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Energy Efficient In-Memory Hyperdimensional Encoding for Spatio-Temporal Signal Processing

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


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  <identifier identifierType="URL">https://zenodo.org/record/5301648</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>Hersche, Michael</creatorName>
      <givenName>Michael</givenName>
      <familyName>Hersche</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>Sebastian, Abu</creatorName>
      <givenName>Abu</givenName>
      <familyName>Sebastian</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Rahimi, Abbas</creatorName>
      <givenName>Abbas</givenName>
      <familyName>Rahimi</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Energy Efficient In-Memory Hyperdimensional Encoding for Spatio-Temporal Signal Processing</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-03-25</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5301648</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TCSII.2021.3068126</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;The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) has been proven to provide a lightweight learning framework for various cognitive tasks compared to the widely used deep learning-based approaches. Spatio-temporal (ST) signal processing, which encompasses biosignals such as electromyography (EMG) and electroencephalography (EEG), is one family of applications that could benefit from an HDC-based learning framework. At the core of HDC lie manipulations and comparisons of large bit patterns, which are inherently ill-suited to conventional computing platforms based on the von-Neumann architecture. In this work, we propose an architecture for ST signal processing within the HDC framework using predominantly in-memory compute arrays. In particular, we introduce a methodology for the in-memory hyperdimensional encoding of ST data to be used together with an in-memory associative search module. We show that the in-memory HDC encoder for ST signals offers at least 1.80&amp;times; energy efficiency gains, 3.36&amp;times; area gains, as well as 9.74&amp;times; throughput gains compared with a dedicated digital hardware implementation. At the same time it achieves a peak classification accuracy within 0.04% of that of the baseline HDC framework.&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>
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