H-H <scp>oudini</scp> : Scalable Invariant Learning

Formal verification is a critical task in hardware design today. Yet, while there has been significant progress in improving technique automation and efficiency, scaling to large hardware designs remains a significant challenge.

Authors

Christopher W. Fletcher (ORCID: https://orcid.org/0000-0002-9995-5995)
Yongye Zhu (ORCID: https://orcid.org/0000-0002-6191-8187)
S. Dinesh

Institutions

University of California, Berkeley (US)

Publication Details

Published: 2025-02-06
DOI: https://doi.org/10.1145/3669940.3707263
Citations: 1
Primary Topic: Machine Learning and Algorithms
Type: article
Field-Weighted Citation Impact: 2.48

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article

H-H oudini : Scalable Invariant Learning

Christopher W. Fletcher, Yongye Zhu, S. Dinesh

Feb 6, 2025

1 citations

Machine Learning and Algorithms

2.48

article

H-H oudini : Scalable Invariant Learning

Christopher W. Fletcher, Yongye Zhu, S. Dinesh

February 6, 2025article en

1 citations

Abstract

University of California, Berkeley (US)

Openalex Percentile: Top 12%

Machine Learning and Algorithms

2.48

DOI Source OpenAlex

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H-H <scp>oudini</scp> : Scalable Invariant Learning — Christopher W. Fletcher, Yongye Zhu, et al. · (2025) | TGRS Research Map | TGRS