NSF AI grant $2.59M: research infrastructure and workforce for on-device "Edge AI" (University of Alabama and six-university collaboration)

The NSF awarded about $2,587,276 as a Research Infrastructure Improvement Track-2 Focused EPSCoR Collaboration (RII Track-2 FEC) grant (NSF Award 2611071; October 2025 – September 2027; the awardee of record is the University of Alabama, with the University of South Alabama leading).

Per the abstract, using AI today generally requires internet access and very large, complex remote computers to make decisions and predictions, which causes delays and privacy and security concerns. The latest techniques, known as "Edge AI," avoid these problems by collecting and analyzing data directly on cameras, smartphones, and wearable devices. Edge AI is still in its infancy, with several important technical problems to solve. The award is a collaboration among six universities (including two minority-serving institutions) and several private-sector partners in Alabama, Arkansas, and North Dakota.

Fundamental contributions to be developed include: (i) lightweight AI-empowered reasoning and machine-learning algorithms for edge platforms; (ii) a new Application-Specific Integrated Circuit (ASIC) design methodology enabling AI ASICs with ultra-low power, reconfigurability, and short development cycles; (iii) a sensor-device platform for Edge AI based on novel functionalized nano-scale sensing materials with nano-3D printing; and (iv) an Edge AI device platform meeting the requirements of different use cases. As a test, the team will design, prototype, and test a low-cost smart wearable that predicts diabetes onset by monitoring a patient's own breath, without a doctor needing to interpret the results. The project also builds an education-to-workforce pipeline from high school through undergraduate, graduate, postdoctoral, junior-faculty, and industry levels.