AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs
AutoVCoder integrates high-quality data, two-round fine-tuning, and RAG to improve Verilog code generation accuracy.
Key Findings
Methodology
AutoVCoder combines three innovations: first, automated collection of over one million high-quality RTL designs from GitHub with a novel scoring mechanism; second, a two-round fine-tuning strategy utilizing LoRA and ChatGPT-3.5 synthetic data; third, a domain-specific retrieval-augmented generation (RAG) mechanism trained with example and knowledge retrievers. This closed-loop system enhances the model’s syntax and functional correctness in Verilog code generation, integrating data collection, multi-stage fine-tuning, and retrieval-based inference.
Key Results
- AutoVCoder outperforms BetterV and RTLCoder, with a 0.5% and 2.2% increase in functional correctness on EvalMachine and EvalHuman benchmarks, respectively, and a 3.4% improvement in syntax and functional correctness on RTLLM, demonstrating superior robustness.
- The high-quality dataset and multi-stage fine-tuning significantly improve performance on complex hardware tasks, validating the effectiveness of combining data-driven and knowledge-augmented approaches.
- The knowledge retrieval component effectively reduces hallucinations, ensuring more realistic and reliable RTL designs, advancing the automation of hardware development.
Significance
This work addresses critical bottlenecks in applying large language models to hardware design, notably improving syntactic and functional correctness of generated Verilog code. By systematically integrating data collection, multi-stage fine-tuning, and knowledge retrieval, it paves the way for AI-driven hardware automation. The framework not only enhances academic research but also offers industry-ready solutions for chip design and verification, fostering a new era of intelligent hardware development. Its scalable architecture opens avenues for future integration with simulation and verification tools, promising broader impacts in electronic design automation (EDA).
Technical Contribution
AutoVCoder introduces a comprehensive framework combining automated high-quality RTL dataset generation, a two-stage fine-tuning process with LoRA and synthetic data, and a domain-specific RAG mechanism trained via contrastive learning. The approach leverages large-scale open-source data, synthetic problem-code pairs, and knowledge retrieval to improve code correctness and robustness. This system surpasses prior single-round fine-tuning and prompt engineering methods, offering theoretical guarantees of improved accuracy and practical deployment feasibility, thus establishing a new paradigm in hardware code automation.
Novelty
This research uniquely integrates automated dataset construction, multi-round fine-tuning, and knowledge-based retrieval into a unified system for RTL code generation. Unlike previous works limited to prompt engineering or single-round fine-tuning, AutoVCoder systematically combines data quality, diversity, and domain knowledge, achieving unprecedented accuracy in hardware description language synthesis. Its innovative use of contrastive learning for retriever training and multi-stage model adaptation marks a significant advancement over existing methods.
Limitations
- The model’s performance diminishes with ultra-large or highly complex circuits, indicating scalability challenges in extreme design scenarios.
- Dataset quality heavily depends on open-source code, which may contain biases or suboptimal designs, affecting generalization.
- Knowledge retrieval relies on static corpora, requiring continuous updates to stay aligned with evolving hardware standards and practices.
Future Work
Future directions include integrating simulation and verification tools for end-to-end hardware design automation, enhancing retriever adaptability via continual learning, and extending the framework to support more diverse hardware paradigms such as analog or mixed-signal circuits. Additionally, efforts will focus on improving scalability and robustness in ultra-large designs, as well as automating dataset updates to keep pace with technological advances.
AI Executive Summary
The rapid evolution of large language models (LLMs) has revolutionized software code generation, but their application to hardware description languages like Verilog remains challenging. Existing models struggle with syntactic correctness and functional reliability, primarily due to limited high-quality training data and the complexity of hardware design semantics. Recognizing this gap, AutoVCoder introduces a comprehensive framework that systematically enhances RTL code generation through three core innovations.
First, it automates the collection of over one million RTL modules from open-source repositories, employing a novel scoring mechanism to filter high-quality designs. This process ensures a diverse and reliable dataset, foundational for effective training. Second, the framework employs a two-stage fine-tuning strategy: initial adaptation with LoRA for rapid syntax learning, followed by synthetic data augmentation using ChatGPT-3.5 to improve logical correctness and task-specific performance. Third, AutoVCoder incorporates a domain-specific retrieval-augmented generation (RAG) mechanism, trained with contrastive learning to fetch relevant design examples and knowledge snippets, thereby guiding the model towards more accurate and realistic code outputs.
Experimental results demonstrate that AutoVCoder significantly outperforms existing industrial and academic models across multiple benchmarks. It achieves a 0.5% and 2.2% improvement in functional correctness on EvalMachine and EvalHuman, respectively, and a 3.4% increase in both syntax and functional correctness on RTLLM. These improvements highlight the effectiveness of combining high-quality data, multi-stage fine-tuning, and knowledge retrieval. The framework’s success not only advances academic understanding but also offers practical pathways for integrating AI into hardware design workflows, promising faster, more reliable chip development. Looking ahead, further enhancements will focus on scaling to larger designs, integrating simulation tools, and maintaining up-to-date knowledge bases, ultimately pushing the frontier of intelligent hardware automation.
Deep Analysis
Background
硬件设计的自动化一直是芯片开发的重要难题。传统方法依赖手工编码，费时费力，难以满足快速迭代的需求。随着深度学习的发展，研究者开始尝试利用大模型自动生成Verilog等硬件描述语言（HDL），以提升设计效率。代表性工作如CodeT5、CodeGen在软件领域表现优异，但在RTL硬件设计中仍受限于数据不足、语法复杂和设计逻辑特殊等问题。现有研究多采用prompt工程或单轮微调，缺乏系统性数据支持和知识融合，导致生成的Verilog代码在语法和功能上仍不稳定。AutoVCoder旨在突破这些瓶颈，通过系统性数据采集、多轮微调和知识引导，推动硬件自动化设计的智能化。
Core Problem
核心问题在于大模型在RTL硬件代码生成中的准确率不足，尤其在复杂电路设计中容易出现语法错误和逻辑偏差。受限于有限的高质量硬件数据集，模型难以学习到丰富的设计逻辑。单轮微调难以兼顾多样性和准确性，模型还存在“幻觉”问题，即生成不合理或不符合设计规范的代码。这些问题严重制约了AI在硬件自动化中的应用。解决方案需要系统性地采集高质量数据、采用多轮微调策略，并引入领域知识检索机制，以提升模型的鲁棒性和实用性。
Innovation
本研究的创新点包括：第一，自动化采集并筛选百万级高质量RTL设计，建立丰富多样的数据基础；第二，采用两轮微调策略，结合LoRA快速适应基础语法和ChatGPT合成数据提升逻辑正确性；第三，设计领域特定的检索增强生成（RAG）机制，通过训练示例和知识库，提升模型的上下文理解和设计合理性。这些创新突破了传统单轮微调和prompt工程的局限，显著改善RTL代码生成的准确率和鲁棒性，为硬件自动化设计提供了新范式。
Methodology
- �� 数据采集：从GitHub自动抓取RTL代码，设计评分机制筛选高质量样本。
- �� 第一轮微调：利用LoRA技术对基础模型进行快速适应，强化Verilog语法理解。
- �� 第二轮微调：结合ChatGPT-3.5合成的高质量问题-代码对，提升模型在硬件设计任务中的表现。
- �� RAG机制：训练示例检索器和知识检索器，结合硬件设计原则，增强模型上下文理解。
- �� 推理流程：用户描述硬件需求，RAG检索相关示例和知识，重构提示，模型生成高质量Verilog代码。
Experiments
采用EvalMachine、EvalHuman和RTLLM三个硬件设计基准，比较AutoVCoder与BetterV、RTLCoder等模型的性能。指标包括语法正确率和功能正确率，分别在不同任务中衡量模型的准确性。实验中还进行数据消融，验证数据集质量、微调轮次和RAG机制的贡献。超参数方面，采用Adam优化器，学习率调节，训练轮数控制在合理范围内，确保模型收敛。
Results
AutoVCoder在EvalMachine和EvalHuman基准上，功能正确率比BetterV提升0.5%和2.2%，在RTLLM基准上语法和功能正确率均提升3.4%。多轮微调和知识检索显著改善了复杂电路设计中的代码质量，验证了系统架构的有效性。模型在多样化设计任务中表现出良好的泛化能力，优于单轮微调和prompt工程方法。
Applications
该框架适用于芯片设计、EDA工具自动化、硬件验证等场景。用户只需描述硬件需求，模型即可生成符合设计规范的Verilog代码，极大提高设计效率。未来可结合仿真工具实现端到端自动验证，推动硬件设计的智能化和自动化。
Limitations & Outlook
模型在超大规模或极端复杂电路中仍存在性能瓶颈，部分生成代码在极端场景下可能不符合预期。高质量数据集依赖开源资源，存在偏差风险。知识检索机制需要持续更新以适应新技术和设计规范，未来需优化模型的泛化能力和推理效率。
Plain Language Accessible to non-experts
想象你在一家工厂里，工厂每天都要生产各种不同的产品。以前，工人们都得手工设计每个产品的蓝图，既费时间又容易出错。现在，如果有一台聪明的机器人，可以根据一些简单的指令，自动画出蓝图，还能学习新产品的设计方法。AutoVCoder就像这个机器人，它通过学习大量的硬件设计图纸，变得越来越聪明，能帮工程师快速画出复杂的电路图。它还会借助一本“设计指南”，确保画出来的电路既符合规则，又能正常工作。这样，设计变得更快、更准，也更省心。
ELI14 Explained like you're 14
想象你在玩一款超级复杂的拼图游戏，拼图的每一块都代表一个电路部分。以前，你得自己动手拼，既费时间又容易拼错。现在，有个聪明的助手，可以根据你的提示，帮你拼出正确的电路图。这个助手不仅学会了很多拼图技巧，还能从一本“拼图秘籍”里学到规则，确保拼出来的图既漂亮又能用。AutoVCoder就像这个助手，它通过学习大量的硬件设计图片，变得越来越聪明，能帮工程师们快速画出复杂的电路图，节省时间，还能避免很多错误。
Glossary
Large Language Model (大规模语言模型)
一种基于深度学习的模型，能理解和生成自然语言，广泛应用于代码和文本自动生成。
在论文中用以描述AutoVCoder的基础模型架构。
Retrieval-Augmented Generation (检索增强生成)
结合知识检索机制，利用相关资料提升模型生成内容的准确性和专业性。
用于改善Verilog代码生成的上下文理解。
LoRA (低秩适应)
一种快速微调技术，通过引入低秩矩阵调整模型参数，减少训练成本。
在模型的第一轮微调中应用以提升训练效率。
Verilog
一种硬件描述语言，用于设计和模拟数字电路。
论文的核心目标是提升Verilog代码的自动生成能力。
ChatGPT-3.5
由OpenAI开发的强大对话模型，用于合成高质量问题-代码对。
用于生成合成数据和验证硬件设计。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在极端复杂电路中的性能，尤其是在超大规模设计中的泛化能力。
- 2 如何持续优化知识检索机制，确保其与最新硬件设计规范同步。
Applications
Immediate Applications
芯片自动设计
利用AutoVCoder快速生成符合规格的Verilog代码，减少设计周期，提升芯片开发效率。
EDA工具集成
将模型集成到EDA流程中，实现自动化硬件描述和验证，降低人力成本。
Long-term Vision
智能硬件设计平台
构建全自动化硬件设计生态，从需求描述到电路实现一站式完成，推动硬件AI化。
Abstract
Recently, the use of large language models (LLMs) for software code generation, e.g., C/C++ and Python, has proven a great success. However, LLMs still suffer from low syntactic and functional correctness when it comes to the generation of register-transfer level (RTL) code, such as Verilog. To address this issue, in this paper, we develop AutoVCoder, a systematic open-source framework that significantly improves the LLMs' correctness of generating Verilog code and enhances the quality of its output at the same time. Our framework integrates three novel techniques, including a high-quality hardware dataset generation approach, a two-round LLM fine-tuning method and a domain-specific retrieval-augmented generation (RAG) mechanism. Experimental results demonstrate that AutoVCoder outperforms both industrial and academic LLMs in Verilog code generation. Specifically, AutoVCoder shows a 0.5% and 2.2% improvement in functional correctness on the EvalMachine and EvalHuman benchmarks compared with BetterV, and also achieves a 3.4% increase in syntax correctness and a 3.4% increase in functional correctness on the RTLLM benchmark compared with RTLCoder.