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Found 5 papers, 2 papers with code
IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability
Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process.
DeepGate2: Functionality-Aware Circuit Representation Learning
Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and logic reasoning tasks.
Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG
(2) Conflict-driven implication and justification have been applied to increase decision accuracy and solving efficiency.
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
On top of the observations that industrial formulae exhibit clear community structure and oversplit substructures lead to the difficulty in semantic formation of logical structures, we propose HardSATGEN, which introduces a fine-grained control mechanism to the neural split-merge paradigm for SAT formula generation to better recover the structural and computational properties of the industrial benchmarks.
SATformer: Transformer-Based UNSAT Core Learning
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem.
