A novel IP-core mapping algorithm in reliable 3D optical network-on-chips

The Optical Network-on-Chip (ONoC) is considered as a promising way to achieve high performance of multiprocessor systems, and it will be a 3-Dimensional (3D) architecture organized by a certain topology where optical routers are optically interconnected with each other. For the design of 3D ONoCs, the highly reliable IP-core mapping is a key problem of properly assigning IP cores onto optical routers for a given communication task, and it has two main challenges: reliability estimation and mapping scheme. As for reliability estimation, crosstalk noise and thermal sensitivity which severely influence Signal-Noise-Ratio (SNR) should be measured. In addition, although standard genetic algorithms have been widely utilized to solve the optimal mapping solution due to the superiority of simple process, there are some deficiencies such as premature convergence and inferior local searching. In this paper, the impact factors of ONoC reliability are measured by SNR and thermal models, and we also design a novel IP-core mapping algorithm called as CGSA (Cataclysm Genetic-based Simulated Annealing) based on proposed models. In CGSA, we integrate genetic with an improved simulated annealing algorithm assorted with cataclysm strategies, in order to speed up the searching process. Furthermore, to enhance the network reliability, CGSA is bound with the topology selection, i.e., CGSA generates the optimal mapping solution with the best matched 3D ONoC topology. Simulation results show that CGSA is effective on achieving the higher reliability than benchmarks.