Advanced architectures distributed systems for the implementation of neural networks

Abstract

Many industries nowadays use management and decision making based on artificial neural networks. However, the major drawback of neural networks lies in their time and computational complexity. The problem with computational complexity could be eliminated using sharing of the computing needs on multiple computing nodes. This article focuses on the architectural design of a distributed system, which aims to solve large neural networks. The article describes the technology GPGPU and the next part of the article deals with an overview of methods for speeding up the calculation and distribution of artificial neural network. The main section describes the design of a model architecture description of the algorithm that allows correct data distribution on computational nodes.