Collaborative Research: DESC: Type I: FLEX: Building Future-proof Learning-Enabled Cyber-Physical Systems with Cross-Layer Extensible and Adaptive Design

合作研究：DESC：类型 I：FLEX：通过跨层可扩展和自适应设计构建面向未来的、支持学习的网络物理系统

• 批准号: 2324937
• 负责人: Jingtong Hu
• 金额: $ 30万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324937&HistoricalAwards=false
• 关键词: Collaborative; Research; DESC; Type; FLEX

When an electronic device does not meet system requirements, it could become a waste of electrical and electronic equipment (WEEE). About 50 million metric tonnes of WEEE are generated annually worldwide, and it is estimated to increase to 111 million tonnes per year by 2050, causing immense environmental, human health, and socioeconomic damage. Retrofitting and reusing these devices may prolong their lifespan and reduce such negative impacts. For instance, in the case of a smartphone, extending its usage by just one year can cut its CO2 impact on the environment by 31%. This project enhances the retrofitting capability for emerging learning-enabled cyber-physical systems (LE-CPSs) to accommodate changes/updates with existing hardware, so as to increase device lifespan, reduce e-waste and improve sustainability. The success of this project could foster a more sustainable future, with far-reaching impacts spanning across environmental, economic, and societal dimensions. By enhancing retrofitting capabilities, the lifespan of electronic devices can be effectively extended, consequently mitigating the necessity for new products and reducing the cumulative quantity of electronic waste generated. Improved retrofitting can also lead to more energy-efficient systems, reducing greenhouse gas emissions and mitigating climate change. Moreover, retrofitting electronic systems can lead to cost savings for both businesses and consumers, increasing access to affordable technology, especially for economically disadvantaged communities. The project catalyzes research in several communities: design automation, cyber-physical systems, machine learning, and domain experts in multi-agent system applications. This project also generates broader impacts through curriculum development, broadening participation in computing, K-12 outreach activities, and international design contests.Retrofitting could be quite challenging, especially for emerging LE-CPSs such as autonomous vehicles, medical devices, and robots, which often operate within a constantly-changing physical environment, have limited resources and stringent timing requirements, and employ complex and resource-consuming machine learning techniques. To bridge the gap between functionality and architecture during retrofitting and prolong system lifetime, this project develops FLEX, a cross-layer framework that on the one hand makes the architecture of LE-CPSs more extensible to facilitate accommodating changes with existing hardware, and on the other hand makes their functionality more adaptive with respect to resource limitations and environment changes. The framework includes novel (1) system-level extensibility-driven design and retrofitting methods that at the design time explore the design space and trades off future extensibility of LE-CPSs with other system objectives and at the retrofitting time leverage the robustness of existing functionality in LE-CPSs to further expand scheduling slack and accommodate retrofitting needs based on a weakly-hard paradigm; (2) resource adaptability-driven neural architecture model and design methods that provide multiple designs of neural networks (e.g., with multiple exists) to enable resource-aware configuration during retrofitting; and (3) continual and on-device learning methods that enable LE-CPSs to effectively adapt to the changing physical environment and system input with little supervision for increasing system lifetime.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.

当电子设备不满足系统要求时，它可能会成为电气和电子设备（WEEE）的废物。全球每年产生约 5000 万吨 WEEE，预计到 2050 年每年将增加到 1.11 亿吨，对环境、人类健康和社会经济造成巨大损害。改造和重复使用这些设备可以延长其使用寿命并减少此类负面影响。例如，就智能手机而言，仅将其使用时间延长一年即可将二氧化碳对环境的影响减少 31%。该项目增强了新兴的学习型网络物理系统（LE-CPS）的改造能力，以适应现有硬件的变化/更新，从而延长设备的使用寿命，减少电子垃圾并提高可持续性。该项目的成功可以培育一个更加可持续的未来，在环境、经济和社会层面产生深远的影响。通过增强改造能力，可以有效延长电子设备的使用寿命，从而减少对新产品的需求，并减少电子废物的累积产生量。改进改造还可以带来更节能的系统，减少温室气体排放并缓解气候变化。此外，改造电子系统可以为企业和消费者节省成本，增加获得负担得起的技术的机会，特别是对于经济上处于不利地位的社区。该项目促进了多个社区的研究：设计自动化、网络物理系统、机器学习和多智能体系统应用领域专家。该项目还通过课程开发、扩大计算参与、K-12 推广活动和国际设计竞赛产生更广泛的影响。改造可能相当具有挑战性，特别是对于自动驾驶汽车、医疗设备和机器人等新兴 LE-CPS 而言，通常在不断变化的物理环境中运行，资源有限，时间要求严格，并且采用复杂且消耗资源的机器学习技术。为了弥合改造过程中功能和架构之间的差距并延长系统寿命，该项目开发了 FLEX，这是一种跨层框架，一方面使 LE-CPS 的架构更具可扩展性，以方便适应现有硬件的变化，另一方面另一方面使它们的功能对于资源限制和环境变化更具适应性。该框架包括新颖的 (1) 系统级可扩展性驱动设计和改造方法，这些方法在设计时探索设计空间并权衡 LE-CPS 的未来可扩展性与其他系统目标，并在改造时利用现有功能的稳健性在 LE-CPS 中，进一步扩大调度余量并适应基于弱硬范式的改造需求； (2)资源适应性驱动的神经架构模型和设计方法，提供神经网络的多种设计（例如，具有多种存在）以在改造期间实现资源感知配置； (3) 持续的设备上学习方法，使 LE-CPS 能够有效地适应不断变化的物理环境和系统输入，无需监督即可延长系统寿命。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。