This project’s objective is to build a dedicated computer system for supporting deep learning applications, particularly training deep neural networks, in scalable and efficient ways.
- Investigators: William Gropp (PI), Roy Campbell (Co-PI), Volodymyr Kindratenko (Co-PI), Jian Peng (Co-PI)
- Start date: October 1, 2017; End date: September 30, 2021 (estimated)
- Award amount: $2,721,983
- Funding source: NSF MRI
Frameworks: Machine Learning and FPGA Computing for Real-Time Application in Big-Data Physics Experiments
This project is pushing the frontiers of deep learning at scale, demonstrating the versatility and scalability of these methods to accelerate and enable new physics in the Big Data era.
- Investigators: Eliu Huerta (PI), Volodymyr Kindratenko (Co-PI), Dan Katz (Co-PI)
- Start date: October 1, 2019; End date: September 30, 2022
- Award amount to date: $651,314
- NSF programs: Office of Multidisciplinary Activities, Computational Physics, Software Institutes
- Funding source: NSF CSSI
This project focuses on the design of deep learning algorithms for real-time data analytics of time-series and image datasets using Field Programmable Gate Arrays to accelerate low-latency inference of machine learning algorithms.
- Investigators: Eliu Huerta (PI), Mark Neubauer (Co-PI), Volodymyr Kindratenko (Co-PI), Zhizhen Zhao (Co-PI)
- Start date: September 1, 2019; End date: August 31, 2021
- Award amount to date: $600,311
- NSF program: Cyberinfrastructure
- Funding source: NSF HDR
This project will make artificial AI models and data more accessible and reusable to accelerate research in AI research and development.
- Investigators: Eliu Huerta (PI), Mark Neubauer (Co-PI), Volodymyr Kindratenko (Co-PI), Zhizhen Zhao (Co-PI), Dan Katz (CO-PI), Roger Rusack (Co-PI), Philip Harris (Co-PI), Javier Duarte (Co-PI)
- Start date: September 2020; End date: August 2023
- Award amount to date: $2,200,000
- Funding source: DOE FAIR
A pilot program to develop a new, interdisciplinary approach combining astronomy Big Data with machine learning tools to build a deep learning algorithm to estimate the masses of supermassive black holes.
- Investigators: Xin Liu (Faculty Fellow) and Volodymyr Kindratenko
- Award year: 2020-2021
- Funding source: NCSA Faculty Fellows
Innovative AI tools will be developed to identify cattle that have the highest genetic potential for milk production and health status and make simplistic assumptions about the relationship between phenotypes and genotypes.
- Investigators: Sandra Rodriguez-Zas (PI), Eliu Huerta (Co-PI)
- Start date: July 1, 2020; End date: June 30, 2021
- Award amount to date: $25,000
- Funding source: Center for Digital Agriculture Seed Funding