Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
Conference
·
OSTI ID:1823535
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1823535
- Report Number(s):
- FERMILAB-CONF-20-622-CMS-SCD; arXiv:2012.01563; oai:inspirehep.net:1834621
- Country of Publication:
- United States
- Language:
- English
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