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

arXiv: https://arxiv.org/abs/2307.07289 · HTML: https://mlsanity.com/paper/2307.07289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2023.

## Built on by (most cited first, 2 of 2)

- [Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision](https://mlsanity.com/paper/2401.04988/) (2024)
- [BitHEP -- The Limits of Low-Precision ML in HEP](https://mlsanity.com/paper/2504.03387/) (2025)

## Abstract (arXiv)

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.