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日益增长的图形和数据处理需求与有限的移动硬件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团队，开发一种创新的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。本研究利用标准工作负载来研究VR/AR系统的设计，以了解计算资源的使用情况，并通过任务分配来减少计算负载，从而提高计算的效率和可扩展性。通过整合人类交互和先进数据处理技术的创新，研究成果将使研究和整个行业都受益。该教育计划通过将计算机图形学、嵌入式系统和机器学习方面的跨学科模块与新开发的教学实践结合起来，加强现有课程和教学方法，并特别关注妇女和代表性不足的少数群体。该项目执行三项任务。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率进行建模。任务2探索了高效的以人为中心的图形绘制框架，以减少VR/AR系统的计算负载。任务3通过平衡图形绘制和深度神经网络(DNN)应用中的计算负载和数据分配来利用新的方案来提高计算效率。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.