Artificial intelligence is increasingly essential for small, power-limited devices that process information in real time, such as wearable health monitors, autonomous drones, and environmental sensor networks. However, most current computer hardware is inefficient for these "edge" applications because it relies on moving massive amounts of data between separate memory and processing units. This constant data movement creates a significant energy bottleneck and slows down operations. This project addresses this challenge by developing a new class of electronic hardware where memory and processing are combined within the same physical material. The research focuses on ionically gated transistors, which are electronic switches whose behavior is controlled by the movement of ions, or charged atoms, within a solid material. By mimicking the way biological brains process information, this "in-materia" approach enables hardware that can both respond dynamically to fast-changing signals and store learned information permanently in place. Beyond the technical innovations, the project provides a public benefit by strengthening the United States' semiconductor workforce. It integrates research with education by training graduate and undergraduate students in advanced microchip fabrication and modeling. Additionally, the project engages K–12 students and the local community through hands-on demonstrations that illustrate how new materials can enable the next generation of energy-efficient computing. Technically, the project establishes the fundamental science and engineering of a dual-mode ionically gated transistor that functions as a Co-located Adaptive Synapse (CAS). The CAS is a single device capable of operating in two distinct regimes controlled by the magnitude of the applied gate voltage. At lower voltages, the device operates as an electric double-layer transistor (EDLT), where ions accumulate at the interface of the channel to produce a volatile, fading-memory resp