TRAINING SET PARALLELISM IN PAHRA ARCHITECTURE
Liberios Vokorokos, Norbert Ádám, Anton Balá · 2007
Multilayered feed-forward neural networks trained with back-propagation algorithm are one of the most popular “online” artificial neural networks. These networks are showing strong inherit parallelism because of the influence of high number of simple computational elements. So it is natural to try to implement this kind of parallelism on parallel computer architecture. The Parallel Hybrid Ring Architecture (PAHRA), which is described in this article, provides flexible platform for simulation of multilayered feed-forward neural networks trained with back-propagation algorithm. The computational model of given architecture, bound to the modified error back-propagation algorithm, allows to describe the formal elements of parallel implementation of multilayered feed-forward neural network. It also allows the mathematical tool for verification of performance, which is used in simulation experiments of multilayered feed-forward network on specific hardware platform.