September 22, 2020 Conference paper Open Access

Cococcioni, Marco; Rossi, Federico; Ruffaldi, Emanuele; Saponara, Sergio

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  <identifier identifierType="DOI">10.5281/zenodo.4042854</identifier>
  <creators>
    <creator>
      <creatorName>Cococcioni, Marco</creatorName>
      <givenName>Marco</givenName>
      <familyName>Cococcioni</familyName>
      <affiliation>University of Pisa</affiliation>
    </creator>
    <creator>
      <creatorName>Rossi, Federico</creatorName>
      <givenName>Federico</givenName>
      <familyName>Rossi</familyName>
      <affiliation>University of Pisa</affiliation>
    </creator>
    <creator>
      <creatorName>Ruffaldi, Emanuele</creatorName>
      <givenName>Emanuele</givenName>
      <familyName>Ruffaldi</familyName>
      <affiliation>MMI spa</affiliation>
    </creator>
    <creator>
      <creatorName>Saponara, Sergio</creatorName>
      <givenName>Sergio</givenName>
      <familyName>Saponara</familyName>
      <affiliation>University of Pisa</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Novel Posit-based Fast Approximation of ELU Activation Function for Deep Neural Networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-09-22</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4042854</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo" resourceTypeGeneral="ConferencePaper">10.1109/SMARTCOMP50058.2020.00053</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4042853</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/1.0/legalcode">Creative Commons Attribution 1.0 Generic</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Nowadays, &amp;nbsp;real-time &amp;nbsp;applications &amp;nbsp;are &amp;nbsp;exploiting DNNs &amp;nbsp;more &amp;nbsp;and &amp;nbsp;more &amp;nbsp;for &amp;nbsp;computer &amp;nbsp;vision &amp;nbsp;and &amp;nbsp;image &amp;nbsp;recognition &amp;nbsp;tasks. &amp;nbsp;Such kind of applications are posing strict constraints in terms of both fast and efficient information representation and processing. New formats for representing real numbers have been proposed and among them the Posit format appears to be very promising, providing means &amp;nbsp;to &amp;nbsp;implement &amp;nbsp;fast &amp;nbsp;approximated &amp;nbsp;version &amp;nbsp;of widely &amp;nbsp;used activation functions in DNNs. Moreover, information processing performance &amp;nbsp;are &amp;nbsp;continuously &amp;nbsp;improved &amp;nbsp;thanks &amp;nbsp;to &amp;nbsp;advanced vectorized &amp;nbsp;SIMD &amp;nbsp;(single-instruction &amp;nbsp;multiple-data) &amp;nbsp;processor architectures &amp;nbsp;and &amp;nbsp;instructions &amp;nbsp;like &amp;nbsp;ARM &amp;nbsp;SVE (Scalable Vector Extension). This &amp;nbsp;paper explores both &amp;nbsp;approaches (Posit-based implementation of activation functions and vectorized SIMD processor architectures) to &amp;nbsp;obtain &amp;nbsp;faster &amp;nbsp;DNNs. &amp;nbsp;The &amp;nbsp;two &amp;nbsp;proposed &amp;nbsp;techniques &amp;nbsp;are able &amp;nbsp;to &amp;nbsp;speed &amp;nbsp;up &amp;nbsp;both &amp;nbsp;DNN training &amp;nbsp;and &amp;nbsp;inference steps.&amp;nbsp;&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/H2020/826647/">826647</awardNumber>
      <awardTitle>SGA1 (Specific Grant Agreement 1) OF THE EUROPEAN PROCESSOR INITIATIVE (EPI)</awardTitle>
    </fundingReference>
  </fundingReferences>
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