Journal article Open Access

In-memory hyperdimensional computing

Karunaratne, Geethan; Le Gallo, Manuel; Cherubini, Giovanni; Benini, Luca; Rahimi, Abbas; Sebastian, Abu


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/3969884</identifier>
  <creators>
    <creator>
      <creatorName>Karunaratne, Geethan</creatorName>
      <givenName>Geethan</givenName>
      <familyName>Karunaratne</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>Cherubini, Giovanni</creatorName>
      <givenName>Giovanni</givenName>
      <familyName>Cherubini</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Benini, Luca</creatorName>
      <givenName>Luca</givenName>
      <familyName>Benini</familyName>
      <affiliation>ETH Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Rahimi, Abbas</creatorName>
      <givenName>Abbas</givenName>
      <familyName>Rahimi</familyName>
      <affiliation>ETH Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Sebastian, Abu</creatorName>
      <givenName>Abu</givenName>
      <familyName>Sebastian</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
  </creators>
  <titles>
    <title>In-memory hyperdimensional computing</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-06-01</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3969884</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1038/s41928-020-0410-3</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;Hyperdimensional computing is an emerging computational framework that takes inspiration from attributes of neuronal&lt;br&gt;
circuits including hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness. When employed for machine learning tasks, such as learning and classification, the framework involves manipulation and comparison of large patterns within memory. A key attribute of hyperdimensional computing is its robustness to the imperfections associated with the computational substrates on which it is implemented. It is therefore particularly amenable to emerging non-von Neumann approaches such as in-memory computing, where the physical attributes of nanoscale memristive devices are exploited to perform computation. Here, we report a complete in-memory hyperdimensional computing system in which all operations are implemented on two memristive crossbar engines together with peripheral digital complementary metal&amp;ndash;oxide&amp;ndash;semiconductor (CMOS) circuits. Our approach can achieve a near optimum trade-off between design complexity and classification accuracy based on three prototypical hyperdimensional computing related learning tasks: language classification, news classification, and hand gesture recognition from electromyography signals. Experiments using 760,000 phase-change memory devices performing analogue in-memory computing achieve comparable accuracies to software implementations.&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>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</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>
11
10
views
downloads
Views 11
Downloads 10
Data volume 5.5 MB
Unique views 10
Unique downloads 9

Share

Cite as