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arXiv AI By Zhiyuan Yan, Xiaofeng Zhou, Ziyue Zheng, Ziyi Yang, Wenbin Che, Wei Zhang, Yangdi Lyu, Hongce Zhang

NeuroAbs: A Neuro-Symbolic RTL Abstraction Framework for Property Checking Acceleration

Read the original on arXiv AI →

NeuroAbs is a neuro‑symbolic framework designed to accelerate property checking in hardware verification by abstracting RTL designs. It employs large language model (LLM) assisted analysis to select signals for abstraction and combines LLM‑based abstraction with an AST‑based symbolic RTL representation to produce sound abstractions verified via SMT solving. When an abstraction is too coarse, NeuroAbs uses counterexample‑guided refinement (CEGAR) to iteratively improve the model, leading to significant efficiency gains across various verification tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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