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

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.