HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery
HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery
批准号:
2117997
负责人:
Shih-Chieh Hsu
金额:
$1500.0万
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:
2021-10-01 至 2026-09-30
中文摘要
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英文摘要
The data revolution is dramatically accelerating the acquisition rate of new information, creating a vast amount of data. Artificial intelligence (AI) has emerged as a solution for rapid processing of complex datasets. New hardware such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) allow AI algorithms to be greatly accelerated. To take full advantage of fast AI, the Institute of Accelerated AI Algorithms for Data-Driven Discovery (A3D3) targets fundamental problems in three fields of science: high energy physics, multi-messenger astrophysics, and systems neuroscience. A3D3 works closely within these domains to develop customized AI solutions to process large datasets in real-time, significantly enhancing their discovery potential. The ultimate goal of A3D3 is to construct the institutional knowledge essential for real-time applications of AI in any scientific field. Through dedicated outreach efforts, A3D3 will empower scientists with new tools to deal with the data deluge. Students mentored through A3D3 research will interact closely with industry partners, creating new career opportunities and strengthening synergies between academia and industry.The approach of A3D3 is to tightly couple AI algorithm innovations, heterogeneous computing platforms, and science-driven application development informed through close collaboration with domain scientists within physics, astronomy, and neuroscience. The common theme across domains is the development of AI strategies accelerated by emerging processor technology, employing hardware-AI co-design as a transformative solution to a wide range of scientific challenges. Hardware architectures such as GPUs and FPGAs have emerged as promising technologies to address many of the challenges in data-intensive science because they provide highly-performant, parallelizable, and configurable data processing pipeline capabilities. When combined with AI algorithms, these architectures significantly accelerate scientific workflows compared to CPU-only computing platforms. Building on the existing Fast Machine Learning community, A3D3 cultivates an ecosystem where scientists across domains collaborate to meet critical challenges, forming a central hub of excellence for innovation in accelerated AI for science. The work is extended to the public at large through a diverse set of educational training programs and by mentoring next-generation scientists.This project is part of the National Science Foundation's Big Idea activities in Harnessing the Data Revolution (HDR) and Windows on the Universe - The Era of Multi-Messenger Astrophysics (WoU-MMA). This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Divisions of Astronomical Sciences and of Physics within the NSF Directorate for Mathematical and Physical Sciences.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.
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DOI:
10.48550/arxiv.2210.16966
发表时间:
2022-10
期刊:
ArXiv
影响因子:
--
作者:
[Siqi Miao;Yunan Luo;Miaoyuan Liu;Pan Li]
通讯作者:
Siqi Miao;Yunan Luo;Miaoyuan Liu;Pan Li
DOI:
10.1140/epjc/s10052-022-10541-4
发表时间:
2021-09
期刊:
The European Physical Journal C
影响因子:
--
作者:
[O. Fischer;B. Mellado;S. Antusch;E. Bagnaschi;S. Banerjee;G. Beck;Benedetta Belfatto;M. Bellis;Z. Berezhiani;M. Blanke;B. Capdevila;K. Cheung;A. Crivellin;N. Desai;Bhupal Dev;R. Godbole;T. Han;P. Harris;M. Hoferichter;M. Kirk;S. Kulkarni;C. Lange;K. Lassila-Perini;Zhen Liu;F. Mahmoudi;C. A. Manzari;D. Marzocca;B. Mukhopādhyāẏa;A. Pich;Yifeng Ruan;Luc Schnell;J. Thaler;S. Westhoff]
通讯作者:
O. Fischer;B. Mellado;S. Antusch;E. Bagnaschi;S. Banerjee;G. Beck;Benedetta Belfatto;M. Bellis;Z. Berezhiani;M. Blanke;B. Capdevila;K. Cheung;A. Crivellin;N. Desai;Bhupal Dev;R. Godbole;T. Han;P. Harris;M. Hoferichter;M. Kirk;S. Kulkarni;C. Lange;K. Lassila-Perini;Zhen Liu;F. Mahmoudi;C. A. Manzari;D. Marzocca;B. Mukhopādhyāẏa;A. Pich;Yifeng Ruan;Luc Schnell;J. Thaler;S. Westhoff
DOI:
10.1109/cvprw59228.2023.00025
发表时间:
2023-06
期刊:
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
影响因子:
--
作者:
[Haotian Tang;Shang Yang;Zhijian Liu;Ke Hong;Zhongming Yu;Xiuyu Li;Guohao Dai;Yu Wang;Song Han]
通讯作者:
Haotian Tang;Shang Yang;Zhijian Liu;Ke Hong;Zhongming Yu;Xiuyu Li;Guohao Dai;Yu Wang;Song Han
DOI:
10.14778/3551793.3551831
发表时间:
2022-02
期刊:
Proc. VLDB Endow.
影响因子:
--
作者:
[Haoteng Yin;Muhan Zhang;Yanbang Wang;Jianguo Wang;Pan Li]
通讯作者:
Haoteng Yin;Muhan Zhang;Yanbang Wang;Jianguo Wang;Pan Li
ScaleHLS: a scalable high-level synthesis framework with multi-level transformations and optimizations: invited
ScaleHLS:具有多级转换和优化的可扩展高级综合框架:受邀
DOI:
10.1145/3489517.3530631
发表时间:
2022
期刊:
ACM
影响因子:
--
作者:
[Ye, Hanchen, Jun, HyeGang, Jeong, Hyunmin, Neuendorffer, Stephen, Chen, Deming]
通讯作者:
Chen, Deming
共 20 条
Conference: NSF Meta-Workshop on AI to Accelerate Scientific and Engineering Discovery (AI2ASED)
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批准号:2337647
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项目类别:Standard Grant
-
资助金额:$10.0万
-
财政年份:2023
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负责人:Shih-Chieh Hsu
-
依托单位:
Collaborative Research: FASER and FASERnu at the Large Hadron Collider
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批准号:2110648
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项目类别:Standard Grant
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资助金额:$25.0万
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财政年份:2021
-
负责人:Shih-Chieh Hsu
-
依托单位:
Accelerating Searches for Beyond the Standard Model Physics and the ATLAS Pixel Detector
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批准号:2110963
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项目类别:Continuing Grant
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资助金额:$45.0万
-
财政年份:2021
-
负责人:Shih-Chieh Hsu
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依托单位:
Collaborative Research: Advancing Science with Accelerated Machine Learning
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批准号:1934360
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项目类别:Continuing Grant
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资助金额:$60.0万
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财政年份:2019
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负责人:Shih-Chieh Hsu
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依托单位:
Beyond the Standard Model Searches using Mono-Boson Final States and the ATLAS Pixel Detector Upgrade
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批准号:1510727
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项目类别:Continuing Grant
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资助金额:$45.0万
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财政年份:2015
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负责人:Shih-Chieh Hsu
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依托单位:
海外基金