We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a similarity measure in order to compare our locally constrained graph building approaches with commonly used k-nearest neighbour building approaches. To demonstrate the feasibility of our solution for particle physics applications, we implemented a real-time graph building approach in a case study for the Belle~II central drift chamber using Field-Programmable Gate Arrays~(FPGAs). Our presented solution adheres to all throughput and latency constraints currently present in the hardware-based trigger of the Belle~II experiment. We achieve constant time complexity at the expense of linear space complexity and thus prove that our automated methodology generates online graph building designs suitable for a wide range of particle physics applications. By enabling an hardware-accelerated pre-processing of graphs, we enable the deployment of novel Graph Neural Networks~(GNNs) in first level triggers of particle physics experiments.
@article{arxiv.2307.07289,
title = {Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics},
author = {Marc Neu and Juergen Becker and Philipp Dorwarth and Torben Ferber and Lea Reuter and Slavomira Stefkova and Kai Unger},
journal= {arXiv preprint arXiv:2307.07289},
year = {2024}
}