What does this research mean for the field?

A newly proposed method for parallelizing the training of convolutional neural networks across multiple GPUs scales significantly better than existing alternative methods. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

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April 23, 2014Open Access

One weird trick for parallelizing convolutional neural networks

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Abstract

I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.

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Alex Krizhevsky

University of Toronto

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Alex Krizhevsky (Wed,) studied this question.

synapsesocial.com/papers/6a0920a9266340834eb620ae — DOI: https://doi.org/10.48550/arxiv.1404.5997

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Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training· 2013 · 67 citations
ImageNet: A large-scale hierarchical image database· 2009 · 61,675 citations
Deep learning with COTS HPC systems· 2013 · 608 citations
Large Scale Distributed Deep Networks· 2012 · 2,916 citations
Building high-level features using large scale unsupervised learning· 2011 · 179 citations

One weird trick for parallelizing convolutional neural networks

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Actions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study

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