IEEE Circuits and Systems Magazine - Q2 2018 - 89

References
[1] L. O. Chua and L. Yang, "Cellular neural networks: Applications,"
IEEE Trans. Circuits Syst., vol. 35, no. 10, pp. 1273-1290, 1988.
[2] T. Roska and L. O. Chua, "The CNN universal machine: An analogic
array computer," IEEE Trans. Circuits Syst. II, vol. 40, no. 3, pp. 163-173,
1993.
[3] Sps02 toshiba smart photosensor. [Online]. Available: http://www.
toshiba-teli.co.jp/en/products/industrial/sps/sps.htm
[4] L. O. Chua and T. Roska, "The CNN paradigm," IEEE Trans. Circuits
Syst. I, vol. 40, no. 3, pp. 147-156, 1993.
[5] L. O. Chua and T. Roska, Cellular Neural Networks and Visual Computing: Foundations and Applications. Cambridge, U.K.: Cambridge Univ.
Press, 2002.
[6] CNN software library v3.1. [Online]. Available: http://cnn-technology.itk.ppke.hu/Template_library_v3.1.pdf
[7] L. O. Chua, T. Roska, and P. L. Venetianer, "The CNN is universal
as the turing machine," IEEE Trans. Circuits Syst. I, vol. 40, no. 4, pp.
289-291, 1993.
[8] L. Nemes, L. Chua, and T. Roska, "Implementation of arbitrary boolean functions on a CNN universal machine," Int. J. Circuit Theory Appl.,
vol. 26, no. 6, pp. 593-610, 1998.
[9] Z. Vörösházi, Z. Nagy, and P. Szolgay, "Fpga-based real time, multichannel emulated-digital retina model implementation," EURASIP J.
Adv. Signal Process., vol. 2009, p. 6, 2009.
[10] A. Kis, F. Kovács, and P. Szolgay, "3D tactile sensor array processed
by CNN-UM: A fast method for detecting and identifying slippage and
twisting motion," Int. J. Circuit Theory Appl., vol. 34, no. 4, pp. 517-531,
2006.
[11] R. Kunz, R. Tetzlaff, and D. Wolf, "SCNN: A universal simulator for
cellular neural networks," in Proc. 4th IEEE Int. Workshop Cellular Neural
Networks and Applications,, pp. 255-259.
[12] H. Harrer, J. A. Nossek, T. Roska, and L. O. Chua, "A current-mode
DTCNN universal chip," in Proc. IEEE Int. Symp. Circuits and Systems,
1994, vol. 4, pp. 135-138.
[13] R. Tetzlaff, C. Niederhofer, and P. Fischer, "Feature extraction in
epilepsy using a cellular neural network based device-first results," in
Proc. Int. Symp. Circuits and Systems, 2003, vol. 3, pp. III-III.
[14] F. Gollas, C. Niederhofer, and R. Tetzlaff, "Prediction of brain electrical activity in epilepsy using a higher-dimensional prediction algorithm for discrete time cellular neural networks (DTCNN)," in Proc. Int.
Symp. Circuits and Systems, 2004, vol. 5, pp. V-V.
[15] T. Roska, A. Zarandy, S. Zold, P. Foldesy, and P. Szolgay, "The computational infrastructure of analogic CNN computing. I. The CNN-UM
chip prototyping system," IEEE Trans. Circuits Syst. I, vol. 46, no. 2, pp.
261-268, 1999.
[16] S. Espejo, A. Rodriguez-Vazquez, R. Carmona, P. Foldesy, A. Zarandy, P. Szolgay, T. Sziranyi, and T. Roska, "0.8 um CMOS two dimensional
programmable mixed-signal focal-plane array processor with on-chip
binary imaging and instruction storage," IEEE J. Solid State Circuits, vol.
32, no. 7, pp. 1013-1026, 1997.
[17] J. Cruz, L. Chua, and T. Roska, "A fast, complex and efficient test
implementation of the CNN universal machine," in Proc. IEEE 3rd Int.
Workshop Cellular Neural Networks and Applications, 1994, pp. 61-66.
[18] A. Paasio, A. Dawidziuk, K. Halonen, and V. Porra, "Minimum size
0.5 micron CMOS programmable 48 by 48 CNN test chip," in Proc. European Conf. Circuit Theory and Design, 1997, vol. 1, pp. 154-156.
[19] A. Zarandy, R. Dominguez-Castro, and S. Espejo, "Ultra-high frame
rate focal plane image sensor and processor," IEEE Sensors J., vol. 2, no.
6, pp. 559-565, 2002.
[20] G. L. Cembrano, Á. Rodríguez-Vázquez, S. Espejo Meana, and R.
Domínguez-Castro, "Ace16k: A 128 × 128 focal plane analog processor
with digital i/o," Int. J. Neural Syst., vol. 13, no. 6, pp. 427-434, 2003.
[21] Á. Zarándy and C. Rekeczky, "Bi-i: A standalone ultra high speed
cellular vision system," IEEE Circuits Syst. Mag., vol. 5, no. 2, pp. 36-45,
2005.
[22] A. Rodríguez-Vázquez, R. Domínguez-Castro, F. Jiménez-Garrido,
S. Morillas, A. García, C. Utrera, M. D. Pardo, J. Listan, and R. Romay,
"A CMOS vision system on-chip with multi-core, cellular sensory-processing front-end," in Cellular Nanoscale Sensory Wave Computing. New
York: Springer, 2010, pp. 129-146.

SECOND quartEr 2018

[23] J. Poikonen, M. Laiho, and A. Paasio, "Mipa4k: A 64 × 64 cell mixedmode image processor array," in Proc. IEEE Int. Symp. Circuits and Systems, 2009, pp. 1927-1930.
[24] Kova1 sensor (kovilta) [Online]. Available: http://www.kovilta.fi/
tech.html
[25] A. Lopich and P. Dudek, "A simd cellular processor array vision
chip with asynchronous processing capabilities," IEEE Trans. Circuits
Syst. I, vol. 58, no. 10, pp. 2420-2431, 2011.
[26] S. J. Carey, A. Lopich, D. R. Barr, B. Wang, and P. Dudek, "A 100,000
fps vision sensor with embedded 535 gops/w 256 × 256 SIMD processor array," in Proc. Symp. Very-Large-Scale Integration Circuits, 2013, pp.
C182-C183.
[27] P. Földesy, Á. Zarándy, C. Rekeczky, and T. Roska, "Digital implementation of cellular sensor-computers," Int. J. Circuit Theory Appl., vol.
34, no. 4, pp. 409-428, 2006.
[28] P. Keresztes, Á. Zarándy, T. Roska, P. Szolgay, T. Bezák, T. Hidvégi,
P. Jónás, and A. Katona, "An emulated digital CNN implementation," J.
VLSI Signal Process., vol. 23, no. 2, pp. 291-303, 1999.
[29] Z. Nagy and P. Szolgay, "Configurable multilayer CNN-UM emulator
on FPGA," IEEE Trans. Circuits Syst. I, vol. 50, no. 6, pp. 774-778, 2003.
[30] R. Yeniceri and M. E. Yalcin, "An emulated digital wave computer
core implementation," 2009.
[31] R. Braunschweig, J. Muller, J. Muller, and R. Tetzlaff, "Nero mastering 300k CNN cells," 2013, pp. 1-4.
[32] Á. Zarándy, "The art of CNN template design," Int. J. Circuit Theory
Appl., vol. 27, no. 1, pp. 5-23, 1999.
[33] N. Takahashi and L. O. Chua, "On the complete stability of nonsymmetric cellular neural networks," IEEE Trans. Circuits Syst. I, vol. 45, no.
7, pp. 754-758, 1998.
[34] M. Forti and A. Tesi, "New conditions for global stability of neural networks with application to linear and quadratic programming
problems," IEEE Trans. Circuits Syst. I, vol. 42, no. 7, pp. 354 -366,
1995.
[35] L. O. Chua and C. W. Wu, "On the universe of stable cellular neural
networks," Int. J. Circuit Theory Appl., vol. 20, no. 5, pp. 497-517, 1992.
[36] M. Radványi, B. Varga, and K. Karacs, "Advanced crosswalk detection for the bionic eyeglass," in Proc. 12th Int. Workshop Cellular Nanoscale Networks Applications, 2010, pp. 1-5.
[37] (2016). SRC benchmarking center. SRC. [Online]. Available: https://
www.src.org/program/nri/benchmarking/
[38] A. Horváth, M. Hillmer, Q. Lou, X. S. Hu, and M. Niemier, "Cellular neural network friendly convolutional neural networks: CNNs with
CNNs," in Proc. IEEE Design, Automation and Test Europe Conf. and Exhibition, 2017, pp. 145-150.
[39] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based
learning applied to document recognition," Proc. IEEE, vol. 86, no. 11,
pp. 2278-2324, Nov. 1998.
[40] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," arXiv Preprint,
arXiv:1512.00567, 2015.
[41] S. Esser, R. Appuswamy, P. A. Merolla, J. V. Arthur, and D. S. Modha,
"Backpropagation for energy-efficient neuromorphic computing," in
Proc. Neural Information Processing Systems, 2015, pp. 1-9.
[42] W. Wen, C. Wu, Y. Wang, K. Nixon, Q. Wu, M. Barnell, H. Li, and Y.
Chen. (2016). "A new learning method for inference accuracy, core occupation, and performance co-optimization on truenorth chip," in Proc.
53rd Annual Design Automation Conf., New York, pp. 18:1-18:6. [Online].
Available: http://doi.acm.org/10.1145/2897937.2897968
[43] S. Kocsárdi, Z. Nagy, Á. Csík, and P. Szolgay, "Two-dimensional compressible flow simulation on emulated digital CNN-UM," in Proc. IEEE
11th Int. Workshop Cellular Neural Networks and Applications, 2008, pp.
169-174.
[44] Z. Nagy, C. Nemes, A. Hiba, Á. Csík, A. Kiss, M. Ruszinkó, and P. Szolgay, "Accelerating unstructured finite volume computations on fieldprogrammable gate arrays," Concurrency Comput. Pract. Exp., vol. 26,
no. 3, pp. 615-643, 2014.
[45] C. Nemes, G. Barcza, Z. Nagy, Ö. Legeza, and P. Szolgay, "The density matrix renormalization group algorithm on kilo-processor architectures: Implementation and trade-offs," Comput. Phys. Commun., vol.
185, no. 6, pp. 1570-1581, 2014.

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http://www.kovilta.fi/tech.html http://www.kovilta.fi/tech.html http://www.toshiba-teli.co.jp/en/products/industrial/sps/sps.htm http://www.toshiba-teli.co.jp/en/products/industrial/sps/sps.htm http://cnn-technology.itk.ppke.hu/Template_library_v3.1.pdf http://cnn-technology.itk.ppke.hu/Template_library_v3.1.pdf https://www.src.org/program/nri/benchmarking/ https://www.src.org/program/nri/benchmarking/ http://doi.acm.org/10.1145/2897937.2897968
Table of Contents for the Digital Edition of IEEE Circuits and Systems Magazine - Q2 2018
