深度神经网络并行训练算法综述

IF 0.2 Q4 ACOUSTICS
Dongsuk Yook, Hyowon Lee, In-Chul Yoo
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引用次数: 2

摘要

由于深度神经网络(dnn)的训练通常需要大量的训练数据,因此需要一种并行训练方法来训练dnn。随机梯度下降(SGD)算法是目前应用最广泛的深度神经网络训练方法之一。然而,由于SGD是一个固有的顺序过程,它需要某种近似方案来并行化SGD算法。在本文中,我们回顾了并行化SGD算法的各种努力,并分析了计算开销、通信开销以及近似的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A survey on parallel training algorithms for deep neural networks
Since a large amount of training data is typically needed to train Deep Neural Networks (DNNs), a parallel training approach is required to train the DNNs. The Stochastic Gradient Descent (SGD) algorithm is one of the most widely used methods to train the DNNs. However, since the SGD is an inherently sequential process, it requires some sort of approximation schemes to parallelize the SGD algorithm. In this paper, we review various efforts on parallelizing the SGD algorithm, and analyze the computational overhead, communication overhead, and the effects of the approximations.
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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