Conference paper Open Access

Computational memory-based inference and training of deep neural networks

Sebastian, Abu; Boybat, Irem; Dazzi, Martino; Giannopoulos, Iason; Jonnalagadda, Varaprasad; Joshi, Vinay; Karunaratne, Geethan; Kersting, Benedikt; Khaddam-Aljameh, Riduan; Nandakumar S. R.; Petropoulos, A.; Piveteau, C.; Antonakopoulos, T.; Rajendran , Bipin; Le Gallo, Manuel; Eleftheriou, Evangelos


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  <identifier identifierType="URL">https://zenodo.org/record/5301571</identifier>
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    <creator>
      <creatorName>Sebastian, Abu</creatorName>
      <givenName>Abu</givenName>
      <familyName>Sebastian</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Boybat, Irem</creatorName>
      <givenName>Irem</givenName>
      <familyName>Boybat</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Dazzi, Martino</creatorName>
      <givenName>Martino</givenName>
      <familyName>Dazzi</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Giannopoulos, Iason</creatorName>
      <givenName>Iason</givenName>
      <familyName>Giannopoulos</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Jonnalagadda, Varaprasad</creatorName>
      <givenName>Varaprasad</givenName>
      <familyName>Jonnalagadda</familyName>
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    <creator>
      <creatorName>Joshi, Vinay</creatorName>
      <givenName>Vinay</givenName>
      <familyName>Joshi</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
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      <creatorName>Karunaratne, Geethan</creatorName>
      <givenName>Geethan</givenName>
      <familyName>Karunaratne</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Kersting, Benedikt</creatorName>
      <givenName>Benedikt</givenName>
      <familyName>Kersting</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Khaddam-Aljameh, Riduan</creatorName>
      <givenName>Riduan</givenName>
      <familyName>Khaddam-Aljameh</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Nandakumar S. R.</creatorName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Petropoulos, A.</creatorName>
      <givenName>A.</givenName>
      <familyName>Petropoulos</familyName>
      <affiliation>University of Patras</affiliation>
    </creator>
    <creator>
      <creatorName>Piveteau, C.</creatorName>
      <givenName>C.</givenName>
      <familyName>Piveteau</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Antonakopoulos, T.</creatorName>
      <givenName>T.</givenName>
      <familyName>Antonakopoulos</familyName>
      <affiliation>University of Patras</affiliation>
    </creator>
    <creator>
      <creatorName>Rajendran , Bipin</creatorName>
      <givenName>Bipin</givenName>
      <familyName>Rajendran</familyName>
      <affiliation>NJIT</affiliation>
    </creator>
    <creator>
      <creatorName>Le Gallo, Manuel</creatorName>
      <givenName>Manuel</givenName>
      <familyName>Le Gallo</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Eleftheriou, Evangelos</creatorName>
      <givenName>Evangelos</givenName>
      <familyName>Eleftheriou</familyName>
      <affiliation>IBM Research, Zurich</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Computational memory-based inference and training of deep neural networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-07-25</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5301571</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.23919/VLSIT.2019.8776518</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;In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.&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/H2020/682675/">682675</awardNumber>
      <awardTitle>PROJECTED MEMRISTOR: A nanoscale device for cognitive computing</awardTitle>
    </fundingReference>
  </fundingReferences>
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