A parallel formulation of back-propagation learning on distributed memory multiprocessors
Academic Article
Overview
Identity
Additional Document Info
Other
View All
Overview
abstract
This paper presents a mapping scheme for parallel pipelined execution of the Back-propagation Learning Algorithm on distributed memory multiprocessors. The proposed implementation exhibits inter-layer or pipelined parallelism, unique to the multilayer neural networks. Simple algorithms have been presented, which allow the data transfer involved in both recall and learning phases of the back-propagation algorithm to be carried out with a small communication overhead. The effectiveness of the mapping scheme has been illustrated, by estimating the speedup of the proposed implementation on an array of T-805 transputers.