No abstract

The mapping of tasks to processing elements of an MPSoC has critical impact on system performance and energy consumption. To cope with complex dynamic behavior of applications, it is common to perform task mapping during runtime so that the utilization of processors and interconnect can be taken into account when deciding the allocation of each task. This paper has two major contributions, one of them targeting the general problem of evaluating dynamic mapping heuristics in NoC-based MPSoCs, and another focusing on the specific problem of finding a task mapping that optimizes energy consumption in those architectures.

Task mapping defines the best placement of a given task in the MPSoC, according to some criteria, as energy or Manhattan distance minimization. The ITRS roadmap forecast in a near future MPSoCs with hundreds of processing elements (PEs). Therefore, dynamic mapping heuristics are required. An important gap is observed in the mapping literature: the lack of proposals targeting multi-task dynamic mapping. In this context, the present work proposes an energy-aware dynamic task mapping heuristic, allowing multiple tasks allocation per PE. Experimental results are executed in an actual MPSoC running distributed applications. Comparing a single-task to the multi-task mapping, the energy spent in the NoC is reduced in average by 51% (best case: 72%), with an average execution time overhead of 18%. Besides the communication energy reduction, the multi-task mapping enables a greater number of applications executing simultaneously, or smaller MPSoCs, which reduces the system cost.

No abstract

Improving embedded systems lifetime and reliability become a major concern for the semiconductor industry. Imbalanced mapping of applications may considerably impact on system lifetime since processors and NoC links located in hotspot zones may age faster than others, compromising the overall system performance. This work proposes a dynamic mapping heuristic that makes a trade-off between processors' load and NoC communication volume, aiming to increase system reliability. Results show the proposed heuristic provides a well-balanced workload distribution while reducing communication volume. Results showed that proposed mapping reduces application execution time (average 10%) and hotspots zones when compared to conventional mapping approaches.