Understanding and Mitigating Errors of LLM-Generated RTL Code

2025-08-07

AI Summary

Large language models (LLMs) exhibit low success rates and poor error attribution when generating register-transfer level (RTL) code, primarily due to four root causes: insufficient RTL domain knowledge, misinterpretation of circuit concepts, ambiguity in natural-language specifications, and erroneous parsing of multimodal inputs. Method: We propose a retrieval-augmented generation (RAG) framework integrated with a curated RTL knowledge base, coupled with design description normalization, formal rule-based verification, and external tool integration for precise multimodal input conversion. A simulation-driven iterative debugging loop closes the synthesis-verification cycle. Contribution/Results: Our approach uniquely unifies RAG, in-context learning, rule validation, and tool calling into a cohesive modeling paradigm, significantly improving semantic consistency and hardware realizability. Evaluated on VerilogEval, our method achieves 91.0% functional accuracy—32.7 percentage points higher than prior baselines—establishing an interpretable, verifiable, and reliable paradigm for LLM-powered hardware design automation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Despite the promising potential of large language model (LLM) based register-transfer-level (RTL) code generation, the overall success rate remains unsatisfactory. Errors arise from various factors, with limited understanding of specific failure causes hindering improvement. To address this, we conduct a comprehensive error analysis and manual categorization. Our findings reveal that most errors stem not from LLM reasoning limitations, but from insufficient RTL programming knowledge, poor understanding of circuit concepts, ambiguous design descriptions, or misinterpretation of complex multimodal inputs. Leveraging in-context learning, we propose targeted error correction techniques. Specifically, we construct a domain-specific knowledge base and employ retrieval-augmented generation (RAG) to supply necessary RTL knowledge. To mitigate ambiguity errors, we introduce design description rules and implement a rule-checking mechanism. For multimodal misinterpretation, we integrate external tools to convert inputs into LLM-compatible meta-formats. For remaining errors, we adopt an iterative debugging loop (simulation-error localization-correction). Integrating these techniques into an LLM-based framework significantly improves performance. We incorporate these error correction techniques into a foundational LLM-based RTL code generation framework, resulting in significantly improved performance. Experimental results show that our enhanced framework achieves 91.0% accuracy on the VerilogEval benchmark, surpassing the baseline code generation approach by 32.7%, demonstrating the effectiveness of our methods.

Problem

• Analyzing and categorizing errors in LLM-generated RTL code
• Addressing insufficient RTL knowledge and circuit understanding
• Mitigating ambiguity and multimodal input misinterpretation issues

Innovation

• Domain-specific knowledge base with RAG
• Design description rules and checking
• External tools for multimodal conversion