Conference paper Open Access

Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations

Bent, Graham; Simpkin, Christopher; Taylor, Ian; Rahimi, Abbas; Karunaratne, Geethan; Sebastian, Abu; Millar, Declan; Martens, Andreas; Roy, Kaushik


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  <identifier identifierType="DOI">10.5281/zenodo.5301585</identifier>
  <creators>
    <creator>
      <creatorName>Bent, Graham</creatorName>
      <givenName>Graham</givenName>
      <familyName>Bent</familyName>
      <affiliation>Cardiff Univ.</affiliation>
    </creator>
    <creator>
      <creatorName>Simpkin, Christopher</creatorName>
      <givenName>Christopher</givenName>
      <familyName>Simpkin</familyName>
      <affiliation>Cardiff Univ.</affiliation>
    </creator>
    <creator>
      <creatorName>Taylor, Ian</creatorName>
      <givenName>Ian</givenName>
      <familyName>Taylor</familyName>
      <affiliation>Cardiff Univ.</affiliation>
    </creator>
    <creator>
      <creatorName>Rahimi, Abbas</creatorName>
      <givenName>Abbas</givenName>
      <familyName>Rahimi</familyName>
      <affiliation>IBM Research - Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Karunaratne, Geethan</creatorName>
      <givenName>Geethan</givenName>
      <familyName>Karunaratne</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>Millar, Declan</creatorName>
      <givenName>Declan</givenName>
      <familyName>Millar</familyName>
      <affiliation>IBM United Kingdom Ltd</affiliation>
    </creator>
    <creator>
      <creatorName>Martens, Andreas</creatorName>
      <givenName>Andreas</givenName>
      <familyName>Martens</familyName>
      <affiliation>IBM United Kingdom Ltd</affiliation>
    </creator>
    <creator>
      <creatorName>Roy, Kaushik</creatorName>
      <givenName>Kaushik</givenName>
      <familyName>Roy</familyName>
      <affiliation>Purdue Univ.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Energy efficient 'in memory' computing to enable decentralised service workflow composition in support of multi-domain operations</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-04-12</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5301585</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5301584</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;Paper Abstract&lt;/p&gt;

&lt;p&gt;Future Multi-Domain Operations (MDO) will require the coordination of hundreds, even thousands, of devices and component services. This will demand the capability to rapidly discover the distributed devices/services and combine them into different work ow configurations, thereby creating the applications necessary to support changing mission needs. To meet these objectives, we envision a distributed Cognitive Computing System (CCS) that consists of humans and software that work together as a &amp;lsquo;Distributed Federated Brain&amp;#39;. Motivated by neuromorphic processing models, we present an approach that uses hyper-dimensional symbolic semantic vector representations of the services/devices and workflows. We show how these can be used to perform decentralized service/device discovery and work ow composition in the context of a dynamic communications re-planning scenario. In this paper, we describe how emerging analogue AI &amp;lsquo;In Memory&amp;#39; and &amp;lsquo;Near Memory&amp;#39; computing devices can be used to efficiently perform some of the required hyper-dimensional vector computation (HDC). We present an evaluation of the performance of an energy-efficient phase change memory device (PCM) that can perform the required vector operations and discuss how such devices could be used in energy-critical &amp;lsquo;edge of network&amp;#39; tactical MDO operations.&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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