Artificial intelligence (AI) systems are rapidly transforming a wide range of industries, from healthcare to manufacturing, by enabling machines to perform complex tasks such as pattern recognition, natural language understanding, and autonomous decision-making. These systems rely on AI hardware accelerators for efficient computing, but designing such accelerators remains a time-consuming, manual process that limits innovation and accessibility. This project addresses the national need for scalable and energy-efficient AI infrastructure by automating the design of AI accelerators. The new framework developed in this project aims to significantly reduce the time and effort needed to produce high-performance, low-power AI hardware. By accelerating the deployment of efficient AI systems, this work can help make advanced AI capabilities more broadly accessible and sustainable, fostering economic growth and scientific progress across domains. This project develops HexAI, a comprehensive design automation framework for next-generation AI accelerators. HexAI integrates architectural optimization, workload and data-awareness, and backend circuit synthesis into a unified framework. It introduces three major innovations: (1) a structured design space representation that supports efficient exploration of architectural and mapping decisions; (2) data- and workload-aware design strategies that adapt hardware to real-world AI tasks, including training and multi-tenant inference; and (3)