Vorion: A RISC-V GPU with Hardware-Accelerated 3D Gaussian Rendering and Training
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909915925184512 |
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| author | Wang, Yipeng Yang, Mengtian Lo, Chieh-pu Kulkarni, Jaydeep P. |
| author_facet | Wang, Yipeng Yang, Mengtian Lo, Chieh-pu Kulkarni, Jaydeep P. |
| contents | 3D Gaussian Splatting (3DGS) has recently emerged as a foundational technique for real-time neural rendering, 3D scene generation, volumetric video (4D) capture. However, its rendering and training impose massive computation, making real-time rendering on edge devices and real-time 4D reconstruction on workstations currently infeasible. Given its fixed-function nature and similarity with traditional rasterization, 3DGS presents a strong case for dedicated hardware in the graphics pipeline of next-generation GPUs. This work, Vorion, presents the first GPGPU prototype with hardware-accelerated 3DGS rendering and training. Vorion features scalable architecture, minimal hardware change to traditional rasterizers, z-tiling to increase parallelism, and Gaussian/pixel-centric hybrid dataflow. We prototype the minimal system (8 SIMT cores, 2 Gaussian rasterizer) using TSMC 16nm FinFET technology, which achieves 19 FPS for rendering. The scaled design with 16 rasterizers achieves 38.6 iterations/s for training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vorion: A RISC-V GPU with Hardware-Accelerated 3D Gaussian Rendering and Training Wang, Yipeng Yang, Mengtian Lo, Chieh-pu Kulkarni, Jaydeep P. Hardware Architecture Graphics 3D Gaussian Splatting (3DGS) has recently emerged as a foundational technique for real-time neural rendering, 3D scene generation, volumetric video (4D) capture. However, its rendering and training impose massive computation, making real-time rendering on edge devices and real-time 4D reconstruction on workstations currently infeasible. Given its fixed-function nature and similarity with traditional rasterization, 3DGS presents a strong case for dedicated hardware in the graphics pipeline of next-generation GPUs. This work, Vorion, presents the first GPGPU prototype with hardware-accelerated 3DGS rendering and training. Vorion features scalable architecture, minimal hardware change to traditional rasterizers, z-tiling to increase parallelism, and Gaussian/pixel-centric hybrid dataflow. We prototype the minimal system (8 SIMT cores, 2 Gaussian rasterizer) using TSMC 16nm FinFET technology, which achieves 19 FPS for rendering. The scaled design with 16 rasterizers achieves 38.6 iterations/s for training. |
| title | Vorion: A RISC-V GPU with Hardware-Accelerated 3D Gaussian Rendering and Training |
| topic | Hardware Architecture Graphics |
| url | https://arxiv.org/abs/2511.16831 |