As the integrated circuit (IC) complexity keeps increasing, the chip design cost is skyrocketing. There is a compelling need for design efficiency improvement through new electronic design automation (EDA) techniques. As a result, AI-driven EDA techniques have been extensively explored for VLSI circuit design applications. In this talk, I will present the recent trend of developing foundation AI models for circuit design, also named circuit foundation models (CFMs). I will categorize existing circuit foundation models into two primary types: 1) encoder-based methods for general circuit representation learning for predictive tasks; and 2) decoder-based methods leveraging large language models (LLMs) for generative tasks. I will introduce representative works in each category, as well as our observed challenges and potential future research directions.

Speaker Bio:  

Prof. Zhiyao Xie is an Assistant Professor in the ECE Department at Hong Kong University of Science and Technology. He received his Ph.D. in 2022 from Duke University. He has received multiple awards, including the RGC Early Career Award 2023, ACM Outstanding Dissertation Award in EDA 2023, EDAA Outstanding Dissertation Award 2023, MICRO 2021 Best Paper Award, ASP-DAC 2023 Best Paper Award, ACM SIGDA SRF Best Poster Award 2022, and 4 other best paper nominations.

References: 

[1] A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA, arXiv preprint arXiv:2504.03711, 2025. 

[2] CircuitFusion: Multimodal Circuit Representation Learning for Agile Chip Design, International Conference on Learning Representations (ICLR), 2025 

[3] NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graph, ACM/IEEE Design Automation Conference (DAC), 2025. 

[4] GenEDA: Towards Generative Netlist Functional Reasoning via Cross-Modal Circuit Encoder-Decoder Alignment, IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2025.