Modular neuro-chip with on-chip learning and adjustable learning parameters

摘要

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.