Performance improvement of application-specific network on chip using machine learning algorithms
by Mahnaz Rafie; Ahmad Khademzadeh; Midia Reshadi
International Journal of High Performance Systems Architecture (IJHPSA), Vol. 5, No. 2, 2014

Abstract: 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.

Online publication date: Sat, 12-Jul-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Systems Architecture (IJHPSA):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email