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

基本信息

  • 批准号:
    2324937
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 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

项目摘要

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.
当电子设备不符合系统要求时,它可能会成为电气和电子设备(WEEE)的浪费。每年在全球每年产生约5000万吨的WEEE,到2050年估计每年增加到1.11亿吨,造成了巨大的环境,人类健康和社会经济损害。对这些设备进行翻新和重复使用可能会延长其寿命并减少这种负面影响。例如,在智能手机的情况下,将其使用量延长一年可以将其对环境的影响减少31%。该项目增强了新兴学习支持的网络物理系统(LE-CPSS)的改造能力,以适应现有硬件的变化/更新,以提高设备寿命,减少电子废物并改善可持续性。该项目的成功可能会促进更可持续的未来,并在环境,经济和社会方面跨越了深远的影响。通过增强改装功能,可以有效地扩展电子设备的寿命,从而减轻新产品的必要性并减少产生的电子废物的累积量。改进的改造还可以导致更节能的系统,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,从而增加获得负担得起的技术的机会,尤其是对于经济弱势群体的社区。该项目催化了几个社区的研究:设计自动化,网络物理系统,机器学习和多代理系统应用程序的域专家。该项目还通过课程开发,扩大计算,K-12外展活动和国际设计竞赛的参与而产生更广泛的影响。进行重新配置可能非常具有挑战性,尤其是对于新兴的LE-CPS,例如自动驾驶汽车,医疗设备和机器人,通常在不断变化的物理环境中运行,并且在物理环境中运作越来越有限,并具有限制的资源和精通的技术和精通的机器,并进行了及时的计算。为了弥合改造和延长系统寿命期间功能与体系结构之间的差距,该项目开发了Flex,这是一个跨层框架,一方面使LE-CPSS的架构更加可扩展,以促进现有硬件的适应变化,另一方面使其功能更适合对资源限制和环境的尊重和环境的变化。该框架包括新颖的(1)系统级可扩展性驱动的设计和改造方法,这些方法在设计时间探索设计空间,并交易LE-CPS与其他系统目标的未来扩展性,以及在修改LECPS中现有功能的稳健性,从而在LE-CPS中的现有功能的鲁棒性,以进一步扩展安排安排和可容纳基于弱点的改装需求,以适应修改的修改需求。 (2)资源适应性驱动的神经体系结构模型和设计方法,这些模型和设计方法提供了多种设计的神经网络(例如,存在多个存在),以启用在翻新过程中的资源感知配置; (3)使LE-CPS能够有效地适应不断变化的物理环境和系统输入的持续不断的设备学习方法,几乎​​没有监督系统的寿命。该奖项反映了NSF的法定任务,并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

项目成果

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Jingtong Hu其他文献

FlexLevel NAND Flash Storage System Design to Reduce LDPC Latency
FlexLevel NAND 闪存存储系统设计可减少 LDPC 延迟
Stack-Size Sensitive On-Chip Memory Backup for Self-Powered Nonvolatile Processors
适用于自供电非易失性处理器的堆栈大小敏感片上内存备份
Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System
实时 POCUS 图像质量评估和采集引导系统的开发
  • DOI:
    10.48550/arxiv.2212.08624
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhenge Jia;Yiyu Shi;Jingtong Hu;Lei Yang;B. Nti
  • 通讯作者:
    B. Nti
Algorithm-hardware Co-design of Attention Mechanism on FPGA Devices
FPGA器件上注意力机制的算法-硬件协同设计
Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs
学习学习用于心内 EGM 室性心律失常检测的个性化神经网络
  • DOI:
    10.24963/ijcai.2021/359
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhenge Jia;Zhepeng Wang;Feng Hong;Lichuan Ping;Yiyu Shi;Jingtong Hu
  • 通讯作者:
    Jingtong Hu

Jingtong Hu的其他文献

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{{ truncateString('Jingtong Hu', 18)}}的其他基金

Collaborative Research: FuSe: R3AP: Retunable, Reconfigurable, Racetrack-Memory Acceleration Platform
合作研究:FuSe:R3AP:可重调、可重新配置、赛道内存加速平台
  • 批准号:
    2328972
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
Collaborative Research: CNS Core: Small: Towards Unsupervised Learning on Resource Constrained Edge Devices with Novel Statistical Contrastive Learning Scheme
合作研究:CNS 核心:小型:利用新颖的统计对比学习方案在资源受限的边缘设备上实现无监督学习
  • 批准号:
    2122320
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core:Small:IMPERIAL: In-Memory Processing Enhanced Racetrack Inspired by Accessing Laterally
协作研究:CNS Core:Small:IMPERIAL:受横向访问启发的内存处理增强赛道
  • 批准号:
    2133267
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Collaborative Research:CNS Core: Small: Intermittent and Incremental Inference with Statistical Neural Network for Energy-Harvesting Powered Devices
合作研究:CNS 核心:小型:利用统计神经网络对能量收集供电设备进行间歇和增量推理
  • 批准号:
    2007274
  • 财政年份:
    2020
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
RAPID:Collaborative:Independent Component Analysis Inspired Statistical Neural Networks for 3D CT Scan Based Edge Screening of COVID-19
RAPID:协作:独立成分分析启发的统计神经网络,用于基于 3D CT 扫描的 COVID-19 边缘筛查
  • 批准号:
    2027546
  • 财政年份:
    2020
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
IRES Track I: International Research Experience for Students on Non-Volatile Processor Based Self-Powered Embedded Systems
IRES Track I:基于非易失性处理器的自供电嵌入式系统学生的国际研究经验
  • 批准号:
    1827009
  • 财政年份:
    2018
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
SHF: Small: Collaborative Research: Multi-level Non-volatile FPGA Synthesis to Empower Efficient Self-adaptive System Implementations
SHF:小型:协作研究:多级非易失性 FPGA 综合,实现高效自适应系统
  • 批准号:
    1820537
  • 财政年份:
    2017
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
CRII: CSR: Enabling Efficient Non-Volatile Processors on Energy Harvesting Powered Embedded Systems
CRII:CSR:在能量收集供电的嵌入式系统上启用高效的非易失性处理器
  • 批准号:
    1830891
  • 财政年份:
    2017
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
SHF: Small: Collaborative Research: Multi-level Non-volatile FPGA Synthesis to Empower Efficient Self-adaptive System Implementations
SHF:小型:协作研究:多级非易失性 FPGA 综合,实现高效自适应系统
  • 批准号:
    1527506
  • 财政年份:
    2015
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
CRII: CSR: Enabling Efficient Non-Volatile Processors on Energy Harvesting Powered Embedded Systems
CRII:CSR:在能量收集供电的嵌入式系统上启用高效的非易失性处理器
  • 批准号:
    1464429
  • 财政年份:
    2015
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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Collaborative Research: Conference: DESC: Type III: Eco Edge - Advancing Sustainable Machine Learning at the Edge
协作研究:会议:DESC:类型 III:生态边缘 - 推进边缘的可持续机器学习
  • 批准号:
    2342498
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Collaborative Research: Conference: DESC: Type III: Eco Edge - Advancing Sustainable Machine Learning at the Edge
协作研究:会议:DESC:类型 III:生态边缘 - 推进边缘的可持续机器学习
  • 批准号:
    2342497
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
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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:
    2324936
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
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合作研究:DESC:类型 II:REFRESH:重新审视扩展 FPGA 空间以实现环境可持续性异构系统
  • 批准号:
    2324865
  • 财政年份:
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  • 批准号:
    2324949
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
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