Conference paper Open Access
Xygkis, Athanasios; Papadopoulos, Lazaros; Moloney, David; Soudris, Dimitrios; Yous, Sofiane
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="URL">https://zenodo.org/record/3347180</identifier> <creators> <creator> <creatorName>Xygkis, Athanasios</creatorName> <givenName>Athanasios</givenName> <familyName>Xygkis</familyName> <affiliation>Intel Corporation, Ireland</affiliation> </creator> <creator> <creatorName>Papadopoulos, Lazaros</creatorName> <givenName>Lazaros</givenName> <familyName>Papadopoulos</familyName> <affiliation>School of ECE, NTUA, Greece</affiliation> </creator> <creator> <creatorName>Moloney, David</creatorName> <givenName>David</givenName> <familyName>Moloney</familyName> <affiliation>Intel Corporation, Ireland</affiliation> </creator> <creator> <creatorName>Soudris, Dimitrios</creatorName> <givenName>Dimitrios</givenName> <familyName>Soudris</familyName> <affiliation>School of ECE, NTUA, Greece</affiliation> </creator> <creator> <creatorName>Yous, Sofiane</creatorName> <givenName>Sofiane</givenName> <familyName>Yous</familyName> <affiliation>Intel Corporation, Ireland</affiliation> </creator> </creators> <titles> <title>Efficient Winograd-based Convolution Kernel Implementation on Edge Devices</title> </titles> <publisher>Zenodo</publisher> <publicationYear>2018</publicationYear> <dates> <date dateType="Issued">2018-09-20</date> </dates> <resourceType resourceTypeGeneral="ConferencePaper"/> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3347180</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3195970.3196041</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><p>The implementation of Convolutional Neural Networks on edge Internet of Things (IoT) devices is a significant programming challenge, due to the limited computational resources and the real-time requirements of modern applications. This work focuses on the efficient implementation of the Winograd convolution, based on a set of application-independent and Winograd-specific software techniques for improving the utilization of the edge devices computational resources. The proposed techniques were evaluated in Intel/Movidius Myriad2 platform, using 4 CNNs of various computational requirements. The results show significant performance improvements, up to 54%, over other convolution algorithms.</p></description> </descriptions> <fundingReferences> <fundingReference> <funderName>European Commission</funderName> <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier> <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/780572/">780572</awardNumber> <awardTitle>Software Development toolKit for Energy optimization and technical Debt elimination</awardTitle> </fundingReference> </fundingReferences> </resource>
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