Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910376666333184 |
|---|---|
| author | Neu, Marc Becker, Juergen Dorwarth, Philipp Ferber, Torben Reuter, Lea Stefkova, Slavomira Unger, Kai |
| author_facet | Neu, Marc Becker, Juergen Dorwarth, Philipp Ferber, Torben Reuter, Lea Stefkova, Slavomira Unger, Kai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_07289 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics Neu, Marc Becker, Juergen Dorwarth, Philipp Ferber, Torben Reuter, Lea Stefkova, Slavomira Unger, Kai High Energy Physics - Experiment Signal Processing 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. |
| title | Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics |
| topic | High Energy Physics - Experiment Signal Processing |
| url | https://arxiv.org/abs/2307.07289 |