Journal article Open Access

Memory devices and applications for in-memory computing

Sebastian, Abu; Le Gallo, Manuel; Khaddam-Aljameh, Riduan; Eleftheriou, Evangelos


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  <identifier identifierType="URL">https://zenodo.org/record/3969876</identifier>
  <creators>
    <creator>
      <creatorName>Sebastian, Abu</creatorName>
      <givenName>Abu</givenName>
      <familyName>Sebastian</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Le Gallo, Manuel</creatorName>
      <givenName>Manuel</givenName>
      <familyName>Le Gallo</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>Eleftheriou, Evangelos</creatorName>
      <givenName>Evangelos</givenName>
      <familyName>Eleftheriou</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Memory devices and applications for in-memory computing</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-03-30</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3969876</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1038/s41565-020-0655-z</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;Traditional von Neumann computing systems involve separate processing and memory units. However, data&lt;br&gt;
movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth&lt;br&gt;
in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the&lt;br&gt;
traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby&lt;br&gt;
certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of&lt;br&gt;
the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory&lt;br&gt;
computing. In this Review, we provide a broad overview of the key computational primitives enabled by these&lt;br&gt;
memory devices as well as their applications spanning scientific computing, signal processing, optimization,&lt;br&gt;
machine learning, deep learning and stochastic computing.&lt;/p&gt;</description>
    <description descriptionType="Other">Invited Review article in Nature Nanotechnology</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>
</resource>
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