Using evolutionary metaheuristics to solve the mapping and routing problem in networks on chip

Task mapping and routing are crucial steps in the Networks on Chip (NoC) based Multiprocessor System on Chip (MPSoC) design. While the mapping must ensure an optimized arrangement of the applications’ tasks on the system cores, the routing must ensure the tasks’ communication with the minimum possible delay. We observe that these two problems are highly dependent since finding a routing solution requires first finding a mapping solution. Based on that, this paper analyzes the mapping and routing problems in NoC-based MPSoC and defines a joint version as the Mapping and Routing Problem (MRP). We propose a mathematical model that generates mapping and routing solutions based on a specific bandwidth of NoC links. We also propose three evolutionary metaheuristic algorithms to find optimized solutions to the MRP: Genetic (GA), Memetic (MA), and Transgenetic Algorithms (TA). Experimental results evaluating communication latency demonstrate that the proposed algorithms suit well for the tackled problem, but the TA stands out among all the compared solutions. Overall, TA achieved up to 8% and 19% better performance than the compared algorithms in Global Average Delay and Maximum Delay. Also, it outperformed the other strategies in 55.76% and 51.58% of all the performed simulations in both respective metrics.