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Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design.
Deep graph library: A graph-centric, highly-performant package for graph neural networks
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Topological ordering of a list of randomly-numbered elements of a network
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Algorithm AS 136: A k-means clustering algorithm
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Think locally, act globally: Highly balanced graph partitioning
Sanders, P.; and Schulz, C. 2013 · 2013
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TAU 2014 contest on removing common path pessimism during timing analysis
Hu, J.; Sinha, D.; and Keller, I. 2014 · 2014
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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TAU 2015 contest on incremental timing analysis
Hu, J.; Schaeffer, G.; and Garg, V. 2015 · 2015
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ePlace: Electrostatics-based placement using fast fourier transform and Nesterov’s method
Lu, J.; Chen, P.; Chang, C.-C.; Sha, L.; Huang, D. J.-H.; Teng, C.-C.; and Cheng, C.-K. 2015 · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Graph attention networks
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2018 · 2018
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RouteNet: Routability prediction for mixed-size designs using convolutional neural network
Xie, Z.; Huang, Y.-H.; Fang, G.-Q.; Ren, H.; Fang, S.-Y.; Chen, Y.; and Hu, J. 2018 · 2018
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OpenROAD: Toward a Self-Driving, Open-Source Digital Layout Implementation Tool Chain
Ajayi, T.; Blaauw, D.; Chan, T.; Cheng, C.; Chhabria, V.; Choo, D.; Coltella, M.; Dobre, S.; Dreslinski, R.; Fogaça, M.; et al. 2019 · 2019
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Machine learning-based pre-routing timing prediction with reduced pessimism
Barboza, E. C.; Shukla, N.; Chen, Y.; and Hu, J. 2019 · 2019
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Dreamplace: Deep learning toolkit-enabled gpu acceleration for modern vlsi placement
Lin, Y.; Dhar, S.; Li, W.; Ren, H.; Khailany, B.; and Pan, D. Z. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 2019
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How powerful are graph neural networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2019 · 2019
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Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis
Chowdhury, A. B.; Tan, B.; Carey, R.; Jain, T.; Karri, R.; and Garg, S. 2022 · 2022
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A timing engine inspired graph neural network model for pre-routing slack prediction
Guo, Z.; Liu, M.; Gu, J.; Zhang, S.; Pan, D. Z.; and Lin, Y. 2022 · 2022
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Maskplace: Fast chip placement via reinforced visual representation learning
Lai, Y.; Mu, Y.; and Luo, P. 2022 · 2022
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Deepgate: Learning neural representations of logic gates
Li, M.; Khan, S.; Shi, Z.; Wang, N.; Yu, H.; and Xu, Q. 2022 · 2022
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DREAMPlace 4.0: Timing-driven global placement with momentum-based net weighting
Liao, P.; Liu, S.; Chen, Z.; Lv, W.; Lin, Y.; and Yu, B. 2022 · 2022
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Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2020
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Simple and deep graph convolutional networks
Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; and Li, Y. 2020 · 2020
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Strategies for pre-training graph neural networks
Hu, W.; Liu, B.; Gomes, J.; Zitnik, M.; Liang, P.; Pande, V.; and Leskovec, J. 2020 · 2020
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On joint learning for solving placement and routing in chip design
Cheng, R.; and Yan, J. 2021 · 2021
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Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
Ghose, A.; Zhang, V.; Zhang, Y.; Li, D.; Liu, W.; and Coates, M. 2021 · 2021
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Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; and Ng, R. 2021 · 2021
Cited alongside, same era.
A graph placement methodology for fast chip design
Mirhoseini, A.; Goldie, A.; Yazgan, M.; Jiang, J. W.; Songhori, E.; Wang, S.; Lee, Y.-J.; Johnson, E.; Pathak, O.; Nazi, A.; et al. 2021 · 2021
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How do vision transformers work?
Park, N.; and Kim, S. 2022 · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Wu, Q.; Zhao, W.; Li, Z.; Wipf, D. P.; and Yan, J. 2022 · 2022
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Pre-routing path delay estimation based on transformer and residual framework
Yang, T.; He, G.; and Cao, P. 2022 · 2022
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Versatile Multi-stage Graph Neural Network for Circuit Representation
Yang, Z.; Li, D.; Zhang, Y.; Zhang, Z.; Song, G.; Hao, J.; et al. 2022 · 2022
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Heterogeneous Graph Neural Network-based Imitation Learning for Gate Sizing Acceleration
Zhou, X.; Ye, J.; Pui, C.-W.; Shao, K.; Zhang, G.; Wang, B.; Hao, J.; Chen, G.; and Heng, P. A. 2022 · 2022
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Hubrouter: Learning global routing via hub generation and pin-hub connection
Du, X.; Wang, C.; Zhong, R.; and Yan, J. 2023 · 2023
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DeepSeq: Deep Sequential Circuit Learning
Khan, S.; Shi, Z.; Li, M.; and Xu, Q. 2023 · 2023
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Chipformer: Transferable chip placement via offline decision transformer
Lai, Y.; Liu, J.; Tang, Z.; Wang, B.; Hao, J.; and Luo, P. 2023 · 2023
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EasySO: Exploration-enhanced Reinforcement Learning for Logic Synthesis Sequence Optimization and a Comprehensive RL Environment
Yuan, J.; Wang, P.; Ye, J.; Yuan, M.; Hao, J.; and Yan, J. 2023 · 2023
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GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
Zhao, W.; Wu, Q.; Yang, C.; and Yan, J. 2023 · 2023
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