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.
@article{arxiv.1312.6186,
title = {GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training},
author = {Thomas Paine and Hailin Jin and Jianchao Yang and Zhe Lin and Thomas Huang},
journal= {arXiv preprint arXiv:1312.6186},
year = {2013}
}