ChatEDA: A Large Language Model Powered Autonomous Agent for EDA

The integration of a complex set of electronic design automation (EDA) tools to enhance interoperability is a critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities in natural language processing and comprehension, of...

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Published in:IEEE transactions on computer-aided design of integrated circuits and systems Vol. 43; no. 10; pp. 3184 - 3197
Main Authors: Wu, Haoyuan, He, Zhuolun, Zhang, Xinyun, Yao, Xufeng, Zheng, Su, Zheng, Haisheng, Yu, Bei
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Data systems
Design automation
Electronic design automation
Electronic design automation (EDA)
Integrated circuit modeling
Large language models
large language models (LLMs)
machine learning algorithms
Mathematical models
Natural language processing
Quantization (signal)
Task analysis
Training
ISSN:0278-0070, 1937-4151
Online Access:Get full text
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Summary:The integration of a complex set of electronic design automation (EDA) tools to enhance interoperability is a critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities in natural language processing and comprehension, offering a novel approach to interfacing with EDA tools. This research article introduces ChatEDA, an autonomous agent for EDA empowered by an LLM, AutoMage, complemented by EDA tools serving as executors. ChatEDA streamlines the design flow from the register-transfer level (RTL) to the graphic data system version II (GDSII) by effectively managing task decomposition, script generation, and task execution. Through comprehensive experimental evaluations, ChatEDA has demonstrated its proficiency in handling diverse requirements, and our fine-tuned AutoMage model has exhibited superior performance compared to GPT-4 and other similar LLMs.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2024.3383347