August 29, 2018 Journal article Open Access

Le Gallo, Manuel; Sebastian, Abu; Cherubini, Giovanni; Giefers, Heiner; Eleftheriou, Evangelos

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  <identifier identifierType="URL">https://zenodo.org/record/3249877</identifier>
  <creators>
    <creator>
      <creatorName>Le Gallo, Manuel</creatorName>
      <givenName>Manuel</givenName>
      <familyName>Le Gallo</familyName>
      <affiliation>IBM Research - Zurich, 8803 Rüschlikon, Switzerland</affiliation>
    </creator>
    <creator>
      <creatorName>Sebastian, Abu</creatorName>
      <givenName>Abu</givenName>
      <familyName>Sebastian</familyName>
      <affiliation>IBM Research - Zurich, 8803 Rüschlikon, Switzerland</affiliation>
    </creator>
    <creator>
      <creatorName>Cherubini, Giovanni</creatorName>
      <givenName>Giovanni</givenName>
      <familyName>Cherubini</familyName>
      <affiliation>IBM Research - Zurich, 8803 Rüschlikon, Switzerland</affiliation>
    </creator>
    <creator>
      <creatorName>Giefers, Heiner</creatorName>
      <givenName>Heiner</givenName>
      <familyName>Giefers</familyName>
    </creator>
    <creator>
      <creatorName>Eleftheriou, Evangelos</creatorName>
      <givenName>Evangelos</givenName>
      <familyName>Eleftheriou</familyName>
    </creator>
  </creators>
  <titles>
    <title>Compressed sensing with approximate message passing using in-memory computing</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Approximate message passing</subject>
    <subject>Compressed sensing</subject>
    <subject>In-memory computing</subject>
    <subject>Phase-change memory</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-08-29</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3249877</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TED.2018.2865352</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">Creative Commons Attribution Non Commercial No Derivatives 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 a promising non-von Neumann approach where certain computational tasks are performed within resistive memory units by exploiting their physical attributes. In this paper, we propose a new method for fast and robust compressed sensing of sparse signals with approximate message passing recovery using in-memory computing. The measurement matrix for compressed sensing is encoded in the conductance states of resistive memory devices organized in a crossbar array. This way, the matrix-vector multiplications associated with both the compression and recovery tasks can be performed by the same crossbar array without intermediate data movements at potential O(1) time complexity. For a signal of size N, the proposed method achieves a potential O(N)-fold recovery complexity reduction compared with a standard software approach. We show the array-level robustness of the scheme through large-scale experimental demonstrations using more than 256k phase-change memory devices.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/682675/">682675</awardNumber>
      <awardTitle>PROJECTED MEMRISTOR: A nanoscale device for cognitive computing</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/780215/">780215</awardNumber>
      <awardTitle>Computation-in-memory architecture based on resistive devices</awardTitle>
    </fundingReference>
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