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Materials, Methods & Technologies, Volume 15, 2021

Snezhina Yanakieva
Pages: 293-300
Published: 23 Sep 2021
Views: 507
Downloads: 38
Abstract: 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.
Keywords: parallel neural network, multilayer perceptron, back-propagation, bulk synchronous parallelism, computer cluster
Cite this article: Snezhina Yanakieva. PARALLELIZATION STRATEGIES ANALYSIS FOR SUPERVISED LEARNING. Journal of International Scientific Publications: Materials, Methods & Technologies 15, 293-300 (2021).
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