The Cream Rises to the Top: Efficient Reranking Method for Verilog Code Generation

2025-09-24

AI Summary

Verilog code generation suffers from insufficient domain knowledge integration and poor semantic alignment, resulting in diverse yet unreliable outputs from large language models (LLMs). To address this, we propose VCD-RNK, a discriminative re-ranking framework that replaces costly logic simulation with three-dimensional expert knowledge distillation—encompassing code semantic analysis, test case generation, and functional correctness evaluation—while incorporating reasoning-aware simulation to model functional consistency of generated code. Crucially, VCD-RNK achieves these improvements without increasing sampling overhead, significantly enhancing both single-solution reliability and pass@k functional correctness. Experimental results demonstrate an average 23.6% improvement in top-1 accuracy across multiple hardware design tasks compared to baseline models. This work provides hardware engineers with an efficient and trustworthy Verilog synthesis assistant grounded in domain-specific reasoning and verification principles.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

LLMs face significant challenges in Verilog generation due to limited domain-specific knowledge. While sampling techniques improve pass@k metrics, hardware engineers need one trustworthy solution rather than uncertain candidates. To bridge this gap, we formulate it as a semantic alignment problem between requirements and Verilog implementations, and propose VCD-RNK, a discriminator model tailored for efficient Verilog code reranking. Specifically, VCD-RNKincorporates Verilog-specific reasoning by distilling expert knowledge across three dimensions: code semantic analysis, test case generation, and functional correctness assessment. By explicitly simulating the above reasoning processes during inference, VCD-RNK effectively avoids computationally intensive test execution in existing methods.

Problem

• Addresses semantic alignment between requirements and Verilog implementations
• Improves trustworthy single solution generation over uncertain candidates
• Avoids computationally intensive test execution through efficient reasoning

Innovation

• Semantic alignment discriminator for Verilog reranking
• Expert knowledge distillation across three dimensions
• Simulates reasoning processes to avoid test execution