CAREER: D3: Addressing Emerging Data-Induced Challenges in Embedded and Real-Time Systems

职业：D3：解决嵌入式和实时系统中新出现的数据引发的挑战

• 批准号: 1750263
• 负责人: Cong Liu
• 金额: $ 53.84万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750263&HistoricalAwards=false
• 关键词: CAREER; D3; Addressing; Emerging; Data

Data-driven embedded systems are here. The ability to create algorithms that process massive amounts of real-time sensor-captured data is enabling designers in many industries to develop intelligent embedded systems that automate actions and decisions, e.g., self-driving vehicles. This emerging data-intensive embedded computing paradigm brings a new set of data-induced challenges around guaranteeing timing predictability and enabling latency constraints to be analytically validated at design time. The goal of this research is to overcome challenges due to real-time processing of massive data in embedded systems in order to guarantee timing predictability. D3, a comprehensive resource management ecosystem with the capability of predictably processing massive real-time data-intensive workloads, is implemented in the operating system. D3 brings in a novel set of fundamental system-level techniques, which enable smart data filtering and characterization, transparent and supervised streaming for execution concurrency optimization, and predictable memory management under heterogeneous architectures. The hard algorithmic challenges due to co-scheduling memory and highly heterogeneous computing resources such that analytical guarantees on timing predictability become quantifiable will be addressed. Developing a comprehensive heterogeneous resource management ecosystem for predictably processing massive real-time data-intensive workloads would be a significant result for many application domains, such as transportation and robotics. The next-generation automotive system is a good example that could greatly benefit from the proposed research. The outcome of this project will pave the way to certifiability of safety-critical autonomous vehicles based on heterogeneous platforms. An innovative undergraduate research project to develop an educational tool that uses real-time data-driven system design concepts as its foundation assists in realizing the educational objectives. All source code and evaluation data will be freely available for direct download from public web servers maintained by the University of Texas at Dallas (UTD) Computer Science Department under an open source Gnu Public License. Additional case-study test programs and scripts will be freely available under the open source BSD license and directly downloadable from public web servers maintained by the UTD Computer Science Department. All data products produced in this project will also be archived in the UTD computer science computing repository for permanent storage. The repository is available at www.utdallas.edu/~cong/CareerRepo.

数据驱动的嵌入式系统在这里。创建算法来处理大量实时传感器捕获数据的能力使许多行业的设计人员能够开发自动化行动和决策（例如自动驾驶工具）的智能嵌入式系统。这种新兴的数据密集型嵌入式计算范式带来了一套新的数据引起的挑战，围绕确保定时性可预测性和在设计时对延迟约束进行分析验证。这项研究的目的是克服嵌入式系统中大量数据的实时处理，以确保定时可预测性。 D3是一个综合资源管理生态系统，具有可预测的处理大量实时数据密集型工作负载的能力，在操作系统中实现了。 D3带来了一组新型的基本系统级技术，该技术可以实现智能数据过滤和表征，透明和监督流的流媒体，以进行执行并发优化，以及在异构体系结构下可预测的内存管理。由于共同测定的内存和高度异质的计算资源，因此将解决时正时可预测性的分析保证，因此将解决艰苦的算法挑战。对于许多应用领域（例如运输和机器人技术）来说，开发一个全面的异质资源管理生态系统将是可预测处理的大量实时数据密集型工作负载的重要结果。下一代汽车系统是一个很好的例子，可以从拟议的研究中受益匪浅。该项目的结果将为基于异构平台的安全至关重要自动驾驶汽车的认证铺平道路。一个创新的本科研究项目，旨在开发一种教育工具，该工具使用实时数据驱动的系统设计概念作为基础，以帮助实现教育目标。所有源代码和评估数据都将免费获得，可从德克萨斯大学达拉斯大学（UTD）计算机科学系的公共网络服务器下载，并根据开源GNU公共许可证。根据开源BSD许可，将免费获得其他案例研究程序和脚本，并可以直接从UTD计算机科学系维护的公共网络服务器下载。该项目中生产的所有数据产品也将在UTD计算机科学计算存储库中存档，以进行永久存储。该存储库可在www.utdallas.edu/~cong/careerrepo上找到。

项目成果

PIFA: An Intelligent Phase Identification and Frequency Adjustment Framework for Time-Sensitive Mobile Computing

PIFA：用于时间敏感移动计算的智能相位识别和频率调整框架

• DOI: 10.1109/rtas.2019.00013
• 发表时间: 2019
• 期刊: 2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS
• 作者: Zhang, Xia;Xiao, Xusheng;He, Liang;Ma, Yun;Huang, Yangyang;Liu, Xuanzhe;Xu, Wenyao;Liu, Cong
• 通讯作者: Liu, Cong

Simulee: Detecting CUDA Synchronization Bugs via Memory-Access Modeling

• DOI: 10.1145/3377811.3380358
• 发表时间: 2020-06
• 期刊: 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)
• 作者: Mingyuan Wu;Yicheng Ouyang;Husheng Zhou;Lingming Zhang;Cong Liu;Yuqun Zhang

NeuOS: A Latency-Predictable Multi-Dimensional Optimization Framework for DNN-driven Autonomous Systems

• 发表时间: 2020
• 作者: Soroush Bateni;Cong Liu

Enabling Latency-Aware Data Initialization for Integrated CPU/GPU Heterogeneous Platform

• DOI: 10.1109/tcad.2020.3013047
• 发表时间: 2020-11
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Zhendong Wang;Zihang Jiang;Zhen Wang;Xulong Tang;Cong Liu;Shouyi Yin;Yang Hu

Diagnosing Vehicles with Automotive Batteries

• DOI: 10.1145/3300061.3300126
• 发表时间: 2019-08
• 期刊: The 25th Annual International Conference on Mobile Computing and Networking
• 作者: Liang He;L. Kong;Ziyang Liu;Yuanchao Shu;Cong Liu