% 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”.

@ARTICLE{Aksoy:1050051,
      author       = {Aksoy, Alperen and Bekman, Ilja and Eguzo, Chimezie and
                      Grewing, Christian and Zambanini, Andre},
      title        = {{FPGA}-{B}ased {R}eal-{T}ime {W}aveform {C}lassification},
      reportid     = {FZJ-2025-05765, arXiv:2511.05479},
      year         = {2025},
      note         = {TWEPP25 proceedings paper pre-print},
      abstract     = {For self-triggered readout of SiPM sum signals, a waveform
                      classification can aid a simple threshold trigger to
                      reliably extract calorimetric particle hit information
                      online at an early stage and thus reduce the volume of
                      transmitted data. Typically, the ADC data acquisition is
                      based on FPGAs for edge data processing. In this study, we
                      consider look-up-table-based neural-networks and address
                      challenges of binary multi-layer neural networks' layout,
                      footprint, performance and training. We show that these
                      structures can be trained using a genetic algorithm and
                      achieve the inference latency compatible with dead-time free
                      processing online.},
      cin          = {PGI-4},
      cid          = {I:(DE-Juel1)PGI-4-20110106},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2511.05479},
      howpublished = {arXiv:2511.05479},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2511.05479;\%\%$},
      url          = {https://juser.fz-juelich.de/record/1050051},
}