1 Ocak 2021 Konferans bildirisi Açık Erişim

Nojehdeh, Mohammadreza Esmali; Parvin, Sajjad; Altun, Mustafa

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/234352</identifier>
  <creators>
    <creator>
      <creatorName>Nojehdeh, Mohammadreza Esmali</creatorName>
      <givenName>Mohammadreza Esmali</givenName>
      <familyName>Nojehdeh</familyName>
      <affiliation>Istanbul Tech Univ, Dept Elect &amp; Commun Engn, TR-34469 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Parvin, Sajjad</creatorName>
      <givenName>Sajjad</givenName>
      <familyName>Parvin</familyName>
      <affiliation>Istanbul Tech Univ, Dept Elect &amp; Commun Engn, TR-34469 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Altun, Mustafa</creatorName>
      <givenName>Mustafa</givenName>
      <familyName>Altun</familyName>
      <affiliation>Istanbul Tech Univ, Dept Elect &amp; Commun Engn, TR-34469 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Efficient Hardware Implementation Of Convolution Layers Using Multiply-Accumulate Blocks</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/234352</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ISVLSI51109.2021.00079</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">In this paper, we propose an efficient method to realize a convolution layer of the convolution neural networks (CNNs). Inspired by the hilly-connected neural network architecture, we introduce an efficient computation approach to implement convolution operations. Also, to reduce hardware complexity, we implement convolutional layers under the time-multiplexed architecture where computing resources are re-used in the multiply-accumulate (MAC) blocks. A comprehensive evaluation of convolution layers shows using our proposed method when compared to the conventional MAC-based method results up to 97% and 50% reduction in dissipated power and computation time, respectively.</description>
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