Title :
A 720 cells interconnection-oriented system for cellular neural networks
Author :
Salerno, Mario ; Sargeni, Fausto ; Bonaiuto, Vincenzo
Author_Institution :
Dept. of Electron. Eng., Rome Univ., Italy
Abstract :
Cellular Neural Networks (CNN´s) represent a remarkable improvement in hardware implementation of Artificial Neural Networks. In fact, their regular structure and their local connectivity feature make this class of neural networks really appealing for VLSI implementations. The CNN are widely used in several application fields, such as image processing and pattern recognition. In this research area, the authors presented a fully digitally programmable CNN chip with 6×6 cells (6×6DPCNN chip). In this paper, a system with twenty of these chips will be presented. This system is made up of twenty boards with one 6×6DPCNN chip each. Its main features are: fully programmability of the templates; digital input/output for logic operation; analog outputs for dynamic analysis; implementation of space-variant as well as space-invariant CNN
Keywords :
VLSI; cellular neural nets; neural chips; VLSI; artificial neural network; cellular neural network; digitally programmable CNN chip; interconnection-oriented system; Artificial neural networks; Cellular neural networks; Image processing; Logic; Neural network hardware; Nonlinear equations; Pattern recognition; Transconductance; Very large scale integration; Voltage;
Conference_Titel :
Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on
Print_ISBN :
0-7803-3583-X
DOI :
10.1109/ISCAS.1997.608945