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