Parallel implementation of backpropagation neural network on a heterogeneous ring processor topology

This paper analyzes the parallel mapping of the backpropagation learning algorithm, onto a heterogeneous multiprocessor ring architecture. Training set parallelism is used as the parallelizing paradigm. A mathematical model is developed in order to obtain an expression for the training time per epoch. This model is then used to find the optimal mapping that minimizes the training time per epoch. It is shown that the optimal mapping results in a mixed integer programming (MIP) problem which is NP-complete. To solve this problem, the genetic algorithmic approach is used and the optimal distribution of training patterns are obtained. A Monte Carlo study is then carried out which statistically verify the proximity of these optimal solutions to the global optimum, for the MIP problem