Abstract
The rapid advancement of AI has driven growing demand for specialized AI hardware, while the increasing complexity of modern integrated circuits poses substantial challenges to design productivity, functional verification, and power, performance, and area (PPA) optimization. Emerging AI technologies, including large language models (LLMs), reinforcement learning (RL), graph neural networks (GNNs), and autonomous AI agents, present new opportunities to advance electronic design automation (EDA) toward more intelligent and adaptive design methodologies. This seminar will provide an overview of the chip design lifecycle, spanning system architecture, register-transfer level (RTL) design, functional verification, physical implementation, and tapeout, with an emphasis on AI-enabled automation. Three research directions will be highlighted: (1) AI-assisted RTL generation and functional verification, leveraging LLMs and intelligent agents to facilitate hardware description generation, coverage-driven verification, and state-space exploration; (2) AI-driven physical design optimization, employing RL and GNNs to enhance floorplanning and placement; and (3) AI-guided design-space exploration, integrating predictive modeling and intelligent search strategies to optimize PPA trade-offs. Furthermore, the seminar will discuss the potential of agentic AI frameworks to orchestrate EDA tools and enable iterative, feedback-driven chip design workflows.
Speaker Bio
Dr. Lujun Zhai is a Research Assistant Professor in the Department of Electrical and Computer Engineering and the Center for Computational Systems Biology (CCSB) at Prairie View A&M University (PVAMU). Her research focuses on artificial intelligence, computer vision, hardware acceleration, and resource-efficient computing systems. Her work integrates deep learning, hardware-aware optimization, and field-programmable gate array (FPGA) technologies to develop efficient AI solutions for resource-constrained and embedded computing platforms. Her research also explores AI applications to electronic design automation (EDA), including intelligent chip design, functional verification, and design optimization.