Nontechnical Description The rapid advancement of deep neural networks (DNNs) and large language models (LLMs) is transforming many facets of modern society. These AI models are trained and deployed in data centers powered by specialized hardware such as graphics processing units (GPUs), resulting in significant energy demands and raising critical concerns around sustainability and energy security. This project aims to explore the use of light for performing neural network computations, enabling the development of energy-efficient AI hardware. Specifically, the project will leverage the integration of thin-film lithium niobate (TFLN) — a high-performance electro-optic material — with silicon photonic chip platforms to fabricate analog optical modulators that offer significantly lower loss and higher speed compared to traditional silicon-based devices. In addition, the project will design new architectures and circuit techniques to achieve high-resolution AI computation using low-precision building blocks, optimizing both efficiency and accuracy. The educational component of this project will train students in both photonic and advanced electronic chip design, equipping them with the skills essential for next-generation AI hardware development. Outreach to high-school students using AI-based projects will help build a pipeline of students to pursue engineering degrees focusing on semiconductors and AI. The industry sponsor will be actively engaged as a strategic partner to he