Parallel Architectures for Artificial Neural Networks
Paradigms and Implementations
Systems
1. Auflage November 1998
412 Seiten, Hardcover
Wiley & Sons Ltd
This excellent reference for all those involved in neural networks
research and application presents, in a single text, the necessary
aspects of parallel implementation for all major artificial neural
network models. The book details implementations on varoius
processor architectures (ring, torus, etc.) built on different
hardware platforms, ranging from large general purpose parallel
computers to custom built MIMD machines using transputers and
DSPs.
Experts who performed the implementations author the chapters and
research results are covered in each chapter. These results are
divided into three parts.
Theoretical analysis of parallel implementation schemes on MIMD
message passing machines.
Details of parallel implementation of BP neural networks on a
general purpose, large, parallel computer.
Four chapters each describing a specific purpose parallel neural
computer configuration.
This book is aimed at graduate students and researchers working in
artificial neural networks and parallel computing. Graduate level
educators can use it to illustrate the methods of parallel
computing for ANN simulation. The text is an ideal reference
providing lucid mathematical analyses for practitioners in the
field.
Torresen).
2. A Review of Parallel Implementations of Backpropagation Neural
Networks (Jim Torresen, Olav Landsverk).
I: Analysis of Parallel Implementations.
3. Network Parallelism for Backpropagation Neural Networks on a
Heterogeneous Architecture (R. Arularasan, P. Saratchandran, N.
Sundararajan, Shou King Foo).
4. Training-Set Parallelism for Backpropagation Neural Networks on
a Heterogeneous Architecture (Shou King Foo, P. Saratchandran, N.
Sundararajan).
5. Parallel Real-Time Recurrent Algorithm for Training Large Fully
Recurrent Neural Networks (Elias S. Manolakos, George
Kechriotis).
6. Parallel Implementation of ART1 Neural Networks on Processor
Ring Architectures (Elias S. Manolakos, Stylianos
Markogiannakis).
II: Implementations on a Big General-Purpose Parallel
Computer.
7. Implementation of Backpropagation Neural Networks on Large
Parallel Computers (Jim Torresen, Shinji Tomita).
III: Special Parallel Architectures and Application Case
Studies.
8. Massively Parallel Architectures for Large-Scale Neural Network
Computations (Yoshiji Fujimoto).
9. Regularly Structured Neural Networks on the DREAM Machine
(Soheil Shams, Jean-Luc Gaudiot).
10. High-Performance Parallel Backpropagation Simulation with
On-Line Learning (Urs A. Muller, Patrick Spiess, Michael Kocheisen,
Beat Flepp, Anton Gunzinger, Walter Guggenbuhl).
11. Training Neural Networks with SPERT-II (Krste Asanovic;, James
Beck, David Johnson, Brian Kingsbury, Nelson Morgan, John
Wawrzynek).
12. Concluding Remarks (N. Sundararajan, P. Saratchandran).
