This $300,000 Project Grant awarded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) aims to develop batteryless technology for Internet of Things (IoT) devices. The project integrates artificial intelligence (AI) and deep learning techniques to harness the potential of batteryless sensors for personalized data analytics, with far-reaching implications for healthcare, IoT, and augmented/virtual reality applications. Key objectives include developing new deep...
This Project Grant award of $174,975 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program supports research at Worcester Polytechnic Institute (WPI) to develop an integrated framework for opportunistic computation offloading from battery-free Internet of Things (IoT) devices to persistently powered edge computing devices. The key objectives are to: (1) design a scheduling system to determine optimal communication and computation strategies...
This $600,000 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 new unifying software environment and ecosystem for building and deploying Internet of Things (IoT) systems. The project aims to create publicly available, extensible IoT software infrastructure that enables new cross-disciplinary discoveries and makes IoT more broadly accessible. The key technical advances...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant (CFDA 47.070), titled "CAREER: ELASTIC INTERMITTENT COMPUTATION ENABLING BATTERYLESS EDGE INTELLIGENCE", will provide $259,488 to the University of Illinois to develop sustainable computing technologies for the Internet of Things (IoT). The key products and services to be delivered through this 5-year project (10/1/2024 - 9/30/2029) include: Advancing the foundations of...
The National Science Foundation (NSF) awarded Virginia Polytechnic Institute & State University (Virginia Tech), a leading public research university, a $450,000 Project Grant under the NSF's Computer and Information Science and Engineering program (CFDA 47.070). The grant supports technical advances in the design of small, low-power computing hardware and software for energy harvesting systems to restore the continuous power abstraction for batteryless Internet of Things (IoT) devices....
This Project Grant award, totaling $375,022, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The objective of this project is to transition from the conventional cloud-centric computing paradigm to a "sense-decide-action" mechanism that enables autonomous, energy-efficient edge intelligence processing of IoT data. The key technical approaches include: Designing and analyzing non-Von Neumann...
This $381,264 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports research at the University of Florida to develop hardware-efficient artificial intelligence techniques for federated learning across diverse Internet of Things devices. Over a three-year period ending September 2025, the research team will work to enable quantization and pruning of neural networks in a way that accounts for the varied computational...
This three-year, $279,334 National Science Foundation project grant funds research at the New Jersey Institute of Technology to develop integrated sensing and normally-off computing architectures for Internet of Things devices. The research focuses on designing processing-in-sensor units and processing-near-sensor units that co-integrate sensing and processing capabilities. These hybrid platforms will feature granularly configurable arithmetic operations to balance accuracy, speed and power...
This National Science Foundation Project Grant of $299,181 supports research and education efforts toward opportunistic, fast, and robust in-cache artificial intelligence acceleration at the edge for Internet of Things devices. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), the award to New Jersey Institute of Technology from January 1, 2023 to December 31, 2025 aims to design and deploy new hardware-oriented AI algorithms for efficient image...
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...
This $219,227 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research at the University of Texas at San Antonio (UTSA) to develop innovative methods for integrating adaptive tiny machine learning models into battery-less Internet of Things (IoT) systems. The key objectives are:
Fundamental redesign and optimization of tiny machine learning models to enable timely data analysis and decision-making in self-powered IoT devices.
Development of a holistic framework to support adaptive data collection, decision-making, and communication strategies that adapt to varying ambient energy environments.
Design of reliable and efficient code update mechanisms to maintain program currency under frequent power interruptions.
This 5-year project, awarded on June 1, 2025, aims to lay the foundations for intelligent, self-powered IoT devices and applications. The research outcomes will include novel cross-layer co-design techniques, tiny machine learning model design methods, software packages, and end-to-end deployment solutions. The project also includes efforts to engage underrepresented groups and K-12 students in STEM fields.