Award Abstract # 2117997
HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery
NSF Org: |
PHY
Division Of Physics
|
Recipient: |
UNIVERSITY OF WASHINGTON
|
Initial Amendment Date: |
September 15, 2021 |
Latest Amendment Date: |
February 15, 2024 |
Award Number: |
2117997 |
Award Instrument: |
Cooperative Agreement |
Program Manager: |
James Shank
jshank@nsf.gov
(703)292-4516
PHY
Division Of Physics
MPS
Direct For Mathematical & Physical Scien
|
Start Date: |
October 1, 2021 |
End Date: |
September 30, 2026 (Estimated) |
Total Intended Award Amount: |
$15,000,000.00 |
Total Awarded Amount to Date: |
$15,250,000.00 |
Funds Obligated to Date: |
FY 2021 = $4,500,000.00
FY 2022 = $5,625,000.00
FY 2023 = $5,125,000.00
|
History of Investigator: |
-
Shih-Chieh
Hsu
(Principal Investigator)
schsu@uw.edu
-
Kate
Scholberg
(Co-Principal Investigator)
-
Mark
Neubauer
(Co-Principal Investigator)
-
Michael
Coughlin
(Co-Principal Investigator)
-
Philip
Harris
(Co-Principal Investigator)
-
Song
Han
(Former Co-Principal Investigator)
|
Recipient Sponsored Research Office: |
University of Washington
4333 BROOKLYN AVE NE
SEATTLE
WA
US
98195-1016
(206)543-4043
|
Sponsor Congressional District: |
07
|
Primary Place of Performance: |
University of Washington
4333 Brooklyn Ave NE
Seattle
WA
US
98105-1016
|
Primary Place of Performance Congressional District: |
07
|
Unique Entity Identifier (UEI): |
HD1WMN6945W6
|
Parent UEI: |
|
NSF Program(s): |
HDR-Harnessing the Data Revolu, WoU-Windows on the Universe: T, HEP-High Energy Physics, OFFICE OF MULTIDISCIPLINARY AC
|
Primary Program Source: |
01002324DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
|
Program Reference Code(s): |
062Z,
069Z,
075Z,
1206,
7483,
9102
|
Program Element Code(s): |
099Y00,
107Y00,
122100,
125300
|
Award Agency Code: |
4900
|
Fund Agency Code: |
4900
|
Assistance Listing Number(s): |
47.049, 47.070
|
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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(Showing: 1 - 24 of 24)
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(Showing: 1 - 24 of 24)
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