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
Salerno, M., Sargeni, F., Bonaiuto, V. (1997). A 720 cells interconnection-oriented system for cellular neural networks. In n. Anon (a cura di), Proceedings of IEEE international symposium on circuits and systems, 1997. ISCAS '97 (Volume 1) (pp. 681-684). IEEE, Piscataway, NJ, United States [10.1109/ISCAS.1997.608945].
A 720 cells interconnection-oriented system for cellular neural networks
SALERNO, MARIO;SARGENI, FAUSTO;BONAIUTO, VINCENZO
1997-01-01
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 CNNI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
