Executing multiple applications on a single MPSoC brings the major challenge of satisfying multiple quality requirements regarding real-time, energy, etc. Hybrid application mapping denotes the combination of design-time analysis with run-time application mapping. In this article, we present such a methodology, which comprises a design space exploration coupled with a formal performance analysis. This results in several resource reservation configurations, optimized for multiple objectives, with verified real-time guarantees for each individual application. The Pareto-optimal configurations are handed over to run-time management which searches for a suitable mapping according to this information. To provide any real-time guarantees, the performance analysis needs to be composable and the influence of the applications on each other has to be bounded. We achieve this either by spatial or a novel temporal isolation for tasks and by exploiting composable NoCs. With the proposed temporal isolation, tasks of different applications can be mapped to the same resource while with spatial isolation, one computing resource can be exclusively used by only one application. The experiments reveal that the success rate in finding feasible application mappings can be increased by the proposed temporal isolation by up to 30% and energy consumption can be reduced compared to spatial isolation.
This paper presents a novel application-driven and resourceaware mapping methodology for tree-structured streaming applications onto NoCs. This includes strategies for mapping the source of streaming applications (seed point selection), as well as embedding strategies so that each process autonomously embeds its own succeeding tasks. The proposed embedding strategies only consider the local view of neighboring cells on the NoC which allows to significantly reduce computation and monitoring overhead. Our vision is that this approach facilitates self-organizing embedded systems that provide the flexibility and fault-tolerance required in future silicon technologies. The results provided in this paper show that our local and decentralized algorithms can compete with previously presented global and centralized algorithms.
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