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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF WASHINGTON
Doing Business As Name:University of Washington
PD/PI:
  • Shih-Chieh Hsu
  • (206) 543-2760
  • schsu@uw.edu
Co-PD(s)/co-PI(s):
  • Kate Scholberg
  • Mark S Neubauer
  • Michael W Coughlin
  • Song Han
Award Date:09/15/2021
Estimated Total Award Amount: $ 15,000,000
Funds Obligated to Date: $ 4,500,000
  • FY 2021=$4,500,000
Start Date:10/01/2021
End Date:09/30/2026
Transaction Type: Cooperative Agreements
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery
Federal Award ID Number:2117997
DUNS ID:605799469
Parent DUNS ID:042803536
Program:HDR-Harnessing the Data Revolu
Program Officer:
  • Amy Walton
  • (703) 292-4538
  • awalton@nsf.gov

Awardee Location

Street:4333 Brooklyn Ave NE
City:Seattle
State:WA
ZIP:98195-0001
County:Seattle
Country:US
Awardee Cong. District:07

Primary Place of Performance

Organization Name:University of Washington
Street:4333 Brooklyn Ave NE
City:Seattle
State:WA
ZIP:98105-1016
County:Seattle
Country:US
Cong. District:07

Abstract at Time of Award

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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