October 19, 2019 Conference paper Open Access

Douthwaite, Matthew; Garcıa-Redondo, Fernando; Georgiou, Pantelis; Das, Shidhartha

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  <identifier identifierType="URL">https://zenodo.org/record/3741945</identifier>
  <creators>
    <creator>
      <creatorName>Douthwaite, Matthew</creatorName>
      <givenName>Matthew</givenName>
      <familyName>Douthwaite</familyName>
      <affiliation>Imperial College London</affiliation>
    </creator>
    <creator>
      <creatorName>Garcıa-Redondo, Fernando</creatorName>
      <givenName>Fernando</givenName>
      <familyName>Garcıa-Redondo</familyName>
      <affiliation>ARM Research</affiliation>
    </creator>
    <creator>
      <creatorName>Georgiou, Pantelis</creatorName>
      <givenName>Pantelis</givenName>
      <familyName>Georgiou</familyName>
      <affiliation>Imperial College London</affiliation>
    </creator>
    <creator>
      <creatorName>Das, Shidhartha</creatorName>
      <givenName>Shidhartha</givenName>
      <familyName>Das</familyName>
      <affiliation>ARM Research</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Time-Domain Current-Mode MAC Engine for Analogue Neural Networks in Flexible Electronics</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Flexible Electronics</subject>
    <subject>MAC Operation</subject>
    <subject>Neural Networks</subject>
    <subject>Analogue Signal Processing</subject>
    <subject>Wearable Sensors</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-10-19</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3741945</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/BIOCAS.2019.8919190</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;Flexible electronics is becoming more prevalent in a wide range of applications, particularly wearable biomedical&lt;br&gt;
devices. These devices would greatly benefit from in-built intelligence allowing them to process data and identify features,&lt;br&gt;
in order to reduce transmission and power requirements. In this work, we present a novel time-domain multiply-accumulate&lt;br&gt;
(MAC) engine architecture that can act as the basic block of an artificial analogue neural network. The design does not require&lt;br&gt;
analogue voltage buffers, making them easier to realise in flexible technologies and consumes less power than conventional methods. The research could be used in future to construct a low power classifier for a low cost, flexible wearable biomedical sensor.&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/H2020/780215/">780215</awardNumber>
      <awardTitle>Computation-in-memory architecture based on resistive devices</awardTitle>
    </fundingReference>
  </fundingReferences>
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