%0 Generic %1 paine2013asynchronous %A Paine, Thomas %A Jin, Hailin %A Yang, Jianchao %A Lin, Zhe %A Huang, Thomas %D 2013 %K deep dl large-scale networks neural %T GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training %U http://arxiv.org/abs/1312.6186 %X The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and data parallelism, e.g. A-SGD, whose large scale has been used mostly in industry. We report early experiments with a system that makes use of both model parallelism and data parallelism, we call GPU A-SGD. We show using GPU A-SGD it is possible to speed up training of large convolutional neural networks useful for computer vision. We believe GPU A-SGD will make it possible to train larger networks on larger training sets in a reasonable amount of time.

@misc{paine2013asynchronous, abstract = {The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and data parallelism, e.g. A-SGD, whose large scale has been used mostly in industry. We report early experiments with a system that makes use of both model parallelism and data parallelism, we call GPU A-SGD. We show using GPU A-SGD it is possible to speed up training of large convolutional neural networks useful for computer vision. We believe GPU A-SGD will make it possible to train larger networks on larger training sets in a reasonable amount of time.}, added-at = {2019-06-04T16:20:53.000+0200}, author = {Paine, Thomas and Jin, Hailin and Yang, Jianchao and Lin, Zhe and Huang, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/23da98903a9d2e9ed2c335441c0d083a0/alrigazzi}, description = {GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training}, interhash = {99711f68dbb04df2498cef7111ef1126}, intrahash = {3da98903a9d2e9ed2c335441c0d083a0}, keywords = {deep dl large-scale networks neural}, note = {cite arxiv:1312.6186Comment: 6 pages, 4 figures}, timestamp = {2019-06-04T16:20:53.000+0200}, title = {GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training}, url = {http://arxiv.org/abs/1312.6186}, year = 2013 }