On Advancing Physical Design Using Graph Neural Networks
Author:
Affiliation:
1. Georgia Institute of Technology
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3508352.3561094
Reference21 articles.
1. VLSI placement parameter optimization using deep reinforcement learning
2. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View
3. A timing engine inspired graph neural network model for pre-routing slack prediction
4. Multilevel hypergraph partitioning: applications in VLSI domain
5. Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
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1. Automated Physical Design Watermarking Leveraging Graph Neural Networks;Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD;2024-09-09
2. GAN-Place: Advancing Open-Source Placers to Commercial-Quality using Generative Adversarial Networks and Transfer Learning;ACM Transactions on Design Automation of Electronic Systems;2023-12-06
3. RL-CCD: Concurrent Clock and Data Optimization using Attention-Based Self-Supervised Reinforcement Learning;2023 60th ACM/IEEE Design Automation Conference (DAC);2023-07-09
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