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@INPROCEEDINGS{Neftci:1025209,
      author       = {Neftci, Emre},
      title        = {{P}re-training and {M}eta-learning for {M}emristor
                      {C}rossbar {A}rrays},
      reportid     = {FZJ-2024-02777},
      year         = {2023},
      abstract     = {Memristive crossbar arrays show promise as non-von Neumann
                      computing technologies, bringing sophisticated neural
                      network processing to the edge and facilitating real-world
                      online learning. However, their deployment for real-world
                      learning problems faces challenges such as non-linearities
                      in conductance updates, variations during operation,
                      fabrication mismatch, conductance drift, and the realities
                      of gradient descent training.This talk will present methods
                      to pre-train neural networks to be largely insensitive to
                      these non-idealities during learning tasks. These methods
                      rely on a phenomenological model of the device, obtainable
                      experimentally, and bi-level optimization. We showcase this
                      effect through meta-learning and a differentiable model of
                      conductance updates on few-shot learning tasks. Since
                      pre-training is a necessary procedure for any online
                      learning scenario at the edge, our results may pave the way
                      for real-world applications of memristive devices without
                      significant adaptation overhead.Furthermore, by considering
                      the programming of memristive devices as a learning problem
                      in its own right, we demonstrate that the developed methods
                      can accelerate existing write-verify techniques.},
      month         = {Feb},
      date          = {2024-02-21},
      organization  = {Neuronics Conference, València
                       (Spain), 21 Feb 2024 - 23 Feb 2024},
      subtyp        = {Invited},
      cin          = {PGI-15},
      cid          = {I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.29363/nanoge.neuronics.2024.027},
      url          = {https://juser.fz-juelich.de/record/1025209},
}