoC architectures and scheduling of tasks are key problems in system level design of embedded systems. In this paper, a cluster slack optimization algorithm is described, in which the tasks in a cluster are simultaneously mapped and scheduled for heterogeneous MPSoC architectures. In our approach, the tasks are iteratively clustered and each cluster is optimized by using the branch and bound technique to capitalize on slack distribution. The proposed static task mapping and scheduling method is applied to pipelined data stream processing as well as for batch processing. In pipelined processing, the tradeoff between throughput and memory cost can be exploited by adjusting a weighting parameter. Furthermore, an energy-aware task mapping and scheduling algorithm based on our cluster slack optimization is developed. Experimental results show improvement in latency, throughput and energy.展开更多
文摘To solve computationally expensive problems, multiple processor SoCs (MPSoCs) are frequently used. Mapping of applications to MPSoC architectures and scheduling of tasks are key problems in system level design of embedded systems. In this paper, a cluster slack optimization algorithm is described, in which the tasks in a cluster are simultaneously mapped and scheduled for heterogeneous MPSoC architectures. In our approach, the tasks are iteratively clustered and each cluster is optimized by using the branch and bound technique to capitalize on slack distribution. The proposed static task mapping and scheduling method is applied to pipelined data stream processing as well as for batch processing. In pipelined processing, the tradeoff between throughput and memory cost can be exploited by adjusting a weighting parameter. Furthermore, an energy-aware task mapping and scheduling algorithm based on our cluster slack optimization is developed. Experimental results show improvement in latency, throughput and energy.
