Abstract
Parallel hardware is an essential requirement for the implementation and study of true-size and real-time neural network applications. General purpose parallel machines, although expected to be slower than special purpose neurocomputers, are generally much more commonly available. For this reason, they present an interesting alternative for supporting neural computations. In the present work we propose a software environment for neural network computing on general purpose parallel machines. A description of the general environment is presented, based on concepts of the GALATEA neurocomputing project. Furthermore, a specific implementation on the PADMAVATI machine is discussed in some details.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
“Synopses of Information Processing Systems,”ESPRIT II projects (October 1991), project 5293: GALATEA (Neurocomputing).
“Synopses of Information Processing Systems,”ESPRIT I projects (Sept. 1990), project 967: PADMAVATI.
Houstis, E. N., T. S. Papatheodorou, S. K. Kortesis and N. B. Tsantanis (1992). A Neural Network Library for General Purpose Parallel Architectures, CTI Technical Report, TR 92.04.06.
Houstis, E. N., H. Byun and S. K. Kortesis (1991). A Workload Partitioning Strategy for Scientific Computations by Generalized Neural Networks, Proceedings of International Joint Conference on Neural Networks, IEEE.
Project GALATEA internal documentation and promotional material.
Soucek, B. and M. Soucek (1988). Neural and Massively Parallel Computers John Wiley & Sons.
ESPRIT conference proceedings 1990, paper on PADMAVATI
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer Science+Business Media New York
About this chapter
Cite this chapter
Houstis, E.N., Papatheodorou, T.S., Kortesis, S.K., Tsantanis, N.B. (1993). A Virtual Software Environment for Artificial Neural Networks on General Purpose Parallel Architectures. In: Kárný, M., Warwick, K. (eds) Mutual Impact of Computing Power and Control Theory. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-2968-2_20
Download citation
DOI: https://doi.org/10.1007/978-1-4615-2968-2_20
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-6291-3
Online ISBN: 978-1-4615-2968-2
eBook Packages: Springer Book Archive