AI Institute for Edge Computing Leveraging Next Generation Networks (Athena)

利用下一代网络的人工智能边缘计算研究所 (Athena)

基本信息

  • 批准号:
    2112562
  • 负责人:
  • 金额:
    $ 2000万
  • 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
  • 起止时间:
    2021-10-01 至 2026-09-30
  • 项目状态:
    未结题

项目摘要

The National AI Institute, named Athena, seeks to kindle and fuel a transformation in modern edge computing by leveraging next generation networks. Led by Duke University, Athena taps the ongoing revolution in Artificial Intelligence (AI) and brings together a multidisciplinary team from seven universities: Duke University, Massachusetts Institute of Technology, North Carolina Agricultural and Technical State University, Princeton University, University of Michigan-Ann Arbor, University of Wisconsin-Madison, and Yale University. Athena organizes its research activities under four interrelated thrusts - AI, Computer Systems, Networking, and Services - which constitute an ambitious and comprehensive research agenda. It will develop AI-driven, next generation technologies for edge computing and new algorithmic and practical foundations of AI. Athena will evaluate the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use-inspired research. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships. Athena aims at scientific contributions in both edge computing and AI. These include (i) edge networking mechanisms across the stack by leveraging a data-driven, AI-based approach; (ii) systems support for efficient and reliable AI across the edge-enhanced mobile networks; (iii) novel practical and algorithmic foundations of AI to ensure the new functionalities, efficiency, scalability, security, privacy, and fairness of the AI solutions adopted in these next-generation networks; and (iv) novel services and applications, focused on diverse cyber-physical systems that leverage the innovations of the other thrusts. Athena’s research outcomes will benefit the network and computer industries at large. This national institute will work closely with external collaborators to translate research outcomes to industrial practice and policymaking. The educational and outreach activities of Athena will empower students and postdocs to develop their interests, build skills, and acquire knowledge about AI and computer and network systems through research experiences, industry internships, and community engagements. The Inclusive AI Initiative – one of Athena’s innovations in education and workforce development - will strengthen the ethical AI competencies of all Athena members to better promote and be aware of equity and fairness in their research and the communities impacted by the institute’s research.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.
名为雅典娜的国家人工智能研究所寻求通过利用下一代网络来点燃和推动现代边缘计算的变革。在杜克大学的领导下,雅典娜利用正在进行的人工智能(AI)革命,并汇集了来自七所大学的多学科团队:杜克大学、麻省理工学院、北卡罗来纳农业技术州立大学、普林斯顿大学、密歇根大学安娜堡分校、威斯康星大学麦迪逊分校和耶鲁大学。雅典娜在四个相互关联的推动下组织其研究活动-人工智能、计算机系统、网络和服务-这构成了一个雄心勃勃和全面的研究议程。它将开发人工智能驱动的下一代边缘计算技术,以及人工智能的新算法和实用基础。雅典娜将通过分析、实验和经验工具的组合来评估研究结果,特别是通过目标使用启发的研究。雅典娜致力于一套强大而全面的教育和劳动力发展努力,以及与包括行业和社区合作伙伴在内的外部利益相关者的合作和知识转移努力。雅典娜的目标是在边缘计算和人工智能方面做出科学贡献。这些措施包括(I)通过利用数据驱动的、基于人工智能的方法在整个堆栈中建立边缘网络机制;(Ii)在边缘增强的移动网络中支持高效和可靠的人工智能;(Iii)人工智能的新的实用和算法基础,以确保这些下一代网络中采用的人工智能解决方案的新功能、效率、可扩展性、安全性、隐私和公平性;以及(Iv)新的服务和应用,专注于利用其他推动力的创新的不同的网络物理系统。雅典娜的研究成果将使整个网络和计算机行业受益。该国家研究所将与外部合作者密切合作,将研究成果转化为工业实践和政策制定。雅典娜的教育和外展活动将使学生和博士后能够通过研究经验、行业实习和社区参与来发展他们的兴趣,培养技能,并获得关于人工智能和计算机和网络系统的知识。包容性人工智能倡议-雅典娜在教育和劳动力发展方面的创新之一-将加强所有雅典娜成员的伦理人工智能能力,以更好地促进和意识到他们的研究和受研究所研究影响的社区的公平和公平。该奖项反映了NSF的法定使命,并通过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,被认为值得支持。

项目成果

期刊论文数量(62)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Software-Defined Programmable Testbed for Beyond 5G Optical-Wireless Experimentation at City-Scale
  • DOI:
    10.1109/mnet.006.2100605
  • 发表时间:
    2022-03
  • 期刊:
  • 影响因子:
    9.3
  • 作者:
    Tingjun Chen;Jiakai Yu;Artur Minakhmetov;Craig L. Gutterman;Michael Sherman;Shengxiang Zhu;Steven Santaniello;Aishik Biswas;I. Seskar;G. Zussman;D. Kilper
  • 通讯作者:
    Tingjun Chen;Jiakai Yu;Artur Minakhmetov;Craig L. Gutterman;Michael Sherman;Shengxiang Zhu;Steven Santaniello;Aishik Biswas;I. Seskar;G. Zussman;D. Kilper
OpenLoRa: Validating LoRa Implementations through an Extensible and Open-sourced Framework
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Manan Mishra;D. J. Koch;Muhammad Osama Shahid;Bhuvana Krishnaswamy;Krishna Chintalapudi;Suman Banerjee
  • 通讯作者:
    Manan Mishra;D. J. Koch;Muhammad Osama Shahid;Bhuvana Krishnaswamy;Krishna Chintalapudi;Suman Banerjee
Sommelier: Curating DNN Models for the Masses
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
NASRec:推荐系统的权重共享神经架构搜索
  • DOI:
    10.1145/3543507.3583446
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhang, Tunhou;Cheng, Dehua;He, Yuchen;Chen, Zhengxing;Dai, Xiaoliang;Xiong, Liang;Yan, Feng;Li, Hai;Chen, Yiran;Wen, Wei
  • 通讯作者:
    Wen, Wei
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
PIDS:3D 点云的联合点交互维度搜索
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Yiran Chen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
TriZone: A Design of MLC STT-RAM Cache for Combined Performance, Energy, and Reliability Optimizations
TriZone:MLC STT-RAM 缓存设计,可实现性能、能耗和可靠性的综合优化
Improving Multilevel Writes on Vertical 3-D Cross-Point Resistive Memory
改进垂直 3D 交叉点电阻存储器的多级写入
Shift-Optimized Energy-Efficient Racetrack-Based Main Memory
基于移位优化的节能赛道主存储器
Essays on the Economics of Networks
网络经济学论文集
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yiran Chen
  • 通讯作者:
    Yiran Chen

Yiran Chen的其他文献

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{{ truncateString('Yiran Chen', 18)}}的其他基金

Conference: 2023 CISE Computer System Research PI Meeting
会议:2023 CISE计算机系统研究PI会议
  • 批准号:
    2341163
  • 财政年份:
    2023
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
Collaborative Research: FuSe: Efficient Situation-Aware AI Processing in Advanced 2-Terminal SOT-MRAM
合作研究:FuSe:先进 2 端子 SOT-MRAM 中的高效态势感知 AI 处理
  • 批准号:
    2328805
  • 财政年份:
    2023
  • 资助金额:
    $ 2000万
  • 项目类别:
    Continuing Grant
Workshop Proposal: Redefining the Future of Computer Architecture from First Principles
研讨会提案:从第一原理重新定义计算机架构的未来
  • 批准号:
    2220601
  • 财政年份:
    2022
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
Collaborative Research: CCRI:NEW: Research Infrastructure for Real-Time Computer Vision and Decision Making via Mobile Robots
合作研究:CCRI:新:通过移动机器人进行实时计算机视觉和决策的研究基础设施
  • 批准号:
    2120333
  • 财政年份:
    2021
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
EAGER: Distributed Heterogeneous Data Analytics via Federated Learning
EAGER:通过联邦学习进行分布式异构数据分析
  • 批准号:
    2140247
  • 财政年份:
    2021
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Medium: Revitalizing EDA from a Machine Learning Perspective
合作研究:SHF:媒介:从机器学习的角度振兴 EDA
  • 批准号:
    2106828
  • 财政年份:
    2021
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
Collaborative Research: Two-dimensional Synaptic Array for Advanced Hardware Acceleration of Deep Neural Networks
合作研究:用于深度神经网络高级硬件加速的二维突触阵列
  • 批准号:
    1955246
  • 财政年份:
    2020
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
Workshop Proposal: Processing-In-Memory (PIM) Technology - Grand Challenges and Applications
研讨会提案:内存处理 (PIM) 技术 - 重大挑战和应用
  • 批准号:
    2027324
  • 财政年份:
    2020
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
RTML: Large: Collaborative: Harmonizing Predictive Algorithms and Mixed Signal/Precision Circuits via Computation-Data Access Exchange and Adaptive Dataflows
RTML:大型:协作:通过计算数据访问交换和自适应数据流协调预测算法和混合信号/精密电路
  • 批准号:
    1937435
  • 财政年份:
    2019
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant
CCRI: Planning: Collaborative Research: Planning to Develop a Low-Power Computer Vision Platform to Enhance Research in Computing Systems
CCRI:规划:协作研究:规划开发低功耗计算机视觉平台以加强计算系统研究
  • 批准号:
    1925514
  • 财政年份:
    2019
  • 资助金额:
    $ 2000万
  • 项目类别:
    Standard Grant

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