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article · 2024

OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection

Fan Cui, Chenyang Yin, Kexing Zhou, Youwei Xiao, Guangyu Sun, Qiang Xu, Qipeng Guo, Yun Liang, Xingcheng Zhang, Demin Song, Dahua Lin

Cited by 21Abstract and details only

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.

Cite this paper

APA 7

Cui, F., Yin, C., Zhou, K., Xiao, Y., Sun, G., Xu, Q., Guo, Q., Liang, Y., Zhang, X., Song, D., & Lin, D. (2024). OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection. https://doi.org/10.1145/3676536.3676830

MLA 9

Cui, Fan, et al. “OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection.” 2024, pp. 1–9, https://doi.org/10.1145/3676536.3676830.

Chicago

Cui, Fan, Chenyang Yin, Kexing Zhou, Youwei Xiao, Guangyu Sun, Qiang Xu, Qipeng Guo, et al. 2024. “OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection.” https://doi.org/10.1145/3676536.3676830.

Harvard

Cui, F. et al. (2024) ‘OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection’. Available at: https://doi.org/10.1145/3676536.3676830.

BibTeX
@article{cui2024origen,
  title = {{OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection}},
  author = {Cui, Fan and Yin, Chenyang and Zhou, Kexing and Xiao, Youwei and Sun, Guangyu and Xu, Qiang and Guo, Qipeng and Liang, Yun and Zhang, Xingcheng and Song, Demin and Lin, Dahua},
  pages = {1--9},
  year = {2024},
  doi = {10.1145/3676536.3676830}
}

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