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A Configurable Digital Cellular Neural Network with Template Decomposition

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Abstract

A configurable digital cellular neural network (DCNN) with decomposable templates is presented. A template decomposition method based on physical circuit decomposition and synthesis is proposed to meet the large template requirement in CNN. The complexity of the template decomposition scheme is only O(⌈(2n+1)/3⌉2), which is significantly reduced when compared to the other methods in the literature. Moreover, the proposed method supports both binary and gray-scale images.

The silicon intellectual property (SIP) concept is employed in the design and a friendly graphical user interface (GUI) is developed to facilitate the operation. When implemented on an embedded system with Virtex II XC2V2000 chip, the DCNN is realized with 48014 gates at 18-bit data format and can run at an optimal frequency of 90 MHz. The result outperforms the other approaches to implement DCNN with much reduced gate counts and higher working frequency.

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References

  1. M.H. Brugge, J.H. Stevens, J.A.G. Nijhuis, L. Spaanenburg, Efficient DTCNN implementations for large-neighborhood functions, in Proceedings of the IEEE CNNA-1998 (1998), pp. 88–93

    Google Scholar 

  2. C. Christopoulos, A. Skodras, T. Ebrahimi, The JPEG2000 still image coding system: an overview. IEEE Trans. Consum. Electron. 46(4), 1103–1127 (2000)

    Article  Google Scholar 

  3. L.O. Chua, L. Yang, Cellular neural networks: theory. IEEE Trans. Circuits Syst. 35, 1257–1272 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  4. L.O. Chua, L. Yang, Cellular neural networks: applications. IEEE Trans. Circuits Syst. 35, 1273–1290 (1988)

    Article  MathSciNet  Google Scholar 

  5. L.O. Chua, T. Roska, The CNN universal machine. I. The architecture: cellular neural networks and their applications, in Proceedings of the IEEE CNNA-92, Munich (1992), pp. 1–10

    Google Scholar 

  6. K.R. Crounse, T. Roska, L.O. Chua, Image halftoning with cellular neural networks. Memo UCB/ERL 91-106 (1991)

  7. R. Dominguez-Castro, S. Espejo, A. Rodriguez-Vazquez, R. Carmona, A CNN universal chip in CMOS technology, in Proceedings of the IEEE CNNA-94, Rome (1994), pp. 91–96

    Google Scholar 

  8. N.A. Fernandez, D.L. Vilarino, V.M. Brea, D. Cabello, On the emulation of large-neighborhood templates with binary CNN-based architectures, in Proceedings of the IEEE CNNA-2005, Hsin-chu (2005), pp. 274–277

    Google Scholar 

  9. P. Foldesy, L. Kek, T. Roska, A. Zarandy, G. Bartfai, Fault-tolerant design of analogic CNN templates and algorithms-part. I. the binary output case. IEEE Trans. Circuits Syst. 46(2), 312–322 (1999)

    Article  Google Scholar 

  10. C.F. Gerald, P.O. Wheatley, Applied Numerical Analysis, 7th edn. (Addison-Wesley, Reading, 2003)

    Google Scholar 

  11. M. Keating, P. Bricaud, Reusable Methodology Manual, 3rd edn. (Kluwer Academic, Norwell, 2002)

    Google Scholar 

  12. S. Kocsardi, Z. Nagy, A. Csik, P. Szolgay, Simulation of two-dimensional supersonic flows on emulated-digital CNN-UM. EURASIP J. Adv. Signal Process. 923404 (2009)

  13. L. Koskinen, A. Paasio, K. Halonen, Improved CNN algorithm for H.264 motion estimation partitions, in Proceedings of the IEEE CNNA-2005, Hsin-chu (2005), pp. 142–145

    Google Scholar 

  14. C.C. Lee, J.P. Gyvez, Color image processing in a cellular neural-network environment. IEEE Trans. Neural Netw. 7, 1086–1098 (1996)

    Article  Google Scholar 

  15. C.T. Lin, C.L. Chang, J.F. Chung, New horizon for CNN: with fuzzy paradigms for multimedia. IEEE Trans. Circuits Syst. 5(2), 20–35 (2005)

    Google Scholar 

  16. C.-N. Lin, S.-N. Yu, W.-C. Chuang, Embedding of multiple functions into a single cellular neural network: an image filtering prospective, in Proceedings of the IEEE CNNA-2005, Hsin-chu (2005), pp. 166–169

    Google Scholar 

  17. G. Liu, S. Oe, Texture image segmentation method based on multi-layer CNN, in Proceedings of the 12th I ICTAI-2000 (2000), pp. 47–150

    Google Scholar 

  18. O. Moreira-Tamayo, J.P. Gyvez, Preprocessing operators for image compression using Cellular neural networks, in Proceedings of IEEE IJCNN-1996, vol. 3, Washington (1996), pp. 1500–1505

    Google Scholar 

  19. Z. Nagy, P. Szolgay, Configurable multilayer CNN-UM emulator on FPGA. IEEE Trans. Circuits Syst. I 50(6), 774–778 (2003)

    Article  Google Scholar 

  20. Z. Nagy, Z. Voroshazi, P. Szolgay, Emulated digital CNN-UM solution of partial differential equations. Int. J. Circuit Theory Appl. 34(4), 445–470 (2006)

    Article  MATH  Google Scholar 

  21. G.E. Pazienza, X. Ponce-Garcia, M. Balsi, X. Vilasis-Cardona, Robot vision with cellular neural networks: a practical implementation of new algorithms. Int. J. Circuit Theory Appl. 35(4), 449–462 (2007)

    Article  Google Scholar 

  22. C. Rekeczky, T. Serrano-Gotarredona, T. Roska, A. Rodriguez-Vazquez, A stored program 2nd order/3-layer complex cell CNN-UM, in Proceedings of the IEEE CNNA-2000, Catania (2000), pp. 213–217

    Google Scholar 

  23. T. Roska, J. Vandewalle (eds.), Cellular Neural Networks (Wiley, New York, 1993)

    Google Scholar 

  24. T. Roska, V. Rodriguez, Towards the visual microprocessor: VLSI design and the use of cellular neural network universal machines. Proc. IEEE 90(7), 1244–1257 (2002)

    Article  Google Scholar 

  25. T. Roska, Cellular wave computers for brain-like spatial-temporal sensory computing. IEEE Trans. Circuits Syst. 5(2), 5–19 (2005)

    Google Scholar 

  26. K. Slot, Large-neighborhood templates implementation in discrete-time CNN Universal Machine with a nearest-neighbor connection pattern, in Proceedings of the IEEE CNNA-1994, Rome (1994), pp. 213–218

    Google Scholar 

  27. Z. Voroshazi, Z. Nagy, P. Szolgay, FPGA-based real time, multichannel emulated-digital retina model implementation. EURASIP J. Adv. Signal Process. 749838 (2009)

  28. K.A. Wen, J.Y. Su, C.Y. Lu, VLSI design of digital cellular neural networks for image processing. J. Vis. Commun. Image Represent. 5(2), 117–126 (1994)

    Article  Google Scholar 

  29. C.Y. Wu, C.H. Cheng, S.H. Chen, L.J. Lin, K.H. Huang, A new structure of large-neighborhood CNN, in Proceedings of the International Joint Conference Neural Networks-2003, vol. 2, Oregon (2003), pp. 1497–1501

    Chapter  Google Scholar 

  30. Xilinx Products Home Page http://www.xilinx.com (last accessed online 7/28/2010)

  31. S.-N. Yu, C.-N. Lin, Preprocessor embedding in cellular neural network. J. Chin. Inst. Eng. 32(5), 599–611 (2009)

    Article  MathSciNet  Google Scholar 

  32. S.-N. Yu, C.-N. Lin, An efficient paradigm for wavelet-based image processing using cellular neural networks. Int. J. Circuit Theory Appl. 38(5), 527–542 (2010)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, National Chung Cheng University, Chia-Yi County, Taiwan

    Sung-Nien Yu

  2. Software Development and Platform Development Division, Compal Communications, Inc., Taipei, Taiwan

    Chien-Nan Lin

  3. Network and Multimedia Institute, Institute for Information Industry, Taipei, Taiwan

    Yun-Kai Hsu

Authors
  1. Sung-Nien Yu
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  2. Chien-Nan Lin
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  3. Yun-Kai Hsu
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Correspondence to Sung-Nien Yu.

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Yu, SN., Lin, CN. & Hsu, YK. A Configurable Digital Cellular Neural Network with Template Decomposition. Circuits Syst Signal Process 30, 463–482 (2011). https://doi.org/10.1007/s00034-010-9221-5

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  • DOI: https://doi.org/10.1007/s00034-010-9221-5

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