Project Grant 2401544
- The National Science Foundation (NSF) awarded a $340,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Missouri System to develop sustainability-aware design methods for approximate deep neural network (DNN) accelerators. The key objectives of this 3-year project are: Investigating efficient neural architecture search techniques for reliable and sustainable approximate DNN accelerator designs. Developing fault mitigation and...
- This $307,266 federal Project Grant award from the National Science Foundation's Mathematical and Physical Sciences Program (CFDA 47.049) supports the development of effective computational methods for training neural networks using an Exploration-Exploitation-Determination (EED) framework. The project, conducted by North Carolina State University, aims to address fundamental challenges in training neural networks, which are core components of modern AI models. The key objectives include: 1)...
- This $900,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) federal grant program supports the development of a new framework called NEUHLS (NEUrosymbolic Framework for High-Level Synthesis of Multi-Task Learning) at the University of California, Irvine. The goal of the NEUHLS framework is to enable the deployment of complex deep neural network (DNN) models on resource-constrained edge devices by optimizing them...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to the University of Texas at Dallas (UTD) aims to develop a new sustainability-aware design flow for approximate deep neural network (DNN) accelerators. The $259,741 award seeks to address the energy consumption and fault vulnerability issues of high-precision DNN accelerators used in safety-critical applications. Key objectives include: (1) designing reliable...
- The National Science Foundation awarded a $1,247,506 Project Grant to the University of Texas at Dallas under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) for the period of October 1, 2022 through September 30, 2025. The grant funds research to comprehend and mitigate errors in analog implementations of on-die neural networks. Specifically, the university will investigate and develop methods to address the impact of manufacturing and operational...
- The National Science Foundation (NSF) Division of Computing and Communication Foundations awarded a $150,000 Project Grant to The Leland Stanford Junior University (Stanford University) to support the "COLLABORATIVE RESEARCH: SHF: SMALL: QUASI WEIGHTLESS NEURAL NETWORKS FOR ENERGY-EFFICIENT MACHINE LEARNING ON THE EDGE" project under the NSF's Computer and Information Science and Engineering (CFDA 47.070) program. The project aims to develop low-energy machine learning hardware that...
- The National Science Foundation (NSF) awarded a $175,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of North Carolina at Charlotte. The project, titled "CRII: CSR: ENABLING ON-DEVICE CONTINUAL LEARNING THROUGH ENHANCING EFFICIENCY OF COMPUTING, MEMORY, AND DATA", aims to develop an efficient on-device continual learning framework that can incrementally learn new knowledge without forgetting prior learnt knowledge, while...
- This Project Grant award of $331,063, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop transformative methods for enhancing the resilience and reliability of machine learning (ML) systems in dynamic, real-world environments. The award will address three key challenges: 1) improving robustness generalization across data distributions, 2) ensuring robustness against multiple attacks simultaneously,...
- The U.S. National Science Foundation (NSF) awarded a $120,120 CAREER grant under the Computer and Information Science and Engineering (CISE) program to The Johns Hopkins University. The project, titled "DEEPMATTER: A Scalable and Programmable Embedded Deep Neural Network", will develop novel methodologies for optimizing deep neural network (DNN) models to enable their deployment on embedded systems with limited hardware resources and power budgets. The research aims to create new DNN...
- This $298,450 National Science Foundation project grant supports research to quantify the error landscape of deep neural networks. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the awardee New York University will employ statistical mechanics methods to characterize the basins of attraction in high-dimensional parameter spaces of deep learning models. The university will measure basin volume distributions and flatness as a function of network parameters...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant awarded to North Carolina State University (NC State) provides $208,745 to develop scalable and stable neural network paradigms to address variability issues in emerging device-based platforms for large-scale neuromorphic computing. The project aims to improve the reliability and sustainability of deep learning accelerators for data centers by explicitly modeling weight uncertainties, designing statistical neural network architectures, and developing variability-aware neural network classifiers and input pre-processing techniques. The research results are expected to advance the state-of-the-art in computer engineering and enable new consumer, business, scientific, and national security applications leveraging deep learning capabilities. The grant will also provide hands-on research opportunities for students, including underrepresented groups, to develop cutting-edge expertise in this critical computing domain.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $208.7k | 12/27/23 |