Journal article Open Access

# Compressed sensing with approximate message passing using in-memory computing

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

### DataCite XML Export

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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="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>
</fundingReferences>
</resource>

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