# SpNeRF: Memory Efficient Sparse Volumetric Neural Rendering Accelerator for Edge Devices

arXiv: https://arxiv.org/abs/2505.08191 · HTML: https://mlsanity.com/paper/2505.08191/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Compressing Volumetric Radiance Fields to 1 MB](https://mlsanity.com/paper/2211.16386/) (2022)
- [Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction](https://mlsanity.com/paper/2304.12467/) (2023)
- [RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Rendering](https://mlsanity.com/paper/2212.01120/) (2022)
- [Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights](https://mlsanity.com/paper/2007.00864/) (2020)
- [Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing](https://mlsanity.com/paper/2305.05766/) (2023)

## Built on by (most cited first, 1 of 1)

- [HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization](https://mlsanity.com/paper/2510.09010/) (2025)

## Abstract (arXiv)

Neural rendering has gained prominence for its high-quality output, which is crucial for AR/VR applications. However, its large voxel grid data size and irregular access patterns challenge real-time processing on edge devices. While previous works have focused on improving data locality, they have not adequately addressed the issue of large voxel grid sizes, which necessitate frequent off-chip memory access and substantial on-chip memory. This paper introduces SpNeRF, a software-hardware co-design solution tailored for sparse volumetric neural rendering. We first identify memory-bound rendering inefficiencies and analyze the inherent sparsity in the voxel grid data of neural rendering. To enhance efficiency, we propose novel preprocessing and online decoding steps, reducing the memory size for voxel grid. The preprocessing step employs hash mapping to support irregular data access while maintaining a minimal memory size. The online decoding step enables efficient on-chip sparse voxel grid processing, incorporating bitmap masking to mitigate PSNR loss caused by hash collisions. To further optimize performance, we design a dedicated hardware architecture supporting our sparse voxel grid processing technique. Experimental results demonstrate that SpNeRF achieves an average 21.07× reduction in memory size while maintaining comparable PSNR levels. When benchmarked against Jetson XNX, Jetson ONX, RT-NeRF.Edge and NeuRex.Edge, our design achieves speedups of 95.1×, 63.5×, 1.5× and 10.3×, and improves energy efficiency by 625.6×, 529.1×, 4×, and 4.4×, respectively.