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.

随着嵌入式系统的激增，多核计算设备实现了将计算从集中式云转移到分布式边缘平台的最新趋势，这种趋势在智能城市的智能基础设施中产生了新的产品和服务。边缘计算平台的性能瓶颈从边云通信转移到片上通信，系统的实时性能给片上网络（NoC）带来了新的系统架构挑战，即可扩展性和安全性。 。这些挑战与不同用户之间的动态数据分布有关，该项目旨在设计一个实时且可扩展的片上网络来实现实时协作学习算法，关键策略是协调系统架构和算法协同设计来探索。边缘计算平台上的新设计空间。为了应对研究挑战，将开发一个综合架构，通过硬件和软件协同设计来解决这些多方面的问题，其中包括三个关键要点：（i）设计互连，这将消除NoC上的不可预测性障碍；（ii）为协作学习系统建立可扩展的虚拟化交易环境，以保证所有实时交易任务能够在正确的时间完成；（iii）实现实时且安全的多方协作学习。 - 根据新提出的架构，边缘平台上的目标跟踪系统将使用物理平台 Equinox 进行评估，并进行模拟以外的室内和室外研究。这项研究将为研究和教育机会开辟新的维度。特别是，该项目的成功将提供可以增强边缘实时协作计算的硬件/软件包所产生的互连和 Equinox 是即用型平台，使专家/研究人员能够轻松检查他们有关协作学习和实时边缘计算的研究设计。 ，从而缩小不同研究领域之间的差距，教育工作将致力于（i）本科生和研究生课程的设计，（ii）中学生和教师的夏令营开发，（iii）扩大参与。韦恩州立大学计算与工程大学。该项目中开发的数据、代码、模拟器和平台将在整个项目期间公开，并在项目结束后至少五年内在韦恩州立大学网站上提供。 (http://zheng.eng.wayne.edu/index.html) 和 CAR 实验室网站 (https://www.thecarlab.org/)。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式支持：利用基金会的智力价值进行评估以及更广泛的影响审查标准。

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