A Routing based Genetic Algorithm for Task Mapping on MPSoC

  • Hiago Rocha UFRGS
  • Antonio Carlos Beck UFRGS
  • Sílvia Maia UFRN
  • Márcio Kreutz UFRN
  • Monica Pereira UFRN


This work proposes an optimized task mapping solution called Routing Model-based Genetic Algorithm (RMGA) that combines task mapping and routing problems using an Integer Linear Programming (ILP) model as a fitness function. We compared our proposed RMGA with other Genetic Algorithms (GA) that address the mapping problem using a classical flow×distance fitness function evaluation. Experimental results evaluating communication latency demonstrate that the proposed algorithm outperforms two GAs from literature. It presents up to 30% lower delay when simulating tasks communication pattern of six different applications and varying packet injection rate and NoC dimension. Additionally, RMGA presents the lowest delay of more than 70% in all simulated scenarios compared to the other GAs.

Palavras-chave: Mapping Problem, Routing Problem, Exact Fitness Evaluation, ILP Routing Model


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ROCHA, Hiago; BECK, Antonio Carlos; MAIA, Sílvia; KREUTZ, Márcio; PEREIRA, Monica. A Routing based Genetic Algorithm for Task Mapping on MPSoC. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 10. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 103-110. ISSN 2237-5430.