PDCNN-MRW: a parallel Winograd convolutional neural network algorithm base on MapReduce

Parallel deep convolutional neural network (DCNN) algorithms have been widely used in the field of big data, but there are still some problems: excessive computation of redundant features, insufficient performance of convolution operation, and poor merging ability of parameter parallelization. Based...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 15; no. 5; pp. 1949 - 1966
Main Authors Wen, Zhanqing, Mao, Yimin, Dai, Jingguo
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Artificial neural networks
Big Data
Classification
Codes
Complex Systems
Computation
Computational Intelligence
Control
Data processing
Efficiency
Engineering
Lung diseases
Mechatronics
Neural networks
Original Article
Parameters
Pattern Recognition
Redundancy
Robotics
Systems Biology
Parallel DCNN algorithm
Load balancing
MapReduce
Parallel Winograd convolution
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Summary:Parallel deep convolutional neural network (DCNN) algorithms have been widely used in the field of big data, but there are still some problems: excessive computation of redundant features, insufficient performance of convolution operation, and poor merging ability of parameter parallelization. Based on the above problems, a parallel DCNN algorithm based on MapReduce and Winograd convolution (PDCNN-MRW) is proposed in this paper, which contains three parts. First, a feature selection method based on cosine similarity and normalized mutual information (FS-CSNMI) is proposed, which reduces redundant feature computation between channels and avoids excessive redundant feature computation. Next, a parallel Winograd convolution method base on MapReduce (PWC-MR) is presented to address the insufficient convolution performance by reducing the number of multiplications. Finally, a load balancing method based on multiway tree and task migration (LB-MTTM) is developed, which improves the capability of parameter merging by balancing the load between nodes and reducing the response time of the cluster. We compared the PDCNN-MRW algorithm with other algorithms on four datasets, including ISIC 2019, BloodCellImages, PatchCamelyon, and ImageNet 1K. Experiment shows that the proposed algorithm has lower training costs and higher efficiency than other parallel DCNN algorithms.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-023-02007-0