Circuit Learning: From Decision Trees to Decision Graphs

Circuit learning has gained significant attention due to machine learning advancements and approximate synthesis applications. The task is to learn a circuit to model an unknown Boolean function subject to different design constraints. When circuit size is hard-constrained, decision-tree-based learn...

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Veröffentlicht in:IEEE transactions on computer-aided design of integrated circuits and systems Jg. 42; H. 11; S. 1
Hauptverfasser: Huang, Yu-Shan, Jiang, Jie-Hong R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Boolean functions
Circuit design
Constraints
Decision trees
Integrated circuit modeling
Machine learning
Merging
Predictive models
Task analysis
Training
ISSN:0278-0070, 1937-4151
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Zusammenfassung:Circuit learning has gained significant attention due to machine learning advancements and approximate synthesis applications. The task is to learn a circuit to model an unknown Boolean function subject to different design constraints. When circuit size is hard-constrained, decision-tree-based learning plays a crucial role in state-of-the-art methods. However, it can be ineffective due to its structural restriction. This work proposes graph learning to overcome the limitation, provide trade-offs between circuit size and accuracy, and enrich the portfolio of circuit learning tools. Experimental results show the superiority of our approach to prior work in accuracy, training time, and circuit size.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2023.3258747