Recently, 3D Gaussian Splatting (3DGS) has emerged as a mainstream rendering technique due to its photorealistic quality and low latency. However, processing massive numbers of non-contributing Gaussian points introduces significant computational overhead on resource-limited edge platforms, limiting its deployment in next-generation AR/VR devices. Contribution-based prior skipping alleviates this inefficiency, yet the resulting contribution-testing workload becomes prohibitive for edge execution. In this paper, we present FLICKER, a contribution-aware 3DGS accelerator based on hardware-software co-design. The proposed framework integrates adaptive leader pixels, pixel-rectangle grouping, hierarchical Gaussian testing, and a mixed-precision architecture to enable near pixel-level, contribution-driven rendering with minimal overhead. Experimental results demonstrate up to $1.5\times$ speedup, $2.6\times$ improvement in energy efficiency, and $14%$ area reduction compared with a state-of-the-art accelerator. Compared with a representative edge GPU, FLICKER achieves a $19.8\times$ speedup and $26.7\times$ higher energy efficiency.

翻译：近年来，3D高斯溅射（3DGS）凭借其逼真的渲染质量和低延迟特性，已成为主流的渲染技术。然而，在资源受限的边缘平台上处理大量无贡献的高斯点会带来显著的计算开销，限制了其在下一代增强现实/虚拟现实设备中的部署。基于贡献度的先验跳过机制虽能缓解这一低效问题，但由此产生的贡献度测试工作负载对边缘设备而言仍难以承受。本文提出FLICKER，一种基于软硬件协同设计的贡献感知型3DGS加速器。该框架集成了自适应引导像素、像素矩形分组、分层高斯测试以及混合精度架构，能够以极低开销实现接近像素级的贡献驱动渲染。实验结果表明，与最先进的加速器相比，FLICKER实现了最高$1.5\times$的加速比、$2.6\times$的能效提升以及$14\%$的面积缩减。与典型的边缘GPU相比，FLICKER获得了$19.8\times$的加速比和$26.7\times$的能效提升。