{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/real-time-graph-based-point-cloud-networks-on","title":"Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining","arxiv_id":"2507.05099","date":"2025-07-07","proceeding":null,"authors":["Marc Neu","Isabel Haide","Timo Justinger","Till Rädler","Valdrin Dajaku","Torben Ferber","Jürgen Becker"],"abstract":"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 {\\mu}s, satisfying the demands of real-time trigger systems in particle physics experiments. An open-source reference implementation is provided.","url_abs":"https://arxiv.org/abs/2507.05099v1","url_pdf":"https://arxiv.org/pdf/2507.05099v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"real-time-graph-based-point-cloud-networks-on","repo_url":"https://github.com/choocie/pcnhlslib","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}