<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>31349</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>209</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-09-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Efficient circuit simulation of a memristive crossbar array with synaptic weight variability</title>
    <abstract language="eng">In this paper, we present a method for highly-efficient circuit simulation of a hardware-based artificial neural network realized in a memristive crossbar array. The statistical variability of the devices is considered by a noise-based simulation technique. For the simulation of a crossbar array with 8 synaptic weights in Cadence Virtuoso the new approach shows a more than 200x speed improvement compared to a Monte Carlo approach, yielding the same results. In addition, first results of an ANN with more than 15,000 memristive devices classifying test data of the MNIST dataset are shown, for which the speed improvement is expected to be several orders of magnitude. Furthermore, the influence on the classification of parasitic resistances of the connection lines in the crossbar is shown.</abstract>
    <parentTitle language="eng">Solid State Electronics</parentTitle>
    <identifier type="issn">0038-1101</identifier>
    <identifier type="doi">10.1016/j.sse.2023.108760</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="RelationnotEU">16ME0092</enrichment>
    <enrichment key="BTUfunderNamenotEU">BMBF</enrichment>
    <enrichment key="Artikelnummer">108760</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Nadine</firstName>
      <lastName>Dersch</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>Emilio</firstName>
      <lastName>Perez-Bosch Quesada</lastName>
    </author>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Roemer</lastName>
    </author>
    <author>
      <firstName>Mike</firstName>
      <lastName>Schwarz</lastName>
    </author>
    <author>
      <firstName>Alexander</firstName>
      <lastName>Kloes</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural network</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
</export-example>
