Mapping a neural network onto a distributed image processing system

Algorithms for mapping a weight matrix of a neural layer onto distributed computer systems with a torus structure are proposed for parallel image processing. It is shown that the choice of the mapping technique depends upon the ratio between the number of neurons and the number of weight coefficients in a neuron (the number of pixels): if this ratio is sufficiently small, the column distribution is more efficient, otherwise the row distribution is more suitable. In particular for the Hopfield network, the row distribution is always more efficient than the column distribution.