A parallel approach for backpropagation learning of neural networks

Authors
Crespo, María Liz; Piccoli, María Fabiana; Printista, Alicia Marcela; Gallard, Raúl Hector
Publication Year
1997
Language
English
Format
conference paper
Status
Published version
Description
Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly describes parallel schemes for a backpropagation algorithm and proposes a distributed system architecture for developing parallel training with a partition pattern scheme. Under this approach, weight changes are computed concurrently, exchanged between system components and adjusted accordingly until the whole parallel learning process is completed. Some comparative results are also shown.
Eje: Procesamiento distribuido y paralelo. Tratamiento de señales
Red de Universidades con Carreras en Informática (RedUNCI)
Subject
Ciencias Informáticas
Architectures
Parallel
Neural nets
Distributed
Neutral networks
parallelised backpropagation
partitioning schemes
pattern partitioning
system architecture
Access level
Open access
License
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
Repository
SEDICI (UNLP)
Institution
Universidad Nacional de La Plata
OAI Identifier
oai:sedici.unlp.edu.ar:10915/23892