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

CNS 核心：中：用于嵌入式自治系统的实时能量弹性 GPU

• 批准号: 1955650
• 负责人: Daniel Wong
• 金额: $ 119.99万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955650&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Real; time

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.

图形处理单元（GPU）提供了巨大的计算能力，可实现现代变革性应用，例如计算机视觉，机器学习和科学发现。但是，这些好处通常仅限于大规模的计算机系统，例如云计算和超级计算机。嵌入式资源受限的环境（例如自主系统和空中无人机）可以有限地使用GPU，因为对可用的能源，计算和通信资源以及时机要求（实时限制）的限制。该项目从根本上重新考虑了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。这项研究由三个主要目标组成：（1）为计算资源和存储结构开发节能和弹性的微体系结构，（2）开发时间安排时会与ARACE ACARE GPU硬件调度程序相协调与操作系统实时任务调度程序，（3）以动态平衡定时需求，并通过整体构建跨度的计算，以实现整体范围，以实现整体范围，以实现整体范围，以实现整体整体范围，以整个整体构建整个整个范围内置。嵌入式GPU提供了更多的计算能力，以使未来的嵌入式自主系统变得更安全，更节能。计算能力的扩散还会导致对社会的无数影响，例如改进的便携式医疗设备，具有较小的功能，功能扩大，汽车碰撞检测系统具有提高的准确性和安全性，以及用于较大范围的GPU计算机系统的电力消耗较低。该项目将为本科生提供研究机会，并扩大了代表性不足的少数群体的参与，为新一代的计算机工程师提供培训，这些计算机工程师将应对未来嵌入式系统的设计挑战。该奖项反映了NSF的法定任务，并通过评估该基金会的知识功能和广泛的影响来评估NSF的法定任务。

项目成果

KRISP: Enabling Kernel-wise RIght-sizing for Spatial Partitioned GPU Inference Servers

• DOI: 10.1109/hpca56546.2023.10071121
• 发表时间: 2023-02
• 期刊: 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
• 作者: M. Chow;Ali Jahanshahi;Daniel Wong

JetStream: Graph Analytics on Streaming Data with Event-Driven Hardware Accelerator

• DOI: 10.1145/3466752.3480126
• 发表时间: 2021-10
• 期刊: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Shafiur Rahman;Mahbod Afarin;N. Abu-Ghazaleh;Rajiv Gupta

BlockMaestro: Enabling Programmer-Transparent Task-based Execution in GPU Systems

• DOI: 10.1109/isca52012.2021.00034
• 发表时间: 2021-06
• 期刊: 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA)
• 作者: AmirAli Abdolrashidi;Hodjat Asghari Esfeden;A. Jahanshahi;Kaustubh Singh;N. Abu-Ghazaleh;Daniel Wong

ScaleServe: a scalable multi-GPU machine learning inference system and benchmarking suite

ScaleServe：可扩展的多 GPU 机器学习推理系统和基准测试套件

• DOI: 10.1145/3530390.3532735
• 发表时间: 2022
• 期刊: Proceedings of the 14th Workshop on General Purpose Processing Using GPU
• 作者: Jahanshahi, Ali;Chow, Marcus;Wong, Daniel
• 通讯作者: Wong, Daniel

Balancing Energy Efficiency and Real-Time Performance in GPU Scheduling

• DOI: 10.1109/rtss52674.2021.00021
• 发表时间: 2021-12
• 期刊: 2021 IEEE Real-Time Systems Symposium (RTSS)
• 作者: Yidi Wang;Mohsen Karimi;Yecheng Xiang;Hyoseung Kim