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@PROCEEDINGS{Lohoff:1016966,
      author       = {Lohoff, Jamie and Yu, Zhenming and Finkbeiner, Jan Robert
                      and Kaya, Anil and Stewart, Kenneth and Wai Lui, Hin and
                      Neftci, Emre},
      title        = {{I}nterfacing {N}euromorphic {H}ardware with {M}achine
                      {L}earning {F}rameworks - {A} {R}eview},
      publisher    = {ACM New York, NY, USA},
      reportid     = {FZJ-2023-03873},
      year         = {2023},
      abstract     = {With the emergence of neuromorphic hardware as a promising
                      low-power parallel computing platform, the need for tools
                      that allowresearchers and engineers to efficiently interact
                      with such hardwareis rapidly growing. Machine learning
                      frameworks like Tensorflow,PyTorch and JAX have been
                      instrumental for the success of machinelearning in recent
                      years as they enable seamless interaction withtraditional
                      machine learning accelerators such as GPUs and TPUs.In stark
                      contrast, interfacing with neuromorphic hardware
                      remainsdifficult since the aforementioned frameworks do not
                      address thechallenges associated with mapping neural network
                      models and al-gorithms to physical hardware. In this paper,
                      we review the variousstrategies employed throughout the
                      neuromorphic computing com-munity to tackle these challenges
                      and categorize them according totheir methodologies and
                      implementation effort. This classificationserves as a
                      guideline for device engineers and software developersalike
                      to enable them to choose the best-fit solution in regard of
                      theirdemands and available resources. Finally, we provide a
                      JAX-basedproof-of-concept implementation of a compilation
                      pipeline tailoredto the needs of researchers in the early
                      stages of device develop-ment, where parts of the
                      computational graph can be mapped ontocustom hardware via
                      operations exposed through a C++ or Pythoninterface. The
                      code is available at https://github.com/PGI15/xbarax.},
      month         = {Aug},
      date          = {2023-08-03},
      organization  = {International Conference on
                       Neuromorphic Systems, Santa Fe (USA), 3
                       Aug 2023 - 5 Aug 2023},
      cin          = {PGI-15},
      cid          = {I:(DE-Juel1)PGI-15-20210701},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / BMBF
                      16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien
                      der künstlichen Intelligenz für die Elektronik der Zukunft
                      - NEUROTEC II - (BMBF-16ME0398K) / BMBF 16ME0399 -
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC II - (BMBF-16ME0399)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K /
                      G:(DE-82)BMBF-16ME0399},
      typ          = {PUB:(DE-HGF)26},
      doi          = {10.1145/3589737.3605967},
      url          = {https://juser.fz-juelich.de/record/1016966},
}