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

图形处理单元（GPU）提供了巨大的计算能力，支持计算机视觉、机器学习和科学发现等现代变革性应用。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。嵌入式资源受限的环境，如自主系统和无人机，由于可用能源、计算和通信资源以及时序要求（实时约束）的限制，可以有限地使用GPU。该项目从根本上重新考虑了嵌入式资源受限环境中支持GPU的计算机系统的实时行为设计。这项研究包括三个主要目标：（1）开发用于计算资源和存储结构的节能且弹性的微架构，（2）开发与操作系统实时任务调度器协调的定时感知GPU硬件调度器，以及（3）通过在整个软件-硬件计算栈上进行整体协调来动态地平衡时序要求和能量弹性微架构。新一代实时嵌入式GPU提供了更强大的计算能力，使未来的嵌入式自治系统变得更安全、更节能。这种计算能力的激增也可能对社会产生无数影响，例如改进的便携式医疗设备更小，功能更强，汽车碰撞检测系统具有更高的准确性和安全性，物联网甚至更大规模的基于GPU的计算机系统的功耗更低。该项目将为本科生提供研究机会，扩大代表性不足的少数民族的参与，为新一代计算机工程师提供培训，他们将迎接未来嵌入式系统的设计挑战。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.