Rendering and inverse-rendering algorithms that drive conventional computer graphics have recently been superseded by neural representations (NR). NRs have recently been used to learn the geometric and the material properties of the scenes and use the information to synthesize photorealistic imagery, thereby promising a replacement for traditional rendering algorithms with scalable quality and predictable performance. In this work we ask the question: Does neural graphics (NG) need hardware support? We studied representative NG applications showing that, if we want to render 4k res. at 60FPS there is a gap of 1.5X-55X in the desired performance on current GPUs. For AR/VR applications, there is an even larger gap of 2-4 OOM between the desired performance and the required system power. We identify that the input encoding and the MLP kernels are the performance bottlenecks, consuming 72%,60% and 59% of application time for multi res. hashgrid, multi res. densegrid and low res. densegrid encodings, respectively. We propose a NG processing cluster, a scalable and flexible hardware architecture that directly accelerates the input encoding and MLP kernels through dedicated engines and supports a wide range of NG applications. We also accelerate the rest of the kernels by fusing them together in Vulkan, which leads to 9.94X kernel-level performance improvement compared to un-fused implementation of the pre-processing and the post-processing kernels. Our results show that, NGPC gives up to 58X end-to-end application-level performance improvement, for multi res. hashgrid encoding on average across the four NG applications, the performance benefits are 12X,20X,33X and 39X for the scaling factor of 8,16,32 and 64, respectively. Our results show that with multi res. hashgrid encoding, NGPC enables the rendering of 4k res. at 30FPS for NeRF and 8k res. at 120FPS for all our other NG applications.

翻译：驱动传统计算机图形的渲染和逆渲染算法最近已被神经表示（NR）所取代。神经表示近年来被用于学习场景的几何与材质属性，并利用这些信息合成逼真图像，从而有望以可扩展的质量和可预测的性能取代传统渲染算法。本文提出疑问：神经图形（NG）是否需要硬件支持？我们针对代表性神经图形应用开展研究，结果表明：若要在当前GPU上实现4K分辨率60FPS渲染，存在1.5至55倍的性能差距；而对于AR/VR应用，所需性能与系统功耗之间的差距更大，达2至4个数量级。研究发现输入编码与多层感知机（MLP）核心是性能瓶颈，在多分辨率哈希网格、多分辨率密集网格和低分辨率密集网格编码中分别占用72%、60%和59%的应用处理时间。为此，我们提出神经图形处理簇（NGPC）——一种可扩展且灵活的硬件架构，通过专用引擎直接加速输入编码与MLP核心，并支持广泛的神经图形应用。同时，我们通过Vulkan融合其余核心，相较于预处理与后处理核心的非融合实现，获得9.94倍的核心级性能提升。实验结果表明，在四类神经图形应用中，NGPC对多分辨率哈希网格编码可实现最高58倍的端到端应用级性能提升；在缩放因子为8、16、32和64时，平均性能增益分别为12倍、20倍、33倍和39倍。研究还表明，采用多分辨率哈希网格编码时，NGPC可使NeRF达到4K分辨率30FPS渲染，并使其他神经图形应用实现8K分辨率120FPS渲染。