# Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining

arXiv: https://arxiv.org/abs/2507.05099 · HTML: https://mlsanity.com/paper/2507.05099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Reconfigurable Computing Challenge: Real-Time Graph Neural Networks for Online Event Selection in Big Science](https://mlsanity.com/paper/2605.10612/) (2026)

## Abstract (arXiv)

Graph-based Point Cloud Networks (PCNs) are powerful tools for processing sparse sensor data with irregular geometries, as found in high-energy physics detectors. However, deploying models in such environments remains challenging due to stringent real-time requirements for both latency, and throughput. In this work, we present a deeply pipelined dataflow architecture for executing graph-based PCNs on FPGAs. Our method supports efficient processing of dynamic, sparse point clouds while meeting hard real-time constraints. We introduce specialized processing elements for core graph operations, such as GraVNet convolution and condensation point clustering, and demonstrate our design on the AMD Versal VCK190. Compared to a GPU baseline, our FPGA implementation achieves up to 5.25x speedup in throughput while maintaining latencies below 10 μs, satisfying the demands of real-time trigger systems in particle physics experiments. An open-source reference implementation is provided.