This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant, award ID 2403723, provides $500,000.00 to Cornell University to develop novel algorithms and hardware designs for energy-efficient, memory-optimized spiking neural network (SNN) systems on edge computing devices. The project aims to advance the practical deployment of neuromorphic computing for applications like drones, autonomous robots, portable medical devices, and wearable...
This Project Grant award from the National Science Foundation (NSF) under its Engineering program (CFDA 47.041) provides $400,000 in funding to Yale University to develop a new hardware system for accelerating the computational modeling of continuous neuronal systems. The project aims to address the significant computational demands required for sophisticated multi-scale brain models by creating a convergent simulator architecture that leverages neuromorphic hardware principles. The resulting...
The National Science Foundation awarded a $599,304 three-year Project Grant to the University of California, Santa Barbara under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The grant will support the development of novel methods and hardware architectures for optimization and acceleration of spiking neural networks. Key products will include algorithms, software design tools, and field-programmable gate array hardware architectures and...
This Project Grant award, totaling $731,058.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070). The primary objective of the award is to design and evaluate an innovative energy-efficient hardware and software framework for implementing deep neural networks (DNNs) on-chip, with a focus on enabling real-time signal classification for next-generation wireless systems. The research aims to...
This $2 million Project Grant from the National Science Foundation's Office of Emerging Frontiers and Multidisciplinary Activities will fund research at Cornell University from October 2022 through September 2026 under the federal Engineering program (CFDA 47.041). The grant aims to advance neuromorphic computing by developing algorithms and computational models inspired by biological neural circuits. Researchers will extract principles from neuroscience to design a neural network architecture...
The Massachusetts Institute of Technology (MIT) received a $500,000 Project Grant award from the National Science Foundation Division of Computing and Communication Foundations on June 1, 2022 to support research titled "AF: SMALL: AN ALGORITHMIC THEORY OF BRAIN BEHAVIOR: CONCEPT REPRESENTATION AND LEARNING IN SPIKING NEURAL NETWORKS." The three-year project will investigate concept representation and learning in spiking neural networks through the lens of algorithmic theory. The award...
The National Science Foundation (NSF) Office of Emerging Frontiers and Multidisciplinary Activities awarded a $1,999,968 Project Grant to the University of California, Santa Barbara (UCSB) under the NSF Engineering (CFDA 47.041) program. This grant aims to develop scalable algorithms and hardware for human-brain-scale neuromorphic systems with practical learning capabilities. The key objectives are to create hardware-friendly local learning algorithms, a framework for continual online...
The National Science Foundation (NSF) awarded a $296,578 five-year Project Grant under the Integrative Activities (IA) program to the University of South Carolina (USC) to develop heterogeneous neuromorphic and edge computing systems for real-time machine learning applications. The project aims to enable computer vision and language models on resource- and energy-constrained devices, with a focus on American Sign Language translation to empower those with hearing and speech impairments. Key...
This $250,000 Project Grant award from the National Science Foundation's Engineering (CFDA 47.041) program to The Pennsylvania State University (Penn State) supports a collaborative research project on "Spintronics Enabled Stochastic Spiking Neural Networks with Temporal Information Encoding". The goal is to advance neuromorphic computing architectures that can achieve brain-scale efficiency for complex machine learning through a multi-disciplinary approach spanning device physics,...
This five-year, $199,995 project grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop a co-designed framework of hardware, software, and algorithms enabling extreme-scale machine learning systems for emerging artificial intelligence of things and internet of senses technologies. Specifically, the Saint Louis University team will pursue five research thrusts: developing hardware and compiler approaches for large-scale split learning...
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 systems in applications such as drones, autonomous robots, and wearable devices. The key innovation involves optimizing SNN algorithms and hardware design to address memory overhead and power constraints of edge computing devices. The research team plans to demonstrate prototype SNN-based chips with novel architectures, shared computations, and compression techniques to enhance the efficiency of SNNs on resource-constrained edge systems. This award reflects NSF's mission to advance scientific knowledge and enable sustainable artificial intelligence through fundamental research and development in computing and information science.