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

University Of California-Riverside, Riverside CA

Investigators

Abstract

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.