This project aims to tackle the growing challenge of energy-efficient computing by developing innovative technology for real-time, on-chip acoustic classification. As traditional computing systems struggle to keep up with the demands of modern applications, especially in remote or low-power environments, there is an urgent need for more efficient alternatives. By using advanced materials known as GCMO memristors, this project will enable machines to process acoustic signals directly at the source, reducing the need for energy-hungry cloud computing. This approach could transform areas like environmental monitoring and security, where fast, local decisions are crucial. The project promises to push the boundaries of edge AI technology, offering a sustainable, low-energy solution for applications that require quick and reliable classification of temporal data in real time.

2026

2027