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@INPROCEEDINGS{Boeshertz:1038063,
      author       = {Boeshertz, Gauthier and Indiveri, Giacomo and Nair, Manu
                      and Renner, Alpha},
      title        = {{A}ccurate {M}apping of {RNN}s on {N}euromorphic {H}ardware
                      with {A}daptive {S}piking {N}eurons},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-01112},
      pages        = {376-380},
      year         = {2024},
      abstract     = {Thanks to their parallel and sparse activity features,
                      recurrent neural networks (RNNs) are well-suited for
                      hardware implementation in low-power neuromorphic hardware.
                      However, mapping rate-based RNNs to hardware-compatible
                      spiking neural networks (SNNs) remains challenging. Here, we
                      present a ΣΔ−low-pass RNN (lpRNN): an RNN architecture
                      employing an adaptive spiking neuron model that encodes
                      signals using ΣΔ-modulation and enables precise mapping.
                      The ΣΔ-neuron communicates graded values using spike
                      timing, and the dynamics of the IpRNN are set to match
                      typical timescales for processing natural signals, such as
                      speech. Our approach integrates rate and temporal coding,
                      offering a robust solution for efficient and accurate
                      conversion of RNNs to SNNs. We demonstrate the
                      implementation of the IpRNN on Intel's neuromorphic research
                      chip Loihi, achieving state-of-the-art classification
                      results on audio benchmarks using 3-bit weights. These
                      results call for a deeper investigation of recurrency and
                      adaptation in event-based systems, which may lead to
                      insights for edge computing applications where
                      power-efficient real-time inference is required.},
      month         = {Jul},
      date          = {2024-07-30},
      organization  = {2024 International Conference on
                       Neuromorphic Systems (ICONS), Arlington
                       (VA), 30 Jul 2024 - 2 Aug 2024},
      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)8},
      UT           = {WOS:001462433900056},
      doi          = {10.1109/ICONS62911.2024.00064},
      url          = {https://juser.fz-juelich.de/record/1038063},
}