Conference paper Open Access

Compressed sensing recovery using computational memory

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


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  <identifier identifierType="URL">https://zenodo.org/record/5301633</identifier>
  <creators>
    <creator>
      <creatorName>Le Gallo, Manuel</creatorName>
      <givenName>Manuel</givenName>
      <familyName>Le Gallo</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Sebastian, Abu</creatorName>
      <givenName>Abu</givenName>
      <familyName>Sebastian</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Cherubini, Giovanni</creatorName>
      <givenName>Giovanni</givenName>
      <familyName>Cherubini</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Giefers, Heiner</creatorName>
      <givenName>Heiner</givenName>
      <familyName>Giefers</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>Compressed sensing recovery using computational memory</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>In-memory computing, phase change memory, compressed sensing</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-01-25</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5301633</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/IEDM.2017.8268469</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;Computational memory (CM) is a promising non-von Neumann approach where certain computational tasks are performed within resistive memory units by exploiting their physical attributes. We propose a new method for fast and robust compressed sensing (CS) recovery of sparse signals using CM. For a signal of size N, this method achieves a potential O(N)-fold complexity reduction compared with a standard software approach. Large-scale experimental demonstrations using more than 256k phase-change memory (PCM) devices are presented along with an in-depth device analysis and array-level considerations.&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>
</resource>
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