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Found 3 papers, 2 papers with code
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.
DeepTPI: Test Point Insertion with Deep Reinforcement Learning
Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement.
DeepGate: Learning Neural Representations of Logic Gates
We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate.
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