Circuit Learning for Logic Regression on High Dimensional Boolean Space

Logic regression aims to find a Boolean model involving binary covariates that predicts the response of an unknown system. It has many important applications, e.g., in data analysis and system design. In the 2019 ICCAD CAD Contest, the challenge of learning a compact circuit representing a black-box...

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Bibliographic Details
Published in2020 57th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors Chen, Pei-Wei, Huang, Yu-Ching, Lee, Cheng-Lin, Jiang, Jie-Hong Roland
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Boolean functions
Circuit optimization
Generators
Integrated circuit modeling
Predictive models
Solid modeling
Task analysis
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Summary:Logic regression aims to find a Boolean model involving binary covariates that predicts the response of an unknown system. It has many important applications, e.g., in data analysis and system design. In the 2019 ICCAD CAD Contest, the challenge of learning a compact circuit representing a black-box input-output pattern generator in a high dimensional Boolean space is formulated as the logic regression problem. This paper presents our winning approach to the problem based on a decision-tree reasoning procedure assisted with a template based preprocessing. Our methods outperformed other contestants in the competition in both prediction accuracy and circuit size.
DOI:10.1109/DAC18072.2020.9218510