The Back Propagation Model for neural network simulation is a very simple and very popular model for solving realworld problems. It does, however, suffer from one major problem, that is, the time taken for the network to adjust its weights in order to response to an input. This paper proposes a computer architecture, specifically optimised for neural networks simulation, which is capable of unlimited expansion without significantly degradation in unit performance
['Mike Turega']
A computer architecture to support neural net simulation