Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7113
Title: Intelligent NOC hotspot prediction
Authors: Soteriou, Vassos 
Kakoulli, Elena 
Theocharides, Theocharis 
Keywords: Computer science;Networks on a chip;Neural networks;Routers (Computer networks)
Category: Electrical Engineering,Electronic Engineering,Information Engineering
Field: Engineering and Technology
Issue Date: 2011
Publisher: Springer
Source: VLSI 2010 annual symposium: selected papers, 2011, Pages 3-16
Abstract: Hotspots are Network on-Chip (NoC) routers or modules which occasionally receive packetized traffic at a higher rate that they can process. This phenomenon reduces the performance of an NoC, especially in the case wormhole flow-control. Such situations may also lead to deadlocks, raising the need of a hotspot prevention mechanism. Such mechanism can potentially enable the system to adjust its behavior and prevent hotspot formation, subsequently sustaining performance and efficiency. This Chapter presents an Artificial Neural Network-based (ANN) hotspot prediction mechanism, potentially triggering a hotspot avoidance mechanism before the hotspot is formed. The ANN monitors buffer utilization and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 and 92%
URI: http://ktisis.cut.ac.cy/handle/10488/7113
ISBN: 978-94-007-1487-8 (print)
ISSN: 978-94-007-1488-5 (online)
DOI: 10.1007/978-94-007-1488-5_1
Rights: © Springer Science+Business Media B.V. 2011
Type: Book Chapter
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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