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应运而生。能够创建处理大量实时传感器捕获数据的算法，使许多行业的设计人员能够开发智能嵌入式系统，使行动和决策自动化，例如自动驾驶车辆。这种新兴的数据密集型嵌入式计算范式带来了一系列新的数据引发的挑战，围绕着保证时间可预测性和支持在设计时对延迟约束进行分析验证。本研究的目的是克服嵌入式系统中海量数据的实时处理带来的挑战，以保证时间的可预测性。D3是一个全面的资源管理生态系统，具有可预测地处理海量实时数据密集型工作负载的能力。D3引入了一套新颖的基本系统级技术，支持智能数据过滤和表征、用于执行并发优化的透明和受监督的流，以及异类体系结构下的可预测内存管理。将解决由于共同调度存储器和高度异构性的计算资源而导致的困难的算法挑战，从而使对时间可预测性的分析保证变得可量化。为可预测地处理海量实时数据密集型工作负载，开发一个全面的异质资源管理生态系统将是许多应用领域的重要成果，例如交通和机器人。下一代汽车系统就是一个很好的例子，可以从拟议的研究中受益匪浅。该项目的成果将为基于不同平台的安全关键自动驾驶车辆的可认证性铺平道路。一个创新的本科生研究项目，旨在开发一种以实时数据驱动的系统设计概念为基础的教育工具，以帮助实现教育目标。所有源代码和评估数据将根据开源GNU公共许可证从德克萨斯大学达拉斯分校(UTD)计算机科学系维护的公共网络服务器上免费下载。其他案例研究测试程序和脚本将根据开放源码BSD许可证免费提供，并可从UTD计算机科学部维护的公共网络服务器直接下载。该项目产生的所有数据产品也将存档在UTD的计算机科学计算储存库中，用于永久存储。该存储库位于www.utdallas.edu/~cong/CareerRepo。

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

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

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

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

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

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