Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design

SystemVerilog Assertions (SVAs) are essential for verifying Register Transfer Level (RTL) designs, as they can be embedded into key functional paths to detect unintended behaviours. During simulation, assertion failures occur when the design’s behaviour deviates from expectations. Solving these failures, i.e., identifying and fixing the issues causing the deviation, requires analysing complex logical and timing relationships between multiple signals. This process heavily relies on human expertise, and there is currently no automatic tool available to assist with it. Here, we present AssertSolver, an opensource Large Language Model (LLM) specifically designed for solving assertion failures. By leveraging synthetic training data and learning from error responses to challenging cases, AssertSolver achieves a bug-fixing pass@1 metric of $88.54 \%$ on our testbench, significantly outperforming OpenAI’s o1-preview by up to $\mathbf{1 1. 9 7 \%}$. We release our model and testbench for public access to encourage further research: https://github.com/SEU-ACAL/reproduce-AssertSolver-DAC-25.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133324
Citations
1
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
2.28
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article

Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design

Nan Guan, Yuan Hu, Yafeng Ji, Jie Zhou et al.
1 citations
Business Process Modeling and Analysis
2.28
article

Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design

Nan Guan, Yuan Hu, Yafeng Ji, Jie Zhou, Xinan Jiao, Xinwei Fang, Shuai Zhao, Ning Wang, Zhe Jiang, Bingkun Yao
article en
1 citations

Abstract

SystemVerilog Assertions (SVAs) are essential for verifying Register Transfer Level (RTL) designs, as they can be embedded into key functional paths to detect unintended behaviours. During simulation, assertion failures occur when the design’s behaviour deviates from expectations. Solving these failures, i.e., identifying and fixing the issues causing the deviation, requires analysing complex logical and timing relationships between multiple signals. This process heavily relies on human expertise, and there is currently no automatic tool available to assist with it. Here, we present AssertSolver, an opensource Large Language Model (LLM) specifically designed for solving assertion failures. By leveraging synthetic training data and learning from error responses to challenging cases, AssertSolver achieves a bug-fixing pass@1 metric of $88.54 \%$ on our testbench, significantly outperforming OpenAI’s o1-preview by up to $\mathbf{1 1. 9 7 \%}$. We release our model and testbench for public access to encourage further research: https://github.com/SEU-ACAL/reproduce-AssertSolver-DAC-25.

Sun Yat-sen University (CN), City University of Hong Kong (HK), National Commission for Science, Technology, and Innovation (KE), University of York (GB), Southeast University (CN)
Openalex Percentile: Top 9%
Business Process Modeling and Analysis
2.28
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Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design — Nan Guan, Yuan Hu, et al. · (2025) | TGRS Research Map | TGRS