Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 10
2950-2956,2961

Parallel deep convolution neural network optimization based on Im2col

Hu Jian1,2
Gong Ke1
Mao Yimin1
Chen Zhigang3
Chen Liang2
1. School of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
2. School of Electronic Information Engineering, Gannan University of Science & Technology, Ganzhou Jiangxi 341000, China
3. School of Computer Science & Engineering, Central South University, Changsha 410083, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0114

Abstract

In the large data environment, there are many problems in the parallel deep convolution neural network(DCNN) algorithm, such as excessive data redundancy, slow convolution layer operation and poor convergence of loss function. This paper proposed a parallel deep convolution neural network optimization algorithm based on Im2col method(IA-PDCNNOA). Firstly, the algorithm proposed a parallel feature extraction strategy based on Marr-Hildreth operator to extract target features from data as input of convolution neural network, which could effectively avoid the problem of excessive data redundancy. Secondly, it designed a parallel model training strategy based on Im2col method, which removed the redundant convolution kernel by designing the Mahalanobis distance center value and improved the convolution layer operation speed by combining MapReduce and Im2col methods. Finally, it proposed an improved small-batch gradient descent strategy, which eliminated the effect of abnormal data on the batch gradient and solved the problem of poor convergence of the loss function. The experimental results show that IA-PDCNNOA algorithm performs well in deep convolution neural network calculation under large data environment and is suitable for parallel DCNN model training of large datasets.

Foundation Support

科技创新2030—“新一代人工智能”重大项目(2020AAA0109605)
国家自然科学基金资助项目(41562019)
江西省教育厅科技项目(GJJ209405,GJJ209406,GJJ209407)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0114
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 10
Section: Algorithm Research & Explore
Pages: 2950-2956,2961
Serial Number: 1001-3695(2022)10-009-2950-07

Publish History

[2022-05-23] Accepted Paper
[2022-10-05] Printed Article

Cite This Article

胡健, 龚克, 毛伊敏, 等. 基于Im2col的并行深度卷积神经网络优化算法 [J]. 计算机应用研究, 2022, 39 (10): 2950-2956,2961. (Hu Jian, Gong Ke, Mao Yimin, et al. Parallel deep convolution neural network optimization based on Im2col [J]. Application Research of Computers, 2022, 39 (10): 2950-2956,2961. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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