课题基金 / 基金详情

AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE)

AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE)
环境中具有计算学习功能的智能网络基础设施人工智能研究所 (ICICLE)
批准号:
2112606
负责人:
Dhabaleswar Panda
金额:
$2000.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-11-01 至 2026-10-31

项目摘要

项目成果

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中文摘要
翻译
尽管世界见证了人工智能(AI)技术在某些领域的巨大成功,但由于缺乏易于使用的人工智能基础设施,许多领域尚未从人工智能中获益。NSF AI环境中计算学习智能网络基础设施研究所(ICICLE)将开发在不同和异构环境下具有透明和高性能执行的智能网络基础设施。它将推动科学家在广泛领域易于使用的即插即用人工智能,从而促进人工智能的民主化。ICICLE汇集了一个多学科的科学家和工程师团队,由俄亥俄州立大学领导,与凯斯西储大学、IC-FOODS、印第安纳大学、爱荷华州立大学、俄亥俄超级计算机中心、伦斯勒理工学院、圣地亚哥超级计算机中心、德克萨斯高级计算中心、犹他大学、加州大学戴维斯分校、加州大学圣地亚哥分校、特拉华大学和威斯康星大学麦迪逊分校合作。最初,在三个应用启发型科学领域的复杂社会挑战将推动ICICLE的研究和劳动力发展议程:智能粮库、精准农业和动物生态学。ICICLE的研究和开发包括:(i)通过推进五个基础领域来增强即插即用的人工智能:知识图谱、模型共享、自适应人工智能、联邦学习和会话人工智能。(ii)提供强大的网络基础设施,能够推动人工智能驱动的科学(CI4AI),解决应用、软件和硬件异质性带来的挑战,并将CI4AI创新传播到使用启发的科学领域。(iii)创造新的人工智能技术,以适应/优化各种CI组件(AI4CI),实现人工智能和CI的良性循环。(iv)开发新技术来解决跨领域问题,包括CI和AI的隐私、问责制和数据完整性;(v)提供由软件、数据和应用程序组成的地理上分布的异构系统,由通用应用程序编程接口和执行中间件进行编排。ICICLE先进的集成边缘、云和高性能计算硬件和软件CI组件简化了人工智能的使用,使其更容易解决新的调查领域。通过这种方式,ICICLE专注于人工智能的研究,通过人工智能创新,加速人工智能的应用。ICICLE正在通过创新的教育、培训和扩大计算参与的方法,建立一支多元化的STEM劳动力队伍,确保在全国范围内,从初中/高中学生到从业者,取得持续的可衡量的成果和影响。作为合作的纽带,ICICLE促进了向行业和其他利益相关者的技术转让,以及其他国家科学基金会人工智能研究所和联邦机构之间的数据共享和协调。作为研究、开发、技术转让、劳动力发展和教育的国家资源,ICICLE正在创建一个广泛可用、更智能、更强大、更多样化、更有弹性和更有效的CI4AI和AI4CI生态系统。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Although the world is witness to the tremendous successes of Artificial Intelligence (AI) technologies in some domains, many domains have yet to reap the benefits of AI due to the lack of easily usable AI infrastructure. The NSF AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) will develop intelligent cyberinfrastructure with transparent and high-performance execution on diverse and heterogeneous environments. It will advance plug-and-play AI that is easy to use by scientists across a wide range of domains, promoting the democratization of AI. ICICLE brings together a multidisciplinary team of scientists and engineers, led by The Ohio State University in partnership with Case Western Reserve University, IC-FOODS, Indiana University, Iowa State University, Ohio Supercomputer Center, Rensselaer Polytechnic Institute, San Diego Supercomputer Center, Texas Advanced Computing Center, University of Utah, University of California-Davis, University of California-San Diego, University of Delaware, and University of Wisconsin-Madison. Initially, complex societal challenges in three use-inspired scientific domains will drive ICICLE’s research and workforce development agenda: Smart Foodsheds, Precision Agriculture, and Animal Ecology. ICICLE’s research and development includes: (i) Empowering plug-and-play AI by advancing five foundational areas: knowledge graphs, model commons, adaptive AI, federated learning, and conversational AI. (ii) Providing a robust cyberinfrastructure capable of propelling AI-driven science (CI4AI), solving the challenges arising from heterogeneity in applications, software, and hardware, and disseminating the CI4AI innovations to use-inspired science domains. (iii) Creating new AI techniques for the adaptation/optimization of various CI components (AI4CI), enabling a virtuous cycle to advance both AI and CI. (iv) Developing novel techniques to address cross-cutting issues including privacy, accountability, and data integrity for CI and AI; and (v) Providing a geographically distributed and heterogeneous system consisting of software, data, and applications, orchestrated by a common application programming interface and execution middleware. ICICLE’s advanced and integrated edge, cloud, and high-performance computing hardware and software CI components simplify the use of AI, making it easier to address new areas of inquiry. In this way, ICICLE focuses on research in AI, innovation through AI, and accelerates the application of AI. ICICLE is building a diverse STEM workforce through innovative approaches to education, training, and broadening participation in computing that ensure sustained measurable outcomes and impact on a national scale, along the pipeline from middle/high school students to practitioners. As a nexus of collaboration, ICICLE promotes technology transfer to industry and other stakeholders, as well as data sharing and coordination across other National Science Foundation AI Institutes and Federal agencies. As a national resource for research, development, technology transfer, workforce development, and education, ICICLE is creating a widely usable, smarter, more robust and diverse, resilient, and effective CI4AI and AI4CI ecosystem.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.
期刊论文(42)
专著(0)
科研奖励(0)
会议论文
Learning Fractals by Gradient Descent
通过梯度下降学习分形
DOI: 10.1609/aaai.v37i2.25342
发表时间: 2023
期刊: Proceedings of the AAAI Conference on Artificial Intelligence
影响因子: --
作者: [Tu, Cheng-Hao, Chen, Hong-You, Carlyn, David, Chao, Wei-Lun]
通讯作者: Chao, Wei-Lun
“Hey CAI” - Conversational AI Enabled User Interface for HPC Tools
–Hey CAI – 适用于 HPC 工具的对话式 AI 用户界面
DOI: 10.1007/978-3-031-07312-0_5
发表时间: 2022
期刊: Proceedings International Conference on High Performance Computing
影响因子: --
作者: [Kousha, P., Jain, A., Kolli, A., Prasanna, S., Miriyala, S., Subramoni, H., Shafi, A., Panda, DK.]
通讯作者: Panda, DK.
Accelerating MPI All-to-All Communication with Online Compression on Modern GPU Clusters
在现代 GPU 集群上通过在线压缩加速 MPI 全面通信
DOI: 10.1007/978-3-031-07312-0_1
发表时间: 2022
期刊: ISC HIGH PERFORMANCE
影响因子: --
作者: [Zhou, Q., Kousha, P., Anthony, Q., Khorassani, K., Shafi, A., Subramoni, H., Panda, DK.]
通讯作者: Panda, DK.
DOI: 10.48550/arxiv.2305.13073
发表时间: 2023-05
期刊:
影响因子: --
作者: [Ziru Chen;Shijie Chen;Michael White;R. Mooney;Ali Payani;Jayanth Srinivasa;Yu Su;Huan Sun]
通讯作者: Ziru Chen;Shijie Chen;Michael White;R. Mooney;Ali Payani;Jayanth Srinivasa;Yu Su;Huan Sun
37
    CSR: Small: CONCERT: Designing Scalable Communication Runtimes with On-the-fly Compression for HPC and AI Applications on Heterogeneous Architectures
    • 批准号:
      2312927
    • 项目类别:
      Standard Grant
    • 资助金额:
      $60.0万
    • 财政年份:
      2023
    • 负责人:
      Dhabaleswar Panda
    • 依托单位:
    Travel: Student Travel Support for MVAPICH User Group (MUG) 2023 Conference
    • 批准号:
      2331223
    • 项目类别:
      Standard Grant
    • 资助金额:
      $1.0万
    • 财政年份:
      2023
    • 负责人:
      Dhabaleswar Panda
    • 依托单位:
    Collaborative Research: Frameworks: Performance Engineering Scientific Applications with MVAPICH and TAU using Emerging Communication Primitives
    • 批准号:
      2311830
    • 项目类别:
      Standard Grant
    • 资助金额:
      $90.0万
    • 财政年份:
      2023
    • 负责人:
      Dhabaleswar Panda
    • 依托单位:
    Travel: Student Travel Support for MVAPICH User group (MUG) 2022 Conference
    • 批准号:
      2231825
    • 项目类别:
      Standard Grant
    • 资助金额:
      $1.0万
    • 财政年份:
      2022
    • 负责人:
      Dhabaleswar Panda
    • 依托单位:
    海外基金