1 Ocak 2021 Konferans bildirisi Açık Erişim

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

Dublin Core

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  <dc:creator>Nojehdeh, Mohammadreza Esmali</dc:creator>
  <dc:creator>Parvin, Sajjad</dc:creator>
  <dc:creator>Altun, Mustafa</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/234352</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:234352</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Efficient Hardware Implementation of Convolution Layers Using Multiply-Accumulate Blocks</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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