A reconfigurable VLSI neural network

Abstract

Due to the variety of architectures that need be considered while attempting solutions to various problems using neural networks, the implementation of a neural network with programmable topology and programmable weights has been undertaken. A new circuit block, the distributed neuron-synapse, has been used to implement a 1024 synapse reconfigurable network on a VLSI chip. In order to evaluate the performance of the VLSI chip, a complete test setup consisting of hardware for configuring the chip, programming the synaptic weights, presenting analog input vectors to the chip, and recording the outputs of the chip, has been built. Following the performance verification of each circuit block on the chip, various sample problems were solved. In each of the problems the synaptic weights were determined by training the neural network using a gradient-based learning algorithm which is incorporated in the experimental test setup. The results of this work indicate that reconfigurable neural networks built using distributed neuron synapses can be used to solve various problems efficiently. >