September 20, 2018 Conference paper Open Access

Xygkis, Athanasios; Papadopoulos, Lazaros; Moloney, David; Soudris, Dimitrios; Yous, Sofiane

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  <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">&lt;p&gt;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.&lt;/p&gt;</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>
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