Benchmarking Large Language Models for Automated Verilog RTL Code Generation
Fine-tuned CodeGen-16B achieves 25.9% syntax correctness and outperforms commercial models in functional accuracy for Verilog code generation.
Key Findings
Methodology
This study fine-tuned transformer-based pre-trained models such as Megatron-LM, CodeGen, J1-Large, and code-davinci-002 on a large-scale Verilog dataset (~400MB) compiled from GitHub repositories and textbooks. A comprehensive evaluation framework was developed, including 17 problems of varying difficulty, with dedicated test benches for syntax and functional correctness. The experiments systematically varied parameters such as temperature, number of completions, and prompt complexity. Performance metrics included compilation success rate, functional correctness, and Pass@k, providing a detailed performance landscape across model sizes and configurations.
Key Results
- Fine-tuning improved overall syntax correctness to 25.9%, with the largest model (CodeGen-16B) achieving 94.2% compilation success and 72.8% functional correctness, surpassing the performance of commercial models like code-davinci-002.
- Model size correlated positively with performance; 16B-parameter models outperformed smaller counterparts across all metrics, validating the capacity-performance relationship.
- Lower temperature settings (around 0.1) yielded higher accuracy, and increasing the number of completions per prompt improved success rates, indicating the importance of sampling strategies.
Significance
This work pioneers a systematic evaluation of large language models for hardware description language generation, demonstrating that with adequate fine-tuning, these models can significantly reduce manual effort and errors in digital circuit design. The results pave the way for integrating AI into EDA workflows, enabling faster, more reliable hardware development, and reducing reliance on human expertise for routine coding tasks. The findings also highlight the importance of model size and prompt engineering in achieving optimal results, influencing future research directions in AI-assisted hardware design.
Technical Contribution
The paper introduces a large-scale Verilog dataset, a multi-model fine-tuning pipeline, and a comprehensive evaluation framework combining syntax and functional tests. It systematically investigates the impact of model size, training data diversity, and prompt complexity on code generation quality. The experimental results establish that larger models, when fine-tuned, outperform existing commercial solutions, providing a new benchmark for AI-driven hardware code synthesis. The approach also emphasizes the significance of prompt design and sampling strategies, offering practical insights for deploying such models in real-world scenarios.
Novelty
This is the first large-scale attempt to fine-tune transformer-based models specifically for Verilog HDL code generation, utilizing a comprehensive dataset from open-source repositories and textbooks. The study systematically compares multiple models of varying sizes, introduces a multi-level evaluation framework, and provides detailed insights into the effects of prompt complexity and sampling parameters. Unlike prior work limited to software code, this research addresses the unique challenges of hardware description language synthesis, filling a critical gap in AI-assisted hardware design.
Limitations
- Despite improvements, the models still struggle with complex structures like LFSRs and truth tables, mainly due to limited training data diversity and insufficient modeling of hardware-specific features.
- The high computational cost of training and fine-tuning large models limits scalability and real-time deployment, especially for resource-constrained environments.
- The evaluation mainly focuses on syntactic and basic functional correctness; comprehensive hardware verification and edge case testing remain future challenges.
Future Work
Future research will focus on expanding the dataset with more diverse hardware structures, improving prompt engineering techniques, and integrating formal verification tools to enhance correctness. Exploring multi-task learning and transfer learning could further improve model robustness. Additionally, developing lightweight models suitable for embedded environments and real-time applications will be crucial for industrial adoption. The ultimate goal is to establish fully automated, reliable hardware design pipelines driven by AI.
AI Executive Summary
As digital hardware complexity surges, traditional manual coding of Verilog becomes increasingly time-consuming and error-prone. Existing automated approaches rely heavily on templates or rule-based systems, which lack flexibility and scalability. Recent advances in transformer-based large language models (LLMs) like GPT-3 and CodeGen have revolutionized software code generation, inspiring researchers to explore their application in hardware description languages.
This study systematically evaluates the capabilities of multiple pre-trained LLMs, including Megatron-LM, CodeGen, J1-Large, and code-davinci-002, in generating synthesizable Verilog code. By constructing a comprehensive dataset from GitHub repositories and textbooks (~400MB), the authors fine-tuned these models, achieving significant performance gains. The evaluation framework encompasses 17 problems of varying difficulty levels, with dedicated test benches for syntax and functional correctness, enabling a rigorous assessment of generated code.
Results demonstrate that fine-tuning markedly improves code quality. The largest model, CodeGen-16B, reaches a syntax correctness rate of 25.9% and a functional correctness rate of 72.8%, outperforming commercial counterparts. The experiments reveal that larger models, lower sampling temperatures, and more completions per prompt enhance performance, emphasizing the importance of model capacity and sampling strategies. These findings suggest that AI-driven code generation can substantially assist hardware designers, reducing manual effort and human error.
The broader impact of this work lies in its potential to transform hardware design workflows. Automating code synthesis not only accelerates development cycles but also improves reliability, especially as designs grow more complex. Integrating such models into EDA tools could lead to fully automated, AI-assisted hardware design pipelines, democratizing access to advanced hardware development. Despite current limitations in handling complex structures and high computational costs, this research lays a solid foundation for future advancements in AI-powered hardware automation, promising a new era of intelligent, efficient digital system design.
Deep Analysis
Background
Hardware description languages like Verilog and VHDL在数字电路设计中扮演核心角色。传统流程依赖工程师手工编码，既耗时又易出错。近年来，深度学习尤其是Transformer模型在自然语言处理和软件代码生成中取得突破，激发了将其应用于硬件描述语言自动生成的兴趣。现有研究多集中在软件领域，缺乏系统性评估Verilog自动生成的能力。开源数据有限，模型泛化能力不足，限制了实际应用。随着大规模预训练模型的发展，微调成为提升性能的关键途径。本研究在此基础上，构建了最大规模的Verilog训练集，设计了多场景评估体系，推动硬件自动化设计的技术进步。
Core Problem
Verilog代码的自动生成面临多重挑战。首先，硬件设计复杂，模型需理解丰富的硬件结构和行为逻辑。其次，缺乏大规模高质量训练数据，限制模型泛化。再次，预训练模型在Verilog上的表现不佳，语法和功能正确率低。最后，缺少系统性评估标准，难以量化模型能力。这些问题阻碍自动硬件设计的产业化，需要通过数据集构建、模型微调和评估体系创新来突破。
Innovation
本研究的创新点包括：1）构建最大规模Verilog数据集，结合GitHub开源代码和教材文本，提升数据多样性；2）采用多模型微调策略，验证不同参数规模模型在硬件描述语言生成中的性能差异；3）设计多层次评估框架，结合语法分析和功能验证，系统量化模型能力；4）分析提示复杂度和生成参数对性能的影响，为优化生成策略提供依据。这些创新突破了现有技术瓶颈，为硬件自动化设计提供了新思路。
Methodology
- �� 数据采集：利用Google BigQuery从GitHub收集Verilog仓库，筛选含模块定义的文件，过滤大文件，形成约300MB的训练集。• 教材文本：下载70本Verilog教材，采用OCR提取文本，过滤无关内容，生成400MB的Verilog文本数据。• 模型选择：采用Megatron-LM、CodeGen、J1-Large和code-davinci-002等预训练模型，参数从345M到16B不等。• 微调策略：在上述数据集上进行多轮微调，采用DeepSpeed进行模型和数据并行，确保训练效率。• 评估设计：设计17个不同难度的Verilog问题，配备专用测试台，检测语法和功能正确性。• 参数调节：调节温度、生成次数和提示详细程度，系统性分析模型性能。• 结果指标：采用编译成功率、功能验证通过率和Pass@k指标，量化模型能力。
Experiments
实验采用多参数模型、多难度问题集，调节温度（0.1-1）、生成次数（1-25）和提示复杂度（低、中、高），评估模型在不同配置下的表现。通过编译成功率和功能验证率衡量语法和功能正确性，分析模型参数规模对性能的影响。还进行了提示工程的敏感性分析，验证不同描述详细程度对生成质量的影响。采用交叉验证和消融实验，比较纯GitHub数据和教材+GitHub数据的微调效果，验证数据多样性对模型性能的作用。
Results
微调模型在整体语法正确率上显著优于预训练模型，最高达25.9%；功能正确率在复杂问题中也表现优异，最大参数模型（CodeGen-16B）达72.8%。参数规模越大，性能越优，验证了模型容量的重要性。调节温度发现，低温（0.1）生成的代码更准确，生成次数越多，正确率提升明显。提示详细程度影响生成质量，越详细的提示效果越好。模型在复杂问题上的表现仍有限，部分问题（如LFSR）未能成功生成正确代码，提示数据多样性不足。整体结果显示，微调极大提升了模型在Verilog代码生成中的实用性。
Applications
该技术可应用于硬件设计自动化、验证流程自动化和EDA工具集成。设计师可以利用微调模型快速生成硬件骨架，节省设计时间，减少错误。未来还可结合硬件验证工具，形成端到端的自动设计流程，推动智能硬件开发。长远来看，自动生成Verilog将极大降低硬件开发门槛，促进硬件创新和产业升级，尤其在AI芯片、边缘计算等领域具有巨大潜力。
Limitations & Outlook
模型在处理高复杂度和特殊边界条件（如异步复位、特殊时序）时仍表现不足，部分生成代码未能通过功能验证。训练成本高昂，依赖大量GPU资源，限制了广泛部署。数据多样性不足，导致部分复杂问题（如LFSR、真值表）无法准确生成。未来需增强训练数据的多样性，优化提示工程，提升模型鲁棒性和适应性。
Plain Language Accessible to non-experts
想象你在一家工厂里，工厂的任务是制造各种复杂的机械。每台机械都需要按照说明书组装，说明书写得很详细，但手工写每台机械既费时又容易出错。现在，工厂引入了一台聪明的机器人，它可以学习大量的说明书，然后根据指令自动组装机械。刚开始，机器人只学会了基础的组装步骤，但经过不断学习和调整，它变得越来越聪明，能自己理解复杂的指令，甚至能解决一些新问题。这个机器人就像论文中的大模型，通过大量的代码和文本学习，然后微调后，能自动生成Verilog代码，帮助工程师快速设计硬件。它的出现大大提高了效率，也减少了错误，让工厂变得更智能、更高效。
ELI14 Explained like you're 14
想象你在学校的科学实验室里，有一台超级聪明的机器人助手。你告诉它你想做一个电子电路，比如一个计数器或者一个简单的开关控制系统。以前，你得花很多时间写详细的说明，然后手工编写电路图或者代码，容易出错。现在，这个机器人已经学会了很多关于电子的知识，它可以根据你的简单描述，自动帮你写出Verilog代码，就像你用手机打字一样快。这个机器人是通过看了成千上万的代码和书本学会的，然后经过特别的训练变得更聪明。虽然它还不能完美解决所有复杂的问题，但它已经可以帮你做大部分工作，让你有更多时间去想创新点。未来，这样的机器人会变得更聪明，甚至可以帮工程师设计出更复杂、更高效的硬件系统！
Glossary
Transformer（变换器）
一种深度学习模型架构，擅长处理序列数据，广泛应用于自然语言和代码生成。
论文中用来构建大规模预训练模型的基础架构。
微调（Fine-tuning）
在预训练模型基础上，使用特定任务的数据进行再训练，以提升模型在该任务上的表现。
用于提升模型生成Verilog代码的准确性。
Pass@k指标
衡量模型在k次建议中，成功生成符合要求代码的比例。
评价模型在代码正确率上的表现。
Verilog
一种硬件描述语言，用于描述数字电路的结构和行为。
本研究的目标代码生成语言。
GPT（生成式预训练变换器）
一种基于Transformer的预训练模型，能生成连贯文本或代码。
论文中提及的模型基础架构。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在复杂硬件设计中的功能正确率，特别是在边界条件和特殊场景下的表现仍未解决。
- 2 缺乏针对Verilog特定硬件特性（如时序、异步复位）的专门训练策略，限制了模型的实用性。
- 3 模型训练成本高，如何降低硬件描述语言生成模型的硬件和时间成本仍是挑战。
Applications
Immediate Applications
硬件设计辅助
工程师可以利用微调模型快速生成硬件骨架代码，节省时间，减少错误，提升设计效率。
自动验证流程
结合模型生成的Verilog代码与自动测试平台，实现快速验证和调试，缩短开发周期。
Long-term Vision
智能EDA工具集成
将AI模型深度集成到EDA工具链中，实现端到端的自动硬件设计与验证，大幅降低门槛。
Abstract
Automating hardware design could obviate a significant amount of human error from the engineering process and lead to fewer errors. Verilog is a popular hardware description language to model and design digital systems, thus generating Verilog code is a critical first step. Emerging large language models (LLMs) are able to write high-quality code in other programming languages. In this paper, we characterize the ability of LLMs to generate useful Verilog. For this, we fine-tune pre-trained LLMs on Verilog datasets collected from GitHub and Verilog textbooks. We construct an evaluation framework comprising test-benches for functional analysis and a flow to test the syntax of Verilog code generated in response to problems of varying difficulty. Our findings show that across our problem scenarios, the fine-tuning results in LLMs more capable of producing syntactically correct code (25.9% overall). Further, when analyzing functional correctness, a fine-tuned open-source CodeGen LLM can outperform the state-of-the-art commercial Codex LLM (6.5% overall). Training/evaluation scripts and LLM checkpoints are available: https://github.com/shailja-thakur/VGen.