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@ARTICLE{Ziegler:894982,
      author       = {Ziegler, Tobias and Waser, R. and Wouters, Dirk J. and
                      Menzel, Stephan},
      title        = {{I}n‐{M}emory {B}inary {V}ector–{M}atrix
                      {M}ultiplication {B}ased on {C}omplementary {R}esistive
                      {S}witches},
      journal      = {Advanced intelligent systems},
      volume       = {2},
      number       = {10},
      issn         = {2640-4567},
      address      = {Weinheim},
      publisher    = {Wiley-VCH Verlag GmbH $\&$ Co. KGaA},
      reportid     = {FZJ-2021-03510},
      pages        = {2000134 -},
      year         = {2020},
      abstract     = {This work studies a computation in-memory concept for
                      binary multiply-accumulate operations based on complementary
                      resistive switches (CRS). By exploiting the in-memory
                      boolean exclusive OR (XOR) operation of single CRS devices,
                      the Hamming Distance (HD) can be calculated if the center
                      electrodes of multiple CRS cells are connected. This HD is
                      linearly encoded in the voltage drop of the common
                      electrode, and from it the result of a binary
                      multiply-accumulate operation can be calculated. A
                      small-scale demonstration is experimentally realized and the
                      feasibility of the in-memory computation concept is
                      confirmed. A simulation study identifies the low resistance
                      state (LRS) variability as the main reason for the
                      variations in the output voltage. The application as a
                      potential hardware accelerator for the inference step of
                      binary neural networks is investigated. Therefore, a 1-layer
                      fully connected neural network is trained on a binarized
                      version of the MNIST data set and the inference step of the
                      test data set is simulated. The concept achieves a
                      prediction accuracy of approximately $86\%.$},
      cin          = {PGI-7 / JARA-FIT},
      ddc          = {620},
      cid          = {I:(DE-Juel1)PGI-7-20110106 / $I:(DE-82)080009_20140620$},
      pnm          = {5233 - Memristive Materials and Devices (POF4-523) /
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC -, Teilvorhaben: Forschungszentrum Jülich
                      (16ES1133K) / BMBF-16ES1134 - Verbundprojekt:
                      Neuro-inspirierte Technologien der künstlichen Intelligenz
                      für die Elektronik der Zukunft - NEUROTEC -
                      (BMBF-16ES1134)},
      pid          = {G:(DE-HGF)POF4-5233 / G:(BMBF)16ES1133K /
                      G:(DE-82)BMBF-16ES1134},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000669790800016},
      doi          = {10.1002/aisy.202000134},
      url          = {https://juser.fz-juelich.de/record/894982},
}