Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7095
Title: HPRA: a pro-active hotspot-preventive high-performance routing algorithm for Networks-on-Chips
Authors: Soteriou, Vassos 
Kakoulli, Elena 
Theocharides, Theocharis 
Keywords: Computer science
Neural networks (Computer science)
Hardware
Microprocessors
Algorithms
Embedded computer systems
Issue Date: 2012
Publisher: IEEE Xplore
Source: 2012 IEEE 30th International conference on computer design, 2012, Pages 249-255
Abstract: The inherent spatio-temporal unevenness of traffic flows in Networks-on-Chips (NoCs) can cause unforeseen, and in cases, severe forms of congestion, known as hotspots. Hotspots reduce the NoC's effective throughput, where in the worst case scenario, the entire network can be brought to an unrecoverable halt as a hotspot(s) spreads across the topology. To alleviate this problematic phenomenon several adaptive routing algorithms employ online load-balancing functions, aiming to reduce the possibility of hotspots arising. Most, however, work passively, merely distributing traffic as evenly as possible among alternative network paths, and they cannot guarantee the absence of network congestion as their reactive capability in reducing hotspot formation(s) is limited. In this paper we present a new pro-active Hotspot-Preventive Routing Algorithm (HPRA) which uses the advance knowledge gained from network-embedded Artificial Neural Network-based (ANN) hotspot predictors to guide packet routing across the network in an effort to mitigate any unforeseen near-future occurrences of hotspots. These ANNs are trained offline and during multicore operation they gather online buffer utilization data to predict about-to-be-formed hotspots, promptly informing the HPRA routing algorithm to take appropriate action in preventing hotspot formation(s). Evaluation results across two synthetic traffic patterns, and traffic benchmarks gathered from a chip multiprocessor architecture, show that HPRA can reduce network latency and improve network throughput up to 81% when compared against several existing state-of-the-art congestion-aware routing functions. Hardware synthesis results demonstrate the efficacy of the HPRA mechanism
URI: http://ktisis.cut.ac.cy/handle/10488/7095
ISBN: 978-1-4673-3051-0
DOI: 10.1109/ICCD.2012.6378648
Rights: © 2012 IEEE
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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