Collaborative Research: CPS: Medium: Real-time Criticality-Aware Neural Networks for Mission-critical Cyber-Physical Systems

合作研究：CPS：中：用于关键任务网络物理系统的实时关键性感知神经网络

• 批准号: 2038923
• 负责人: Heechul Yun
• 金额: $ 32.14万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038923&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Real

Advances in artificial intelligence (AI) make it clear that intelligent systems will account for the next leap in scientific progress to enable a myriad of future applications that improve the quality of life, contribute to the economy, and enhance societal resilience to a broad spectrum of disruptions. Yet, advances in AI come at a considerable resource costs. To reduce the cost of AI, this project takes inspiration from biological systems. It is well-known that a key bottleneck in AI is the perception subsystem. It is the part that allows AI to perceive and understand its surroundings. Humans are very good at understanding what’s critical in their environment and the human perceptual system automatically focuses limited cognitive resources on those elements of the scene that matter most, saving a significant amount of “brain processing power”. Current AI pipelines do not have a similar mechanism, resulting in significantly higher resource costs. The project refactors data analytics and machine intelligence pipelines to allow for better prioritization of external stimuli leveraging and significantly extending advances in scheduling previously developed in the real-time systems research community. The refactored AI pipeline will improve the efficiency and efficacy of AI-enabled systems, allowing them to be safer and more responsive, while at the same time significantly lowering their cost. If successful, the project will help bring machine intelligence solutions to the benefit of all society. This is achieved through interactions between research, education, and outreach, as well as integration of multiple scientific communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts. The work is an example of cyber-physical computing research, where a new generation of digital algorithms learn to exploit a better understanding of physical systems in order to improve societal outcomes. The project removes systemic priority inversion from machine intelligence pipelines in modern neural-network-based cyber-physical applications. In general, priority inversion occurs in real-time systems when computations that are less critical (or with longer deadlines) are performed ahead of those that are more critical (or with shorter deadlines). The current state of machine intelligence software suffers from significant priority inversion on the path from perception to decision-making, resulting in vastly inferior system responsiveness to critical events, thereby jeopardizing safety and increasing the cost of hardware to meet application needs. By resolving this problem, this project shall improve system ability to react to critical inputs, while at the same time significantly reducing platform cost. The intellectual merit of the project lies in investigating the intersection of two core areas in cyber-physical computing: (i) data analytics and machine learning and (ii) real-time systems. Specifically, the project refactors data analytics and machine intelligence pipelines to remove priority inversion. Mitigation of priority inversion problems in different systems has been one of the key contributions of the real-time community. Removal of priority inversion from machine intelligence pipelines makes several other scientific contributions. Namely, (i) the refactored AI pipeline improves the efficiency and efficacy of AI-enabled mission-critical systems, (ii) it enables autonomous systems to be more responsive, while lowering their cost, and (iii) it contributes to safety of intelligent systems by ensuring that critical inputs are processed first. The project expects to demonstrate significant improvements in performance of modern machine-learning-based inference protocols, while offering service differentiation that dramatically improves predictability and timeliness of reactions to critical situations. If successful, the project will significantly reduce the cost of deploying machine intelligence solutions in future cyber-physical systems, while improving predictability and temporal guarantees. In addition to delivering the technical contributions of this project, an explicit purpose of the work is to advance education and workforce development on Intelligent CPS topics. This is achieved through interactions between activities for research, education, and broadening participation, as well as integration of multiple communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts.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.

人工智能 (AI) 的进步清楚地表明，智能系统将推动科学进步的下一次飞跃，从而实现未来的无数应用，从而改善生活质量、为经济做出贡献并增强社会对各种问题的适应能力。然而，人工智能的进步需要付出相当大的资源成本，众所周知，人工智能的一个关键瓶颈是感知子系统。让人工智能能够感知和理解人类周围的环境。非常善于理解环境中的关键因素，人类感知系统会自动将有限的认知资源集中在场景中最重要的元素上，从而节省大量的“大脑处理能力”。当前的人工智能管道没有类似的机制，该项目重构了数据分析和机器智能管道，以便更好地优先考虑外部刺激的利用，并显着扩展实时系统研究社区先前开发的调度方面的进步。重构的人工智能管道将提高效率。和功效支持人工智能的系统，使它们更加安全、反应更快，同时显着降低成本，如果成功，该项目将有助于将机器智能解决方案造福全社会。这是通过研究、教育和推广，以及多个科学界的整合，包括（i）提供平台和调度程序的嵌入式计算研究人员，（ii）物联网和网络研究人员，以及（iii）智能应用程序研究人员和应用领域专家。这项工作是网络物理计算研究的一个例子，其中新一代数字算法学习利用对物理系统的更好理解来改善社会成果，该项目消除了现代基于神经网络的网络物理应用中的机器智能管道中的系统优先级倒置。当不太重要（或期限较长）的计算先于更关键（或期限较短）的计算执行时，机器智能软件的当前状态在从感知到路径的过程中会遇到严重的优先级倒置。决策，从而产生巨大系统对关键事件的响应能力较差，从而危及安全并增加满足应用需求的硬件成本。通过解决此问题，该项目将提高系统对关键输入的反应能力，同时显着降低平台成本。该项目旨在研究网络物理计算的两个核心领域的交叉点：（i）数据分析和机器学习以及（ii）实时系统具体而言，该项目重构了数据分析和机器智能管道以消除优先级倒置。减轻优先级不同系统中的倒置问题一直是实时社区的关键贡献之一，从机器智能管道中消除优先级倒置还做出了其他几项科学贡献，即，（i）重构的人工智能管道提高了人工智能的效率和功效。启用任务关键型系统，（ii）它使自主系统能够更具响应性，同时降低其成本，以及（iii）它通过确保首先处理关键输入来促进智能系统的安全。该项目预计将展示显着的改进。在表现基于现代机器学习的推理协议，同时提供服务差异化，显着提高对关键情况的反应的可预测性和及时性。如果成功，该项目将显着降低在未来网络物理系统中部署机器智能解决方案的成本，同时提高可预测性。除了提供该项目的技术贡献外，该工作的一个明确目的是促进智能 CPS 主题的教育和劳动力发展，这是通过研究、教育和扩大参与活动之间的互动来实现的。以及多个社区的整合，包括 (i) 提供平台和调度程序的嵌入式计算研究人员，(ii) 物联网和网络研究人员，以及 (iii) 智能应用研究人员和应用领域专家。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。

项目成果

Cache Bank-Aware Denial-of-Service Attacks on Multicore ARM Processors

针对多核 ARM 处理器的缓存组感知拒绝服务攻击

• DOI: 10.1109/rtas58335.2023.00023
• 发表时间: 2023-05
• 期刊: 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS
• 作者: Bechtel, Michael;Yun, Heechul
• 通讯作者: Yun, Heechul

DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems

DeepPicarMicro：将 TinyML 应用于自主网络物理系统

• DOI: 10.1109/rtcsa55878.2022.00019
• 发表时间: 2022-08
• 期刊: 2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA
• 作者: Bechtel, Michael;Weng, QiTao;Yun, Heechul
• 通讯作者: Yun, Heechul

Anytime-Lidar: Deadline-aware 3D Object Detection

随时激光雷达：截止日期感知 3D 物体检测

• DOI: 10.1109/rtcsa55878.2022.00010
• 发表时间: 2022-08
• 期刊: IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA
• 作者: Soyyigit, Ahmet;Yao, Shuochao;Yun, Heechul
• 通讯作者: Yun, Heechul

Denial-of-Service Attacks on Shared Resources in Intel’s Integrated CPU-GPU Platforms

针对 Intel 集成 CPU-GPU 平台中共享资源的拒绝服务攻击

• DOI: 10.1109/isorc52572.2022.9812711
• 发表时间: 2022-05
• 期刊: 2022 IEEE 25th International Symposium On Real-Time Distributed Computing (ISORC
• 作者: Bechtel, Michael;Yun, Heechul
• 通讯作者: Yun, Heechul