Using Artificial Intelligence (AI) currently requires access to the internet and very large and complex remote computers for making decisions and predictions. This causes long delays and privacy and security concerns. The latest techniques in AI, known as “Edge AI”, avoid these problems by collecting and analyzing data directly on cameras, smart phones, and wearable devices. However, Edge AI is still in its infancy and there are several important technical problems that need to be solved. This Research Infrastructure Improvement Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC) award is a collaboration between six universities (including two minority-serving institutions) and several private-sector partners in Alabama, Arkansas, and North Dakota. As a test of the project's new technology, the project team will build a smart wearable device to predict the onset of diabetes by monitoring a patient's own breath without the need for a doctor to interpret the results. It will provide research training opportunities for advanced college students and will also train high-school teachers in lessons to educate their own students in the principles of Edge AI to seed the future US workforce in these essential concepts for tomorrow’s world. The goal of this RII Track-2 FEC award is to develop integrated research infrastructure and workforce in Edge AI. Fundamental contributions and technical innovations to be developed by the team include: (i) light-weight AI-empowered reasoni