Ada: A Distributed, Power-Aware, Real-Time Scene Provider for XR.

IF 6.5
Yihan Pang, Sushant Kondguli, Shenlong Wang, Sarita Adve
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Abstract

Real-time scene provisioning-reconstructing and delivering scene data to requesting XR applications during runtime-is central to enabling spatial computing in modern XR systems. However, existing solutions struggle to balance latency, power and scene fidelity under XR device constraints, and often rely on designs that are either closed, application-specific designs, or both. We present Ada, the first open distributed, power-aware, application-agnostic real-time scene provisioning system. Through computation offloading along with algorithmic and system innovations, Ada provides high-fidelity scenes with stable performance across all evaluated scene sizes and with low power consumption. To isolate the benefits of Ada's algorithmic and design innovations over the closest prior work [82], which is on-device and CPU-based, we configure a comparable on-device, CPU-based variant of Ada (AdaLocal- CPU). We show this variant achieves up to 6.8× lower scene request latency and higher scene fidelity compared to the prior work. Furthermore, Ada's final distributed GPU-accelerated implementation reduces latency by an additional 2×, highlighting the benefits of GPU acceleration and distributed computing. Additionally, Ada also lowers the incremental power cost of scene provisioning by 24% compared to the best on-device variant (AdaLocal-GPU). Finally, Ada flexibly adapts to diverse latency, power, scene fidelity, and network bandwidth requirements.

Ada:面向XR的分布式、功率感知、实时场景提供商。
实时场景配置——在运行期间重建并向请求XR应用程序提供场景数据——是现代XR系统中实现空间计算的核心。然而,现有的解决方案很难在XR设备的限制下平衡延迟、功耗和场景保真度,并且通常依赖于封闭的、特定于应用程序的设计,或者两者兼而有之。我们提出Ada,第一个开放的分布式、功率感知、应用无关的实时场景配置系统。通过计算卸载以及算法和系统创新,Ada提供高保真场景,在所有评估的场景规模和低功耗下都具有稳定的性能。为了将Ada的算法和设计创新的优势与最近的基于设备和CPU的工作[82]隔离开来,我们配置了一个类似的基于设备和CPU的Ada变体(adallocal - CPU)。我们表明,与之前的工作相比,这种变体实现了高达6.8倍的低场景请求延迟和更高的场景保真度。此外,Ada的最终分布式GPU加速实现将延迟额外减少了2倍,突出了GPU加速和分布式计算的好处。此外,与最好的设备上版本(adallocal - gpu)相比,Ada还将场景配置的增量功耗降低了24%。最后,Ada灵活适应各种延迟、功耗、场景保真度和网络带宽需求。
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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