Year: 2018
Type: preprint
Abstract: The training of Deep Neural Networks usually needs tremendous computing resources. Therefore many deep models are trained in large cluster instead of single machine or GPU. Though major researchs at present try to run whole model on all machines by using asynchronous asynchronous stochastic gradient descent (ASGD), we present a new approach to train deep model parallely -- split the model and then seperately train different parts of it in different speed.
Source: arXiv (Cornell University)
Authors: Dong, Hao, Li, Shuai, Xu, Dongchang, Ren, Yi, Zhang, Di
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2807522660