NeuRex: A Case for Neural Rendering Acceleration
Junseo Lee, Kwanseok Choi, Jungi Lee, Seokwon Lee, Joonho Whangbo, Jaewoong Sim · 2023
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 9.88× and 3.11× speedups against the mobile and high-end consumer GPUs with a substantially small area overhead and lower energy consumption.