FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing
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arXiv
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| Format: | Preprint |
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2025
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| author | Fu, Yuzhe Zhou, Changchun Ye, Hancheng Duan, Bowen Huang, Qiyu Wei, Chiyue Guo, Cong Li, Hai "Helen'' Chen, Yiran |
| author_facet | Fu, Yuzhe Zhou, Changchun Ye, Hancheng Duan, Bowen Huang, Qiyu Wei, Chiyue Guo, Cong Li, Hai "Helen'' Chen, Yiran |
| contents | Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis, originally targeting small-scale inputs. However, as PNNs evolve to process large-scale point clouds with hundreds of thousands of points, all-to-all computation and global memory access in point cloud processing introduce substantial overhead, causing $O(n^2)$ computational complexity and memory traffic where n is the number of points}. Existing accelerators, primarily optimized for small-scale workloads, overlook this challenge and scale poorly due to inefficient partitioning and non-parallel architectures. To address these issues, we propose FractalCloud, a fractal-inspired hardware architecture for efficient large-scale 3D point cloud processing. FractalCloud introduces two key optimizations: (1) a co-designed Fractal method for shape-aware and hardware-friendly partitioning, and (2) block-parallel point operations that decompose and parallelize all point operations. A dedicated hardware design with on-chip fractal and flexible parallelism further enables fully parallel processing within limited memory resources. Implemented in 28 nm technology as a chip layout with a core area of 1.5 $mm^2$, FractalCloud achieves 21.7x speedup and 27x energy reduction over state-of-the-art accelerators while maintaining network accuracy, demonstrating its scalability and efficiency for PNN inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing Fu, Yuzhe Zhou, Changchun Ye, Hancheng Duan, Bowen Huang, Qiyu Wei, Chiyue Guo, Cong Li, Hai "Helen'' Chen, Yiran Hardware Architecture Artificial Intelligence Three-dimensional (3D) point clouds are increasingly used in applications such as autonomous driving, robotics, and virtual reality (VR). Point-based neural networks (PNNs) have demonstrated strong performance in point cloud analysis, originally targeting small-scale inputs. However, as PNNs evolve to process large-scale point clouds with hundreds of thousands of points, all-to-all computation and global memory access in point cloud processing introduce substantial overhead, causing $O(n^2)$ computational complexity and memory traffic where n is the number of points}. Existing accelerators, primarily optimized for small-scale workloads, overlook this challenge and scale poorly due to inefficient partitioning and non-parallel architectures. To address these issues, we propose FractalCloud, a fractal-inspired hardware architecture for efficient large-scale 3D point cloud processing. FractalCloud introduces two key optimizations: (1) a co-designed Fractal method for shape-aware and hardware-friendly partitioning, and (2) block-parallel point operations that decompose and parallelize all point operations. A dedicated hardware design with on-chip fractal and flexible parallelism further enables fully parallel processing within limited memory resources. Implemented in 28 nm technology as a chip layout with a core area of 1.5 $mm^2$, FractalCloud achieves 21.7x speedup and 27x energy reduction over state-of-the-art accelerators while maintaining network accuracy, demonstrating its scalability and efficiency for PNN inference. |
| title | FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing |
| topic | Hardware Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2511.07665 |