An analog feed-forward neural network with on-chip learning

An analog continuous-time neural network with on-chip learning is presented. The 4-3-2 feed-forward network with a modified back-propagation learning scheme was build using micropower building blocks in a double poly, double metal 2 CMOS process. The weights are stored in non-volatile UV-light programmable analog floating gate memories. A differential signal representation is used to design simple building blocks which may be utilized to build very large neural networks. Measured results from on-chip learning are shown and an example of generalization is demonstrated. The use of micro-power building blocks allows very large networks to be implemented without significant power consumption.