<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>29207</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>83</pageFirst>
    <pageLast>88</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analytical Calculation of Inference in Memristor-based Stochastic Artificial Neural Networks</title>
    <abstract language="eng">The impact of artificial intelligence on human life&#13;
has increased significantly in recent years. However, as the&#13;
complexity of problems rose aswell, increasing system features&#13;
for such amount of data computation became troublesome due&#13;
to the von Neumann’s computer architecture. Neuromorphic&#13;
computing aims to solve this problem by mimicking the parallel&#13;
computation of a human brain. For this approach, memristive&#13;
devices are used to emulate the synapses of a human brain. Yet,&#13;
common simulations of hardware based networks require time&#13;
consuming Monte-Carlo simulations to take into account the&#13;
stochastic switching of memristive devices. This work presents&#13;
an alternative concept making use of the convolution of the&#13;
probability distribution functions (PDF) of memristor currents&#13;
by its equivalent multiplication in Fourier domain. An artificial&#13;
neural network is accordingly implemented to perform the&#13;
inference stage with handwritten digits.</abstract>
    <parentTitle language="eng">29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES), 23-24 June 2022 , Wrocław, Poland</parentTitle>
    <identifier type="doi">10.23919/MIXDES55591.2022.9838321</identifier>
    <identifier type="isbn">978-83-63578-22-0</identifier>
    <identifier type="isbn">978-83-63578-21-3</identifier>
    <identifier type="isbn">978-1-6654-6176-4</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</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>Nicolas</firstName>
      <lastName>Bogun</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>Alexander</firstName>
      <lastName>Kloes</lastName>
    </author>
    <author>
      <firstName>Mike</firstName>
      <lastName>Schwarz</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>
