Skip to main content
Log in

On the issue of application of cellular automata and neural networks methods in VLSI design

  • Published:
Optical Memory and Neural Networks Aims and scope Submit manuscript

Abstract

Comparative analysis of applications of two conceptually similar methods used for VLSI design is performed. The models are the cellular automata and the neural networks Specific features of each method are particularized. For the first time the end-to-end strategy of application of the cellular automata for the whole design flow correlating with block-hierarchical approach is proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Stempkovsky, A.L., Vlasov, P.A., and Kozin, G.V., Algorithmic environment for VLSI design on cellular automata, Proc. Joint Symposium: Information Processing and Software, Systems Design Automation, Academy of Sciences of the USSR, Siemens AG,FRG, Moscow, June 5/6, 1990, Springer-Verlag, pp. 308–312.

    Google Scholar 

  2. Hafid Zaabab, A., Qi-Jun Zhang, and Nakhla, M., A neural network modeling approach to circuit optimization and statistical design, Proc. IEEE Transactions on Microwave Theory and Techniques, 1995, vol. 43, no. 6, pp. 1349–1358.

    Article  Google Scholar 

  3. Creech, G.L., Paul, B.J., Lesniak, C.D., Jenkins, T.J., and Calcatera, M.C., Artificial neural networks for fast and accurate EM-CAD of microwave circuits, Proc. IEEE Transactions on Microwave Theory and Techniques, 1997, vol. 45, no. 5, pp. 794–802.

    Article  Google Scholar 

  4. Youngwook Kim, Sean Keely, Joydeep Ghosh, and Hao Ling, Application of artificial neural networks to broadband antenna design based on a parametric frequency model, Proc. IEEE Transactions on Antennas and Propagation, 2007, vol. 55, no. 3, pp. 669–674.

    Article  Google Scholar 

  5. Fang Wang, Vijaya Kumar Devabhaktuni, and Qi-Jun Zhang, A hierarchical neural network approach to the development of a library of neural models for microwave design, Proc. IEEE Transactions on Microwave Theory and Techniques, 1998, vol. 46, no. 12, pp. 2391–2403.

    Article  Google Scholar 

  6. Smith, K.A., Neural networks for combinatorial optimization: a review of more than a decade of research, Informs Journal on Computing, 1999, vol. 11, no. 1, pp. 15–34.

    Article  MathSciNet  MATH  Google Scholar 

  7. José Ernesto Rayas-Sánchez, EM-Based optimization of microwave circuits using artificial neural networks: The State-of-the-Art, Proc. IEEE Transactions on Microwave Theory and Techniques, 2004, vol. 52, no. 1, pp. 420–435.

    Article  Google Scholar 

  8. Mriganka Chakraborty, Artificial neural network for performance modeling and optimization of CMOS analog circuits, International Journal of Computer Applications (0975–8887), 2012, vol. 58, no. 18, pp. 6–12.

    Article  Google Scholar 

  9. Djeffal, F., Dibi, Z., Hafiane, M.L., and Arar, D., Design and simulation of a nanoelectronic DGMOSFET current source using artificial neural networks, Materials Science and Engineering C 27, 2007, pp. 1111–1116. doi 10.1016/jmsec.2006.09.005

    Google Scholar 

  10. Nihan Kahraman and Tulay Yildirim, Technology independent circuit sizing for fundamental analog circuits using artificial neural networks, Research in Microelectronics and Electronics, 2008. PRIME 2008, Ph.D., 2008, pp. 1–4. doi 10.1109/RME.2008.4595710

    Google Scholar 

  11. Stopjaková, V., Malošek, P., Matej, M., Nagy, V., and Margala, M., Defect detection in analog and mixed circuits by neural networks using wavelet analysis, Proc. IEEE Transactions on Reliability, 2005, vol. 54, no. 3, pp. 441–448.

    Article  Google Scholar 

  12. Than, O. and Buttgenbach, S., Simulation of anisotropic chemical etching of crystalline silicon using a cellular automata model, Sensors and Actuators, Part A, 1994, vol. 45, no. 1, p. 85.

    Article  Google Scholar 

  13. Sirakoulis, G., et al., A new simulator for the oxidation process in integrated circuits fabrication based on cellular automata, Materials Science and Engineering, 1999, vol. 7, pp. 631–640.

    Google Scholar 

  14. Agaphonov, A.N., Volkov, A.V., Konygin, S.B., and Sanoyan, A.G., Razrabotka fizicheskih principov i algoritmov komp’yuternogo modelirovaniya bazovyh processov formirovaniya mikrostruktur metodami veroyatnostnogo kletochnogo avtomata, Vestn. Samar. Gos. Tekhn. Univ. Ser. Fiz.-Mat. Nauki, 2007, vol. 1, no. 14, p. 99–107 [in Russian].

    Article  Google Scholar 

  15. Fawaz, S., Al-Anzi., Efficient cellular automata algorithms for planar graph and VLSI layout homotopic compaction, International Journal of Computing and Information Sciences, 2003, vol. 1, no. 1, p. 1.

    MathSciNet  Google Scholar 

  16. Biplab, K., et al., Cellular automata based test structures with logic folding, Proc. of 18th International Conference on VLSI Design (VLSID’05), 2005, pp. 71–74.

    Google Scholar 

  17. Benny Applebaum, Yuval Ishai, and Eya Kushilevitz, Cryptography by cellular automata or how fast can complexity emerge in nature?, Proc. of Conference “Innovations in Computer Science” (ICS’2010), held in Beijing, China, January 5–7, 2010.

    Google Scholar 

  18. Somanath Tripathy and Sukumar Nandi, LCASE: Lightweight Cellular Automata-based Symmetric-key Encryption, International Journal of Network Security, 2009, vol. 8, no. 2, pp. 243–252.

    Google Scholar 

  19. Takeshi Ikenaga, Highly-parallel two-dimensional cellular automaton architecture and its application to realtime image processing, PhD Thesises, Japan: Waseda University, 2001.

    Google Scholar 

  20. Pradipta Maji, et al., Theory and application of cellular automata for pattern classification, Fundamenta Informaticae, 2003, vol. 58, pp. 321–354.

    MathSciNet  MATH  Google Scholar 

  21. Woudenberg, M., Using FPGAs to speed up cellular automata computations, Master’s Thesis for University of Amsterdam, 2006.

    Google Scholar 

  22. Stempkovskij, A.L., Osipov, L.B., and Seleznev, S.Z., Issledovanie voprosov realizacii nejroseti po SIP-tekhnologii dlya postroeniya otkazoustojchivyh odnorodnyh arhitektur, Informacionnye Tekhnologii i Vychislitel’nye Sistemy, M., 1995, no. 9, p. 58 [in Russian].

    Google Scholar 

  23. Stempkovskij, A.L., Osipov, L.B., and Seleznev, S.Z., Problemy realizacii otkazoustojchivyh arhitektur nejrochipov po tekhnologii sistem s integraciej na plastine, Informacionnye Tekhnologii, M., 1997, no. 5, p. 15 [in Russian]. M., 2001, nos. 2–3, p. 40 [in Russian].

    Google Scholar 

  24. Stempkovskij, A.L., Otkazoustojchivye arhitektury mikroehlektronnyh vychislitel’nyh sistem, Informacionnye Tekhnologii i Vychislitel’nye Sistemy, M., 2001, nos. 2–3, p. 40.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I. V. Matyushkin.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stempkovsky, A.L., Gavrilov, S.V., Matyushkin, I.V. et al. On the issue of application of cellular automata and neural networks methods in VLSI design. Opt. Mem. Neural Networks 25, 72–78 (2016). https://doi.org/10.3103/S1060992X16020065

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1060992X16020065

Keywords

Navigation