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Phase I IUCRC University of Missouri-Kansas City: Center for Big Learning (CBL)

Phase I IUCRC University of Missouri-Kansas City: Center for Big Learning (CBL)
第一阶段 IUCCRC 密苏里大学堪萨斯城分校:大学习中心 (CBL)
批准号:
1747751
负责人:
Zhu Li
金额:
$75.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2018
资助国家:
美国
项目状态:
未结题
起止时间:
2018-02-01 至 2025-01-31

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中文摘要
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英文摘要
This project establishes the NSF Industry/University Collaborative Research Center (I/UCRC) for Big Learning (CBL) to accelerate innovation and impact of Deep Learning in various embedded applications. The vision is to create intelligence towards intelligence-driven society. Through catalyzing the fusion of diverse expertise from the consortium of faculty members, students, industry partners, and federal agencies, CBL seeks to create state-of-the-art deep learning methodologies and technologies and enable intelligent applications, transforming broad domains, such as business, healthcare, Internet-of-Things, and cybersecurity. This timely initiative creates a unique platform for empowering our next-generation talents with cutting-edge technologies of societal relevance and significance. The University of Missouri at Kansas City (UMKC) site focuses on the deep learning in embedded systems for mobile and IoT applications. It is based on a framework called DeepLite for deep learning model compression and acceleration that can fit cutting edge deep learning capabilities in embedded systems with very limited computing, storage, communication and power capabilities. DeepLite allows embedded deep learning model training and compression for power, storage, computation complexity tradeoffs with learning performances for targeted embedded applications like immersive content capture, depth and action sensing, visual surveillance, next gen image and video compression and communication. CBL is expected to make wide ranging and long lasting impact to machine learning algorithm, system and application research, accelerating deep learning technology innovation and adoption in the real world, enable transformative new capabilities and new applications in all aspect of society, from education, medicine, media, to security and defense. CBL seamlessly integrates innovation, engineering education, technology business incubation, and community engagement. It facilitates closer interactions and cross pollination of ideas between academia and industry, broaden the research horizon for faculties and students, while help shrink the time to impact and time to market of new technology. The center repository will be hosted at http://nsfcbl.org. The data, code, documents will be well organized and maintained on the CBL servers for the duration of the center for more than five years and beyond. The internal code repository will be managed by GitLab. After the software packages are well documented and tested, they will be released and managed by popular public code hosting services, such as GitHub and Bitbucket.
期刊论文(40)
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会议论文
DOI: 10.1109/jstsp.2019.2963154
发表时间: 2020-01
期刊: IEEE Journal of Selected Topics in Signal Processing
影响因子: 7.5
作者: [Li Li-Li;Ning Yan;Zhu Li;Shan Liu;Houqiang Li]
通讯作者: Li Li-Li;Ning Yan;Zhu Li;Shan Liu;Houqiang Li
DOI: 10.1109/vcip49819.2020.9301815
发表时间: 2020-12
期刊: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
影响因子: --
作者: [B. Kathariya;Li Li-Li;Zhu Li;Ling-yu Duan;Shan Liu]
通讯作者: B. Kathariya;Li Li-Li;Zhu Li;Ling-yu Duan;Shan Liu
DOI: 10.1109/icip.2019.8804199
发表时间: 2019-09
期刊: 2019 IEEE International Conference on Image Processing (ICIP)
影响因子: --
作者: [Hang Zhang;Li Li-Li;Li Song;Xiaokang Yang;Zhu Li]
通讯作者: Hang Zhang;Li Li-Li;Li Song;Xiaokang Yang;Zhu Li
DOI: 10.1145/3301304
发表时间: 2019-03
期刊: ACM Transactions on Knowledge Discovery from Data (TKDD)
影响因子: --
作者: [A. Katib;P. Rao;Kobus Barnard;Charles A. Kamhoua]
通讯作者: A. Katib;P. Rao;Kobus Barnard;Charles A. Kamhoua
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    University of Missouri-Kansas City Planning Grant: I/UCRC for Big Learning
    海外基金