ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
This addresses interoperability issues for circuit designers by providing a novel LLM-based agent, though it appears incremental as it builds on existing LLM capabilities for a specific domain.
The paper tackles the problem of integrating Electronic Design Automation (EDA) tools by introducing ChatEDA, an autonomous agent powered by a large language model (AutoMage) that streamlines the design flow from RTL to GDSII, with experiments showing it handles diverse requirements and outperforms GPT-4 and other LLMs.
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 paper 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.