% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Neftci:1025210,
      author       = {Neftci, Emre and Yu, Zhenming and Leroux, Nathan},
      title        = {{T}raining-to-{L}earn with {M}emristive {D}evices},
      reportid     = {FZJ-2024-02778},
      year         = {2023},
      abstract     = {Memristive crossbar arrays are promising non-von Neumann
                      computing technologies to enable real-world, onlinelearning
                      in neural networks. However, their deployment to real-world
                      learning problems is hindered by their non-linearitiesin
                      conductance updates, variation during operation, fabrication
                      mismatch and the realities of gradient descent training. In
                      thiswork, we show that, with a phenomenological model of the
                      device and bi-level optimization, it is possible to
                      pre-train the neuralnetwork to be largely insensitive to
                      such non-idealities on learning tasks. We demonstrate this
                      effect using Model Agnostic Meta Learning (MAML) and a
                      differentiable model of the conductance update on the
                      Omniglot few-shot learning task. Since pre-training is a
                      necessary procedure for any on-line learning scenario at the
                      edge, our results may pave the way towards real-world
                      applications of memristive devices without significant
                      adaption overhead.},
      month         = {Jan},
      date          = {2023-01-23},
      organization  = {Neuromorphic Materials, Devices,
                       Circuits and Systems, València
                       (Spain), 23 Jan 2023 - 25 Jan 2023},
      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.neumatdecas.2023.013},
      url          = {https://juser.fz-juelich.de/record/1025210},
}