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dc.contributor.authorChang, Wen-Hsiangen_US
dc.contributor.authorLin, Chien-Hsuehen_US
dc.contributor.authorMu, Szu-Pangen_US
dc.contributor.authorChen, Li-Deen_US
dc.contributor.authorTsai, Cheng-Hongen_US
dc.contributor.authorChiu, Yen-Chihen_US
dc.contributor.authorChao, Mango C. -T.en_US
dc.date.accessioned2018-08-21T05:54:26Z-
dc.date.available2018-08-21T05:54:26Z-
dc.date.issued2017-08-01en_US
dc.identifier.issn0278-0070en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TCAD.2017.2648842en_US
dc.identifier.urihttp://hdl.handle.net/11536/145953-
dc.description.abstractAs technology node keeps scaling and design complexity keeps increasing, power distribution networks (PDNs) require more routing resource to meet IR-drop and electromigration (EM) constraints. This paper presents a design flow to generate a PDN that can result in near-minimal overhead for the routing of the underlying standard cells while satisfying both IR-drop and EM constraints based on a given cell placement. The design flow relies on a machine-learning model to quickly predict the total wire length of global route associated with a given PDN configuration in order to speed up the search process. The experimental results based on various 28 nm industrial block designs have demonstrated the accuracy of the learned model for predicting the routing cost and the effectiveness of the proposed framework for reducing the routing cost of the final PDN.en_US
dc.language.isoen_USen_US
dc.subjectElectro-migration (EM)en_US
dc.subjectIR dropen_US
dc.subjectmachine learningen_US
dc.subjectpower grid designen_US
dc.subjectrouting cost modelen_US
dc.subjectrouting-drivenen_US
dc.titleGenerating Routing-Driven Power Distribution Networks With Machine-Learning Techniqueen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TCAD.2017.2648842en_US
dc.identifier.journalIEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMSen_US
dc.citation.volume36en_US
dc.citation.spage1237en_US
dc.citation.epage1250en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000408147200001en_US
Appears in Collections:Articles