The rapid advancement of foundation models has brought powerful new capabilities to Electronic Design Automation (EDA). Unlike traditional task-specific Artificial Intelligence (AI) approaches, foundation models leverage self-supervised learning and large-scale data pre-training to acquire strong generalization abilities. These models can then be efficiently fine-tuned on EDA-specific datasets, enabling a wide range of downstream applications such as hardware code generation, debug and optimization, EDA agents, circuit representation learning and understanding, etc.
This workshop aims to foster collaboration among researchers, engineers, and industry experts to explore the evolving intersection of foundation models and EDA methodologies. By identifying key challenges and exchanging ideas, we seek to inspire comparative analysis between the unique demands of EDA and those of other application domains, and to promote novel solutions that leverage the strengths of foundation models for more accurate, efficient, and scalable design automation. Through talks, paper presentations, and interactive discussions, the workshop will showcase recent progress and explore new ways to apply foundation models in EDA—helping to drive innovation and advance the future of intelligent circuit design.
For general inquiries, please email us at izzywenjiayi@gmail.com