原则引导的Verilog优化：通过本地-云端协作实现IP安全的知识迁移
Principle-Guided Verilog Optimization: IP-Safe Knowledge Transfer via Local-Cloud Collaboration
AI总结：
提出IP安全的边缘-云端协作框架，利用本地小模型提取设计原则，指导云端大模型优化Verilog代码，在保护知识产权的同时显著提升优化成功率。
摘要：
近年来，采用大型语言模型（LLMs）进行寄存器传输级（RTL）代码优化的兴趣日益增长。虽然强大的云端LLMs提供了卓越的优化能力，但在处理专有硬件设计时，它们会带来不可接受的知识产权（IP）泄露风险。在本文中，我们提出了一种新场景，即Verilog代码必须在不泄露敏感IP信息的情况下针对特定属性进行优化。我们引入了首个IP保护的边缘-云端协作框架，该框架利用了两种范式的优势。我们的方法采用本地小型LLMs（例如，Qwen-2.5-Coder-7B）在配对的高质量目标设计与新手草稿代码之间进行安全的比较分析，生成概括关键改进见解的通用设计原则。随后，这些原则被用于查询更强大的云端LLMs（例如，Deepseek-V3）以进行有针对性的代码改进，确保只有抽象且IP安全的指导信息到达外部服务。我们的实验结果表明，与基线方法相比，该框架实现了显著更高的优化成功率。例如，将Qwen-2.5-Coder-7B与Deepseek-V3相结合，在功耗利用率方面实现了66.67%的优化成功率，优于单独使用Deepseek-V3（49.81%）以及商业模型如GPT-4o（55.81%）。对本地和云端LLM组合的进一步研究表明，不同的模型配对在特定优化目标上表现出不同的优势，并且在改变比较代码对数量时出现了有趣的趋势。我们的工作为安全硬件设计优化建立了一种新范式，在性能提升与IP保护之间取得了平衡。
英文摘要：
Recent years have witnessed growing interest in adopting large language models (LLMs) for Register Transfer Level (RTL) code optimization. While powerful cloud-based LLMs offer superior optimization capabilities, they pose unacceptable intellectual property (IP) leakage risks when processing proprietary hardware designs. In this paper, we propose a new scenario where Verilog code must be optimized for specific attributes without leaking sensitive IP information. We introduce the first IP-preserving edge-cloud collaborative framework that leverages the benefits of both paradigms. Our approach employs local small LLMs (e.g., Qwen-2.5-Coder-7B) to perform secure comparative analysis between paired high-quality target designs and novice draft codes, yielding general design principles that summarize key insights for improvements. These principles are then used to query stronger cloud LLMs (e.g., Deepseek-V3) for targeted code improvement, ensuring that only abstracted and IP-safe guidance reaches external services. Our experimental results demonstrate that the framework achieves significantly higher optimization success rates compared to baseline methods. For example, combining Qwen-2.5-Coder-7B and Deepseek-V3 achieves a 66.67\% optimization success rate for power utilization, outperforming Deepseek-V3 alone (49.81\%) and even commercial models like GPT-4o (55.81\%). Further investigation of local and cloud LLM combinations reveals that different model pairings exhibit varying strengths for specific optimization objectives, with interesting trends emerging when varying the number of comparative code pairs. Our work establishes a new paradigm for secure hardware design optimization that balances performance gains with IP protection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
