September 20, 2018 Conference paper Open Access

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

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  <dc:creator>Xygkis, Athanasios</dc:creator>
  <dc:creator>Papadopoulos, Lazaros</dc:creator>
  <dc:creator>Moloney, David</dc:creator>
  <dc:creator>Soudris, Dimitrios</dc:creator>
  <dc:creator>Yous, Sofiane</dc:creator>
  <dc:date>2018-09-20</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/3347180</dc:identifier>
  <dc:identifier>10.1145/3195970.3196041</dc:identifier>
  <dc:identifier>oai:zenodo.org:3347180</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/780572/</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Efficient Winograd-based Convolution Kernel Implementation on Edge Devices</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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