Preprint

FZJ-2025-05765

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png FPGA-Based Real-Time Waveform Classification

Aksoy, A.FZJ* ; Bekman, I. (Corresponding author)FZJ* ; Eguzo, C.FZJ* ; Grewing, C.FZJ* ; Zambanini, A.FZJ*

2025

This record in other databases:

Report No.: arXiv:2511.05479

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.

Note: TWEPP25 proceedings paper pre-print

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Contributing Institute(s):

1. Integrated Computing Architectures (PGI-4)

Research Program(s):

1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)

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