This paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC). As the\nnumber of intellectual property (IP) cores inmultiprocessor system-on-chip (MPSoC) increases, NoC application mapping to find\noptimum core-to-topology mapping becomes more challenging. Besides, the conflicting cost and performance trade-off makes\nmultiobjective application mapping techniques even more complex. This paper proposes an application mapping technique that\nincorporates domain knowledge into genetic algorithm (GA). The initial population of GA is initialized with network partitioning\n(NP) while the crossover operator is guided with knowledge on communication demands. NP reduces the large-scale application\nmapping complexity and providesGAwith a potential mapping search space.The proposed genetic operator is comparedwith stateof-\nthe-art genetic operators in terms of solution quality. In this work, multiobjective optimization of energy and thermal-balance\nis considered.Through simulation, knowledge-based initial mapping shows significant improvement in Pareto front compared to\nrandom initial mapping that is widely used. The proposed knowledge-based crossover also shows better Pareto front compared to\nstate-of-the-art knowledge-based crossover.
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