VLSI neural network with digital weights and analog multipliers

Abstract

A VLSI feedforward neural network is presented that makes use of digital weights and analog multipliers. The network is trained in a chip-in-loop fashion with a host computer implementing the training algorithm. The chip uses a serial digital weight bus implemented by a long shift register to input the weights. The inputs and outputs of the network are provided directly at pins on the chip. The training algorithm used is a parallel weight perturbation technique. Training results are shown for a 2 input, 1 output network trained with an AND function, and for a 2 input, 2 hidden unit, I output network trained with an XOR function.

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