Real-Time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics

Abstract 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 preprocessing of graphs, we enable the deployment of novel Graph Neural Networks (GNNs) in first-level triggers of particle physics experiments.