6 papers
Commissioning and Low Latency Operation of the Graph Neural Network Electromagnetic Calorimeter Trigger at the Belle II Experiment
M. Neu, F. Baptist, I. Haide +10
We present the commissioning and operation of the Graph Neural Network Electromagnetic Calorimeter Trigger Module (GNN-ETM) of the Belle II experiment at the SuperKEKB collider. Th…
RTL Fault Injection of a Deployed Graph Neural Network Trigger for Belle II
Georgios Sotiropoulos, Marc Neu, Tanja Harbaum +2
As particle physics detectors grow in scale, High Energy Physics experiments must process ever-increasing data volumes. Level-1 trigger systems, implemented on Field-Programmable G…
Reconfigurable Computing Challenge: Real-Time Graph Neural Networks for Online Event Selection in Big Science
Marc Neu, Frank Baptist, Thomas Lobmaier +3
Graph neural networks are increasingly adopted in trigger systems for collider experiments, where strict latency and throughput constraints render deployment on embedded platforms…
Real-Time Stream Compaction for Sparse Machine Learning on FPGAs
Marc Neu, Isabel Haide, Torben Ferber +1
Machine learning algorithms are being used more frequently in the first-level triggers in collider experiments, with Graph Neural Networks pushing the hardware requirements of FPGA…
Hardware-Accelerated GNN-based Hit Filtering for the Belle II Level-1 Trigger
Greta Heine, Fabio Mayer, Marc Neu +2
We present a hardware-accelerated hit filtering system employing Graph Neural Networks (GNNs) on Field-Programmable Gate Arrays (FPGAs) for the Belle II Level-1 Trigger. The GNN ex…
Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining
Marc Neu, Isabel Haide, Timo Justinger +4
Graph-based Point Cloud Networks (PCNs) are powerful tools for processing sparse sensor data with irregular geometries, as found in high-energy physics detectors. However, deployin…