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Among the NoC components, links that connect the NoC routers are the most power\u2010hungry components. Several attempts have been made to reduce the link power consumption at both the circuit level and the system level. Most past research efforts have proposed selective on\/off link state switching based on system\u2010level information based on link utilization levels. Most of these proposed algorithms focus on a pessimistic and simple static threshold mechanism which determines whether or not a link should be turned on\/off. This paper presents an intelligent dynamic power management policy for NoCs with improved predictive abilities based on supervised online learning of the system status (i.e., expected future utilization link levels), where links are turned off and on via the use of a small and scalable neural network. Simulation results with various synthetic traffic models over various network topologies show that the proposed work can reach up to 13% power savings when compared to a trivial threshold computation, at very low (<4%) hardware overheads.<\/jats:p>","DOI":"10.1155\/2012\/107821","type":"journal-article","created":{"date-parts":[[2012,2,28]],"date-time":"2012-02-28T21:01:20Z","timestamp":1330462880000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent On\/Off Dynamic Link Management for On\u2010Chip Networks"],"prefix":"10.1155","volume":"2012","author":[{"given":"Andreas G.","family":"Savva","sequence":"first","affiliation":[]},{"given":"Theocharis","family":"Theocharides","sequence":"additional","affiliation":[]},{"given":"Vassos","family":"Soteriou","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2012,2,28]]},"reference":[{"key":"e_1_2_6_1_2","unstructured":"SoteriouV.andPehL.-S. 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