Time multiplexed color image processing based on a CNN with cell-state outputs

A practical system approach for time-multiplexing cellular neural network (CNN) implementations suitable for processing large and complex images using small CNN arrays is presented. For real size applications, due to hardware limitations, it is impossible to have a one-on-one mapping between the CNN hardware cells and all the pixels in the image involved. This paper presents a practical solution by processing the input image, block by block, with the number of pixels in a block being the same as the number of CNN cells in the array. Furthermore, unlike other implementations in which the output is observed at the hard-limiting block, the very large scale integrated (VLSI) architecture hereby described monitors the outputs from the state node. While previous implementations are mostly suitable for black and white applications because of the thresholded outputs, our approach is especially suitable for applications in color (gray) image processing due to the analog nature of the state node. Experimental complementary metal-oxide-semiconductor (CMOS) chip results in good agreement with theoretical results are presented.

[1]  Leon O. Chua,et al.  Design of high-speed, high-density CNNS in cmos technology , 1992, Int. J. Circuit Theory Appl..

[2]  Tamás Roska,et al.  The CNN universal machine: an analogic array computer , 1993 .

[3]  Wai-Chi Fang,et al.  Analog VLSI design of cellular neural networks with annealing agility , 1994, Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94).

[4]  Wai-Chi Fang,et al.  Optimal solutions of selected cellular neural network applications by the hardware annealing method , 1994, Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94).

[5]  Peter R. Kinget,et al.  A programmable analog cellular neural network CMOS chip for high speed image processing , 1995, IEEE J. Solid State Circuits.

[6]  J.L. Huertas,et al.  Accurate design of analog CNN in CMOS digital technologies , 1990, IEEE International Workshop on Cellular Neural Networks and their Applications.

[7]  Tamás Roska,et al.  A fast, complex and efficient test implementation of the CNN Universal Machine , 1994, Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94).

[8]  J. Ramirez-Angulo,et al.  Wide g/sub m/ adjustment range, highly linear OTA with linear programmable current mirrors , 1992, [Proceedings] 1992 IEEE International Symposium on Circuits and Systems.

[9]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[10]  Patrick K. Simpson.  Neural Networks Applications , 1997 .

[11]  José Pineda de Gyvez,et al.  Color image processing in a cellular neural-network environment , 1996, IEEE Trans. Neural Networks.

[12]  Krzysztof Slot,et al.  Multiple-input OTA based circuit for cellular neural network-implementation in VLSI CMOS technology , 1992, CNNA '92 Proceedings Second International Workshop on Cellular Neural Networks and Their Applications.

[13]  Mancia Anguita,et al.  Analog CMOS implementation of a discrete time CNN with programmable cloning templates , 1993 .

[14]  Leon O. Chua,et al.  The CNN paradigm , 1993 .

[15]  Hubert Harrer.  Discrete time cellular neural networks , 1992, Int. J. Circuit Theory Appl..

[16]  Leon O. Chua,et al.  Cellular neural networks: applications , 1988 .

[17]  J.L. Huertas,et al.  Analog VLSI implementation of cellular neural networks , 1992, CNNA '92 Proceedings Second International Workshop on Cellular Neural Networks and Their Applications.

[18]  J.L. Huertas,et al.  Switched-current techniques for image processing cellular neural networks in MOS VLSI , 1992, [Proceedings] 1992 IEEE International Symposium on Circuits and Systems.

[19]  S. Graffi,et al.  CMOS implementation of an analogically programmable cellular neural network , 1993 .

[20]  Jaime Ramirez-Angulo,et al.  Extended transconductance adjustment/linearisation technique , 1991 .

[21]  Edgar Sanchez-Sinencio,et al.  A current-mode cellular neural network implementation , 1993 .

[22]  K. Halonen,et al.  The non-idealities of the IC-realization and the stability of CNN-networks , 1990, IEEE International Workshop on Cellular Neural Networks and their Applications.

[23]  Josef A. Nossek,et al.  An analog implementation of discrete-time cellular neural networks , 1992, IEEE Trans. Neural Networks.

[24]  Leon O. Chua,et al.  Cellular neural networks: Theory and circuit design , 1992, Int. J. Circuit Theory Appl..

[25]  J. Pineda de Gyvez,et al.  Time-multiplexing CNN simulator , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.