Project Grant 2326895
- The University of Texas at Austin was awarded a $450,000 Project Grant from the National Science Foundation (NSF) Division of Computing and Communication Foundations (CFDA 47.070 Computer and Information Science and Engineering) to conduct collaborative research on energy-efficient machine learning hardware for edge computing applications. The key research objectives are to: (1) develop novel weightless neural network architectures that combine the benefits of traditional deep neural networks...
- The National Science Foundation (NSF) awarded a $225,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to The Leland Stanford Junior University. The 3-year grant, effective July 1, 2024, aims to gain a deeper theoretical understanding of the statistical properties of neural networks, which have revolutionized science and engineering. Key research directions include studying the distinguishing features of deep neural networks compared to classical statistical...
- This $618,159 National Science Foundation project grant supports research into developing energy-efficient hardware and software for machine learning and artificial intelligence systems. Funded under the Computer and Information Science and Engineering program, the award supports The Washington University in investigating frameworks using quantum-tunneling dynamic-analog memory devices and novel online learning algorithms. Specific objectives include exploring Fowler-Nordheim dynamic analog...
- 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,...
- This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $199,829 to Kennesaw State University Research And Service Foundation, Inc. to advance fundamental research on federated neuromorphic learning in wireless edge networks. The key objectives are: Overcoming constraints of system memory, communication bandwidth, and adversarial perturbations in wireless edge environments to enable distributed spiking neural network (SNN) learning....
- The Leland Stanford Junior University received a three-year $231,036 Project Grant from the National Science Foundation Division of Computing and Communication Foundations under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support research exploring hardware-aware matrix computations for deep learning applications. Specifically, the university will study how to use structured matrices in concert with modern hardware constraints to...
- The National Science Foundation (NSF) has awarded a $299,999 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, titled "COLLABORATIVE RESEARCH: CNS CORE: SMALL: A COMPILATION SYSTEM FOR MAPPING DEEP LEARNING MODELS TO TENSORIZED INSTRUCTIONS (DELITE)," will fund research to develop a compilation system that can optimize deep neural network (DNN) workloads for emerging tensorized instruction...
- The National Science Foundation (NSF) Division of Computer and Network Systems awarded a $174,178 project grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the President and Board of Trustees of Santa Clara College. The project aims to develop methods and a system for deploying complex machine learning (ML) models on network processing units (NPUs) to enable ultra low-latency performance for modern applications such as self-driving, security threat...
- The National Science Foundation awarded a $525,000 Project Grant to the University of California, San Diego under the Computer and Information Science and Engineering program (CFDA 47.070) to support research investigating energy-efficient persistent learning-in-memory with quantum tunneling dynamic synapses from October 1, 2022 to September 30, 2025. The award will fund the development of novel learning hardware and software tools to significantly improve the energy efficiency of artificial...
- This $500,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program supports a collaborative research effort between Yale University and its partners to develop energy-efficient algorithms and hardware for spike-based edge computing. The project aims to integrate spiking neural networks (SNNs), a brain-inspired computing paradigm, with modern integrated circuits to enable practical deployment of neuromorphic...
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 combines the benefits of traditional deep neural networks (DNNs) and the computationally efficient weightless neural networks (WNNs). Key research activities include: (1) designing multi-layer and hierarchical WNN architectures, (2) devising novel training algorithms for WNNs, (3) exploring quasi-weightless neural networks using emerging memory technologies, and (4) creating energy-efficient edge intelligence systems. This collaborative project between Stanford University and the University of Texas involves underrepresented STEM communities, including minority and women students, undergraduates, and first-generation college students. The project will run from October 1, 2023 to September 30, 2026.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 8/30/23 |