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Learning process is essential for good performance when a neural network is applied to a practical application. The backpropagation algorithm is a well-known learning method widely used in most neural networks. However, since the backpropagation algorithm is time-consuming, much research have been done to speed up the process. The block backpropagation algorithm, which seems to be more efficient than the backpropagation, is recently proposed by Coetzee in [2]. In this paper, we propose an efficient parallel algorithm for the block backpropagation method and its performance model in mesh-connected parallel computer systems. The proposed algorithm adopts master-slave model for weight broadcasting and data parallelism for computation of weights. In order to validate our performance model, a neural network is implemented for printed character recognition application in the TiME which is a prototype parallel machine consisting of 32 transputers connected in mesh topology. It is shown that speedup by our performance model is very close to that by experiments.

- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.8 pp.1622-1630

- Publication Date
- 2000/08/25

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Theory and Models of Software

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Han-Wook LEE, Chan-Ik PARK, "An Efficient Parallel Block Backpropagation Learning Algorithm in Transputer-Based Mesh-Connected Parallel Computers" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 8, pp. 1622-1630, August 2000, doi: .

Abstract: Learning process is essential for good performance when a neural network is applied to a practical application. The backpropagation algorithm is a well-known learning method widely used in most neural networks. However, since the backpropagation algorithm is time-consuming, much research have been done to speed up the process. The block backpropagation algorithm, which seems to be more efficient than the backpropagation, is recently proposed by Coetzee in [2]. In this paper, we propose an efficient parallel algorithm for the block backpropagation method and its performance model in mesh-connected parallel computer systems. The proposed algorithm adopts master-slave model for weight broadcasting and data parallelism for computation of weights. In order to validate our performance model, a neural network is implemented for printed character recognition application in the TiME which is a prototype parallel machine consisting of 32 transputers connected in mesh topology. It is shown that speedup by our performance model is very close to that by experiments.

URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_8_1622/_p

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@ARTICLE{e83-d_8_1622,

author={Han-Wook LEE, Chan-Ik PARK, },

journal={IEICE TRANSACTIONS on Information},

title={An Efficient Parallel Block Backpropagation Learning Algorithm in Transputer-Based Mesh-Connected Parallel Computers},

year={2000},

volume={E83-D},

number={8},

pages={1622-1630},

abstract={Learning process is essential for good performance when a neural network is applied to a practical application. The backpropagation algorithm is a well-known learning method widely used in most neural networks. However, since the backpropagation algorithm is time-consuming, much research have been done to speed up the process. The block backpropagation algorithm, which seems to be more efficient than the backpropagation, is recently proposed by Coetzee in [2]. In this paper, we propose an efficient parallel algorithm for the block backpropagation method and its performance model in mesh-connected parallel computer systems. The proposed algorithm adopts master-slave model for weight broadcasting and data parallelism for computation of weights. In order to validate our performance model, a neural network is implemented for printed character recognition application in the TiME which is a prototype parallel machine consisting of 32 transputers connected in mesh topology. It is shown that speedup by our performance model is very close to that by experiments.},

keywords={},

doi={},

ISSN={},

month={August},}

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TY - JOUR

TI - An Efficient Parallel Block Backpropagation Learning Algorithm in Transputer-Based Mesh-Connected Parallel Computers

T2 - IEICE TRANSACTIONS on Information

SP - 1622

EP - 1630

AU - Han-Wook LEE

AU - Chan-Ik PARK

PY - 2000

DO -

JO - IEICE TRANSACTIONS on Information

SN -

VL - E83-D

IS - 8

JA - IEICE TRANSACTIONS on Information

Y1 - August 2000

AB - Learning process is essential for good performance when a neural network is applied to a practical application. The backpropagation algorithm is a well-known learning method widely used in most neural networks. However, since the backpropagation algorithm is time-consuming, much research have been done to speed up the process. The block backpropagation algorithm, which seems to be more efficient than the backpropagation, is recently proposed by Coetzee in [2]. In this paper, we propose an efficient parallel algorithm for the block backpropagation method and its performance model in mesh-connected parallel computer systems. The proposed algorithm adopts master-slave model for weight broadcasting and data parallelism for computation of weights. In order to validate our performance model, a neural network is implemented for printed character recognition application in the TiME which is a prototype parallel machine consisting of 32 transputers connected in mesh topology. It is shown that speedup by our performance model is very close to that by experiments.

ER -