MRI: Development of an Instrument for Deep Learning Research

MRI：深度学习研究仪器的开发

• 批准号: 1725729
• 负责人: William Gropp
• 金额: $ 272.2万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725729&HistoricalAwards=false
• 关键词: MRI; Development; Instrument; Deep; Learning

This project will develop and deploy a novel instrument for accelerating deep learning research at the University of Illinois (UI). The instrument will integrate the latest computing, storage, and interconnect technologies in a purpose-built shared-use system. This Instrument will deliver unprecedented performance levels for extreme data intensive emerging fields of research with far-reaching impacts in many areas, such as computer vision, natural language processing, artificial intelligence, healthcare and education. The instrument development will be driven by the UI deep learning (DL) community needs and will be carried out in collaboration with IBM and Nvidia. The instrument will serve as a focal point for the rapidly growing DL research community at UI, enable expansion of several research programs at UI, and contribute to STEM education and training.Specifically, the proposed instrument is a far-reaching cyberinfrastructure development for the research community and industry engaged with deep learning. The work will result in an advanced high-performing scalable instrument with capabilities far beyond those currently deployed in academia or industry to tackle large-scale deep learning projects. This instrument will serve as a focal point for a community-driven effort to advance the field of DL, integrating the work of computer scientists, systems engineers, and software developers. This project is transformative both in the systems architecture and domain science fields it will imbue, with new knowledge to be developed via new interactions and synergies that will emerge as part of this effort.The proposed development of this well-integrated instrument will improve the quality and expand the scope of research and training, provide inter- and intra-organizational use amongst many disciplines, and engage private sector partners. The work will have deep and long-lasting effects on future computer architectures for compute- and data-intensive applications by making the blueprints of the novel system architecture of the instrument publically available. Access to the high-performance software developed through this project will aid numerous science domains that utilize DL frameworks. The unprecedented computational capabilities of many applications will make it possible to tackle complex science, engineering and societal problems in many important fields ranging from education, to healthcare, to artificial intelligence (AI). This project will strive to include participants from under-represented minority and female students, to make new discoveries, train, and educate a new generation of users fluent with DL tools and methodologies, contributing to the development of a highly educated, and diverse workforce with specialized skillsets. Finally, the work will enable new industry-academic collaborations benefiting both the scientific community and industry nationwide.

该项目将开发和部署一种新颖的工具，以加速伊利诺伊大学（UI）的深度学习研究。该仪器将将最新的计算，存储和互连技术集成在专用的共享使用系统中。该工具将在许多领域（例如计算机视觉，自然语言处理，人工智能，医疗保健和教育）中为极端数据密集的新兴研究领域提供前所未有的绩效水平。仪器开发将由UI深度学习（DL）社区需求驱动，并将与IBM和NVIDIA合作执行。该工具将成为UI快速增长的DL研究社区的焦点，可以扩展UI的几个研究计划，并为STEM教育和培训做出了贡献。具体来说，拟议的仪器是一个遥远的Cyber​​infrastructure Instruction，用于研究社区和深入学习的研究社区和行业。这项工作将导致一种高级高性能的可扩展工具，其能力远远超出了目前在学术界或行业中部署的能力，以应对大规模的深度学习项目。该工具将成为以社区为导向的努力来推进DL领域，整合计算机科学家，系统工程师和软件开发人员的工作的焦点。该项目在系统架构和领域科学领域都具有变革性，它将通过新的互动和协同作用来开发新知识，这些知识将成为这项工作的一部分。该良好整合工具的拟议开发将提高质量并扩大研究和培训的范围，并在许多跨组织和内部和内部的培训范围内提供众多私人和私人私人群体和私人私人群体和私人群体和私人群体的分配。这项工作将对未来的计算机架构产生深厚的效果，以通过使该仪器的新型系统体系结构的蓝图公开提供计算和数据密集型应用程序。访问通过该项目开发的高性能软件将有助于使用DL框架的众多科学领域。许多应用程序的前所未有的计算能力将使从教育，医疗保健到人工智能（AI）等许多重要领域的复杂科学，工程和社会问题。该项目将努力包括来自代表性不足的少数民族和女学生的参与者，以使新的发现，培训并教育新一代的用户使用DL工具和方法论，这有助于发展高等教育且多样化的员工，并以专业技能为特定的技能。最后，这项工作将使新的行业学术合作使科学界和全国各个行业都受益。

项目成果

Exploring HW/SW Co-Design for Video Analysis on CPU-FPGA Heterogeneous Systems

探索 CPU-FPGA 异构系统上视频分析的硬件/软件协同设计

• DOI: 10.1109/tcad.2021.3093398
• 发表时间: 2021
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Zhang, Xiaofan;Ma, Yuan;Xiong, Jinjun;Hwu, Wen-mei;Kindratenko, Volodymyr;Chen, Deming
• 通讯作者: Chen, Deming

Unsupervised Discovery of Dynamic Neural Circuits

动态神经回路的无监督发现

• 发表时间: 2019
• 期刊: 33rd Conference on Neural Information Processing Systems
• 作者: Colin Graber, Ryan Loh

Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

• DOI: 10.1186/s40537-020-00361-2
• 发表时间: 2020-10-16
• 期刊: JOURNAL OF BIG DATA
• 影响因子: 8.1
• 作者: Huerta, E. A.;Khan, Asad;Saxton, Aaron
• 通讯作者: Saxton, Aaron

Graph Structured Prediction Energy Networks

• 发表时间: 2019-10
• 期刊: ArXiv
• 作者: Colin Graber;A. Schwing

Critical Minerals Map Feature Extraction Using Deep Learning

使用深度学习提取关键矿物图特征

• DOI: 10.1109/lgrs.2023.3310915
• 发表时间: 2023
• 期刊: IEEE Geoscience and Remote Sensing Letters
• 影响因子: 4.8
• 作者: Luo, Shirui;Saxton, Aaron;Bode, Albert;Mazumdar, Priyam;Kindratenko, Volodymyr
• 通讯作者: Kindratenko, Volodymyr