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Efficient Allocation of Computationally Intensive Application Tasks in NoC-based Heterogeneous MPSoCs
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This work presents a novel approach to optimize the mapping of computationintensive applications onto Network-on-Chip (NoC)-based Multi-ProcessorSystems-on-Chip (MPSoCs). The aim is to minimize execution time and energyconsumption, critical metrics in embedded systems. The mapping process, whichassigns functional units to specific NoC nodes, significantly affects implementationefficiency and power consumption. Given the NP-hard nature of the problem, theBinary Multi-Objective Salp Swarm Algorithm (BMSSA) is employed, leveragingPareto dominance and an archive-based mechanism for non-dominated solutions.Experimental results on benchmark applications highlight the proposed algorithm’ssuperiority in energy efficiency and execution speed compared to state-of-the-artmethods, including NSGA-II, MOPSO, and MOACO, where our solution outperforms these algorithms in most scenarios. This demonstrates the scalability andeffectiveness of the proposed approach for next-generation NoC based MPSoCs.
Title: Efficient Allocation of Computationally Intensive Application Tasks in NoC-based Heterogeneous MPSoCs
Description:
This work presents a novel approach to optimize the mapping of computationintensive applications onto Network-on-Chip (NoC)-based Multi-ProcessorSystems-on-Chip (MPSoCs).
The aim is to minimize execution time and energyconsumption, critical metrics in embedded systems.
The mapping process, whichassigns functional units to specific NoC nodes, significantly affects implementationefficiency and power consumption.
Given the NP-hard nature of the problem, theBinary Multi-Objective Salp Swarm Algorithm (BMSSA) is employed, leveragingPareto dominance and an archive-based mechanism for non-dominated solutions.
Experimental results on benchmark applications highlight the proposed algorithm’ssuperiority in energy efficiency and execution speed compared to state-of-the-artmethods, including NSGA-II, MOPSO, and MOACO, where our solution outperforms these algorithms in most scenarios.
This demonstrates the scalability andeffectiveness of the proposed approach for next-generation NoC based MPSoCs.
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