An Adaptive Discrete Particle Swarm Optimization for Mapping Real-Time Applications onto Network-on-a-Chip based MPSoCs

This paper presents a modified version of the well-known Particle Swarm Optimization (PSO) algorithm as an alternative for the single-objective Genetic Algorithm (GA) that is currently the state-of-the-art method to map real-time applications tasks onto Multiple Processors System-on-a-Chip (MPSoC) using preemptive capable wormhole-based Network-on-a-Chip (NoC) as their communication architecture. A statistical study based on an experimental setup has been performed to compare the GA-based task mapper and the proposed method by using a real-time application as a benchmark, as well as a group of randomly generated ones. Preliminary results have shown that our method is capable of achieving quicker convergence than the GA-based method, and it even produces better results when the application utilization is smaller than the available processing capacity, i.e., a fully schedulable mapping solution exists.