NeuRex: A Case for Neural Rendering Acceleration
Junseo Lee, Kwanseok Choi, Jungi Lee, Seokwon Lee, Joonho Whangbo, Jaewoong Sim
Abstract
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.