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research-article

Lookahead Placement Optimization with Cell Library-based Pin Accessibility Prediction via Active Learning

Authors:

Hsien-Shih Chiu,

Philip Hui-Yuh Tai,

Cindy Chin-Fang Shen,

Henry ShengAuthors Info & Claims

ISPD '20: Proceedings of the 2020 International Symposium on Physical Design

Pages 65 - 72

https://doi.org/10.1145/3372780.3375562

Published: 30 March 2020 Publication History

Abstract

With the development of advanced process nodes of semiconductor, the problem of pin access has become one of the major factors to impact the occurrences of design rule violations (DRVs) due to complex design rules and limited routing resource. Many state-of-the-art works address the problem of DRV prediction by adopting supervised machine learning approaches. However, those supervised learning approaches extract the labels of training data by generating a great number of routed designs in advance, giving rise to large effort on training data preparation. In addition, the pre-trained model could hardly predict unseen data and thus may not be applied to predict other designs containing cells that are not used in the training data. In this paper, we propose the first work of cell library-based pin accessibility prediction (PAP) by using active learning techniques. A given set of standard cell libraries is served as the only input for model training. Unlike most of existing studies that aim at design-specific training, we propose a library-based model which can be applied to all designs referencing to the same standard cell library set. Experimental results show that the proposed model can be applied to predict two different designs with different reference library sets. The number of remaining DRVs and M2 shorts of the designs optimized by the proposed model are also much fewer than those of design-specific models.

References

[1]

W. T. J. Chan, P.-H. Ho, A. B. Kahng, and P. Saxena, "Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning," In Proc. International Symposium on Physical Design (ISPD), 2017.

[2]

Q. Zhou, X. Wang, Z. Qi, Z. Chen, Q. Zhou, and Y. Cai. "An accurate detailed routing routability prediction model in placement." In Proc. Asia Symposium on Quality Electronic Design (ASQED), 2015.

[3]

A. F. Tabrizi, N. K. Darav, S. Xu, L. Rakai, I. Bustany, A. Kennings and, L. Behjat. "A Machine Learning Framework to Identify Detailed Routing Short Violations from a Placed Netlist." Proc. Design Automation Conference (DAC), 2018.

[4]

Z. Qi, Y. Cai and Q. Zhou. "Accurate Prediction of Detailed Routing Congestion using Supervised Data Learning." In Proc. International Conference on Computer Design (ICCD), 2014.

[5]

T. C. Yu, S. Y. Fang, H. S. Chiu, K. S. Hu, P. H. Y. Tai, C. C. F. Shen, and H. Sheng. "Pin Accessibility Prediction and Optimization with Deep Learning-based Pin Pattern Recognition." Proc. Design Automation Conference (DAC), 2019.

[6]

A. F. Tabrizi, N. K. Darav, L. Rakai, A. Kennings and L. Behjat. "Detailed Routing Violation Prediction During Placement Using Machine Learning." Proc. VLSI Design, Automation and Test (VLSI-DAT), 2017.

[7]

W. T. J. Chan, Y. Du, A. B. Kahng, S. Nath and K. Samadi. "BEOL stack-aware routability prediction from placement using data mining techniques" In Proc. IEEE International Conference on Computer Design (ICCD), 2016.

[8]

Z. Xie, Y. H. Huang, G. C. Fang, H. Ren, S. Y. Fang, Y. Chen and J. Hu. "RouteNet: Routability Prediction for Mixed-Size Designs Using Convolutional Neural Network." In Proc. International Conference on Computer-Aided Design (ICCAD), 2018.

Digital Library

[9]

X. Xu, B. Yu, J. R. Gao, C. L. Hsu and D. Z. Pan. "PARR: Pin Access Planning and Regular Routing for Self-Aligned Double Patterning." ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 21, no. 3, article 42, 2016.

Digital Library

[10]

X. Xu, Y. Lin, V. Livramento and D. Z. Pan. "Concurrent Pin Access Optimization for Unidirectional Routing." Proc. Design Automation Conference (DAC), 2017.

[11]

J. Seo, J. Jung, S. Kim and Y. Shin. "Pin Accessibility-Driven Cell Layout Redesign and Placement Optimization." Proc. Design Automation Conference (DAC), 2017.

[12]

W. Ye, B. Yu, D. Z. Pan, Y. C. Ban and L. Liebmann. "Standard Cell Layout Regularity and Pin Access Optimization Considering Middle-of-Line." Proc. Great Lakes Symposium on VLSI (GLSVLSI), 2015.

Digital Library

[13]

Y. Ding, C. Chu and W. K. Mak. "Pin Accessibility-Driven Detailed Placement Refinement." In Proc. International Symposium on Physical Design (ISPD), 2017.

[14]

M. M. Ozdal. "Detailed-Routing Algorithms for Dense Pin Clusters in Integrated Circuits." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 28, no. 3, pp. 340--349, 2009.

Digital Library

[15]

X. Xu, B. Cline, G. Yeric, B. Yu and D. Z. Pan. "Self-Aligned Double Patterning Aware Pin Access and Standard Cell Layout Co-Optimization." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 34, no. 5, pp. 699--712, 2015.

Digital Library

[16]

D. Vasisht, A. Damianou, M. Varma, and A. Kapoor. "Active learning for sparse Bayesian multilabel classification." In Proc. ACM SIGKDD. New York, NY, USA, 2014, pp. 472--481.

[17]

P. Melville and R. J. Mooney. "Diverse ensembles for active learning." In Proc. 21th Int. Conf. Mach. Learn. Banff, AB, Canada, 2004, p. 74.

[18]

I. Guyon, G. Cawley, V. Lemaire, and G. Dror. "Results of the active learning challenge." J. Mach. Learn. Res. vol. 16, pp. 19--45, 2011.

[19]

M. Wang and X.-S. Hua. "Active learning in multimedia annotation and retrieval: A survey." ACM Trans. Intell. Syst. Technol. vol. 2, no. 2, pp. 1--21, 2011.

Digital Library

[20]

Y. Chen and A. Krause. "Near-optimal batch mode active learning and adaptive submodular optimization." J. Mach. Learn. Res. vol. 28, no. 1, pp. 160--168, 2013.

[21]

S. Tong and D. Koller. "Support vector machine active learning with applications to text classification." J. Mach. Learn. Res. vol. 2, pp. 45--66, 2001.

Digital Library

[22]

J. Zhu and M. Ma. "Uncertainty-based active learning with instability estimation for text classification." ACM Trans. Speech Lang. Process. vol. 8, no. 4, pp. 1--21, 2012.

Digital Library

[23]

M. Park and J. W. Pillow. "Bayesian active learning with localized priors for fast receptive field characterization." In Proc. NIPS. Nevada City, CA, USA, 2012, pp. 2357--2365.

[24]

W. Luo, A. Schwing, and R. Urtasun. "Latent structured active learning." In Proc. NIPS. Lake Tahoe, CA, USA, 2013, pp. 728--736.

[25]

N. V. Cuong, W. S. Lee, N. Ye, K. M. A. Chai, and H. L. Chieu. "Active learning for probabilistic hypotheses using the maximum Gibbs error criterion." In Proc. NIPS. Lake Tahoe, CA, USA, 2013, pp. 1457--1465.

[26]

B. Du, Z. Wang, L. Zhang, L. Zhang, W. Liu, J. Shen, and D. Tao. "Exploring representativeness and informativeness for active learning." IEEE Trans. Cybern. 47(1), 14--26, 2017.

[27]

X. Li, D. Kuang, and C. X. Ling. "Active learning for hierarchical text classification." In Advances in Knowledge Discovery and Data Mining. Berlin, Germany: Springer, 2012, pp. 14--25.

[28]

I. Dino, B. Albert, Z. Indre, and P. Bernhard. "Clustering based active learning for evolving data streams." In Discovery Science Berlin, Germany: Springer, 2013, pp. 79--93.

[29]

R. Chattopadhyay et al. "Batch mode active sampling based on marginal probability distribution matching." In Proc. ACM SIGKDD. China, 2012, pp. 741--749.

[30]

Y. Fu, B. Li, X. Zhu, and C. Zhang. "Active learning without knowing individual instance labels." IEEE Trans. Knowl. Data Eng. vol. 26, no. 4, pp. 808--822, Apr. 2014.

Digital Library

[31]

T. Reitmaier, A. Calma, and B. Sick. "Transductive active learning A new semisupervised learning approach based on iteratively refined generative models to capture structure in data." Inf. Sci. vol. 293, pp. 275--298, Feb. 2015.

[32]

Synopsys ICC2 user guide. https://www.synopsys.com

[33]

Tensorflow C++ Reference. https://www.tensorflow.org/api_docs/cc/

Cited By

Park HBaek KKim SChoi KKim T(2024)Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction for Designs in Advanced Technology Nodes With Consolidated Practical Applicability and SustainabilityIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.340589443:12(4786-4799)Online publication date: Dec-2024
https://doi.org/10.1109/TCAD.2024.3405894
Peneda DAzevedo FLourenço NHorta NMartins R(2024)Effective Routing Probability Maps via Convolutional Neural Networks for Analog IC Layout Automation2024 20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)10.1109/SMACD61181.2024.10745385(1-4)Online publication date: 2-Jul-2024
https://doi.org/10.1109/SMACD61181.2024.10745385
Lin JChen YYang YHung WTsai CFu DChao MThapliyal HDeMara RPartin-Vaisband IKatkoori S(2023)DRC Violation Prediction with Pre-global-routing Features Through Convolutional Neural NetworkProceedings of the Great Lakes Symposium on VLSI 202310.1145/3583781.3590216(313-319)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1145/3583781.3590216
Show More Cited By

Index Terms

Lookahead Placement Optimization with Cell Library-based Pin Accessibility Prediction via Active Learning
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
      1. Placement

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cover image ACM Conferences

ISPD '20: Proceedings of the 2020 International Symposium on Physical Design

March 2020

160 pages

ISBN:9781450370912

DOI:10.1145/3372780

General Chair:
William Swartz
TimberWolf Systems and University of Texas at Dallas, USA
,
Program Chair:
Jens Lienig
Dresden University of Technology, Germany

Copyright © 2020 ACM.

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Published: 30 March 2020

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ISPD '20: International Symposium on Physical Design

September 20 - 23, 2020

Taipei, Taiwan

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Overall Acceptance Rate 62 of 172 submissions, 36%

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Cited By

Park HBaek KKim SChoi KKim T(2024)Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction for Designs in Advanced Technology Nodes With Consolidated Practical Applicability and SustainabilityIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.340589443:12(4786-4799)Online publication date: Dec-2024
https://doi.org/10.1109/TCAD.2024.3405894
Peneda DAzevedo FLourenço NHorta NMartins R(2024)Effective Routing Probability Maps via Convolutional Neural Networks for Analog IC Layout Automation2024 20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)10.1109/SMACD61181.2024.10745385(1-4)Online publication date: 2-Jul-2024
https://doi.org/10.1109/SMACD61181.2024.10745385
Lin JChen YYang YHung WTsai CFu DChao MThapliyal HDeMara RPartin-Vaisband IKatkoori S(2023)DRC Violation Prediction with Pre-global-routing Features Through Convolutional Neural NetworkProceedings of the Great Lakes Symposium on VLSI 202310.1145/3583781.3590216(313-319)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1145/3583781.3590216
Li LCai YZhou Q(2023)Global Routing Under a Congestion-Aware Reinforcement Learning Model2023 International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA59274.2023.10218371(213-218)Online publication date: 8-May-2023
https://doi.org/10.1109/ISEDA59274.2023.10218371
Martins RLourenço N(2023)Analog Integrated Circuit Routing Techniques: An Extensive ReviewIEEE Access10.1109/ACCESS.2023.326548111(35965-35983)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3265481
Kim SKim T(2022)Pin Accessibility-driven Placement Optimization with Accurate and Comprehensive Prediction Model2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE54114.2022.9774753(778-783)Online publication date: 14-Mar-2022
https://doi.org/10.23919/DATE54114.2022.9774753
Liang RXiang HPandey DReddy LRamji SNam GHu J(2022)Design Rule Violation Prediction at Sub-10-nm Process Nodes Using Customized Convolutional NetworksIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.312667641:10(3503-3514)Online publication date: Oct-2022
https://doi.org/10.1109/TCAD.2021.3126676
Netto RFabre SFontana TLivramento VPilla LBehjat LGuntzel J(2022)Algorithm Selection Framework for Legalization Using Deep Convolutional Neural Networks and Transfer LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.307912641:5(1481-1494)Online publication date: May-2022
https://doi.org/10.1109/TCAD.2021.3079126
Lu YPentapati SLim SLienig JBehjat LYang S(2021)The Law of AttractionProceedings of the 2021 International Symposium on Physical Design10.1145/3439706.3447045(7-14)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3439706.3447045
Li LCai YZhou Q(2021)An Efficient Approach for DRC Hotspot Prediction with Convolutional Neural Network2021 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS51556.2021.9401274(1-5)Online publication date: May-2021
https://doi.org/10.1109/ISCAS51556.2021.9401274

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