Fully-Programmable Analogue VLSI Devices for the Implementation of Neural Networks

Abstract

A neural network is a massively parallel array of simple computational units (neurons) that models some of the functionality of the human nervous system and attempts to capture some of its computational strengths (see Grossberg 1968, Hopfield 1982, Lippmann 1987). The abilities that a synthetic neural net might aspire to mimic include the ability to consider many solutions simultaneously, the ability to work with corrupted or incomplete data without explicit error-correction, and a natural fault-tolerance. This latter attribute, which arises from the parallelism and distributed knowledge representation gives rise to graceful degradation as faults appear. This is attractive for VLSI.