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....

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Yang, Mengtian, Wang, Yipeng, Lo, Chieh-Pu, Zhang, Xiuhao, Oruganti, Sirish, Kulkarni, Jaydeep P.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
DOI:10.1109/DAC63849.2025.11133032