Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7104
Title: Intelligent on/off dynamic link management for on-chip networks
Authors: Savva, Andreas 
Theocharides, Theocharis 
Soteriou, Vassos 
Keywords: Computer science
Embedded computer systems
Microprocessors
Algorithms
Electric network topology
Networks on a chip
Open access publishing
Issue Date: 2012
Publisher: Hindawi
Source: Journal of electrical and computer engineering, 2012, Volume 2012, Pages 1-12
Abstract: Networks-on-chips (NoCs) provide scalable on-chip communication and are expected to be the dominant interconnection architectures in multicore and manycore systems. Power consumption, however, is a major limitation in NoCs today, and researchers have been constantly working on reducing both dynamic and static power. Among the NoC components, links that connect the NoC routers are the most power-hungry 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-level 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
URI: http://ktisis.cut.ac.cy/handle/10488/7104
ISSN: 2090-0147 (print)
2090-0155 (online)
DOI: 10.1155/2012/107821
Rights: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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