提出分形架构FractalCloud，高效处理超大规模点云。
FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing
- 用分形思想实现形状感知的分块划分，减少计算冗余。
- 支持块并行操作，在28nm芯片上实现21.7倍加速与27倍节能。
- 适合自动驾驶、机器人等需要实时处理海量点云的场景。
三维点云在自动驾驶、机器人和虚拟现实等领域应用日益广泛。基于点的神经网络（PNNs）在点云分析中表现优异，但最初针对小规模输入设计。当处理包含数十万点的大规模点云时，全连接计算与全局内存访问带来显著开销，导致计算复杂度达O(n²)，内存流量剧增。现有加速器主要针对小规模任务优化，对大规模负载缺乏有效支持，因分区效率低且架构非并行而扩展性差。为此，我们提出FractalCloud——一种受分形启发的硬件架构，用于高效处理大规模3D点云。该架构引入两项关键优化：(1) 联合设计的分形方法，实现形状感知且硬件友好的分块划分；(2) 块级并行点操作，将所有点操作分解并行执行。专用硬件设计结合片上分形结构与灵活并行机制，可在有限内存下实现完全并行处理。该芯片在28 nm工艺下实现核心面积1.5 mm²，相较当前最先进加速器，推理速度提升21.7倍，能耗降低27倍，同时保持网络精度，验证了其在大规模点云处理中的可扩展性与高效性。
原文摘要 · Abstract (English)
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.
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