用ILLIXR探索延展实境：建筑研究的新天地
Exploring Extended Reality with ILLIXR: A New Playground for Architecture Research
摘要：
当我们进入特定于领域的体系结构时代时，系统研究人员必须了解新兴应用领域的需求。增强和虚拟现实（AR/VR）或延展实境（XR）就是这样一个重要领域。本文介绍了ILLIXR，第一个开源端到端XR系统（1）具有最先进的组件，（2）集成了模块化和可扩展的多线程运行时，（3）为XR应用程序提供了OpenXR兼容接口（例如，游戏引擎），以及（4）具有报告（和权衡）若干体验质量（QoE）度量的能力。我们分析完整ILLIXR系统及其各个组件的性能、功耗和QoE指标。我们的分析揭示了几个属性与架构和系统研究的影响。这些包括苛刻的性能、功率和QoE要求，大量多样的关键任务，在调度和资源管理方面具有挑战的相互依赖的执行流水线，以及性能/功率与人类感知相关的QoE度量之间的大的权衡空间。ILLIXR和我们的分析有可能推动架构和系统研究的新方向，特别是影响XR。ILLIXR是开源的，可在https://illixr.github.io上获得
英文摘要：
As we enter the era of domain-specific architectures, systems researchers must understand the requirements of emerging application domains. Augmented and virtual reality (AR/VR) or extended reality (XR) is one such important domain. This paper presents ILLIXR, the first open source end-to-end XR system (1) with state-of-the-art components, (2) integrated with a modular and extensible multithreaded runtime, (3) providing an OpenXR compliant interface to XR applications (e.g., game engines), and (4) with the ability to report (and trade off) several quality of experience (QoE) metrics. We analyze performance, power, and QoE metrics for the complete ILLIXR system and for its individual components. Our analysis reveals several properties with implications for architecture and systems research. These include demanding performance, power, and QoE requirements, a large diversity of critical tasks, inter-dependent execution pipelines with challenges in scheduling and resource management, and a large tradeoff space between performance/power and human perception related QoE metrics. ILLIXR and our analysis have the potential to propel new directions in architecture and systems research in general, and impact XR in particular. ILLIXR is open-source and available at https://illixr.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
