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About this paper
Marc Neu, Frank Baptist, Thomas Lobmaier, Fabio Papagno, Torben Ferber, Jürgen Becker
The research introduces a real-time deployment of a dynamic Graph Neural Network on an AMD Versal heterogeneous System-on-Chip for online event selection in particle physics, demonstrating a 53% throughput increase over FPGA-only solutions while significantly reducing FPGA DSP utilization at sub-10 μs latency.
Key insights
- Heterogeneous SoC platforms like AMD Versal effectively overcome resource bottlenecks of traditional FPGA-only GNN deployments by offloading regular computations to specialized AI Engines.
- A tailored semi-automated design flow, incorporating specific optimizations like operator fusion and kernel-level tuning, is essential for achieving ultra-low latency and high throughput on complex hardware.
- Dynamic GNNs can be successfully deployed in hard real-time hardware triggers, making them viable for processing increasing data volumes from advanced particle detectors.
Summary by alphaXiv.
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