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

arXiv comment20 pages

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Cited by in corpus (4)

• End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II
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• BitHEP -- The Limits of Low-Precision ML in HEP
• Real-time graph neural networks on FPGAs for the Belle II electromagnetic calorimeter