Parallel Architectures for Artificial Neural Networks: Paradigms and Implementations

An excellent reference for neural networks research and application, this book covers the parallel implementation aspects of all major artificial neural network models in a single text. Parallel Architectures for Artificial Neural Networks details implementations on various processor architectures built on different hardware platforms, ranging from large, general purpose parallel computers to custom built MIMD machine.

Working experts describe their implementation research including results that are then divided into three sections:

• The theoretical analysis of parallel implementation schemes on MIMD message passing machines
• The details of parallel implementation of BP neural networks on general purpose, large, parallel computers
• Four specific purpose parallel neural computer configuration

Aimed at graduate students and researchers working in artificial neural networks and parallel computing this work can be used by graduate level educators to illustrate parallel computing methods for ANN simulation. Practitioners will also find the text an ideal reference tool for lucid mathematical analyses.