No abstract

Mapping parallel applications onto a network on chip (NoC) that is based on heterogeneous MPSoCs is considered as an instance of an NP-hard and a multi-objective problem. Various multi-objective algorithms have been proposed in the literature to handle this issue. Metaheuristics stand out as highly appropriate approaches to deal with this kind of problem. These metaheuristics are classified into two sets: population-based metaheuristics and single solution-based ones. To take advantage of the both sets, the trend is to use hybrid solutions that have shown to give better results. In this article, the authors propose to hybridize these two metaheuristics sets to find good Pareto mapping solutions to optimize the execution time and the energy consumption simultaneously. The experimental results have shown that the proposed hybrid algorithms give high quality non-dominated mapping solutions in a reasonable runtime.

To meet the growing requirements of today's applications, multiprocessor architectures (MPSoCs) interconnected with a network on chip (NoC) are considered as a major solution for future powerful embedded systems. Mapping phase is one of the most critical challenge in designing these systems. It consists of assigning application' tasks on the target platform which can have a considerable influence on the performance of the final system. Due to the large solutions' research space generated by both the application complexity and the platforms, this mapping phase can no longer be done manually and hence it requires powerful exploration tools called DSE (Design Space Exploration Environment). This paper proposes a new tool for static mapping applications on NoC based on heterogeneous MPSoCs. This tool integrates several multiobjective optimization algorithms that can be specified in order to explore different solutions' spaces, mainly: exact method, metaheuristics (population-based metaheuristics and single solution-based ones) as well as hybrid ones; it offers different cost functions (defined using analytical or simulation models). The user can specify them or define others easily and it provides an easy way to evaluate the performance of the Pareto front returned by different algorithms using multiple quality indicators. We also present a series of experiments by considering several scenarios and give guidelines to designers on choosing the appropriate algorithm based on the characteristics of the mapping problem considered.