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 - 10 of 24)
Huang, Shi-Yu and Yang, Yun-Chen and Su, Yu-Ru and Lai, Bo-Cheng and Duarte, Javier and Hauck, Scott and Hsu, Shih-Chieh and Hu, Jin-Xuan and Neubauer, Mark S. "Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs" , 2023 https://doi.org/10.1109/FPL60245.2023.00050 Citation Details
Ye, Hanchen and Jun, HyeGang and Jeong, Hyunmin and Neuendorffer, Stephen and Chen, Deming "ScaleHLS: a scalable high-level synthesis framework with multi-level transformations and optimizations: invited" , 2022 https://doi.org/10.1145/3489517.3530631 Citation Details
Yin, Haoteng and Zhang, Muhan and Wang, Jianguo and Li, Pan "SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning" SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning , 2023 Citation Details
S Miao and Y Luo and M Liu and P Li "Interpretable Geometric Deep Learning via Learnable Randomness Injection" International Conference on Learning Representations , 2023 Citation Details
Luo, Yuhong and Li, Pan "Neighborhood-aware Scalable Temporal Network Representation Learning" Neighborhood-aware Scalable Temporal Network Representation Learning , 2022 Citation Details
Yin, Haoteng and Zhang, Muhan and Wang, Jianguo and Li, Pan "SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning" SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning , 2023 Citation Details
Luo, Yuhong and Li, Pan "Neighborhood-aware Scalable Temporal Network Representation Learning" Neighborhood-aware Scalable Temporal Network Representation Learning , v.198 , 2022 Citation Details
Miao, Siqi and Liu, Mia and Li, Pan "Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism" Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism , 2022 Citation Details
Yin, Haoteng and Zhang, Muhan and Wang, Yanbang and Wang, Jianguo and Li, Pan "Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning" Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning , 2022 Citation Details
Gunny, Alec and Rankin, Dylan and Harris, Philip and Katsavounidis, Erik and Marx, Ethan and Saleem, Muhammed and Coughlin, Michael and Benoit, William "A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics" FlexScience'22 , 2022 https://doi.org/10.1145/3526058.3535454 Citation Details
Gunny, Alec and Rankin, Dylan and Krupa, Jeffrey and Saleem, Muhammed and Nguyen, Tri and Coughlin, Michael and Harris, Philip and Katsavounidis, Erik and Timm, Steven and Holzman, Burt "Hardware-accelerated inference for real-time gravitational-wave astronomy" Nature Astronomy , v.6 , 2022 https://doi.org/10.1038/s41550-022-01651-w Citation Details
(Showing: 1 - 10 of 24)

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