ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning Model

2025-04-20

AI Summary

To address the challenges of scarce high-quality training data, limited reasoning capability, and excessive computational overhead in large language models (LLMs) for Verilog code generation, this paper proposes a systematic solution for AI-driven hardware design automation. First, we construct a high-quality, 5K-sample Verilog dataset featuring complete reasoning traces, rigorously validated through multi-dimensional quality checks. Second, we introduce a two-stage hybrid training paradigm integrating parameter-efficient fine-tuning with full-parameter optimization. Third, we propose a complexity-aware dynamic adaptive inference depth mechanism to balance accuracy and efficiency. Experiments demonstrate that our method achieves a pass@1 score of 57.8% on VerilogEval-human—surpassing the prior state-of-the-art open-source model by 10.4 percentage points and approaching Gemini-2.0-flash (59.5%)—while reducing inference token consumption by 75%.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large Language Models (LLMs) have advanced Verilog code generation significantly, yet face challenges in data quality, reasoning capabilities, and computational efficiency. This paper presents ReasoningV, a novel model employing a hybrid reasoning strategy that integrates trained intrinsic capabilities with dynamic inference adaptation for Verilog code generation. Our framework introduces three complementary innovations: (1) ReasoningV-5K, a high-quality dataset of 5,000 functionally verified instances with reasoning paths created through multi-dimensional filtering of PyraNet samples; (2) a two-stage training approach combining parameter-efficient fine-tuning for foundational knowledge with full-parameter optimization for enhanced reasoning; and (3) an adaptive reasoning mechanism that dynamically adjusts reasoning depth based on problem complexity, reducing token consumption by up to 75% while preserving performance. Experimental results demonstrate ReasoningV's effectiveness with a pass@1 accuracy of 57.8% on VerilogEval-human, achieving performance competitive with leading commercial models like Gemini-2.0-flash (59.5%) and exceeding the previous best open-source model by 10.4 percentage points. ReasoningV offers a more reliable and accessible pathway for advancing AI-driven hardware design automation, with our model, data, and code available at https://github.com/BUAA-CLab/ReasoningV.

Problem

• Improves Verilog code generation quality and efficiency
• Addresses data quality and reasoning capability challenges
• Reduces computational costs while maintaining performance

Innovation

• Hybrid reasoning integrates trained and dynamic capabilities
• Two-stage training combines efficient and full optimization
• Adaptive mechanism adjusts depth, reduces token use