The National Science Foundation (NSF) Division of Information and Intelligent Systems awarded a $200,000 Project Grant to Temple University to develop transformative machine learning and data analytics technologies for enabling AI-based applications on resource-constrained edge computing devices. The project aims to address gaps between the complexity of data and the limited computing resources on edge devices, as well as the need for robust predictive models across heterogeneous edge...
The National Science Foundation (NSF) awarded a $299,999 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Chicago. The grant, titled "COLLABORATIVE RESEARCH: RI: SMALL: ACTIVE CURRICULUM AND ENVIRONMENT DESIGN FOR REINFORCEMENT LEARNING", aims to enhance the performance of reinforcement learning (RL) systems in complex environments. The key objectives are to develop active task and environment design strategies that...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program Project Grant (CFDA 47.070) of $151,593 awarded to Cornell University on March 1, 2024 will support the development of new reinforcement learning (RL) algorithms that can learn efficiently and reliably from limited training data. The key products of this 5-year project will be RL algorithms that can be safely deployed in real-world applications like autonomous driving and generative AI where...
This federal Project Grant award of $474,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop a suite of neural bandit learning algorithms that leverage recent advances in deep learning theory for efficient neural network model training with limited feedback.
The key products and services to be delivered under this 4-year award, which started on October 1, 2024, include:
Advancing bandit learning...
This National Science Foundation (NSF) grant award under the Computer and Information Science and Engineering program (CFDA 47.070) provides $250,000 over 3 years to Northeastern University, a private non-profit research university based in Boston, MA, to develop advanced techniques for continual online learning on energy-constrained edge computing devices.
The key products and services to be delivered through this research project include: (1) an attention-guided smart layer freezing...
This $398,856 National Science Foundation project grant supports research at Northwestern University to improve reinforcement learning algorithms. Specifically, the grant funds the development of sample-efficient and computationally-efficient algorithms for both online and offline reinforcement learning with function approximation. The researchers aim to incorporate optimistic exploration and pessimistic exploitation techniques using faithful uncertainty quantification for neural networks....
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) provides $300,000 to Arizona State University to enhance the performance of reinforcement learning (RL) systems in complex environments. The project aims to develop task and environment representations specifically for active design in RL, which can optimize resource allocation and reduce the need for expensive environment interactions. The key...
This $300,000 Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CFDA 47.070) program supports collaborative research on the mathematical and algorithmic foundations of multi-task reinforcement learning. The research aims to address the challenge of data efficiency in reinforcement learning, developing new approaches that can learn multiple related tasks simultaneously using less data and computational resources compared to learning...
This $100,000 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 enabling technologies for integrating distributed intelligence into the network edge to power next-generation smart applications.
The project focuses on three main thrusts: 1) developing techniques for the life cycle of distributed intelligence, including data curation, decentralized learning/fine-tuning, and...
The National Science Foundation awarded a $600,000 Project Grant to Princeton University under the Computer and Information Science and Engineering program (CFDA 47.070).
The grant will support research toward developing a mathematical foundation for deep reinforcement learning over the period from October 1, 2022 to September 30, 2026. Specifically, the project aims to bridge current gaps in theoretical reinforcement learning and deep neural networks by investigating guarantees achievable by...
This Project Grant award of $224,478 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CFDA 47.070) program will fund research to develop methods for enabling state-of-the-art reinforcement learning techniques to run on resource-constrained edge devices. The key products and services to be delivered under this three-year project (10/1/2024 - 9/30/2027) include:
Innovating and co-designing techniques spanning devices, circuits, system architectures, and algorithms to enable efficient execution of reinforcement learning on edge devices. This will involve integrating a novel approximate computing circuit technique and flash device-based computing to achieve at least an order of magnitude improvement in computing efficiency over conventional approaches. The project also aims to adopt meta-learning frameworks to address the limited write cycles of flash devices and leverage approximate computing for fault tolerance and graceful precision degradation. Additionally, the project will co-design neural network models and their hardware mapping for further resource efficiency.
The proposed methodology will be validated through a silicon prototype and a robotic control system. The research outcomes are expected to significantly advance the state of knowledge on deploying reinforcement learning on resource-constrained edge devices, with potential applications in personal computing platforms, biomedical devices, precision agriculture, smart manufacturing, and other domains.