Optimal application mapping to 2D-mesh NoCs by using a tabu-based particle swarm methodology

A hybrid optimization scheme is presented in this paper that combines Tabu-search, Force Directed Swapping and Discrete Particle Swarm Optimization for Network-on-Chip (NoC) mapping problem. The main goal of the optimization is to map an application core graph such that the overall communication latency and energy consumption of the NoC are minimal. Discrete Particle Swarm Optimization is used as the main optimization scheme where each particle move is influenced by a force derived from the network traffic matrix. We also employ a Tabu-list to discourage swarm particles to re-visit the explored search space. This is done through particle reflection which proposes an alternative route towards the intended move direction. The methodology is tested for some multimedia application core graphs as well as randomly generated large network of synthetic cores. It was found that on average, this hybrid algorithm required less number of iterations to reach an optimal solution as compared to other existing and past algorithms without losing the quality of NoC mapping.