CSR: Small: Collaborative Research: EUReCa: Enabling Untethered VR/AR System via Human-centric Graphic Computing and Distributed Data Processing

虚拟现实（VR）和增强现实（AR）设备，特别是它们的移动版本是新兴的技术。然而，VR/AR技术面临的一个主要挑战是对图形和数据处理日益增长的需求与移动硬件有限的计算能力之间的差距。来自GMU和杜克大学的两名研究人员组成了一个团队，开发了一个创新的VR/AR系统，即“EUReCa”，该系统解决了以人为中心的图形处理和分布式数据处理的挑战。本研究采用标准工作负载对VR/AR系统设计进行研究，了解计算源的利用情况，进而开发使用模型，通过任务分配减少计算负荷，从而提高计算效率和可扩展性。研究成果将整合人类互动的创新和先进的数据处理技术，对研究和工业都有好处。该教育计划通过将计算机图形学、嵌入式系统和机器学习的跨学科模块与新开发的教学实践相结合，加强现有的课程和教学法，并特别关注妇女和代表性不足的少数群体。该项目执行三个任务。任务1通过考虑系统配置和用户操作的动态，对VR/AR系统的计算资源利用进行建模。任务2探索有效的以人为中心的图形渲染框架，以减少VR/AR系统的计算负荷。任务3利用新的方案，通过平衡图形渲染和深度神经网络（DNN）应用中的计算负载和数据分配来提高计算效率。这些技术将在移动设备上进行评估。

Virtual Reality (VR) and Augmented Reality (AR) devices, especially their mobile versions are newly emergent technologies. However, a major challenge that VR/AR technologies faces is the gap between the increasing needs for graphic and data processing and the limited computing capability of the mobile hardware. Two researchers from GMU and Duke form a team to develop an innovative VR/AR system, namely, "EUReCa", which tackles the challenge of human-centric graphic processing and distributed data processing. This research studies VR/AR system design using standard workloads to understand the computation source utilization that leads to development of usage model, and reduces the computation loads via task allocation, thereby enhancing the efficiency and scalability of computation. The research outcomes will benefit both research and industry at large by integrating the innovations of human interaction and advanced data processing technologies. The education plan enhances existing curricula and pedagogy by integrating interdisciplinary modules on computer graphic, embedded systems, and machine learning with newly developed teaching practices, and gives special attention to women and underrepresented minority groups. The project performs three tasks. Task 1 models the computation resource utilization of VR/AR systems by considering the system configuration and dynamics of user operations. Task 2 explores efficient human-centric graphic rendering framework for reducing computation loads of VR/AR systems. Task 3 exploits novel schemes to enhance computation efficiency via balancing the computation loads and data allocations in graphic rendering and deep neural network (DNN) applications. The techniques will be evaluated on mobile devices.