Title :
A submicron analog neural network with an adjustable-level output unit
Author :
Abutalebi, A.H. ; Fakhraie, S.M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Tehran Univ., Iran
fDate :
6/20/1905 12:00:00 AM
Abstract :
A submicron feedforward analog neural network is described. This network uses submicron Gilbert multipliers for its synapses and a novel circuit based on the current-comparator circuit for its neuron. The XOR problem is solved by this network to demonstrate the capability of implementing multi-layer networks. The network is designed in a 0.5 μm technology. HSPICE simulation shows the validity of the operation of the network
Keywords :
CMOS analogue integrated circuits; SPICE; analogue multipliers; circuit simulation; current comparators; feedforward neural nets; integrated circuit design; neural chips; 0.5 mum; CMOS technology; HSPICE simulation; XOR problem; adjustable-level output unit; current-comparator circuit; multi-layer networks; neuron; submicron Gilbert multipliers; submicron analog neural network; submicron feedforward analog neural network; synapses; Artificial neural networks; Biomedical engineering; CMOS technology; Circuit simulation; Multi-layer neural network; Neural network hardware; Neural networks; Neurons; Parallel processing; Systems engineering and theory;
Conference_Titel :
Microelectronics, 1998. ICM '98. Proceedings of the Tenth International Conference on
Conference_Location :
Monastir
Print_ISBN :
0-7803-4969-5
DOI :
10.1109/ICM.1998.825622
