Application of an Adaptive Genetic Algorithm for Task Mapping Optimisation on a Wormhole-based Real-time Network-on-Chip

  • Jesse de Barros UnB
  • Mauricio Ayala-Rincón UnB
  • Carlos Quintero UnB


The task mapping problem of real-time applications onto a homogeneous multiple processors system-on-a-chip (MPSoC) is an NP-hard problem that can be addressed using search-based heuristic methods. A particular case is the priority preemptive wormhole-based Network-on-a-Chip (NoC) communication architecture that takes into consideration the end-to-end schedulability analysis. In this paper, we present a parameter Adaptive Genetic Algorithm (AGA) capable of efficiently achieving schedulable task placements. In our experiments, we perform analytic methods in different scenarios with multiple synthetically generated real-time applications, as well as various platforms with a ranging number of processors to provide a statistical study comparing our algorithm against a standard genetic algorithm approach.

Palavras-chave: Multiprocessor/Multicore/Manycore Systems, Performance Evaluation and Optimization


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DE BARROS, Jesse; AYALA-RINCÓN, Mauricio; QUINTERO, Carlos. Application of an Adaptive Genetic Algorithm for Task Mapping Optimisation on a Wormhole-based Real-time Network-on-Chip. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 9. , 2019, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 17-24. ISSN 2237-5430.