Publications

2024

1. ASPLOS24

   GSCore: Efficient Radiance Field Rendering via Architectural Support for 3D Gaussian Splatting

   Junseo Lee, Seokwon Lee , Jungi Lee , Junyong Park , and Jaewoong Sim

   In Proceedings of the 2024 International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) , San Diego, CA, 2024

   Abs PDF ACM DL Lightning Talk Poster Slides

   This paper presents GSCore, a hardware acceleration unit that efficiently executes the rendering pipeline of 3D Gaussian Splatting with algorithmic optimizations. GSCore builds on the observations from an in-depth analysis of Gaussian-based radiance field rendering to enhance computational efficiency and bring the technique to wide adoption. In doing so, we present several optimization techniques, Gaussian shape-aware intersection test, hierarchical sorting, and subtile skipping, all of which are synergistically integrated with GSCore. We implement the hardware design of GSCore, synthesize it using a commercial 28nm technology, and evaluate the performance across a range of synthetic and real-world scenes with varying image resolutions. Our evaluation results show that GSCore achieves a 15.86× speedup on average over the mobile consumer GPU with a substantially smaller area and lower energy consumption.

2023

1. ISCA-50

   NeuRex: A Case for Neural Rendering Acceleration

   Junseo Lee, Kwanseok Choi , Jungi Lee , Seokwon Lee , Joonho Whangbo , and Jaewoong Sim

   In Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA) , Orlando, FL, 2023

   Abs PDF ACM DL Lightning Talk Slides

   This paper presents NeuRex, an accelerator architecture that efficiently performs the modern neural rendering pipeline with an algorithmic enhancement and supporting hardware. NeuRex leverages the insights from an in-depth analysis of the state-of-the-art neural scene representation to make the multi-resolution hash encoding, which is the key operational primitive in modern neural renderings, more hardware-friendly and features a specialized hash encoding engine that enables us to effectively perform the primitive and the overall rendering pipeline. We implement and synthesize NeuRex using a commercial 28nm process technology and evaluate two versions of NeuRex (NeuRex-Edge, NeuRex-Server) on a range of scenes with different image resolutions for mobile and high-end computing platforms. Our evaluation shows that NeuRex achieves up to 9.88x and 3.11x speedups against the mobile and high-end consumer GPUs with a substantially small area overhead and lower energy consumption.