3D Gaussian Splatting (3DGS) is a breakthrough in 3D reconstruction using 3D Gaussians. However, even on highend GPUs like the NVIDIA A100, reconstructing complex scenes remains time-consuming, taking over 15 minutes. The main bottleneck is α-computation, which accounts for 71.25 % of training workload, yet 93.03 % of it is invalid due to the localized influence of Gaussians. To address this issue, we propose Cambricon-GS, an accelerator for 3DGS training with Gaussian-Pixel hybrid parallelism. At the software level, we introduce a hybrid parallel workflow that breaks the limitation of conventional pixel-only parallelism through two key techniques: Center-Pixel Gaussian Culling (CPGC), which eliminates invalid Gaussians early, and SeedDriven Gaussian Region Exploration (SDGRE), which reduces invalid computation for partially valid Gaussians by selectively exploring valid regions. Overall, the workflow significantly reduces α computations, lowering the workload to 17.99 %. At the hardware level, Cambricon-GS decouples α-computation and α blending into GUnits and PUnits, organized in a 2D mesh-based NoC that supports asynchronous execution and efficient data routing. We further boost performance via Gaussian/Pixel load balancing and tiled SSIM based pipelining. The evaluation results show that Cambricon-GS achieves 19.63×, 14.86×, 15.42×, 2.98× and 2.63× speedup, and 78.62×, 63.00×, 61.72×, 3.89× and 3.22× energy saving, compared to A100, GSCore, GBU, GSArch, and GauSPU, respectively, with negligible image quality loss.

Neural Scene Representation (NSR) is a promising technique for representing real scenes.By learning from dozens of 2D photos captured from different viewpoints, NSR computes the 3D representation of real scenes.However, the performance of NSR processing running on GPU is insufficient for applications.Cambricon-R achieves high performance of more than 60 scenes per second, but at the cost of modeling quality.

Neural scene representation (NSR) initiates a new methodology of encoding a 3D scene with neural networks by learning from dozens of photos taken from different camera positions. NSR not only achieves significant improvement in the quality of novel view synthesis and 3D reconstruction but also reduces the camera cost from the expensive laser cameras to the cheap color cameras on the shelf. However, performing 3D scene encoding using NSR is far from real-time due to the extremely low hardware utilization (only utilization of hardware peak performance), which greatly limits its applications in real-time AR/VR interactions