Abstract
A majority of training algorithms for deep neural networks (DNNs) use variants of stochastic gradient descent (SGD). Since a large amount of time is typically required to train DNNs, many attempts have been made to speed-up the training process by parallelizing the SGD algorithms. However, such parallelization efforts introduce approximation due to the inherent sequential nature of the SGD methods. In this paper, we revisit and analyze parallel SGD algorithms, and propose a novel pipelined SGD that is more efficient than previous algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
