Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG

no code implementations • 4 Mar 2023 • Hui-Ling Zhen, Naixing Wang, Junhua Huang, Xinyue Huang, Mingxuan Yuan, Yu Huang

(2) Conflict-driven implication and justification have been applied to increase decision accuracy and solving efficiency.

DeepTPI: Test Point Insertion with Deep Reinforcement Learning

1 code implementation • 7 Jun 2022 • Zhengyuan Shi, Min Li, Sadaf Khan, Liuzheng Wang, Naixing Wang, Yu Huang, Qiang Xu

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.

Graph Neural Network Q-Learning +2

DeepGate: Learning Neural Representations of Logic Gates

1 code implementation • 26 Nov 2021 • Min Li, Sadaf Khan, Zhengyuan Shi, Naixing Wang, Yu Huang, Qiang Xu

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.

Graph Neural Network Representation Learning