Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 4 , 30 May 2023


Open Access | Article

Interpretable machine learning in VLSI physical design

Ben Sun * 1
1 Dept of Engineering, University College London, London, UK

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 4, 13-19
Published 30 May 2023. © 30 May 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Ben Sun. Interpretable machine learning in VLSI physical design. ACE (2023) Vol. 4: 13-19. DOI: 10.54254/2755-2721/4/20230338.

Abstract

Today's popularisation of portable devices largely depends on the progress in integrated circuits. Modern Very Large Scale Integration technology (VLSI) allows billions of transistors to be packed into the same chip. In the past years, digital design in VLSI has been developed compared to analogue design. The traditional method is hard to model the performance change in analogue or mixed-signal components caused by physical design. In the early 2000s, rapid advances in machine learning and computing power made analogue design automation possible. Despite their outstanding performance, the transparency issue has become significant. This paper introduces the history of VLSI physical design, which includes placement and routing in the early stages. The change that machine learning (ML) has made is mentioned in the third section. Analysis of the potential problem has been proposed, followed by a brief category of some well-known work in interpretable Machine Learning, which could be the primary direction for VLSI automation to be further popularised in the future.

Keywords

VLSI, ROUTING, LAYOUT, explainable Artificial Intelligence (XAI)

References

1. "The Nobel Prize in Physics 2000", NobelPrize.org, 2022. [Online]. Available: https://www.nobelprize.org/prizes/physics/2000/kilby/facts/. [Accessed: 30- Jul- 2022].

2. P. Cook, W. Donath, G. Lemke and A. Brennemann, "Automatic Artwork Generation for Large Scale Integration", IEEE Journal of Solid-State Circuits, vol. 2, no. 4, pp. 190-196, 1967. Available: https://ieeexplore.ieee.org/document/1049817. [Accessed 30 July 2022].

3. K. Chen, M. Feuer, K. Khokhani, N. Nan and S. Schmidt, "The chip layout problem: an automatic wiring procedure", Papers on Twenty-five years of electronic design automation - 25 years of DAC, 1988. Available: 10.1145/62882.62897 [Accessed 30 July 2022].

4. R. Prim, "Shortest Connection Networks And Some Generalizations", Bell System Technical Journal, vol. 36, no. 6, pp. 1389-1401, 1957. Available: 10.1002/j.1538-7305.1957.tb01515.x [Accessed 30 July 2022].

5. D. Hightower, "A solution to line-routing problems on the continuous plane", Proceedings of the 6th annual conference on Design Automation - DAC '69, 1969. Available: 10.1145/800260.809014 [Accessed 30 July 2022].

6. S. Kimura, N. Kubo, T. Chiba and I. Nishioka, "An Automatic Routing Scheme for General Cell LSI", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 2, no. 4, pp. 285-292, 1983. Available: 10.1109/tcad.1983.1270046 [Accessed 30 July 2022].

7. U. Lauther, "A Data Structure for Gridless Routing," 17th Design Automation Conference, 1980, pp. 603-609, doi: 10.1145/800139.804593 [Accessed 30 July 2022].

8. L. Corrigan, "A Placement Capability Based on Partitioning", 16th Design Automation Conference, 1979. Available: 10.1109/dac.1979.1600145 [Accessed 30 July 2022].

9. H. Shiraishi and F. Hirose, "Efficient Placement and Routing Techniques for Master Slice LSI," 17th Design Automation Conference, 1980, pp. 458-464, doi: 10.1145/800139.804570 [Accessed 30 July 2022].

10. C. -L. Ding, C. -Y. Ho and M. J. Irwin, "A new optimization driven clustering algorithm for large circuits," Proceedings of EURO-DAC 93 and EURO-VHDL 93- European Design Automation Conference, 1993, pp. 28-32, doi: 10.1109/EURDAC.1993.410612 [Accessed 30 July 2022].

11. M. Aoyagi, Y. Hamazaki, H. Nakagawa, I. Kurosawa, M. Maezawa and S. Takada, "Chip layout design of a Josephson LSI circuit for examining high-speed operability by using a standard cell automatic placement and routing technique," in IEEE Transactions on Applied Superconductivity, vol. 4, no. 3, pp. 169-176, Sept. 1994, doi: 10.1109/77.317833 [Accessed 30 July 2022].

12. A. B. Kahng, "INVITED: Reducing Time and Effort in IC Implementation: A Roadmap of Challenges and Solutions," 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), 2018, pp. 1-6, doi: 10.1109/DAC.2018.8465871 [Accessed 30 July 2022].

13. C. Sechen and A. Sangiovanni-Vincentelli, "The TimberWolf placement and routing package", IEEE Journal of Solid-State Circuits, vol. 20, no. 2, pp. 510-522, 1985. Available: 10.1109/jssc.1985.1052337 [Accessed 30 July 2022].

14. A. Al-Kawam and H. M. Harmanani, "A Parallel GPU Implementation of the Timber Wolf Placement Algorithm," 2015 12th International Conference on Information Technology - New Generations, 2015, pp. 792-795, doi: 10.1109/ITNG.2015.144 [Accessed 30 July 2022].

15. K. Zhu et al., "GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance," 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942164 [Accessed 30 July 2022].

16. H. Chen et al., "MAGICAL: An Open- Source Fully Automated Analog IC Layout System from Netlist to GDSII", IEEE Design & Test, vol. 38, no. 2, pp. 19-26, 2021. Available: 10.1109/mdat.2020.3024153 [Accessed 30 July 2022].

17. H. Chen et al., "MAGICAL 1.0: An Open-Source Fully-Automated AMS Layout Synthesis Framework Verified With a 40-nm 1GS/s Δ∑ ADC," 2021 IEEE Custom Integrated Circuits Conference (CICC), 2021, pp. 1-2, doi: 10.1109/CICC51472.2021.9431521 [Accessed 30 July 2022].

18. B. Xu et al., "WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout," 2019 56th ACM/IEEE Design Automation Conference (DAC), 2019, pp. 1-6. [Accessed 30 July 2022].

19. M. Liu et al., "Towards Decrypting the Art of Analog Layout: Placement Quality Prediction via Transfer Learning," 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020, pp. 496-501, doi: 10.23919/DATE48585.2020.9116330 .[Accessed 30 July 2022].

20. C. Lee, "An Algorithm for Path Connections and Its Applications", IEEE Transactions on Electronic Computers, vol. -10, no. 3, pp. 346-365, 1961. Available: 10.1109/tec.1961.5219222 [Accessed 30 July 2022].

21. R. Mina, C. Jabbour and G. Sakr, "A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation", Electronics, vol. 11, no. 3, p. 435, 2022. Available: 10.3390/electronics11030435[Accessed 31 July 2022].

22. L. Breiman, ‘‘Statistical modeling: The two cultures (with comments and a rejoinder by the author),’’ Stat. Sci., vol. 16, no. 3, pp. 199–231, 2001

23. A. Santoro et al., "A simple neural network module for relational reasoning." Available: https://arxiv.org/abs/1706.01427. [Accessed 6 August 2022].

24. A. Goldstein, A. Kapelner, J. Bleich and E. Pitkin, "Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation", Journal of Computational and Graphical Statistics, vol. 24, no. 1, pp. 44-65, 2015. Available: 10.1080/10618600.2014.907095 [Accessed 30 July 2022].

25. P. Kumar and M. Sharma, “Feature-Importance Feature-Interactions (FIFI) graph: A graph-based Novel Visualization for Interpretable Machine Learning,” 2021 International Conference on Intelligent Technologies (CONIT), 2021, pp. 1-7, doi: 10.1109/CONIT51480.2021.9498467 [Accessed 30 July 2022].

26. L. Breiman, "Random Forests", Machine Learning, vol. 45, pp. 5-32, 2001. Available: https://link.springer.com/article/10.1023/A:1010933404324. [Accessed 30 July 2022].

27. J. Friedman and B. Popescu, "Predictive learning via rule ensembles", The Annals of Applied Statistics, vol. 2, no. 3, 2008. Available: 10.1214/07-aoas148 [Accessed 30 July 2022].

28. N. Radulovic, A. Bifet and F. Suchanek, "Confident Interpretations of Black Box Classifiers," 2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-8, doi: 10.1109/IJCNN52387.2021.9534234 [Accessed 31 July 2022].

29. M. Ribeiro, S. Singh and C. Guestrin, ""Why Should I Trust You?"", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. Available: 10.1145/2939672.2939778[Accessed 31 July 2022].

30. P. Krishnamurthy, A. B. Chowdhury, B. Tan, F. Khorrami and R. Karri, "Explaining and Interpreting Machine Learning CAD Decisions: An IC Testing Case Study," 2020 ACM/IEEE 2nd Workshop on Machine Learning for CAD (MLCAD), 2020, pp. 129-134, doi: 10.1145/3380446.3430643 [Accessed 31 July 2022].

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-55-3
ISBN (Online)
978-1-915371-56-0
Published Date
30 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/4/20230338
Copyright
30 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated