Work

An Overview of Neural Rendering Accelerators: Challenges, Trends, and Future Directions

Year: 2025

Type: article

Abstract: Rapid advancements in neural rendering have revolutionized the fields of augmented reality (AR) and virtual reality (VR) by enabling photorealistic 3D modeling and rendering. However, deploying neural rendering on edge devices presents significant challenges due to computational complexity, memory inefficiencies, and energy constraints. This paper provides a comprehensive overview of neural rendering accelerators, identifying the major hardware inefficiencies across sampling, positional encoding, and multi-layer perception (MLP) stages. We explore hardware-software co-optimization techniques that address these challenges and provide a summary for in-depth analysis. Additionally, emerging trends like 3D Gaussian Splatting (3DGS) and hybrid rendering approaches are briefly introduced, highlighting their potential to improve rendering quality and efficiency. By presenting a unified analysis of challenges, solutions, and future directions, this work aims to guide the development of next-generation neural rendering accelerators, especially for resource-constrained environments.

Source: IEEE Journal on Emerging and Selected Topics in Circuits and Systems

DOI: 10.1109/jetcas.2025.3561777

Authors: Junha Ryu, Hoi-Jun Yoo

Institution: Korea Advanced Institute of Science and Technology

Language: English

Topic: Computer Graphics and Visualization Techniques

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: Institute for Information and Communications Technology Promotion