Neural Radiance Field (NeRF) has emerged as a promising 3D reconstruction method, delivering high-quality results for AR/VR applications. While quantization methods and hardware accelerators have been proposed to enhance NeRF's computational efficiency, existing approaches face crucial limitations. Current quantization methods operate without considering hardware architecture, resulting in sub-optimal solutions within the vast design space encompassing accuracy, latency, and model size. Additionally, existing NeRF accelerators heavily rely on human experts to explore this design space, making the optimization process time-consuming, inefficient, and unlikely to discover optimal solutions. To address these challenges, we introduce HERO, a reinforcement learning framework performing hardware-aware quantization for NeRF. Our framework integrates a NeRF accelerator simulator to generate real-time hardware feedback, enabling fully automated adaptation to hardware constraints. Experimental results demonstrate that HERO achieves 1.31-1.33 $\times$ better latency, 1.29-1.33 $\times$ improved cost efficiency, and a more compact model size compared to CAQ, a previous state-of-the-art NeRF quantization framework. These results validate our framework's capability to effectively navigate the complex design space between hardware and algorithm requirements, discovering superior quantization policies for NeRF implementation. Code is available at https://github.com/ypzhng/HERO.

翻译：神经辐射场（NeRF）已成为一种前景广阔的3D重建方法，为增强现实/虚拟现实（AR/VR）应用提供了高质量的结果。尽管已有量化方法和硬件加速器被提出以提升NeRF的计算效率，但现有方法面临关键局限。当前的量化方法在操作时未考虑硬件架构，导致在涵盖精度、延迟和模型大小的广阔设计空间中只能获得次优解。此外，现有的NeRF加速器严重依赖人类专家来探索此设计空间，使得优化过程耗时、低效且难以发现最优解。为应对这些挑战，我们提出了HERO，一个执行硬件感知NeRF量化的强化学习框架。我们的框架集成了一个NeRF加速器模拟器以生成实时硬件反馈，从而实现完全自动化的硬件约束适应。实验结果表明，与先前的先进NeRF量化框架CAQ相比，HERO实现了1.31-1.33倍的延迟降低、1.29-1.33倍的成本效率提升以及更紧凑的模型大小。这些结果验证了我们的框架能够有效探索硬件与算法需求之间的复杂设计空间，为NeRF实现发现更优的量化策略。代码可在 https://github.com/ypzhng/HERO 获取。