TY  - EJOUR
AU  - Aksoy, Alperen
AU  - Bekman, Ilja
AU  - Eguzo, Chimezie
AU  - Grewing, Christian
AU  - Zambanini, Andre
TI  - FPGA-Based Real-Time Waveform Classification
IS  - arXiv:2511.05479
M1  - FZJ-2025-05765
M1  - arXiv:2511.05479
PY  - 2025
N1  - TWEPP25 proceedings paper pre-print
AB  - 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.
LB  - PUB:(DE-HGF)25
UR  - https://juser.fz-juelich.de/record/1050051
ER  -