Project Grant 2428656

Award Date 9/1/24
Completion Date 8/31/27
Dollars Obligated $300K
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Austin, TX 78712, USA
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This Project Grant award, valued at $219,227, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) Federal Grant Program. The grant seeks to address challenges in integrating adaptive tiny machine learning models into battery-less Internet of Things (IoT) devices, enabling energy-efficient data analysis and decision-making in dynamic, energy-variable environments. The key objectives of this 5-year project are to: 1)...
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This $300,000 Project Grant awarded by the National Science Foundation's Engineering program (CFDA 47.041) aims to develop batteryless technology for Internet of Things (IoT) devices, particularly in wearable applications such as fitness trackers, smartwatches, and medical devices. The project will integrate artificial intelligence (AI) and deep learning techniques to harness the potential of batteryless sensors for personalized data analytics. Key objectives include:

  1. Developing new deep learning algorithms tailored for the unstructured data generated by batteryless sensors, and optimizing energy usage strategies of batteryless sensors with the requirements of machine learning models.

  2. Creating a novel approach for selecting sensors specifically for batteryless systems, and building real prototypes using kinetic energy harvesting to validate simulations.

  3. Establishing an interdisciplinary, research-based curriculum that combines machine learning and batteryless system design to foster a new generation of innovators.

The project, awarded on September 1, 2024, will be executed by the University of Texas at Austin over a 3-year period ending on August 31, 2027. No subawards are planned for this grant.

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