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@ARTICLE{Karilanova:1038217,
      author       = {Karilanova, Sanja and Fabre, Maxime and Neftci, Emre and
                      Özçelikkale, Ayça},
      title        = {{Z}ero-{S}hot {T}emporal {R}esolution {D}omain {A}daptation
                      for {S}piking {N}eural {N}etworks},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-01253},
      year         = {2024},
      abstract     = {Spiking Neural Networks (SNNs) are biologically-inspired
                      deep neural networks that efficiently extract temporal
                      information while offering promising gains in terms of
                      energy efficiency and latency when deployed on neuromorphic
                      devices. However, SNN model parameters are sensitive to
                      temporal resolution, leading to significant performance
                      drops when the temporal resolution of target data at the
                      edge is not the same with that of the pre-deployment source
                      data used for training, especially when fine-tuning is not
                      possible at the edge. To address this challenge, we propose
                      three novel domain adaptation methods for adapting neuron
                      parameters to account for the change in time resolution
                      without re-training on target time-resolution. The proposed
                      methods are based on a mapping between neuron dynamics in
                      SNNs and State Space Models (SSMs); and are applicable to
                      general neuron models. We evaluate the proposed methods
                      under spatio-temporal data tasks, namely the audio keyword
                      spotting datasets SHD and MSWC as well as the image
                      classification NMINST dataset. Our methods provide an
                      alternative to - and in majority of the cases significantly
                      outperform - the existing reference method that simply
                      scales the time constant. Moreover, our results show that
                      high accuracy on high temporal resolution data can be
                      obtained by time efficient training on lower temporal
                      resolution data and model adaptation.},
      keywords     = {Machine Learning (cs.LG) (Other) / FOS: Computer and
                      information sciences (Other)},
      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)25},
      doi          = {10.48550/ARXIV.2411.04760},
      url          = {https://juser.fz-juelich.de/record/1038217},
}