标题： GCC：一种具有逐高斯和跨阶段条件处理的3DGS推理架构 显示英文标题

标题： GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing

摘要： 3D高斯点云（3DGS）已成为高质量视图合成的领先神经渲染技术，这促使了针对移动应用的专用3DGS加速器的发展。 通过深入分析，我们发现现有加速器采用的传统解耦预处理-渲染数据流存在两个主要限制：1）大量预处理的高斯分布在渲染中未被使用，2）相同的高斯分布在不同的图块渲染中被重复加载，导致大量的计算和数据移动开销。 为了解决这些问题，我们提出了GCC，一种专为快速且节能的3DGS推理设计的新型加速器。 在数据流层面，GCC引入了：1）跨阶段条件处理，将预处理和渲染交错进行，以动态跳过不必要的高斯预处理；以及 2）按高斯分布的渲染，确保对给定高斯分布的所有渲染操作在移动到下一个高斯分布之前完成，从而消除重复的高斯加载。 我们还提出了一种基于alpha的边界识别方法，以获得紧凑且精确的高斯区域，从而降低渲染成本。 我们在28nm技术中实现了我们的GCC加速器。 大量实验表明，GCC在性能和能效方面显著优于最先进的3DGS推理加速器GSCore。 显示英文摘要

摘要： 3D Gaussian Splatting (3DGS) has emerged as a leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for mobile applications. Through in-depth analysis, we identify two major limitations in the conventional decoupled preprocessing-rendering dataflow adopted by existing accelerators: 1) a significant portion of preprocessed Gaussians are not used in rendering, and 2) the same Gaussian gets repeatedly loaded across different tile renderings, resulting in substantial computational and data movement overhead. To address these issues, we propose GCC, a novel accelerator designed for fast and energy-efficient 3DGS inference. At the dataflow level, GCC introduces: 1) cross-stage conditional processing, which interleaves preprocessing and rendering to dynamically skip unnecessary Gaussian preprocessing; and 2) Gaussian-wise rendering, ensuring that all rendering operations for a given Gaussian are completed before moving to the next, thereby eliminating duplicated Gaussian loading. We also propose an alpha-based boundary identification method to derive compact and accurate Gaussian regions, thereby reducing rendering costs. We implement our GCC accelerator in 28nm technology. Extensive experiments demonstrate that GCC significantly outperforms the state-of-the-art 3DGS inference accelerator, GSCore, in both performance and energy efficiency.