Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Understanding and Mitigating Errors of LLM-Generated RTL Code

Jiazheng Zhang, Cheng Liu, Long Cheng, Xiaowei Li, Huawei Li

TL;DR

This work systematically analyzes errors in LLM-generated RTL code and shows that most failures arise from RTL-domain knowledge gaps, ambiguous specifications, misinterpreted multimodal inputs, and insufficient circuit understanding rather than pure reasoning limits. It introduces a targeted correction toolkit—rule-based specification refinement, multimodal data conversion, a retrieval-augmented domain knowledge base, and an iterative simulation-based debugging loop—and integrates these into a unified RTL generation framework. The approach yields strong gains, achieving $98.1\%$ accuracy on VerilogEval with DeepSeek-v3.2-Speciale and outperforming multiple baselines and agent frameworks. This demonstrates that LLM-assisted hardware design can reach high correctness without additional training by leveraging knowledge retrieval, specification clarification, and systematic debugging. The work also provides open-source RTL samples, labeled results, and correction code to foster further research in LLM-assisted RTL design.

Abstract

Despite limited success in large language model (LLM)-based register-transfer-level (RTL) code generation, the root causes of errors remain poorly understood. To address this, we conduct a comprehensive error analysis, finding that most failures arise not from deficient reasoning, but from a lack of RTL programming knowledge, insufficient circuit understanding, ambiguous specifications, or misinterpreted multimodal inputs. Leveraging in-context learning, we propose targeted correction techniques: a retrieval-augmented generation (RAG) knowledge base to supply domain expertise; design description rules with rule-checking to clarify inputs; external tools to convert multimodal data into LLM-compatible formats; and an iterative simulation-debugging loop for remaining errors. Integrating these into an LLM-based framework yields significant improvement, achieving 98.1% accuracy on the VerilogEval benchmark with DeepSeek-v3.2-Speciale, demonstrating the effectiveness of our approach.

Understanding and Mitigating Errors of LLM-Generated RTL Code

TL;DR

This work systematically analyzes errors in LLM-generated RTL code and shows that most failures arise from RTL-domain knowledge gaps, ambiguous specifications, misinterpreted multimodal inputs, and insufficient circuit understanding rather than pure reasoning limits. It introduces a targeted correction toolkit—rule-based specification refinement, multimodal data conversion, a retrieval-augmented domain knowledge base, and an iterative simulation-based debugging loop—and integrates these into a unified RTL generation framework. The approach yields strong gains, achieving accuracy on VerilogEval with DeepSeek-v3.2-Speciale and outperforming multiple baselines and agent frameworks. This demonstrates that LLM-assisted hardware design can reach high correctness without additional training by leveraging knowledge retrieval, specification clarification, and systematic debugging. The work also provides open-source RTL samples, labeled results, and correction code to foster further research in LLM-assisted RTL design.

Abstract

Despite limited success in large language model (LLM)-based register-transfer-level (RTL) code generation, the root causes of errors remain poorly understood. To address this, we conduct a comprehensive error analysis, finding that most failures arise not from deficient reasoning, but from a lack of RTL programming knowledge, insufficient circuit understanding, ambiguous specifications, or misinterpreted multimodal inputs. Leveraging in-context learning, we propose targeted correction techniques: a retrieval-augmented generation (RAG) knowledge base to supply domain expertise; design description rules with rule-checking to clarify inputs; external tools to convert multimodal data into LLM-compatible formats; and an iterative simulation-debugging loop for remaining errors. Integrating these into an LLM-based framework yields significant improvement, achieving 98.1% accuracy on the VerilogEval benchmark with DeepSeek-v3.2-Speciale, demonstrating the effectiveness of our approach.

Paper Structure

This paper contains 24 sections, 25 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs for RTL Code Generation
  4. LLMs for RTL Code Debugging
  5. Error Analysis of LLM-Generated RTL Code
  6. Insufficient Knowledge of Specialized RTL Programming
  7. Misinterpretation of Design Specifications
  8. Ambiguous Design Descriptions (ADD.)
  9. Misinterpretation of Multimodal Data (MMD.)
  10. Insufficient Understanding of Circuit Concepts (IUCC.)
  11. Missing Details of Long Descriptions (MDLD)
  12. Error Distribution Statistics Under Misinteroretation of Design Specifications
  13. Combination of Multiple Errors
  14. LLM-based Error Correction
  15. Error Correction Mechanisms
  16. ...and 9 more sections

Figures (25)

  • Figure 1: The distribution of error type ratios in RTL code generation scenarios.
  • Figure 2: Examples of ambigious design descriptions
  • Figure 3: Distribution of error subtypes under ADD.
  • Figure 4: Examples of multimodal data in hardware circuit design
  • Figure 5: Distribution of error subtypes under MMD.
  • ...and 20 more figures