The amount of data processed by AI-based analytics has grown exponentially year over year. Unfortunately, this has unveiled a critical deficiency of contemporary computers: they physically separate their computation chips from their data storage chips, requiring each piece of data to move between the two whenever it needs to be processed. This separation wastes an increasing amount of energy and time on data movement, often 100 to 1000 times more than the computation itself, hindering the performance, efficiency, and potential uses of computers. For mobile computers such as smartphones, small drones, and sensors, the waste forces them to offload most analytics to the cloud. Recent breakthroughs in computer hardware have realized a near-60-year vision to unite computation and storage onto the same chip, potentially eliminating most data movement waste. If fully realized, this new hardware could unleash the next revolution in mobile computing, with ultra-low-cost devices that no longer need the cloud or an Internet connection to perform AI data analytics in mobile computers. This project addresses the major challenges preventing this realization, by jointly designing the hardware and software foundations required to support and integrate these new chips. The research produced by this project can enable many new uses of computers, from automated crop monitoring to all-day extended reality gaming to rural healthcare to infrastructure anomaly detection. All research findings will be publicly disseminated through conference publications, openly available software tools, and a project website. The project will introduce new university classes, K-12 programs, and public outreach campaigns to introduce learners to hardware/software co-design skills that are critical to train the next generation of computer engineers. Specifically, this project will make cloud-free edge AI analytics a reality by evolving today's processing-using-memory (PUM; a.k.a. in-memory computing) acc