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@INPROCEEDINGS{Bekman:1047703,
      author       = {Bekman, Ilja and Eguzo, Chimezie Vincent},
      title        = {{FPGA}-{B}ased {R}eal-{T}ime {W}aveform {C}lassification
                      and {R}eduction in {P}article {D}etectors},
      reportid     = {FZJ-2025-04469},
      year         = {2025},
      abstract     = {The SHiP SBT self-triggered readout will process SiPM sum
                      signals sampled at 800 MSPS and 12 bit resolution to extract
                      calorimetric particle hit information. A waveform
                      classification is being deployed on the readout FPGA to
                      reduce the volume of transmitted data.The classification
                      divides events into expected signal, expected noise or
                      containing unexpected features applying different degrees of
                      compression.In this study, we consider LUT-based
                      neural-networks and address challenges of NN layout,
                      footprint and performance. Preliminary planning indicates
                      significant data reduction, easing constraints on
                      infrastructure.},
      month         = {Oct},
      date          = {2025-10-06},
      organization  = {Topical Workshop on Electronics for
                       Particle Physics 2025, Rethymno
                       (Greece), 6 Oct 2025 - 10 Oct 2025},
      subtyp        = {After Call},
      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)6},
      url          = {https://juser.fz-juelich.de/record/1047703},
}