  Neural Radiance Field (NeRF) is widely seen as an alternative to traditional
physically-based rendering. However, NeRF has not yet seen its adoption in
resource-limited mobile systems such as Virtual and Augmented Reality (VR/AR),
because it is simply extremely slow. On a mobile Volta GPU, even the
state-of-the-art NeRF models generally execute only at 0.8 FPS. We show that
the main performance bottlenecks are both algorithmic and architectural. We
introduce, CICERO, to tame both forms of inefficiencies. We first introduce two
algorithms, one fundamentally reduces the amount of work any NeRF model has to
execute, and the other eliminates irregular DRAM accesses. We then describe an
on-chip data layout strategy that eliminates SRAM bank conflicts. A pure
software implementation of CICERO offers an 8.0x speed-up and 7.9x energy
saving over a mobile Volta GPU. When compared to a baseline with a dedicated
DNN accelerator, our speed-up and energy reduction increase to 28.2x and 37.8x,
respectively - all with minimal quality loss (less than 1.0 dB peak
signal-to-noise ratio reduction).
