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

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This paper addresses energy-aware application mapping for large-scale Network-on-chip (NoC). The increasing number of intellectual property (IP) cores in multi-processor system-on-chips (MPSoCs) makes NoC application mapping more challenging to find optimum core-to-topology mapping. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA) to minimize the energy consumption of NoC communication. The GA is initialized with knowledge on network partition whereas the genetic crossover operator is guided with inter-core communication demands. NoC energy estimation is based on analytical energy model and cycle-accurate Noxim simulation. For large-scale NoC, application mapping using knowledge-based genetic operator saves up to 28% energy compared to the one on conventional GA. Adding knowledge-based initial mapping speeds up convergence by 81% and further saves energy by 5% compared to only knowledge-based crossover GA. Furthermore, cycle-accurate simulations of applications with traffic dependency show the effectiveness of the proposed application mapping for large-scale NoC.