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Abstract
The complexity of modern silicon designs necessitates advanced validation strategies to ensure timely product development. Machine Learning (ML) techniques have been increasingly integrated into silicon validation workflows to automate and enhance debugging processes. This paper evaluates different ML-assisted debugging approaches, categorizes their methodologies, and benchmarks their effectiveness. This paper discusses strengths, limitations, and future research directions in the context of real-world silicon validation environments.
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