Layout Congestion Prediction Based on Regression-ViT
Article No.: 4, Pages 1 - 21
Abstract
To accelerate the back-end design flow of integrated circuit (IC), numerous studies have made exploratory advancements in machine learning (ML) for electronic design automation (EDA). However, most research works are limited to deep learning (DL) models predominantly based on convolutional neural networks, and the models often suffer from poor generalization due to the scarcity of data. In this study, we propose the Double generative adversarial networks (D-GAN) model to enrich the dataset and propose the Regression Vision Transformer (R-ViT) model to predict layout congestion information. Compared with the baseline model, experimental results show improvements of 3.03% and 2.64% in Receiver Operating Characteristic-Area under Curve (ROC-AUC) and Precision-Recall Curve-Area under Curve (PRC-AUC) respectively. To further enhance the prediction accuracy of the model, an adaptive Huber loss function is designed to optimize the training process, resulting in an improvement of up to 11.03% in ROC-AUC compared with the baseline model. Lastly, extended experiments are conducted to study the effects of parameters and convolutional kernel size on performance, which find a better configuration.
References
[1]
Wei-Ting, J. Chan, Pei-Hsin Ho, Andrew B. Kahng, and Prashant Saxena. 2017. Routability optimization for industrial designs at sub-14 nm process nodes using machine learning. In Proc. ISPD (2017), 15–27.
[2]
Wei Ye, Mohamed Baker Alawieh, Yibo Lin, and David Z. Pan. 2019. Tackling signal electromigration with learning-based detection and multistage mitigation. In Proc. ASPDAC (2019), 167–172.
[3]
Subhendu Roy, Yuzhe Ma, Jin Miao, and Bei Yu. 2017. A learning bridge from architectural synthesis to physical design for exploring power efficient high-performance adders. In Proc. ISLPED (2017), 1–6.
[4]
Haoyu Yang, Jing Su, Yi Zou, Yuzhe Ma, Bei Yu, and E. F. Y. Young. 2019. Layout hotspot detection with feature tensor generation and deep biased learning. IEEE Trans. Comput.-Aided Design Integr. Circuits Syst. 38, 6 (2019), 1175–1187.
[5]
Siting Liu, Qi Sun, Peiyu Liao, Yibo Lin, and Bei Yu. 2021. Global placement with deep learning-enabled explicit routability optimization[C] In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE (2021), 1821–1824.
[6]
Yu-Hung. Huang, Zhiyao Xie, Guan-Qi Fang, Tao-Chun Yu, Haoxing Ren, and Shao-Yun Fang. 2019. Routability-driven macro placement with embedded CNN-based prediction model. In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE) (2019), 180–185.
[7]
C. Yu and Z. Zhang. 2019. Painting on piacement: Forecasting routing congestion using conditional generative adversarial nets. In 2019 56th ACM/IEEE Design Automation Conference (DAC), Las Vegas, NV, USA, (2019), 1–6.
[8]
Wang Bowen, Guibao Shen, Dong Li, Jianye Hao, Wulong Liu, Yu Huang, Hongzhong Wu, Yibo Lin, Guanyong Chen, and Pheng Ann Heng. 2022. LHNN: Lattice hypergraph neural network for VLSI congestion prediction. In Proceedings of the 59th ACM/IEEE Design Automation Conference. 2022.
[9]
Baek Kyeonghyeon, Hyunbum Park, Suwan Kim, Kyumyung Choi, and Taewhan Kim. 2022. Pin accessibility and routing congestion aware drc hotspot prediction using graph neural network and u-net. In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design. 2022.
[10]
Zheng Su, Lancheng Zou, Siting Liu, Yibo Lin, Bei Yu, and Martin Wong. 2023. Mitigating distribution shift for congestion optimization in global placement. In 2023 60th ACM/IEEE Design Automation Conference (DAC). IEEE, 2023.
[11]
A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi. 2020. A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review 53, 8 (2020), 5455–5516.
[12]
Z. Zhang, Q. Gao, L. Liu, and Y. He. 2023. A high-quality rice leaf disease image data augmentation method based on a dual GAN[J]. IEEE Access 11 (2023), 21176--21191. DOI:
[13]
S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon. 2018. CBAM: Convolutional block attention module. In Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VII, Berlin, 2018: Springer-Verlag, 3–19.
[14]
H. Geng, H. Yang, L. Zhang, F. Yang, X. Zheng, and B. Yu. 2022. Hotspot detection via attention-based deep layout metric learning. IEEE Trans. Comput.- Aided Design Integr. Circuits Syst. 41, 8 (2022), 2685–2698.
[15]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and L. Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2017: Curran Associates Inc., 6000–6010.
[16]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021.
[17]
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2020. Generative adversarial networks[J]. Communications of the ACM 63, 11 (2020), 139–144.
[18]
Z. Chai, Y. Zhao, W. Liu, Y. Lin, R. Wang, and R. Huang. 2023. CircuitNet: An open-source dataset for machine learning in VLSI CAD applications with improved domain-specific evaluation metric and learning strategies. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 42, 12 (2023), 5034--5047. DOI:
[19]
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. e. J'egou. 2020. Training data-efficient image transformers & distillation through attention. In International Conference on Machine Learning, 2020.
[20]
Xie Zhiyao, Yu-Hung Huang, Guan-Qi Fang, Haoxing Ren, Shao-Yun Fang, and Yiran Chen. 2018. RouteNet: Routability prediction for mixed-size designs using convolutional neural network. In 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2018.
[21]
S. Kazeminia, C. Baur, A. Kuijper, B. Ginneken, N. Navab, S. Albarqouni, and A. Mukhopadhyay. 2020. GANs for medical image analysis[J]. Artificial Intelligence in Medicine 109 (2020), 101938.
[22]
Y. Zhang, M. Li, R. Li, K. Jia, and L. Zhang. 2022. Exact feature distribution matching for arbitrary style transfer and domain generalization[C]. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022): 8035–8045.
[23]
S. Pascual, A. Bonafonte, and J. Serrà. 2017. SEGAN: Speech enhancement generative adversarial network. Proc. Interspeech (2017), 3642--3646. DOI:
[24]
V. A. Chhabria, K. Kunal, M. Zabihi, and S. S. Sapatnekar. 2021. BeGAN: Power grid benchmark generation using a process-portable GAN-based methodology. In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD) (2021), 1–8.
[25]
K. Lin, L. Wang, and Z. Liu. 2021. End-to-end human pose and mesh reconstruction with transformers. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 1954–1963.
[26]
K. Li, S. Wang, X. Zhang, Y. Xu, W. Xu, and Z. Tu. 2021. Pose recognition with cascade transformers. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, (2021), 1944–1953, DOI:
[27]
Y. Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, and A. M. López. 2022. Multimodal end-to-end autonomous driving. IEEE Transactions on Intelligent Transportation Systems 23, 1 (2022), 537–547.
[28]
J. Sun, Z. Shen, Y. Wang, H. Bao, and X. Zhou. 2021. LoFTR: Detector-free local feature matching with transformers. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), 8918–8927.
[29]
T. Perrett, A. Masullo, T. Burghardt, M. Mirmehdi, and D. Damen. 2021. Temporal-relational crosstransformers for few-shot action recognition. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 475–484.
[30]
M. Arjovskyrjovsky, S. Chintalahintala, and L. Bottouottou. 2017. Wasserstein generative adversarial networks[C]//In Proceedings of the 2017 International Conference on Machine Learning. PMLR, (2017), 214–223.
[31]
M. Arjovsky, S. Chintala, and L. O. Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70, Sydney, NSW, Australia, 2017: JMLR.org, 214–223.
[32]
R. Strudel, R. Garcia, I. Laptev, and C. Schmid. 2021. Segmenter: Transformer for semantic segmentation. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021), 7242–7252.
[33]
C.-C. Chang, J. Pan, T. Zhang, Z. Xie, H. Jiang, W. Qi, C. -W. Lin, R. Liang, J. Mitra, E. Fallon, and Y. Chen. 2021. Automatic routability predictor development using neural architecture search. In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD) (2021), 1–9.
[34]
K. H. Brodersen, C. S. Ong, K. E. Stephan, and J. M. Buhmann. 2010. The binormal assumption on precision-recall curves. In 2010 20th International Conference on Pattern Recognition (2010), 4263–4266.
[35]
Devlin Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. North American Chapter of the Association for Computational Linguistics (2019).
[36]
K. He, R. Girshick, and P. Dollar. 2019. Rethinking imagenet pre-training. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019), 4917–4926.
[37]
Y. Wang, C. Wu, L. Herranz, J. van de Weijer, A. Gonzalez-Garcia, and B. Raducanu. 2018. Transferring GANs: Generating images from limited data. In Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part VI, Berlin, 2018: Springer-Verlag (2018), 220–236.
[38]
V. Badrinarayanan, A. Kendall, and R. Cipolla. 2017. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 12 (2017), 2481–2495.
[39]
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie. 2022. A ConvNet for the 2020s. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), 11966–11976.
[40]
Cadence Design Systems. Innovus Implementation System. Version 20.10. San Jose, CA: Cadence Design Systems. Retrieved from https://www.cadence.com.
Index Terms
- Layout Congestion Prediction Based on Regression-ViT
Recommendations
Remaining Useful Life Prediction of Bearing Based on Deep Perceptron Neural Networks
BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of ThingsThe life assessment and prediction research of the bearing is the most important content of the bearing long life and high reliable research. A novel remaining useful life prediction of bearing model that is deep learning based on deep perceptron neural ...
Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network
AbstractTime series prediction problems are a difficult type of predictive modeling problem. In this paper, we propose a time series prediction method based on a variant long short-term memory (LSTM) recurrent neural network. In the proposed method, we ...
New Results for Prediction of Chaotic Systems Using Deep Recurrent Neural Networks
AbstractPrediction of nonlinear and dynamic systems is a challenging task, however with the aid of machine learning techniques, particularly neural networks, is now possible to accomplish this objective. Most common neural networks used are the multilayer ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Journal Family
Publication History
Published: 09 November 2024
Online AM: 30 September 2024
Accepted: 15 September 2024
Revised: 30 May 2024
Received: 29 January 2024
Published in TODAES Volume 30, Issue 1
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Key-Area Research and Development Program of Guangdong Province
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 110Total Downloads
- Downloads (Last 12 months)110
- Downloads (Last 6 weeks)83
Reflects downloads up to 19 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in