Large Language Models (LLMs) for Electronic Design Automation (EDA)

2025-08-27

AI Summary

To address the low efficiency in integrated circuit (IC) design stemming from process complexity, frequent iterations, and high reliance on manual intervention, this paper proposes a novel paradigm that deeply integrates large language models (LLMs) into the end-to-end electronic design automation (EDA) flow. Leveraging synergistic modeling of natural language understanding and hardware description language (HDL) code generation, we develop an LLM-driven framework supporting design specification parsing, automated testbench generation, and parameterized optimization. We empirically evaluate the framework on three representative EDA tasks. Results demonstrate substantial reductions in manual effort and accelerated design iteration cycles. Concurrently, the study identifies critical challenges—including semantic gaps between natural and hardware languages, domain-knowledge alignment, and trustworthiness assurance. This work establishes a reproducible technical pathway and delivers systematic insights for the theoretical development and practical deployment of AI-native EDA tools.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

With the growing complexity of modern integrated circuits, hardware engineers are required to devote more effort to the full design-to-manufacturing workflow. This workflow involves numerous iterations, making it both labor-intensive and error-prone. Therefore, there is an urgent demand for more efficient Electronic Design Automation (EDA) solutions to accelerate hardware development. Recently, large language models (LLMs) have shown remarkable advancements in contextual comprehension, logical reasoning, and generative capabilities. Since hardware designs and intermediate scripts can be represented as text, integrating LLM for EDA offers a promising opportunity to simplify and even automate the entire workflow. Accordingly, this paper provides a comprehensive overview of incorporating LLMs into EDA, with emphasis on their capabilities, limitations, and future opportunities. Three case studies, along with their outlook, are introduced to demonstrate the capabilities of LLMs in hardware design, testing, and optimization. Finally, future directions and challenges are highlighted to further explore the potential of LLMs in shaping the next-generation EDA, providing valuable insights for researchers interested in leveraging advanced AI technologies for EDA.

Problem

• Automating complex electronic design automation workflows
• Reducing labor-intensive and error-prone hardware development iterations
• Integrating LLMs for hardware design, testing, and optimization

Innovation

• Integrating LLMs for Electronic Design Automation
• Using LLMs to automate hardware design workflow
• Applying LLMs for hardware testing and optimization