Parameter server and method for sharing distributed deep learning parameter using the same
US10990561B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 18, 2018 |
| Grant date | Apr 27, 2021 |
| Priority date | — |
| Expiry date | Oct 9, 2038 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L67/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed herein are a parameter server and a method for sharing distributed deep-learning parameters using the parameter server. The method for sharing distributed deep-learning parameters using the parameter server includes initializing a global weight parameter in response to an initialization request by a master process; performing an update by receiving a learned local gradient parameter from the worker process, which performs deep-learning training after updating a local weight parameter using the global weight parameter; accumulating the gradient parameters in response to a request by the master process; and performing an update by receiving the global weight parameter from the master process that calculates the global weight parameter using the accumulated gradient parameters of the one or more worker processes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.