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OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection
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Abstract
Recent studies have demonstrated the significant potential of Large Language Models (LLMs) in generating Register Transfer Level (RTL) code, with notable advancements showcased by commercial models such as GPT-4 and Claude3-Opus. However, these proprietary LLMs often raise concerns regarding privacy and security. While open-source LLMs offer solutions to these concerns, they typically underperform commercial models in RTL code generation tasks, primarily due to the scarcity of high-quality open-source RTL datasets. To address this challenge, we introduce OriGen, a fully open-source framework that incorporates self-reflection capabilities and a novel dataset augmentation methodology for generating high-quality, large-scale RTL code. Our approach employs a code-to-code augmentation technique to enhance the quality of open-source RTL code datasets. Furthermore, OriGen can rectify syntactic errors through a self-reflection process that leverages compiler feedback.
@misc{cui2024origen,
title = {OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection},
author = {Fan Cui and Chenyang Yin and Kexing Zhou and Youwei Xiao and Guangyu Sun and Qiang Xu and Qipeng Guo and Yun Eric Liang and Xingcheng Zhang and Demin Song and Dahua Lin},
year = {2024},
pages = {1--9},
doi = {10.1145/3676536.3676830},
}