iHPSA: An improved bio-inspired hybrid optimization algorithm for task mapping in Network on Chip

System on a chip (SoC) is the leading technology in the recent global world of digitization. The classical bus-based regular communication infrastructures of SoCs cannot handle the growing number of cores. Designers came up with new communication methods known as the network on chip (NoC) to solve this problem. NoC offers scalability, flexibility, modularity, and efficiency. As the number of cores of SoC keeps on increasing due to advancement in technology and shrinking size of transistors, the arrangement of cores on the NoC becomes more significant that can influence the Network on Chip’s overall performance and efficiency. Therefore, there is always a need for efficient and intelligent mapping algorithms for achieving optimal mapping solutions since existing mapping techniques either do not achieve optimal results or possess more execution time. To resolve this issue, we proposed an improved hybrid particle swarm and simulated annealing optimization algorithm iHPSA. The proposed iHPSA combines Improved Particle Swarm Optimization and Simulated Annealing for NoC mapping and incorporates a machine-learning K-means clustering algorithm to segregate tasks into clusters based on communication bandwidth. The Elbow method is used in the K-means clustering algorithm to predict the number of clusters in large applications intelligently.