scholarly journals Computational Resource Consumption in Convolutional Neural Network Training – A Focus on Memory

2021 ◽  
Vol 8 (1) ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Resource Consumption ◽  
Neural Network Training ◽  
Computational Resource ◽  
Network Training

scholarly journals Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs

2020 ◽  
Vol 12 (5) ◽  
pp. 1-15
Author(s):  
Zhenghao Han ◽  
Li Li ◽  
Weiqi Jin ◽  
Xia Wang ◽  
Gangcheng Jiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Neural Network Training ◽  
Network Training ◽  
Color Restoration ◽  
Image Pairs

scholarly journals Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture

2021 ◽  
Vol 2062 (1) ◽  
pp. 012008
Author(s):  
Sunil Pandey ◽  
Naresh Kumar Nagwani ◽  
Shrish Verma
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Processing Unit ◽  
Neural Network Training ◽  
Central Processing ◽  
Network Training ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Performance Computing

Abstract The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neural network implementations have been observed to train to nearly 80% training accuracy in 200 epochs. In effect, therefore, the comparison is on the time taken to complete the training epochs.

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