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@INPROCEEDINGS{Yu:1022198,
      author       = {Yu, Zhenming and Menzel, Stephan and Strachan, John Paul
                      and Neftci, Emre},
      title        = {{I}ntegration of {P}hysics-{D}erived {M}emristor {M}odels
                      with {M}achine {L}earning {F}rameworks},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-01319},
      pages        = {1142-1146},
      year         = {2022},
      comment      = {2022 56th Asilomar Conference on Signals, Systems, and
                      Computers : [Proceedings] - IEEE, 2022. - ISBN
                      978-1-6654-5906-8 - doi:10.1109/IEEECONF56349.2022.10052010},
      booktitle     = {2022 56th Asilomar Conference on
                       Signals, Systems, and Computers :
                       [Proceedings] - IEEE, 2022. - ISBN
                       978-1-6654-5906-8 -
                       doi:10.1109/IEEECONF56349.2022.10052010},
      abstract     = {Simulation frameworks such MemTorch, DNN+NeuroSim, and
                      aihwkit are commonly used to facilitate the end-to-end
                      co-design of memristive machine learning (ML) accelerators.
                      These simulators can take device nonidealities into account
                      and are integrated with modern ML frameworks. However,
                      memristors in these simulators are modeled with either
                      lookup tables or simple analytic models with basic
                      nonlinearities. These simple models are unable to capture
                      certain performance-critical aspects of device
                      nonidealities. For example, they ignore the physical cause
                      of switching, which induces errors in switching timings and
                      thus incorrect estimations of conductance states. This work
                      aims at bringing physical dynamics into consideration to
                      model nonidealities while being compatible with GPU
                      accelerators. We focus on Valence Change Memory (VCM) cells,
                      where the switching nonlinearity and SET/RESET asymmetry
                      relate tightly with the thermal resistance, ion mobility,
                      Schottky barrier height, parasitic resistance, and other
                      effects. The resulting dynamics require solving an ODE that
                      captures changes in oxygen vacancies. We modified a
                      physics-derived SPICE-level VCM model, integrated it with
                      the aihwkit simulator and tested the performance with the
                      MNIST dataset. Results show that noise that disrupts the
                      SET/RESET matching affects network performance the most.
                      This work serves as a tool for evaluating how physical
                      dynamics in memristive devices affect neural network
                      accuracy and can be used to guide the development of future
                      integrated devices.},
      month         = {Oct},
      date          = {2022-10-31},
      organization  = {2022 56th Asilomar Conference on
                       Signals, Systems, and Computers,
                       Pacific Grove (CA), 31 Oct 2022 - 2 Nov
                       2022},
      cin          = {PGI-15},
      cid          = {I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / BMBF
                      16ES1133K - Verbundprojekt: Neuro-inspirierte Technologien
                      der künstlichen Intelligenz für die Elektronik der Zukunft
                      - NEUROTEC -, Teilvorhaben: Forschungszentrum Jülich
                      (16ES1133K)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(BMBF)16ES1133K},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000976687600210},
      doi          = {10.1109/IEEECONF56349.2022.10052010},
      url          = {https://juser.fz-juelich.de/record/1022198},
}