Abstract
This paper proposes DropSignSGD, a communication-efficient and network-fault tolerant algorithm for training deep neural networks in a distributed and synchronous fashion. In DropSignSGD, all numerical elements communicated between machines are either 1 or −1, represented by only one bit. More importantly, DropSignSGD does not decline the benchmark accuracy on the ImageNet dataset when compared with the traditional distributed stochastic gradient descent algorithm, owing to a little trick in memorizing unused gradients. Experimental results are supported by a mathematical proof showing that DropSignSGD converges under standard assumptions.
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.
