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

IF 3.8 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2025-04-17 DOI:10.1109/JETCAS.2025.3561777

Junha Ryu;Hoi-Jun Yoo

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引用次数: 0

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.

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神经渲染加速器概述：挑战、趋势和未来方向

神经渲染的快速发展通过实现逼真的3D建模和渲染，彻底改变了增强现实（AR）和虚拟现实（VR）领域。然而，由于计算复杂性、内存效率低下和能量限制，在边缘设备上部署神经渲染存在重大挑战。本文提供了神经渲染加速器的全面概述，确定了采样，位置编码和多层感知（MLP）阶段的主要硬件效率低下。我们将探讨解决这些挑战的软硬件协同优化技术，并为深入分析提供总结。此外，还简要介绍了3D高斯喷溅（3DGS）和混合渲染方法等新兴趋势，强调了它们提高渲染质量和效率的潜力。通过对挑战、解决方案和未来方向的统一分析，本工作旨在指导下一代神经渲染加速器的开发，特别是在资源受限的环境中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal on Emerging and Selected Topics in Circuits and Systems ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

8.50

自引率

2.20%

发文量

期刊介绍： The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.