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dc.contributor.authorXiong, Zen
dc.contributor.authorZilberman, Noaen
dc.date.accessioned2019-10-29T00:30:39Z
dc.date.available2019-10-29T00:30:39Z
dc.date.issued2019-11-13en
dc.identifier.isbn9781450370202en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/298114
dc.description.abstractMachine learning is currently driving a technological and societal revolution. While programmable switches have been proven to be useful for in-network computing, machine learning within programmable switches had little success so far. Not using network devices for machine learning has a high toll, given the known power efficiency and performance benefits of processing within the network. In this paper, we explore the potential use of commodity programmable switches for in-network classification, by mapping trained machine learning models to match-action pipelines. We introduce IIsy, a software and hardware based prototype of our approach, and discuss the suitability of mapping to different targets. Our solution can be generalized to additional machine learning algorithms, using the methods presented in this work.
dc.rightsAll rights reserved
dc.titleDo switches dream of machine learning?: Toward in-network classificationen
dc.typeConference Object
prism.endingPage33
prism.publicationDate2019en
prism.publicationNameHotNets 2019 - Proceedings of the 18th ACM Workshop on Hot Topics in Networksen
prism.startingPage25
dc.identifier.doi10.17863/CAM.45171
dcterms.dateAccepted2019-09-14en
rioxxterms.versionofrecord10.1145/3365609.3365864en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-11-13en
dc.contributor.orcidZilberman, Noa [0000-0002-3655-2873]
rioxxterms.typeConference Paper/Proceeding/Abstracten
pubs.funder-project-idLeverhulme Trust (ECF-2016-289)
pubs.funder-project-idIsaac Newton Trust (1608(as))
cam.orpheus.successThu Nov 05 11:54:58 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2020-11-13


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