Performance analysis of a pipelined backpropagation parallelalgorithm
Petrowski, A.; Dreyfus, G.; Girault, C.
Neural Networks, IEEE Transactions on
Volume 4, Issue 6, Nov 1993 Page(s):970 - 981
Digital Object Identifier 10.1109/72.286892
Summary:The supervised training of feedforward neural networks is often
based on the error backpropagation algorithm. The authors consider the
successive layers of a feedforward neural network as the stages of a
pipeline which is used to improve the efficiency of the parallel
algorithm. A simple placement rule is used to take advantage of
simultaneous executions of the calculations on each layer of the
network. The analytic expressions show that the parallelization is
efficient. Moreover, they indicate that the performance of this
implementation is almost independent of the neural network architecture.
Their simplicity assures easy prediction of learning performance on a
parallel machine for any neural network architecture. The experimental
results are in agreement with analytical estimates
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