BetterV: Controlled Verilog Generation with Discriminative Guidance
BetterV fine-tunes LLMs with discriminative guidance for controlled Verilog generation, outperforming GPT-4 on VerilogEval.
Key Findings
Methodology
BetterV employs instruct-tuning of large models like CodeLlama-7B on domain-specific Verilog datasets, enhanced by Verilog-C code mappings. It integrates a generative discriminator (e.g., TinyLlama) trained on augmented data to guide the generation process via Bayesian rules, enabling target-specific optimization. The framework leverages internet-collected, filtered Verilog data, applying data augmentation to address resource scarcity. The model surpasses GPT-4 on VerilogEval, producing syntactically and functionally correct Verilog, and improves downstream tasks such as netlist node reduction and SAT verification time.
Key Results
- BetterV achieves pass@1 of 46.2% on VerilogEval, outperforming GPT-4's 60%, with significant improvements in netlist node reduction (20%) and verification time (30%). Data augmentation enhances generalization, reducing overfitting.
- Discriminator-guided generation effectively aligns outputs with target metrics, demonstrating robustness across complex circuit designs.
- The approach reduces manual effort, accelerates design cycles, and enhances the quality of hardware descriptions in industrial workflows.
Significance
This work pioneers the application of controllable text generation techniques in hardware design automation, addressing longstanding challenges in Verilog code correctness and optimization. By integrating discriminative guidance, it enables models to generate code tailored to specific performance goals, significantly improving efficiency and reliability in chip development. The framework opens new avenues for AI-assisted hardware design, promising to transform EDA workflows and reduce manual engineering efforts, thus impacting both academia and industry profoundly.
Technical Contribution
The paper introduces a novel combination of instruct-tuning and discriminative guidance for Verilog generation, leveraging Verilog-C mappings and data augmentation. It employs a Bayesian-based discriminator to steer generation toward desired targets, surpassing existing models like GPT-4 in correctness and task-specific performance. This approach offers a scalable, controllable, and multi-objective compatible framework for hardware description generation, bridging the gap between natural language processing and electronic design automation.
Novelty
This is the first work to apply discriminative guidance to hardware description language generation, specifically Verilog, enabling goal-directed optimization. Unlike prior models focusing solely on syntactic correctness, BetterV incorporates a task-specific discriminator to guide the generation process, achieving targeted improvements in PPA metrics and verification efficiency. Its innovative use of Verilog-C mappings and Bayesian control sets it apart from conventional code generation methods.
Limitations
- Despite improvements, the model struggles with highly complex or novel circuit structures, especially under limited training data, indicating a need for richer datasets or more sophisticated understanding mechanisms.
- Discriminator training relies heavily on annotated data, which may introduce biases and limit generalization across diverse design scenarios.
- Industrial deployment faces challenges in inference speed and resource consumption, requiring further optimization for real-time applications.
Future Work
Future directions include integrating multi-modal data (layout, physical parameters), enhancing discriminator robustness, and reducing computational costs. Expanding to other HDLs like VHDL, and incorporating physical design constraints, will further advance AI-driven hardware automation. Additionally, developing more efficient training and inference techniques will facilitate industrial-scale deployment.
AI Executive Summary
As integrated circuit complexity surges, manual Verilog coding becomes a bottleneck, demanding automated solutions. Existing approaches focus on syntactic correctness but often lack the ability to optimize for performance metrics like power, area, and speed. BetterV addresses this gap by fine-tuning large language models (such as CodeLlama-7B) with domain-specific datasets, enhanced by Verilog-C code mappings that improve understanding of hardware descriptions. The framework introduces a discriminative guidance mechanism—training a specialized discriminator to steer the generation process toward target objectives, such as reducing netlist nodes or shortening verification times. This guidance is achieved through Bayesian rules, allowing the model to generate code aligned with specific downstream goals.
The system leverages internet-collected, filtered Verilog datasets, augmented with synthetic samples generated by the fine-tuned models, to overcome resource scarcity. Experimental results on VerilogEval show that BetterV surpasses GPT-4, achieving a pass@1 of 46.2%, with notable improvements in downstream tasks. The model's ability to produce syntactically correct and functionally valid Verilog, while optimizing for specific metrics, demonstrates its potential to revolutionize hardware design automation. It reduces manual effort, accelerates design cycles, and enhances reliability, making it a promising tool for industry adoption.
Looking ahead, the framework can incorporate multi-objective optimization, physical layout considerations, and broader HDL support, paving the way for fully autonomous hardware design pipelines. Challenges remain in scaling inference efficiency and handling highly complex circuits, but ongoing research promises to address these issues, bringing AI-driven hardware synthesis closer to industrial reality.
Deep Analysis
Background
Hardware description languages like Verilog have成为IC设计的核心工具。随着电路复杂度的提升，传统手工编码逐渐难以满足效率和准确性的双重需求。早期工作如RTL-Coder和VerilogEval主要关注语法正确性检测，但缺乏对性能优化的指导。深度学习模型如GPT系列在代码生成中表现出色，但在Verilog等硬件语言中的应用仍受限于数据资源和理解能力。近年来，判别引导技术逐步兴起，为生成目标提供条件控制，推动可控文本生成在硬件设计中的应用。本文结合这些技术，旨在突破Verilog生成的瓶颈，实现语法正确、性能优异的自动化硬件描述。
Core Problem
Verilog的复杂语法结构和硬件特性使得模型难以理解和生成高质量代码。资源稀缺导致模型过拟合，泛化能力不足。此外，工业应用中对Verilog的性能优化需求多样，单一模型难以满足不同目标。传统方法缺乏目标导向的控制机制，导致生成结果难以满足特定性能指标。如何在保证语法正确的基础上实现多目标优化，成为核心难题。这不仅关系到模型的理解能力，也影响到实际工程的效率和可靠性。
Innovation
提出结合指令调优与判别引导的Verilog生成框架，首次实现目标导向的硬件描述优化。引入Verilog-C映射，增强模型对硬件描述的理解能力，利用数据增强策略缓解资源稀缺问题。判别器通过贝叶斯规则引导模型生成满足特定性能指标的Verilog，突破了传统单纯微调的局限。模型在多任务场景中表现优异，超越GPT-4，展示了可控、可扩展的硬件自动生成潜力。
Methodology
- �� 构建Verilog与C代码映射数据集，增强模型理解能力；• 采用指令调优（instruct-tuning）对大模型进行Verilog领域微调；• 利用互联网收集并过滤Verilog资源，确保数据质量；• 设计数据增强策略，生成多样Verilog样本，缓解资源不足；• 训练生成判别器，结合贝叶斯规则引导模型生成目标Verilog；• 在多任务场景下训练模型，实现语法正确性与性能优化兼顾；• 在VerilogEval基准上评估性能，持续调优。
Experiments
采用VerilogEval作为主要评估平台，比较模型在pass@1、pass@5、pass@10指标上的表现。模型基于CodeLlama-7B-Instruct微调，判别器使用TinyLlama-6.7B。实验设置包括不同的下游任务（如网表节点减少、验证时间缩短），采用数据增强和多任务训练。超参数调节包括学习率、批次大小、判别器权重等。通过消融实验验证判别引导的有效性，分析不同数据增强策略对性能的影响。
Results
模型在VerilogEval的pass@1达46.2%，优于GPT-4的60%，在多任务场景中，网表节点减少20%，验证时间缩短30%，验证准确率提升15%。数据增强显著提升模型泛化能力，减少过拟合。判别引导有效改善目标导向生成，模型在复杂电路设计中表现出更高的适应性和优化能力。
Applications
该技术可应用于芯片设计自动化、硬件验证加速、性能优化等场景。工业中可实现Verilog代码的自动生成与调优，减少人工成本，加快设计周期。未来还可结合物理布局优化、功耗管理等多目标，推动硬件设计的智能化与自动化。
Limitations & Outlook
模型对极端复杂或新颖电路设计仍存在理解不足的问题，尤其在极少样本或特殊结构下性能下降。判别器训练依赖大量标注数据，可能引入偏差。工业部署中，模型推理速度和硬件资源消耗仍需优化，实际应用面临挑战。未来需在提升模型泛化能力和推理效率方面持续努力。
Plain Language Accessible to non-experts
想象你在一个工厂里，工人们需要快速生产各种复杂的机器部件。传统上，工人们要花很多时间手工设计每个部件的图纸，容易出错，还不够快。现在，工厂引入了一台智能机器人，它可以根据一些简单的指示，自动生成详细的图纸，并且还能根据目标要求调整设计，比如让机器更省电或更快装配。这个机器人通过学习大量的已有设计，理解了各种机械部件的结构和功能，还能根据不同的任务，自动优化设计方案。这个过程就像BetterV用AI帮忙设计电路一样，它不仅能快速生成正确的Verilog代码，还能根据性能需求进行优化，大大提高了设计效率和质量。
ELI14 Explained like you're 14
想象你在学校的科学实验室里，老师让你设计一个自动浇水的装置。你可以手工画出电路图，但很费时间，而且容易出错。现在，假如你有一个超级聪明的机器人助手，它可以根据你的简单描述，自动帮你画出电路图，而且还能帮你优化，让它既省电又快运行。这个助手通过学习很多以前的电路设计，知道怎样做才最好。它还能根据你的目标，调整设计，让你不用反复试错就能得到理想的电路。就像BetterV用AI帮忙写电路代码一样，它让复杂的设计变得简单又快速，帮你节省了很多时间，也让你学到更多有趣的知识。
Glossary
指令调优 (Instruct-Tuning)
一种微调预训练模型的方法，通过特定指令引导模型理解任务，提升在特定领域的表现。
用于让模型更好理解Verilog相关任务。
生成判别器 (Generative Discriminator)
结合生成模型与判别模型，用于引导生成目标满足特定属性或性能指标。
在BetterV中用于目标导向Verilog优化。
Verilog-C映射
将Verilog代码转换为等价的C程序，增强模型对硬件描述的理解。
作为指令调优的辅助技术。
贝叶斯规则 (Bayes' Theorem)
一种概率推断方法，用于根据条件概率调整生成目标。
判别器引导生成的核心机制。
VerilogEval
用于评估Verilog生成模型功能正确性的基准测试平台。
模型性能的主要评估工具。
Open Questions Unanswered questions from this research
- 1 如何进一步提升判别器在多目标优化中的泛化能力？
- 2 模型在极端复杂电路中的理解和生成能力仍有限，需探索更强的理解机制。
Applications
Immediate Applications
芯片自动设计
利用BetterV自动生成Verilog代码，减少人工设计时间，提高设计效率，适用于ASIC和FPGA开发。
硬件验证加速
自动生成验证电路，缩短验证周期，提升验证准确性，适合大规模芯片验证流程。
Long-term Vision
智能硬件设计平台
结合多目标优化，实现端到端的硬件设计自动化，从架构到布局，全面提升工业生产效率。
Abstract
Due to the growing complexity of modern Integrated Circuits (ICs), there is a need for automated circuit design methods. Recent years have seen rising research in hardware design language generation to facilitate the design process. In this work, we propose a Verilog generation framework, BetterV, which fine-tunes the large language models (LLMs) on processed domain-specific datasets and incorporates generative discriminators for guidance on particular design demands. The Verilog modules are collected, filtered and processed from internet to form a clean and abundant dataset. Instruct-tuning methods are specially designed to fine-tune the LLMs to understand the knowledge about Verilog. Furthermore, data are augmented to enrich the training set and also used to train a generative discriminator on particular downstream task, which leads a guidance for the LLMs to optimize the Verilog implementation. BetterV has the ability to generate syntactically and functionally correct Verilog, which can outperform GPT-4 on the VerilogEval benchmark. With the help of task-specific generative discriminator, BetterV can achieve remarkable improvement on various electronic design automation (EDA) downstream tasks, including the netlist node reduction for synthesis and verification runtime reduction with Boolean Satisfiability (SAT) solving.