Abstract
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 2μ 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.
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Berg, Y., Sigvartsen, R.L., Lande, T.S. et al. An analog feed-forward neural network with on-chip learning. Analog Integr Circ Sig Process 9, 65–75 (1996). https://doi.org/10.1007/BF00158853
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DOI: https://doi.org/10.1007/BF00158853