Rendering and inverse-rendering algorithms that drive conventional computer graphics have recently been superseded by neural representations (NR).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学的渲染与逆向渲染算法近期已被神经表示所取代。神经表示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替代传统渲染算法。本研究提出核心问题：神经图形学是否需要硬件支持？通过分析代表性神经图形学应用发现，若要在当前GPU上实现4K分辨率、60FPS的渲染效果，存在1.5倍至55倍的性能缺口。对于增强现实/虚拟现实应用，所需性能与系统功耗之间的缺口更为显著，达到2-4个数量级。研究识别出输入编码和MLP内核是性能瓶颈，在多重分辨率哈希网格、多重分辨率密集网格和低分辨率密集网格编码中分别消耗了72%、60%和59%的应用运行时间。我们提出神经图形处理集群——一种可扩展的灵活硬件架构，通过专用引擎直接加速输入编码和MLP内核，并支持多种神经图形学应用。同时通过Vulkan融合其余内核，使预处理和后处理内核相比未融合实现获得9.94倍的内核级性能提升。实验表明，采用多重分辨率哈希网格编码时，神经图形处理集群在四个神经图形学应用上实现最高58倍的端到端应用级性能提升，其中缩放因子为8、16、32和64时平均性能增益分别为12倍、20倍、33倍和39倍。结果表明，结合多重分辨率哈希网格编码，神经图形处理集群可实现NeRF的4K@30FPS渲染，以及所有其他神经图形学应用的8K@120FPS渲染。