Project Grant 2311768

Award Date 10/1/23
Completion Date 9/30/26
Dollars Obligated $550K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Urbana, IL 61801, USA
Similar Awards
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program Project Grant award of $749,993 to the University of Chicago provides funding for the DIAMOND project. DIAMOND is a service designed to democratize access to cutting-edge deep learning methods by abstracting the use of high-performance computing resources. The project aims to reduce barriers for domain scientists to adopt deep learning by providing a web service-enabled programming interface...
The U.S. National Science Foundation (NSF) awarded a $949,528 Project Grant under the Computer and Information Science and Engineering (CISE) program to Rutgers, The State University to develop a service called DIAMOND. DIAMOND aims to democratize access to cutting-edge deep learning methods for domain scientists by abstracting the use of high-performance computing resources. The project combines novel computer science research with translational efforts to reduce barriers to adopting deep...
The National Science Foundation (NSF) has awarded a $750,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Wisconsin System for the "COLLABORATIVE RESEARCH: FRAMEWORKS: DIAMOND: DEMOCRATIZING LARGE NEURAL NETWORK MODEL TRAINING FOR SCIENCE" project. This 3-year effort aims to develop the DIAMOND service, which will democratize access to cutting-edge deep learning (DL) methods by abstracting the use of high-performance...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $549,999 to the University of North Texas to develop a time-sensitive platform for dynamically refining and adapting large-scale deep learning models. The primary objectives are to: Create methods to parallelize model training and minimize latency, 2) Progressively grow models from pre-trained small models to reduce training iterations, and 3)...
This $131,959 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research conducted by Rutgers, The State University to develop new deep learning training methods that can efficiently scale to utilize high-performance computing (HPC) systems. The key goals are to: 1) Explore techniques like second-order information approximation, computation-communication tradeoffs, and data compression to enhance the speed...
This $175,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of a novel "United Learning" framework. The project aims to efficiently integrate low-resource computing devices, such as personal computers, smartphones, and IoT devices, into the training of complex deep learning models. The key products and services to be delivered include: Knowledge Expansion:...
This is a $292,237 Project Grant awarded by the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to the University of Illinois. The project aims to address two major challenges in building high-quality AI-based scientific models: (1) fully utilizing the compute power of modern hardware with massive parallelism, and (2) making it easier for non-computer science researchers to harness this power. To address these...
This Project Grant award of $200,000 from the National Science Foundation's STEM Education (47.076) program aims to promote AI readiness and democratize AI technologies for a broad spectrum of advanced cyberinfrastructure users and researchers. The key products and services provided under this 4-year award, which began on September 1, 2023, include: Developing a comprehensive suite of experiential learning modules, including flexible micro-modules and immersive extended reality experiences, to...
This Project Grant from the National Science Foundation's $201,262 Computer and Information Science and Engineering program (CFDA 47.070) supports the development of interactive training materials and workshops on deep learning systems and applications in advanced GPU cyberinfrastructure. The University of North Texas will lead the effort in collaboration with Southern Illinois University Carbondale from December 1, 2022 to November 30, 2024. Under the award, the University of North Texas will...
The National Science Foundation (NSF) awarded a $6,999,294 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Texas at Austin for the CHISHIKI.AI initiative. This 5-year project aims to build a sustainable, diverse, and integrated community of computational infrastructure (CI) professionals working at the intersection of artificial intelligence (AI) and civil/environmental engineering (CEE). The project will foster collaboration between...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant award, with a total funding of $549,999.00, supports the DIAMOND project aimed at democratizing access to cutting-edge deep learning methods for scientific applications. The University of Illinois, as the prime awardee, will develop a web service-enabled platform that abstracts the use of high-performance computing resources, allowing domain scientists to focus on neural network architecture design without worrying about infrastructure management. The project will contribute to educational outcomes by engaging PhD students, and by incorporating the developed tools into undergraduate/graduate courses and summer programs, with a focus on recruiting from underserved communities. The DIAMOND framework will provide capabilities such as container configuration, distributed training, hyperparameter tuning, and model sharing, while applying performance optimizations to enable long training jobs and cross-cluster training. The goal is to make advanced deep learning methods more accessible to the broader scientific community.

Generated 7/2/24, 11:29 AM