Improved Mapping Algorithms Performance in NoCs Design Based on Cellular Learning Automata

NOC technology is a solution to cover the communication challenges of complex systems. The important note in the matter of application mapping for those of NoCs who are based on mesh architecture is their NP-hard problem. Also some methods have been proposed trying to overcome the mentioned problem. A low complexity mapping algorithm cannot present the optimal mapping for all applications. Then, adding an optimization phase to mapping algorithms can have an impact on their performance. This study presents an optimization phase based on Cellular Learning Automata to achieve this goal. To evaluate the proposed algorithm, we compare mapping algorithm of Nmap, CastNet, and Onyx before and after optimization. Mathematical analysis and simulation of mapping algorithms for five real applications shows that using the proposed algorithm optimizes efficiency in mapping algorithms.