Network-on-chip heuristic mapping algorithm based on isomorphism elimination for NoC optimisation

摘要

With the development of network-on-chip (NoC) theory, lots of mapping algorithm have been proposed to solve the application mapping problem which is an NP-hard (non-polynomial hard) problem. Most algorithms are based on a heuristic algorithm. They are trapped by iterations limited, not by the distance between iterations, because of the isomorphism of mapping sequence. In this study, the authors define and analyse the isomorphism with the genetic algorithm (GA) which is a heuristic algorithm. Then, they proposed an approach called density direction transform algorithm to eliminate the isomorphism of mapping sequence and accelerate the convergence of population. To verify this approach, they developed a density-direction-based genetic mapping algorithm (DDGMAP) and make a comparison with genetic mapping algorithm (GMA). The experiment demonstrates that compared to the random algorithm, their algorithm (DDGMAP) can achieve on an average 23.48% delay reduction and 7.15% power reduction. And DDGMAP gets better performance than GA in searching the optimal solution.