Ph.D. Project Investigating Chiplet Interfaces for Efficient Near-Sensor Computing in Visual On-Device Intelligence

The ever-growing demand for intelligent devices capable of visual processing in real time at the edge requires a paradigm shift in computing architectures. Near-sensor computing offers a promising solution by bringing computation closer to the data source, enabling faster response times and reduced power consumption. However, traditional monolithic chip design struggles to meet the efficiency and flexibility demands of near- sensor visual intelligence tasks. This Ph.D. project investigates the transformative potential of chiplet interfaces in revolutionizing near-sensor computing for on-device visual intelligence. Chiplet technology offers a modular approach that enables the integration of heterogeneous cores and specialized hardware accelerators into a single package. Using this modularity, the project aims to achieve the following key objectives: (1) design and explore novel chiplet interface architectures, (2) hardware-software co-design for chiplet-based near-sensor processing targeting visual data, (3) power efficiency exploration and finally real-world application validation. The successful completion of this Ph.D. project is expected to yield significant contributions to the field of near- sensor computing. With expectations of proposing some novel chiplet interfaces for efficient visual on-device intelligence, the project has the potential to pave the way for a new generation of intelligent devices capable of real-time visual processing at the edge, with minimal reliance on cloud-based resources.