CNS Core: Medium: Real-time Energy-elastic GPUs for Embedded Autonomous Systems

图形处理单元（gpu）提供巨大的计算能力，使计算机视觉、机器学习和科学发现等现代变革性应用成为可能。然而，这些好处通常仅限于大型计算机系统，如云计算和超级计算机。嵌入式资源受限环境，如自主系统和空中无人机，由于可用能源、计算和通信资源以及时间要求（实时约束）的限制，可以限制gpu的使用。该项目从根本上重新思考了在具有实时行为的嵌入式资源约束环境中启用gpu的计算机系统的设计。本研究包括三个主要目标：(1)为计算资源和存储结构开发节能和弹性微架构；(2)开发与操作系统实时任务调度器协调的时间感知GPU硬件调度器；(3)通过跨整个软硬件计算堆栈的整体协调来动态平衡时间需求和能量弹性微架构。这些下一代实时嵌入式gpu提供了更强的计算能力，使未来的嵌入式自主系统变得更安全、更节能。计算能力的激增也会对社会产生无数影响，例如改进便携式医疗设备，使其体积更小，功能更强大，提高汽车碰撞检测系统的准确性和安全性，降低物联网甚至大型基于gpu的计算机系统的用电量。该项目将为本科生提供研究机会，并扩大未被充分代表的少数民族的参与，为新一代计算机工程师提供培训，以应对未来嵌入式系统的设计挑战。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Graphical Processing Units (GPUs) provide massive computational power that enables modern transformative applications such as computer vision, machine learning, and scientific discovery. However, these benefits are typically confined to large-scale computer systems, such as cloud computing and supercomputers. Embedded resource-constrained environments, such as autonomous systems and aerial drones, can make limited use of GPUs due to constraints on available energy, computing and communication resources, and timing requirements (real-time constraints). This project fundamentally rethinks the design of GPU-enabled computer systems in embedded resource-constrained environments with real-time behavior. This research consists of three main goals: (1) to develop energy-efficient and elastic microarchitectures for computational resources and storage structures, (2) to develop timing-aware GPU hardware schedulers that coordinate with an operating system real-time task scheduler, and (3) to dynamically balance timing requirements and energy-elastic microarchitectures by holistically coordinating across the entire software-hardware computing stack.These next-generation real-time embedded GPUs provide more computational power to enable future embedded autonomous systems to become safer and more energy-efficient. This proliferation of computing power can also lead to myriad impacts on society such as improved portable medical devices that are smaller and with expanded capabilities, automobile collision detection systems with improved accuracy and safety, and lower electricity consumption for internet-of-things and even for larger-scale GPU-based computer systems. This project will offer research opportunities to undergraduate students, and broaden participation of underrepresented minorities, providing training to a new generation of computer engineers who will meet the design challenges of future embedded systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.