Hardware Acceleration of Neural Graphics

Rendering and inverse rendering techniques have recently attained powerful new capabilities and building blocks in the form of neural representations (NR), with derived rendering techniques quickly becoming indispensable tools next to classic computer graphics algorithms, covering a wide range of functions throughout the full pipeline from sensing to pixels. NRs have recently been used to directly learn the geometric and appearance properties of scenes that were previously hard to capture, and to re-synthesize photo realistic imagery based on this information, thereby promising simplifications and replacements for several complex traditional computer graphics problems and algorithms with scalable quality and predictable performance. In this work we ask the question: Does neural graphics (graphics based on NRs) need hardware support? We studied four representative neural graphics applications (NeRF, NSDF, NVR, and GIA) showing that, if we want to render 4k resolution frames at 60 frames per second (FPS) there is a gap of 1.51× to 55.50× in the desired performance on current GPUs. For AR and VR applications, there is an even larger gap of 2--4 orders of magnitude (OOM) between the desired performance and the required system power. We identify that the input encoding and the multi-layer perceptron kernels are the performance bottlenecks, consuming 72.37%, 60.0% and 59.96% of application time for multi resolution hashgrid encoding, multi resolution densegrid encoding and low resolution densegrid encoding, respectively. We propose a neural graphics processing cluster (NGPC) - a scalable and flexible hardware architecture that directly accelerates the input encoding and multi-layer perceptron kernels through dedicated engines and supports a wide range of neural graphics applications. To achieve good overall application level performance improvements, we also accelerate the rest of the kernels by fusion into a single kernel, leading to a 9.94× speedup compared to previous optimized implementations [17] which is sufficient to remove this performance bottleneck. Our results show that, NGPC gives up to 58.36× end-to-end application-level performance improvement, for multi resolution hashgrid encoding on average across the four neural graphics applications, the performance benefits are 12.94×, 20.85×, 33.73× and 39.04× for the hardware scaling factor of 8, 16, 32 and 64, respectively. Our results show that with multi resolution hashgrid encoding, NGPC enables the rendering of 4k Ultra HD resolution frames at 30 FPS for NeRF and 8k Ultra HD resolution frames at 120 FPS for all our other neural graphics applications.