A Survey of Research in Large Language Models for Electronic Design Automation

2025-01-16

AI Summary

This paper systematically investigates the adaptation pathways and application boundaries of large language models (LLMs) in electronic design automation (EDA). Addressing critical challenges—including significant semantic gaps between LLMs and EDA tasks, difficulties in domain-knowledge integration, and insufficient end-to-end coverage—we propose the first comprehensive classification framework for deep LLM–EDA integration. Our methodology introduces a customized paradigm encompassing architectural evolution, scaling laws, task-specific modeling, knowledge-augmented reasoning, and domain-aware prompt engineering. Through empirical evaluation across frontend synthesis, physical design, and verification tasks, we derive an extensible LLM application taxonomy, precisely delineating capability boundaries and requirement-alignment mechanisms. We identify six fundamental technical challenges and articulate concrete, practice-oriented research directions. This work establishes both theoretical foundations and actionable technical roadmaps for deploying LLMs in EDA.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Within the rapidly evolving domain of Electronic Design Automation (EDA), Large Language Models (LLMs) have emerged as transformative technologies, offering unprecedented capabilities for optimizing and automating various aspects of electronic design. This survey provides a comprehensive exploration of LLM applications in EDA, focusing on advancements in model architectures, the implications of varying model sizes, and innovative customization techniques that enable tailored analytical insights. By examining the intersection of LLM capabilities and EDA requirements, the paper highlights the significant impact these models have on extracting nuanced understandings from complex datasets. Furthermore, it addresses the challenges and opportunities in integrating LLMs into EDA workflows, paving the way for future research and application in this dynamic field. Through this detailed analysis, the survey aims to offer valuable insights to professionals in the EDA industry, AI researchers, and anyone interested in the convergence of advanced AI technologies and electronic design.

Problem

• Large Language Models
• Electronic Design Automation
• Model Optimization

Innovation

• Large Language Models
• Electronic Design Automation
• AI-EDA Synergy