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Computer-Sensors: Spatial-Temporal Computers for Analog Array Signals, Dynamically Integrated with Sensors

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Abstract

In this paper, first, an overview is given about the whole scenario of analogic CNN computing, as a paradigm of Spatial-temporal Instruction Set Computer (StISC) operating on flows of signal arrays. Next, two areas on CNN Computing Technology are considered briefly: (i) the architectural advances, especially the variable resolution and adaptation in space, time, and value and (ii) the computational infrastructure from high level language and compiler to physical implementations. Three basic physical implementations are supposed: analogic CMOS, emulated digital CMOS and optical. The computational infrastructure is the same for all implementations, except the physical interfaces. Finally, the systematic description of the Non-equilibrium Spatial-temporal (NEST) algorithms is given, as a new way of array signal processing, and some practical aspects of NEST algorithms are discussed.

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References

  1. G. Linán, S. Espejo, R. Dominguez-Castro, E. Roca, and A. Rodriguez-Vázquez, “CNNUC3: A mixed signal 64 × 64 CNN universal chip,” Proceedings of MicroNeuro, pp. 61–68, Granada, 1999.

  2. P. Saffo, “Sensors: The next wave of infotech innovation,” Institute for the Future, Menlo Park, 1998.

  3. L.O. Chua and L. Yang, “Cellular neural networks: Theory and applications,” IEEE Transactions on Circuits and Systems, Vol. 35, pp. 1257–1290, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  4. T. Roska, J. Hámori, E. Lábos, K. Lotz, L. Orzó, J. Takács, P. Venetianer, Z. Vidnyánszky and Á. Zarándy, “The use of CNN models in the subcortical visual pathway,” Special Issue on Cellular Neural Networks, IEEE Trans. Circuits and Systems-I, Vol. 40. No. 3. pp. 182–195, 1993.

    Article  MATH  Google Scholar 

  5. T. Roska and L.O. Chua, “The CNN universal machine: An analogic array computer,” IEEE Transactions on Circuits and Systems-II, Vol. 40, pp. 163–173, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  6. F. Werblin, T. Roska, and L.O. Chua, “The analogic cellular neural network as a bionic eye,” Int. J. Circuit Theory and Applications, Vol. 23, pp. 541–549, 1995.

    Article  Google Scholar 

  7. L.O. Chua and T. Roska, Cellular Neural Networks-Foundations and Primer, Lecture Notes EE-129, Berkeley, 1997-99 (under publication).

  8. L.O. Chua and T. Roska, “The CNN paradigm,” IEEE Transactions on Circuits and Systems-I, Vol. CAS-40, pp. 147–156, 1993.

    Article  MathSciNet  Google Scholar 

  9. R. Dominguez-Castro, S. Espejo, A. Rodriguez-Vazquez, and R. Carmona, “A CNN universal chip in CMOS technology,” Proc. of the Third IEEE Int. Workshop on Cellular Neural Networks and their Application (CNNA-94), Rome, pp. 91–96, 1994.

  10. J.M. Cruz, L.O. Chua, and T. Roska, “A fast, complex and efficient test implementation of the CNN universal machine,” Proc. of the Third IEEE Int. Workshop on Cellular Neural Networks and their Application (CNNA-94), Rome, pp. 61–66, Dec. 1994.

  11. A. Paasio, A. Dawidziuk, K. Halonen, and V. Porra, “Minimumsize 0.5 micron CMOS programmable 48 by 48 test chip,” Proceedings of the ECCTD-97, pp. 154–156, 1997.

  12. R. Carmona, A. Rodriguez-Vázquez, S. Espejo, R. Dominguez-Castro, T. Roska, T. Kozek, and L.O. Chua, “An 0.5 micron CMOS analog random access memory for TeraOPS speed multimedia video processing, IEEE Transactions on Multimedia, Vol. 1, pp. 121–135, 1999.

    Article  Google Scholar 

  13. L. Blum, F. Cucker, M. Shub, and S. Smale, Complexity and Real Computation, Springer, New York, 1998.

    Book  Google Scholar 

  14. T. Roska and L.O. Chua, “On a framework of complexity of computations on flows implemented on the CNN universal machine,” Technical Report, DNS-15-1995, Computer and Automation Institute, Budapest, 1995.

    Google Scholar 

  15. T. Szirányi and M. Csapodi, “Texture classification by CNN and genetic learning,” Proc. of IEEE Int. Conf. Pattern Recognition, Jerusalem, ICPR'94, Vol. III, pp. 381–383, 1994.

    Google Scholar 

  16. T. Roska, “CNN chip set architecture and the visual mouse,” in Proceedings of 4th International Workshop on Cellular Neural Networks and Their Applications, Seville, pp. 369–374, June 1996.

  17. Cs. Rekeczky, Á. Tahy, Z. Végh, and T. Roska, “CNN-based spatio-temporal nonlinear filtering and endocardial boundary detection in echocardiography,” Proc. ECCTD-97, Vol. II, pp. 667–672, 1997; Int. J. Circuit Theory and Appl.,Vol. 27, pp. 171-207, 1999.

    Google Scholar 

  18. T. Kozek, Chai Wah Wu, Á. Zarándy, Hua Chen, T. Roska, M. Kunt, and L.O. Chua, “New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 7, No. 4, pp. 606–614, Aug. 1997.

    Article  Google Scholar 

  19. A. Schultz, Cs. Rekeczky, I. Szatmári, T. Roska, and L.O. Chua, “Spatio-temporal CNN algorithms for object segmentation and object recognition,” in Proceedings of 4th International Workshop on Cellular Neural Networks and Their Applications, pp. 369–374, London, April 1998.

  20. T. Kozek, T. Roska, and L.O. Chua, “Genetic algorithm forCNN template learning,” IEEE Transactions on Circuits and Systems, Vol. 40, No. 6, pp. 392–402, 1993.

    Article  Google Scholar 

  21. P. Szolgay, I. Szatmári, and K. László, “A fast fixed point learning method to implement associative memory on CNN's,” IEEE Trans. on Circuits and Systems, Ser. I., Vol. 44, pp. 362–366, 1997.

    Article  Google Scholar 

  22. CADETWin (CNN Applications Development Environment and Toolkit), User's Guide, Analogical and Neural Computing Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences (MTA-SzTAKI), Budapest, 1997.

  23. CCPS; (CNN Chip Prototyping System), User's Guide, Analogical and Neural Computing Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences (MTA-SzTAKI), Budapest, 1997.

  24. "CNN Software Library (Templates, subroutines, and Algorithms) Version 8.1,” T. Roska, L. Kék, L. Nemes, and A. Zarándy (Eds.), Computer and Automation Institute of the Hungarian Academy of Sciences, Budapest, 1999.

    Google Scholar 

  25. P. Szolgay, G. Vörös, and Gy. Eröss, “Applications of the cellular neural network paradigm in mechanical vibrating systems,” IEEE Transactions on Circuits and Systems-I, Vol. 40, pp. 222–227, March 1993.

    Article  MATH  Google Scholar 

  26. T. Roska, L.O. Chua, D. Wolf, T. Kozek, R. Tetzlaff, and F. Puffer, “Simulating nonlinear waves and partial differential equations via CNN-Part I: Basic Techniques,” ibid, Vol. 42, pp. 807–815, Oct. 1995.

    Article  Google Scholar 

  27. T. Kozek, L.O. Chua, T. Roska, D. Wolf, R. Tetzlaff, F. Putter, and K. Lotz, “Simulating nonlinearwaves and partial differential equations via CNN-Part II: Typical examples,” ibid, Vol. 42, pp. 816–821, Oct. 1995.

    Article  Google Scholar 

  28. Á. Zarándy, A. Stoffels, T. Roska, and L.O. Chua: "Implementation of binary and gray-scale mathematical morphology on the CNN universal machine,” IEEE Transactions on Circuits and Systems-I, Vol. 8, pp. 1998.

  29. L. Alvarez and J.-M. Morel, “Morphological approach to multiscale analysis,” in Geometry-Driven Diffusion in Computer Vision, B.M.H. Romeny (Ed.), Kluwer Academic Publishers, pp. 229–249, 1994.

  30. P. Maragos, “Differential morphology and image processing,” IEEE Trans. Image Processing, Vol. 5, pp. 922–937, 1966.

    Article  Google Scholar 

  31. R. Malladi and J.A. Sethian, “A unified approach to noise removal, image enhancement, and shape recovery,” ibid, pp. 1554–1568, 1996.

  32. L.O. Chua, “CNN-A paradigm for complexity, Part I, Part II, and Part III,” Int. J. Bifurcation and Chaos, Vol. 7, Aug. 1997.

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  1. Analogical and Neural Computing Laboratory, Hungarian Academy of Sciences, H-1111, Kende u. 13-17, Budapest, Hungary

    Tamás Roska

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  1. Tamás Roska
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Roska, T. Computer-Sensors: Spatial-Temporal Computers for Analog Array Signals, Dynamically Integrated with Sensors. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 23, 221–237 (1999). https://doi.org/10.1023/A:1008132715897

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