QiMeng-CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis

2026-03-15

AI Summary

Existing general-purpose large language models exhibit limited performance on the natural language to SystemVerilog assertion (NL2SVA) task, primarily due to the scarcity of high-quality real-world SVA corpora and the absence of reliable methods for assessing semantic equivalence between natural language specifications and SVA code. To address this, this work proposes an RTL-guided bidirectional data synthesis framework that leverages open-source RTL designs to generate large-scale NL↔SVA translation pairs, followed by semantic consistency filtering to construct high-quality training data. Using this approach, we train CodeV-SVA, the first family of specialized large models for NL2SVA. The CodeV-SVA-14B variant achieves 75.8% and 84.0% Func.@1 accuracy on the NL2SVA-Human and NL2SVA-Machine benchmarks, respectively, matching or surpassing the performance of state-of-the-art models such as GPT-5 and DeepSeek-R1.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

SystemVerilog Assertions (SVAs) are crucial for hardware verification. Recent studies leverage general-purpose LLMs to translate natural language properties to SVAs (NL2SVA), but they perform poorly due to limited data. We propose a data synthesis framework to tackle two challenges: the scarcity of high-quality real-world SVA corpora and the lack of reliable methods to determine NL-SVA semantic equivalence. For the former, large-scale open-source RTLs are used to guide LLMs to generate real-world SVAs; for the latter, bidirectional translation serves as a data selection method. With the synthesized data, we train CodeV-SVA, a series of SVA generation models. Notably, CodeV-SVA-14B achieves 75.8% on NL2SVA-Human and 84.0% on NL2SVA-Machine in Func.@1, matching or exceeding advanced LLMs like GPT-5 and DeepSeek-R1.

Problem

• SystemVerilog Assertions
• NL2SVA
• data scarcity
• semantic equivalence
• hardware verification

Innovation

• RTL-Grounded Synthesis
• Bidirectional Data Selection
• NL2SVA
• Specialized LLMs
• Hardware Assertion Generation