HyperGS: Efficient Real-Time 3D Gaussian Rendering Processor Through Hierarchical Sorting

This paper proposes HyperGS, the first complete hardware accelerator implementation for 3D Gaussian Splatting with algorithmic optimization. Through an in-depth analysis of the Gaussian rendering pipeline characteristics, we designed an efficient and practical hardware architecture with optimized resource utilization. Specifically, we proposed a parallel large-scale sorting unit to enable parallel processing of depth sorting and rasterization, saving 22% of computing time. We also designed a fully pipelined preprocessing calculation unit with low resource usage for model parameter preprocessing, generating key-value pairs. We propose an efficient rasterization unit based on factorization. The rendering pipeline has been improved by reducing preprocessing parameters and decreasing the number of external memory accesses. Experimental results show that HyperGS reduces power consumption by a factor of 84 and improves performance by a factor of 44 compared with existing NVIDIA Jetson GPUs. Using only 19.2GB/s of bandwidth, we achieved rendering speeds ranging from 20.76 to 43.38 frames per second. Our work provides a feasible solution for efficient real-time 3D Gaussian rendering on resource-constrained edge computing devices.