# Cicero: Addressing Algorithmic and Architectural Bottlenecks in Neural Rendering by Radiance Warping and Memory Optimizations

arXiv: https://arxiv.org/abs/2404.11852 · HTML: https://mlsanity.com/paper/2404.11852/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2024.

## Built on by (most cited first, 11 of 11)

- [GauRast: Enhancing GPU Triangle Rasterizers to Accelerate 3D Gaussian Splatting](https://mlsanity.com/paper/2503.16681/) (2025)
- [Neo: Real-Time On-Device 3D Gaussian Splatting with Reuse-and-Update Sorting Acceleration](https://mlsanity.com/paper/2511.12930/) (2025)
- [VR-Pipe: Streamlining Hardware Graphics Pipeline for Volume Rendering](https://mlsanity.com/paper/2502.17078/) (2025)
- [STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support](https://mlsanity.com/paper/2506.09070/) (2025)
- [Uni-Render: A Unified Accelerator for Real-Time Rendering Across Diverse Neural Renderers](https://mlsanity.com/paper/2503.23644/) (2025)
- [VersaQ-3D: Architecture Support for Visual Geometry Grounded Transformers via Versatile Quantization](https://mlsanity.com/paper/2601.20317/) (2026)
- [GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering](https://mlsanity.com/paper/2601.20429/) (2026)
- [DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization](https://mlsanity.com/paper/2608.02099/) (2026)
- [PatchINR: Patch-Based Implicit Neural Representations for Efficient and Scalable Inference](https://mlsanity.com/paper/2606.25534/) (2026)
- [Kaleido: Algorithm-Hardware Co-Design for Video Diffusion Transformers by Exploiting Latent Space Correlations](https://mlsanity.com/paper/2607.13770/) (2026)
- [Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inference](https://mlsanity.com/paper/2608.04428/) (2026)

## Abstract (arXiv)

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).