Performance improvement of application-specific network on chip using machine learning algorithms

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This paper presents a novel and efficient mapping algorithm based on machine learning methods. It produces the best mappings with different metrics which were totally evaluated by support vector machine SVM, decision tree classifier DTC, and multiple clustering. It helps to find the optimal application-specific network-on-chip NoC based on user's demands on how to customise and prioritise the impact of three key metrics on special mapping. The parameters are robustness index, contention factor and communication cost. In fact, as mapping generator produces a mapping, these parameters will be calculated and compared with some rules. The rules are extracted by SVM and DTC. They highly affect the fitness function of the genetic algorithm GA. So the algorithm can be controlled. Simulation results show that the proposed algorithm achieves more than fifteen times performance improvement and more supervision in finding the best mappings from numerous generated solutions in comparison with the ordinary algorithm.