Abstract
Reference placement is promising to handle the increasing complexity in printed circuit board (PCB) designs, which aims to find the isomorphism of the placed template in component combination to reuse the placement. In this paper, we convert the netlist information into a graph and then model the reference placement as a subgraph matching problem. Since the state-of-the-art subgraph matching methods usually recursively search the solutions and suffer from high time and memory consumption in large-scale designs, we develop a novel subgraph matching algorithm D2BS with diversity tolerance and improved backtracking to guarantee matching quality and efficiency. The D2BS algorithm is founded on a data structure called the candidate space (CS) structure. We build and filter the candidate set for each query node according to our designed features to construct the CS structure. During the CS optimization process, a graph diversity tolerance strategy is adopted to achieve efficient inexact matching. Then, hierarchical matching is developed to search the template embeddings in the CS structure guided by branch backtracking and matched-node snatching strategies. Based on the industrial PCB designs, experimental results show that D2BS outperforms the state-of-the-art subgraph matching method in matching accuracy and running time.


















Similar content being viewed by others
Data availability
No datasets were generated or analyzed during the current study.
References
Su M, Xiao Y, Zhang S, Su H, Xu J, He H, Zhu Z, Chen J, Chang Y-W (2022) Late breaking results: Subgraph matching based reference placement for pcb designs. In: Proceedings of the 59th ACM/IEEE Design Automation Conference, pp 1400–1401
Khandpur RS (2006) Printed circuit boards design, fabrication, and assembly. The McGraw-Hill Companies, New York
Zhang D, Ren Q, Su D (2021) A novel authentication methodology to detect counterfeit pcb using pcb trace-based ring oscillator. IEEE Access 9:28525–28539. https://doi.org/10.1109/ACCESS.2021.3059100
Ismail FS, Yusof R, Khalid M (2012) Optimization of electronics component placement design on pcb using self organizing genetic algorithm (soga). J Intell Manuf 23:883–895
Satomi Y, Hachiya K, Kanamoto T, Watanabe R, Kurokawa A (2020) Thermal placement on pcb of components including 3d ics. IEICE Electron Express 17(3):20190737–20190737
Badriyah T, Setyorini F, Yuliawan N (2016) The implementation of genetic algorithm and routing lee for pcb design optimization. In: International Conference on Informatics and Computing (ICIC)
Kureichik V, Kuliev E (2020) Integrated algorithm for elements placement on the printed circuit board. In: IOP Conference Series: Materials Science and Engineering, vol 734, p 012146
Cheng C-K, Ho C-T, Holtz C (2022) Net separation-oriented printed circuit board placement via margin maximization. In: 27th Asia and South Pacific Design Automation Conference (ASP-DAC), pp 288–293
Zhang C, Jin H, Chen J, Zhu J, Luo J (2020) A hierarchy mcts algorithm for the automated pcb routing. In: 16th International Conference on Control & Automation (ICCA), pp 1366–1371
Kunal K, Dhar T, Madhusudan M, Poojary J, Sharma AK, Xu W, Burns SM, Hu J, Harjani R, Sapatnekar SS (2023) Gnn-based hierarchical annotation for analog circuits. IEEE Trans Comput-Aided Des Integr Circuit Syst 42(9):2801–2814. https://doi.org/10.1109/TCAD.2023.3236269
Kunal K, Dhar T, Madhusudan M, Poojary J, Sharma A, Xu W, Burns SM, Hu J, Harjani R, Sapatnekar SS (2020) Gana: graph convolutional network based automated netlist annotation for analog circuits. In: 2020 design, automation & test in Europe conference & exhibition (DATE), pp 55–60. https://doi.org/10.23919/DATE48585.2020.9116329
Shang H, Zhang Y, Lin X, Yu JX (2008) Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. Proc LDB Endow 1(1):364–375
Zhao P, Han J (2010) On graph query optimization in large networks. Proc VLDB Endow 3(1–2):340–351
Han W-S, Lee J, Lee J-H (2013) Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases. In: Proceedings of the 2013 ACM SIGMOD international conference on management of data, pp. 337–348
Zampelli S, Deville Y, Solnon C (2010) Solving subgraph isomorphism problems with constraint programming. Constraints 15(3):327–353
Solnon C (2010) All different-based filtering for subgraph isomorphism. Artif Intell 174(12):850–864
Kotthoff L, McCreesh C, Solnon C (2016) Portfolios of subgraph isomorphism algorithms. In: International Conference on Learning and Intelligent Optimization, Springer. pp 107–122
Ullmann JR (1976) An algorithm for subgraph isomorphism. J ACM (JACM) 23(1):31–42
Cordella LP, Foggia P, Sansone C, Vento M (2001) An improved algorithm for matching large graphs. In: 3rd IAPR-TC15 Workshop on Graph-Based Representations in Pattern Recognition, pp 149–159. Citeseer
Cordella LP, Foggia P, Sansone C, Vento M (2004) A (sub) graph isomorphism algorithm for matching large graphs. IEEE Trans Pattern Anal Mach Intell 26(10):1367–1372
Carletti V, Foggia P, Saggese A, Vento M (2017) Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with vf3. IEEE Trans Pattern Anal Mach Intell 40(4):804–818
Battiti R, Mascia F (2007) An algorithm portfolio for the sub-graph isomorphism problem. In: International Workshop on Wngineering Stochastic Local Search Algorithms, Springer. pp 106–120
Almasri I, Gao X, Fedoroff N (2014) Quick mining of isomorphic exact large patterns from large graphs. In: 2014 IEEE International Conference on Data Mining Workshop, pp 517–524. IEEE
Bonnici V, Giugno R (2016) On the variable ordering in subgraph isomorphism algorithms. IEEE/ACM Trans Comput Biol Bioinform 14(1):193–203
Han M, Kim H, Gu G, Park K, Han W-S (2019) Efficient subgraph matching: harmonizing dynamic programming, adaptive matching order, and failing set together. In: Proceedings of the 2019 International Conference on Management of Data, pp 1429–1446
Zhu Z, Mei Y, Li Z, Lin J, Chen J, Yang J, Chang Y-W (2022) High-performance placement for large-scale heterogeneous fpgas with clock constraints. In: Proceedings of the 59th ACM/IEEE Design Automation Conference, pp 643–648
Zhu Z, Chen J, Peng Z, Zhu W, Chang Y-W (2018) Generalized augmented lagrangian and its applications to vlsi global placement. In: Proceedings of ACM/ESDA/IEEE Design Automation Conference, pp 1–6
Bi F, Chang L, Lin X, Qin L, Zhang W (2016) Efficient subgraph matching by postponing cartesian products. In: Proceedings of the 2016 International Conference on Management of Data, pp 1199–1214
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China under Grants 62104037 and 92373207 and the MOST of Taiwan under Grant MOST 110-2221-E-002-177-MY3.
Author information
Authors and Affiliations
Contributions
ZZ, YL, and MS gave the idea and wrote the main manuscript text. YL, MS, HS, and YX did the experiments. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Preliminary version of this paper was presented at the 2022 ACM/IEEE Design Automation Conference (DAC’22), San Francisco, CA, USA, July 2022 [1].
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhu, Z., Li, Y., Su, M. et al. Subgraph matching-based reference placement for printed circuit board designs. J Supercomput 80, 24324–24357 (2024). https://doi.org/10.1007/s11227-024-06338-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-024-06338-9