Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability

22 Sep 2016Janis KeuperFranz-Josef Pfreundt

This paper presents a theoretical analysis and practical evaluation of the main bottlenecks towards a scalable distributed solution for the training of Deep Neuronal Networks (DNNs). The presented results show, that the current state of the art approach, using data-parallelized Stochastic Gradient Descent (SGD), is quickly turning into a vastly communication bound problem... (read more)

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