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dc.contributor.authorBazrafkan, Shabab
dc.contributor.authorCorcoran, Peter
dc.date.accessioned2018-10-08T13:29:12Z
dc.date.available2018-10-08T13:29:12Z
dc.date.issued2018-02-08
dc.identifier.citationBazrafkan, S., & Corcoran, P. M. (2018). Pushing the AI Envelope: Merging Deep Networks to Accelerate Edge Artificial Intelligence in Consumer Electronics Devices and Systems. IEEE Consumer Electronics Magazine, 7(2), 55-61. doi: 10.1109/MCE.2017.2775245en_IE
dc.identifier.issn2162-2248)
dc.identifier.urihttp://hdl.handle.net/10379/14583
dc.description.abstractDeep neural networks (DNNs) are widely used by both academic and industry researchers to solve many long-standing problems in machine learning. There has been such a growth of research in this field, and it has been applied to so many varying problems, that it would be accurate to say that we may be living through the precursor of the singularity [1]. But regardless of one's views on artificial intelligence (AI), there is no doubt that there is a wealth of recent research that leverages the use of various DNNs to solve a broad range of pattern recognition and classification problems. Examples range from the introduction of smart speakers with intelligent assistants to the application of DNNs to solve recalcitrant problems in computer vision for autonomous vehicles. Many of these problems can have very useful applications in the design of smarter consumer electronics (CE) systems and devices. The question for CE engineers is how to leverage this wealth of academic and industry research efforts, turning them into practical DNN solutions suitable for deployment in practical devices and electronic systems.en_IE
dc.description.sponsorshipThis research was funded under the Science Foundation Ireland (SFI) Strategic Partnership Program by SFI and FotoNation Ltd., project 13/SPP/I2868 on “Next Generation Imaging for Smartphone and Embedded Platforms.” We gratefully acknowledge the support of NVIDIA Corp. with the donation of a Titan X GPU used for this research. Portions of the research in this article use the CASIA-IrisV4 collected by the Chinese Academy of Sciences’ Institute of Automation.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherIEEEen_IE
dc.relation.ispartofIEEE Consumer Electronics Magazineen
dc.subjectIRIS AUTHENTICATIONen_IE
dc.subjectDEVICESen_IE
dc.subjectArtificial intelligenceen_IE
dc.subjectComputer visionen_IE
dc.subjectTask analysisen_IE
dc.subjectIris recognitionen_IE
dc.subjectNeural networksen_IE
dc.titlePushing the AI envelope: merging deep networks to accelerate edge artificial intelligence in consumer electronics devices and systemsen_IE
dc.typeArticleen_IE
dc.date.updated2018-09-27T13:38:21Z
dc.identifier.doi10.1109/MCE.2017.2775245
dc.local.publishedsourcehttps://dx.doi.org/10.1109/MCE.2017.2775245en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderFotoNation Ltden_IE
dc.internal.rssid13957495
dc.local.contactPeter Corcoran, Electrical & Electronic Eng, Room 3041, Engineering Building, Nui Galway. 2764 Email: peter.corcoran@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionSUBMITTED
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Strategic Partnership Programme/13/SPP/I2868/IE/Next Generation Imaging for Smartphone and Embedded Platforms/en_IE
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