CNS Core: Small: Enabling Real-time, Scalable and Secure Collaborative Intelligence on the Edge

CNS 核心：小型：在边缘实现实时、可扩展且安全的协作智能

• 批准号: 2140346
• 负责人: Zheng Dong
• 金额: $ 49.99万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140346&HistoricalAwards=false
• 关键词: CNS; Core; Small; Enabling; Real

With the proliferation of embedded systems, multicore computing devices enable the recent trend of moving computation from the centralized cloud to distributed edge platforms. This trend yields new products and services across smart infrastructures in smart cities. However, as real-time workloads are executed at the edge computing platforms, the performance bottleneck is transferred from the edge-cloud communication to on-chip communication. The system’s real-time performance faces new system-architectural challenges for the Network-On-Chip (NoC), which are scalability and security. These challenges are hinged with dynamic data distributions across different users. This project aims to design a real-time and scalable NoC for implementing real-time collaborative learning algorithms. The key strategy is to orchestrate a system-architecture and algorithm co-design to explore the new design space on the edge computing platform.To cope with the research challenges, a comprehensive architecture will be developed to address these multifaceted problems through a hardware and software co-design, which consists of three key thrusts: (i) designing an interconnect, which will eliminate non-predictability barrier on the NoC; (ii) establishing a scalable virtualized transaction environment for the collaborative learning system to guarantee that all the real-time transaction tasks can complete at the right time; (iii) implementing a real-time and secure multi-target tracking system on the edge platform in light of the newly proposed architecture. The proposed research will be evaluated using the physical platform Equinox, with indoor and outdoor studies beyond simulation.This research will open a new dimension of research and educational opportunities. In particular, the success of the project will provide a hardware/software package that can enhance the real-time collaborative computing on the edge. The resulted interconnect and Equinox are ready-to-use platforms that will allow experts/researchers to easily examine their research designs regarding collaborative learning and real-time edge computing, thereby sealing the gap between different research fields. Educational efforts will be devoted to (i) curriculum design for the undergraduate and graduate program, (ii) summer camp development for middle and high school students, and teachers, (iii) broadening participation in computing and engineering, at the Wayne State University.The data, codes, simulators, and platforms developed in this project will be made available publicly throughout the duration of the project and for at least five years after the end of the project. The project repository will be available on Wayne State University website (http://zheng.eng.wayne.edu/index.html) and the website of the CAR Lab (https://www.thecarlab.org/.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.

随着嵌入式系统的扩散，多核算计算设备可以将计算从集中式云转移到分布式边缘平台的最新趋势。这种趋势可在智能城市的智能基础设施中产生新产品和服务。但是，随着在边缘计算平台上执行实时工作负载，性能瓶颈从边缘云通信转移到芯片交流。该系统的实时性能面临着针对网络芯片（NOC）的新系统构造挑战，这些挑战是可扩展性和安全性的。这些挑战是在不同用户的动态数据分布中取决的。该项目旨在设计实时可扩展的NOC，以实现实时协作学习算法。 The key strategy is to orchestrate a system-architecture and algorithm co-design to explore the new design space on the edge computing platform.To cope with the research challenges, a comprehensive architecture will be developed to address these multifaceted problems through a hardware and software co-design, which consists of three key thrusts: (i) designing an interconnect, which will eliminate non-predictability barrier on the NoC; （ii）为协作学习系统建立可扩展的虚拟交易环境，以确保所有实时交易任务都可以在正确的时间完成； （iii）根据新提出的架构，在边缘平台上实现实时且安全的多目标跟踪系统。拟议的研究将使用较外的室内和室外研究来评估拟议的研究。这项研究将开放研究和教育机会的新维度。尤其是，该项目的成功将提供一个硬件/软件包，可以增强实时的协作计算，从而使所得的互连和等级的研究者可轻松地进行研究，以便允许其互助和equinox的研究者，以便他们的研究人员可以轻松地进行研究。边缘计算，从而密封不同的研究领域之间的差距。教育工作将致力于（i）针对本科和研究生课程的课程设计，（ii）中学生和高中生的夏季训练营发展，以及在韦恩州立大学的计算和工程学的教师，（iii）扩大参与计算和工程。该项目存储库将在韦恩州立大学网站（http://zheng.eng.wayne.edu/index.html）和汽车实验室的网站（https://www.thecarlab.org/..this奖中，该奖项均通过评估律师的知识范围及其范围的范围，这一奖项被认为是众所周知的。

项目成果

BlueScale: a scalable memory architecture for predictable real-time computing on highly integrated SoCs

BlueScale：可扩展内存架构，用于在高度集成的 SoC 上进行可预测的实时计算

• DOI: 10.1145/3489517.3530612
• 发表时间: 2022
• 期刊: The 59th ACM/IEEE Design Automation Conference
• 作者: Jiang, Zhe;Yang, Kecheng;Audsley, Neil;Fisher, Nathan;Shi, Weisong;Dong, Zheng
• 通讯作者: Dong, Zheng

AXI-IC^{RT}: Towards a Real-Time AXI-Interconnect for Highly Integrated SoCs

AXI-IC^{RT}：面向高度集成 SoC 的实时 AXI 互连

• DOI: 10.1109/tc.2022.3179227
• 发表时间: 2023
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Jiang, Zhe;Yang, Kecheng;Fisher, Nathan;Gray, Ian;Audsley, Neil;Dong, Zheng
• 通讯作者: Dong, Zheng

Prophet: Realizing a Predictable Real-time Perception Pipeline for Autonomous Vehicles

• DOI: 10.1109/rtss55097.2022.00034
• 发表时间: 2022-12
• 期刊: 2022 IEEE Real-Time Systems Symposium (RTSS)
• 作者: Liangkai Liu;Zheng-hong Dong;Yanzhi Wang;Weisong Shi

Towards Hard Real-Time and Energy-Efficient Virtualization for Many-Core Embedded Systems

• DOI: 10.1109/tc.2022.3207115
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Zhe Jiang;Kecheng Yang;Yunfeng Ma;N. Fisher;N. Audsley;Zheng Dong

A Utilization-based Test for Non-preemptive Gang Tasks on Multiprocessors

• DOI: 10.1109/rtss55097.2022.00019
• 发表时间: 2022-12
• 期刊: 2022 IEEE Real-Time Systems Symposium (RTSS)
• 作者: Zheng Dong;Cong Liu