{"ID":2830350,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22139","arxiv_id":"2512.22139","title":"HLS4PC: A Parametrizable Framework For Accelerating Point-Based 3D Point Cloud Models on FPGA","abstract":"Point-based 3D point cloud models employ computation and memory intensive mapping functions alongside NN layers for classification/segmentation, and are executed on server-grade GPUs. The sparse, and unstructured nature of 3D point cloud data leads to high memory and computational demand, hindering real-time performance in safety critical applications due to GPU under-utilization. To address this challenge, we present HLS4PC, a parameterizable HLS framework for FPGA acceleration. Our approach leverages FPGA parallelization and algorithmic optimizations to enable efficient fixed-point implementations of both mapping and NN functions. We explore several hardware-aware compression techniques on a state-of-the-art PointMLP-Elite model, including replacing FPS with URS, parameter quantization, layer fusion, and input-points pruning, yielding PointMLP-Lite, a 4x less complex variant with only 2% accuracy drop on ModelNet40. Secondly, we demonstrate that the FPGA acceleration of the PointMLP-Lite results in 3.56x higher throughput than previous works. Furthermore, our implementation achieves 2.3x and 22x higher throughput compared to the GPU and CPU implementations, respectively.","short_abstract":"Point-based 3D point cloud models employ computation and memory intensive mapping functions alongside NN layers for classification/segmentation, and are executed on server-grade GPUs. The sparse, and unstructured nature of 3D point cloud data leads to high memory and computational demand, hindering real-time performanc...","url_abs":"https://arxiv.org/abs/2512.22139","url_pdf":"https://arxiv.org/pdf/2512.22139v1","authors":"[\"Amur Saqib Pal\",\"Muhammad Mohsin Ghaffar\",\"Faisal Shafait\",\"Christian Weis\",\"Norbert Wehn\"]","published":"2025-12-11T17:09:12Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AI\",\"cs.AR\",\"cs.RO\"]","methods":"[]","has_code":false}