article · IEEE Transactions on Neural Networks · 1993
Performance analysis of a pipelined backpropagation parallel algorithm
Alain Pétrowski, Gérard Dreyfus, Claude Girault
Cited by 62Abstract and details only
Abstract
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.
Cite this paper
Pétrowski, A., Dreyfus, G., & Girault, C. (1993). Performance analysis of a pipelined backpropagation parallel algorithm. IEEE Transactions on Neural Networks, 4(6), 970–981. https://doi.org/10.1109/72.286892
Pétrowski, Alain, et al. “Performance analysis of a pipelined backpropagation parallel algorithm.” IEEE Transactions on Neural Networks, vol. 4, no. 6, 1993, pp. 970–81, https://doi.org/10.1109/72.286892.
Pétrowski, Alain, Gérard Dreyfus, and Claude Girault. 1993. “Performance analysis of a pipelined backpropagation parallel algorithm.” IEEE Transactions on Neural Networks 4 (6): 970–81. https://doi.org/10.1109/72.286892.
Pétrowski, A., Dreyfus, G. and Girault, C. (1993) ‘Performance analysis of a pipelined backpropagation parallel algorithm’, IEEE Transactions on Neural Networks, 4(6), pp. 970–981. Available at: https://doi.org/10.1109/72.286892.
@article{petrowski1993performance,
title = {{Performance analysis of a pipelined backpropagation parallel algorithm}},
author = {Pétrowski, Alain and Dreyfus, Gérard and Girault, Claude},
journal = {IEEE Transactions on Neural Networks},
volume = {4},
number = {6},
pages = {970--981},
year = {1993},
doi = {10.1109/72.286892}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.