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.

[2022-05-23] Accepted Paper /doc/2022.03.0114/v1

[2022-10-05] Printed Article /doc/2022.03.0114/v2