PARALLELIZATION STRATEGIES ANALYSIS FOR SUPERVISED LEARNING
Snezhina Yanakieva
Страницы: 293-300
Опубликовано: 23 Sep 2021
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Аннотация: The algorithms for supervised learning in artificial neural networks (ANN) require time and high computational power. As these algorithms gain popularity in a variety of domains, it is critical for them to run fast. Following a brief survey of the different dimensions of parallelism in ANN this paper analyses the performance comparison between different parallelization techniques to show the advantages and disadvantages of these strategies.
Ключевые слова: parallel neural network, multilayer perceptron, back-propagation, bulk synchronous parallelism, computer cluster
Цитировать эту статью: Snezhina Yanakieva. PARALLELIZATION STRATEGIES ANALYSIS FOR SUPERVISED LEARNING. Journal of International Scientific Publications: Materials, Methods & Technologies 15, 293-300 (2021). https://www.scientific-publications.net/en/article/1002220/
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