GSAcc: Accelerate 3D Gaussian Splatting via Depth Speculation and Gaussian-centric Rasterization
D Gaussian Splatting (3DGS) has emerged as a promising real-time photorealistic radiance field rendering technique. Existing GPU and hardware accelerators face limitations due to insufficient parallelism in sequential rendering pipeline stages and the memory overhead associated with interim results....
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| Published in: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7 |
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| Format: | Conference Proceeding |
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IEEE
22.06.2025
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| Abstract | D Gaussian Splatting (3DGS) has emerged as a promising real-time photorealistic radiance field rendering technique. Existing GPU and hardware accelerators face limitations due to insufficient parallelism in sequential rendering pipeline stages and the memory overhead associated with interim results. This paper presents GSAcc, a hardware accelerator co-designed with dataflow to render compressed 3DGS models on edge platforms efficiently. GSAcc enhances 3DGS rendering performance through several key innovations. First, it introduces Gaussian depth speculation, parallelizing preprocessing and sorting tasks. Second, GSAcc adopts a Gaussian-centric dataflow that interleaves preprocessing and rasterization, allowing all rendering steps to execute concurrently without storing intermediate results. Finally, it employs dedicated hardware acceleration to address sorting and rasterization bottlenecks within the optimized dataflow. We implemented and synthesized GSAcc using Intel16 PDK and evaluated its performance on real-world 3DGS scenes. Compared with desktop GPUs, GSAcc achieves up to 1.66 \times 10^{4} \mathrm{x} Power-Performance-Area (PPA) improvement as well as 48.7 x energy savings. Additionally, GSAcc outperforms the state-of-the-art hardware accelerator GSCore with up to 2.3x PPA improvement and 2.9x energy savings. |
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| AbstractList | D Gaussian Splatting (3DGS) has emerged as a promising real-time photorealistic radiance field rendering technique. Existing GPU and hardware accelerators face limitations due to insufficient parallelism in sequential rendering pipeline stages and the memory overhead associated with interim results. This paper presents GSAcc, a hardware accelerator co-designed with dataflow to render compressed 3DGS models on edge platforms efficiently. GSAcc enhances 3DGS rendering performance through several key innovations. First, it introduces Gaussian depth speculation, parallelizing preprocessing and sorting tasks. Second, GSAcc adopts a Gaussian-centric dataflow that interleaves preprocessing and rasterization, allowing all rendering steps to execute concurrently without storing intermediate results. Finally, it employs dedicated hardware acceleration to address sorting and rasterization bottlenecks within the optimized dataflow. We implemented and synthesized GSAcc using Intel16 PDK and evaluated its performance on real-world 3DGS scenes. Compared with desktop GPUs, GSAcc achieves up to 1.66 \times 10^{4} \mathrm{x} Power-Performance-Area (PPA) improvement as well as 48.7 x energy savings. Additionally, GSAcc outperforms the state-of-the-art hardware accelerator GSCore with up to 2.3x PPA improvement and 2.9x energy savings. |
| Author | Kulkarni, Jaydeep P. Oruganti, Sirish Wang, Yipeng Lo, Chieh-Pu Zhang, Xiuhao Yang, Mengtian |
| Author_xml | – sequence: 1 givenname: Mengtian surname: Yang fullname: Yang, Mengtian email: mengtian.yang@utexas.edu organization: The University of Texas at Austin – sequence: 2 givenname: Yipeng surname: Wang fullname: Wang, Yipeng email: yipeng.wang@utexas.edu organization: The University of Texas at Austin – sequence: 3 givenname: Chieh-Pu surname: Lo fullname: Lo, Chieh-Pu email: kcplo@utexas.edu organization: The University of Texas at Austin – sequence: 4 givenname: Xiuhao surname: Zhang fullname: Zhang, Xiuhao email: zhxhao007@utexas.edu organization: The University of Texas at Austin – sequence: 5 givenname: Sirish surname: Oruganti fullname: Oruganti, Sirish email: sirishoruganti@utexas.edu organization: The University of Texas at Austin – sequence: 6 givenname: Jaydeep P. surname: Kulkarni fullname: Kulkarni, Jaydeep P. email: jaydeep@austin.utexas.edu organization: The University of Texas at Austin |
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| Snippet | D Gaussian Splatting (3DGS) has emerged as a promising real-time photorealistic radiance field rendering technique. Existing GPU and hardware accelerators face... |
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| SubjectTerms | 3D Gaussian Splatting Accelerator Architecture Energy conservation Energy efficiency Graphics processing units Hardware acceleration Pipelines Radiance Field Rendering Real-time systems Rendering (computer graphics) Software/Hardware Co-design Sorting Technological innovation Three-dimensional displays |
| Title | GSAcc: Accelerate 3D Gaussian Splatting via Depth Speculation and Gaussian-centric Rasterization |
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