An expandable on-chip BP learning neural network chip

Abstract

An expandable on-chip back-propagation (BP) learning neural network chip is designed. The chip has four neurons and 16 synapses. Large-scale neural networks with arbitrary layers and discretional neurons per layer can be constructed by combining a certain number of such unit chips. A novel neuron circuit with programmable parameters, which generates not only the sigmoid function but also its derivative, is proposed. The neuron has a push-pull output stage to gain strong driving ability in both charge and discharge processes, which is very important in heavy load situations. An improved version of the Gilbert multiplier is also proposed. It has large linear range and accurate zero point. The chip is fabricated with a standard 0.5 μm CMOS, double-poly, double-metal technology. The results of parity experiments demonstrate its ability of on-chip BP learning.