DISTRIBUTED CONVOLUTIONAL NEURAL NETWORK MODEL ON RESOURCE-CONSTRAINED CLUSTER
| Autor: | Rezeda R. Khaydarova, Dmitry I. Mouromtsev, Maxim V. Lapaev, Vladislav D. Fishenko |
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| Jazyk: | English<br />Russian |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki, Vol 20, Iss 5, Pp 739-746 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2226-1494 2500-0373 |
| DOI: | 10.17586/2226-1494-2020-20-5-739-746 |
| Popis: | Subject of Research. The paper presents the distributed deep learning particularly convolutional neural network problem for resource-constrained devices. General architecture of convolutional neural network and its specificity is considered, existing constraints that appear while the deployment process on such architectures as LeNet, AlexNet, VGG-16/VGG-19 are analyzed. Deployment of convolutional neural network for resource-constrained devices is still a challenging task, as there are no existing and widely-used solutions. Method. The method for distribution of feature maps into smaller pieces is proposed, where each part is a determined problem. General distribution model for overlapped tasks within the scheduler is presented. Main Results. Distributed convolutional neural network model for a resource-constrained cluster and a scheduler for overlapped tasks is developed while carrying out computations mostly on a convolutional layer since this layer is one of the most resource-intensive, containing a large number of hyperparameters. Practical Relevance. Development of distributed convolutional neural network based on proposed methods provides the deployment of the convolutional neural network on a cluster that consists of 24 RockPro64 single board computers performing tasks related to machine vision, natural language processing, and prediction and is applicable in edge computing. |
| Databáze: | Directory of Open Access Journals |
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