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Found 5 papers, 2 papers with code
Knowledge Distillation for Anomaly Detection
Unsupervised deep learning techniques are widely used to identify anomalous behaviour.
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints.
Graph Neural Networks for Charged Particle Tracking on FPGAs
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC).
Charged particle tracking via edge-classifying interaction networks
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks.
