Journal article Open Access

Memory devices and applications for in-memory computing

Sebastian, Abu; Le Gallo, Manuel; Khaddam-Aljameh, Riduan; Eleftheriou, Evangelos


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="942" ind1=" " ind2=" ">
    <subfield code="a">2020-09-30</subfield>
  </datafield>
  <controlfield tag="005">20200930002650.0</controlfield>
  <datafield tag="500" ind1=" " ind2=" ">
    <subfield code="a">Invited Review article in Nature Nanotechnology</subfield>
  </datafield>
  <controlfield tag="001">3969876</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">IBM Research - Zurich</subfield>
    <subfield code="a">Le Gallo, Manuel</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">IBM Research - Zurich</subfield>
    <subfield code="a">Khaddam-Aljameh, Riduan</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">IBM Research - Zurich</subfield>
    <subfield code="a">Eleftheriou, Evangelos</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">1736083</subfield>
    <subfield code="z">md5:8b68f8633e658be827fcd9e9edfa486b</subfield>
    <subfield code="u">https://zenodo.org/record/3969876/files/Sebastian_Manuscript_RF.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-03-30</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o">oai:zenodo.org:3969876</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">IBM Research - Zurich</subfield>
    <subfield code="a">Sebastian, Abu</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Memory devices and applications for in-memory computing</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">682675</subfield>
    <subfield code="a">PROJECTED MEMRISTOR: A nanoscale device for cognitive computing</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Traditional von Neumann computing systems involve separate processing and memory units. However, data&lt;br&gt;
movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth&lt;br&gt;
in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the&lt;br&gt;
traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby&lt;br&gt;
certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of&lt;br&gt;
the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory&lt;br&gt;
computing. In this Review, we provide a broad overview of the key computational primitives enabled by these&lt;br&gt;
memory devices as well as their applications spanning scientific computing, signal processing, optimization,&lt;br&gt;
machine learning, deep learning and stochastic computing.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.1038/s41565-020-0655-z</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
  </datafield>
</record>
23
23
views
downloads
Views 23
Downloads 23
Data volume 39.9 MB
Unique views 23
Unique downloads 22

Share

Cite as