LLM for EDA in Front-End Design: Challenges and Opportunities
For EDA researchers, this is a survey/position paper that outlines challenges and opportunities, but it is incremental as it does not introduce novel methods or results.
The paper discusses the potential of LLMs as a unified interface for front-end EDA tasks (HDL generation, testbench construction, design space exploration) and reviews recent advances. It highlights the shift from localized assistance to autonomous agentic execution, but does not present new results or concrete numbers.
As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development. Recently, Large Language Models (LLMs) have shown great potential in Electronic Design Automation (EDA). Beyond specification understanding, LLMs show the potential to serve as a unified intelligent interface for hardware description language (HDL) generation, testbench construction, and design space exploration. The rise of agentic AI, represented by pioneering systems such as OpenClaw, offers a strategic roadmap for the next generation EDA. From this perspective, this paper discusses the evolution of EDA from localized assistance to autonomous agentic execution. Then, we review representative advances of LLMs in front-end design, focusing on key tasks such as circuit and testbench generation from a shared specification, as well as design quality improvement in established workflows such as high-level synthesis. Finally, we discuss the key challenges and limitations of integrating LLMs into EDA, and outline future opportunities for advancing LLM-enabled front-end design, offering a systematic perspective for researchers interested in leveraging agentic AI technologies for EDA.