@inproceedings{liu-etal-2025-deeprtl2,
title = "{D}eep{RTL}2: A Versatile Model for {RTL}-Related Tasks",
author = "Liu, Yi and
Zhang, Hongji and
Zhou, Yunhao and
Shi, Zhengyuan and
Xu, Changran and
Xu, Qiang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.336/",
doi = "10.18653/v1/2025.findings-acl.336",
pages = "6485--6500",
ISBN = "979-8-89176-256-5",
abstract = "The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in register transfer level (RTL) code generation and understanding. While previous studies have demonstrated the efficacy of fine-tuning LLMs for these generation-based tasks, embedding-based tasks, which are equally critical to EDA workflows, have been largely overlooked. These tasks, including natural language code search, RTL code functionality equivalence checking, and performance prediction, are essential for accelerating and optimizing the hardware design process. To address this gap, we present $\textbf{DeepRTL2}$, a family of versatile LLMs that unifies both generation- and embedding-based tasks related to RTL. By simultaneously tackling a broad range of tasks, DeepRTL2 represents the first model to provide a comprehensive solution to the diverse challenges in EDA. Through extensive experiments, we show that DeepRTL2 achieves state-of-the-art performance across all evaluated tasks."
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<abstract>The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in register transfer level (RTL) code generation and understanding. While previous studies have demonstrated the efficacy of fine-tuning LLMs for these generation-based tasks, embedding-based tasks, which are equally critical to EDA workflows, have been largely overlooked. These tasks, including natural language code search, RTL code functionality equivalence checking, and performance prediction, are essential for accelerating and optimizing the hardware design process. To address this gap, we present DeepRTL2, a family of versatile LLMs that unifies both generation- and embedding-based tasks related to RTL. By simultaneously tackling a broad range of tasks, DeepRTL2 represents the first model to provide a comprehensive solution to the diverse challenges in EDA. Through extensive experiments, we show that DeepRTL2 achieves state-of-the-art performance across all evaluated tasks.</abstract>
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%0 Conference Proceedings
%T DeepRTL2: A Versatile Model for RTL-Related Tasks
%A Liu, Yi
%A Zhang, Hongji
%A Zhou, Yunhao
%A Shi, Zhengyuan
%A Xu, Changran
%A Xu, Qiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liu-etal-2025-deeprtl2
%X The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in register transfer level (RTL) code generation and understanding. While previous studies have demonstrated the efficacy of fine-tuning LLMs for these generation-based tasks, embedding-based tasks, which are equally critical to EDA workflows, have been largely overlooked. These tasks, including natural language code search, RTL code functionality equivalence checking, and performance prediction, are essential for accelerating and optimizing the hardware design process. To address this gap, we present DeepRTL2, a family of versatile LLMs that unifies both generation- and embedding-based tasks related to RTL. By simultaneously tackling a broad range of tasks, DeepRTL2 represents the first model to provide a comprehensive solution to the diverse challenges in EDA. Through extensive experiments, we show that DeepRTL2 achieves state-of-the-art performance across all evaluated tasks.
%R 10.18653/v1/2025.findings-acl.336
%U https://aclanthology.org/2025.findings-acl.336/
%U https://doi.org/10.18653/v1/2025.findings-acl.336
%P 6485-6500
Markdown (Informal)
[DeepRTL2: A Versatile Model for RTL-Related Tasks](https://aclanthology.org/2025.findings-acl.336/) (Liu et al., Findings 2025)
ACL
- Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, and Qiang Xu. 2025. DeepRTL2: A Versatile Model for RTL-Related Tasks. In Findings of the Association for Computational Linguistics: ACL 2025, pages 6485–6500, Vienna, Austria. Association for Computational Linguistics.