Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/4292
DC FieldValueLanguage
dc.contributor.authorKakoulli, Elena-
dc.contributor.authorTheocharides, Theocharis-
dc.contributor.authorSoteriou, Vassos-
dc.contributor.otherΣωτηρίου, Βάσος-
dc.contributor.otherΚακουλλή, Έλενα-
dc.date.accessioned2013-02-15T14:24:15Zen
dc.date.accessioned2013-05-17T10:38:29Z-
dc.date.accessioned2015-12-09T12:04:24Z-
dc.date.available2013-02-15T14:24:15Zen
dc.date.available2013-05-17T10:38:29Z-
dc.date.available2015-12-09T12:04:24Z-
dc.date.issued2010-
dc.identifier.citation2010 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2010, pp. 339-344en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4292-
dc.description.abstractHotspots are network on-chip (NoC) routers or modules in systems on-chip (SoCs) which occasionally receive packetized traffic at a rate higher than they can consume it. This adverse phenomenon greatly reduces the performance of an NoC, especially in the case of today's widely-employed wormhole flow-control, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion that can cause the network to saturate. Even worse, such situations may lead to deadlocks. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance and efficiency. Unfortunately, hotspots cannot be known a-priori in NoCs used in general-purpose systems as application demands are not predetermined unlike in application-specific SoCs, making hotspot prediction and subsequently prevention difficult. In this paper we present an artificial neural network-based hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance mechanism for handling an unforeseen hotspot formation efficiently. The network uses buffer utilization statistical data to dynamically monitor the interconnect fabric, 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% to 92% when evaluated on two different mesh NoCsen_US
dc.language.isoenen_US
dc.rights© Copyright 2010 IEEEen_US
dc.subjectComputer scienceen_US
dc.subjectNeural networksen_US
dc.subjectNetworks on a chipen_US
dc.subjectHardwareen_US
dc.subjectRouters (Computer networks)en_US
dc.titleAn artificial neural network-based hotspot prediction mechanism for NoCsen_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceIEEE Computer Society Annual Symposium on VLSIen_US
dc.identifier.doi10.1109/ISVLSI.2010.50en_US
dc.dept.handle123456789/134en
cut.common.academicyear2010-2011en_US
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-1489-807X-
crisitem.author.orcid0000-0002-2818-0459-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
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