Principle-Guided Verilog Optimization: IP-Safe Knowledge Transfer via Local-Cloud Collaboration

2025-08-05

AI Summary

To address the risk of sensitive IP information leakage during Verilog RTL optimization, this paper proposes the first IP-protection-oriented edge-cloud collaborative optimization paradigm. Methodologically, a lightweight local model (e.g., Qwen-2.5-Coder-7B) extracts high-level design principles from RTL code without transmitting raw source code to the cloud; a large cloud-based model (e.g., DeepSeek-V3) then generates concrete optimization suggestions solely from these abstractions, ensuring zero sensitive data exposure. Evaluated on industrial benchmarks, the framework achieves a 66.67% power optimization success rate—significantly outperforming pure cloud-only baselines (49.81% for DeepSeek-V3 and 55.81% for GPT-4o) while preserving IP confidentiality. The core contribution is the introduction of a novel “principle extraction–cloud reconstruction” mechanism into RTL optimization, uniquely balancing security, interpretability, and practical deployability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

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.

Problem

• Optimize Verilog code without IP leakage
• Balance performance gains with IP protection
• Enable secure edge-cloud collaboration for RTL optimization

Innovation

• Edge-cloud collaboration for secure Verilog optimization
• Local LLMs extract IP-safe design principles
• Cloud LLMs apply principles for targeted improvements