Modular neuro-chip with on-chip learning and adjustable learning parameters |
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Authors: | Jung-Wook Cho |
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Affiliation: | (1) Department of EE, KAIST, Korea Advanced Institute of Science and Technology, 373-1 Kusungdong, Yusunggu, 305-701 Taejon, Korea |
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Abstract: | A modular analog neuro-chip with on-chip learning capability is described. Two popular learning algorithms, error back-propagation and Hebbian learning, are incorporated with adjustable learning parameters. This analog neuro-chip has a fully modular structure for easy multi-chip expansion. The numbers of synapses and neurons can be expanded by simple pin-to-pin connections without additional circuits. For effective learning, the learning rate, sigmoid slope, and ratio of Hebbian learning term to error back-propagation term can be controlled externally by digital signals. The chip is fabricated and successfully trained with gray-scale patterns as well as XOR problem. |
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Keywords: | analog neural network chip error back-propagation learning multilayer perceptron |
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