- 专利标题: Distributed deep learning system using a communication network for stochastic gradient descent calculations
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申请号: US16967702申请日: 2019-02-06
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公开(公告)号: US12008468B2公开(公告)日: 2024-06-11
- 发明人: Junichi Kato , Kenji Kawai , Huycu Ngo , Yuki Arikawa , Tsuyoshi Ito , Takeshi Sakamoto
- 申请人: Nippon Telegraph and Telephone Corporation
- 申请人地址: JP Tokyo
- 专利权人: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
- 当前专利权人: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
- 当前专利权人地址: JP Tokyo
- 代理机构: SLATER MATSIL, LLP
- 优先权: JP 18025940 2018.02.16
- 国际申请: PCT/JP2019/004213 2019.02.06
- 国际公布: WO2019/159783A 2019.08.22
- 进入国家日期: 2020-08-05
- 主分类号: G06N3/08
- IPC分类号: G06N3/08 ; G06N3/04 ; G06N3/063
摘要:
Each of learning nodes calculates gradients of a loss function from an output result obtained by inputting learning data to a learning target neural network, converts a calculation result into a packet, and transmits the packet to a computing interconnect device. The computing interconnect device receives the packet transmitted from each of the learning nodes, acquires a value of the gradients stored in the packet, calculates a sum of the gradients, converts a calculation result into a packet, and transmits the packet to each of the learning nodes. Each of the learning nodes receives the packet transmitted from the computing interconnect device and updates a constituent parameter of a neural network based on a value stored in the packet.
公开/授权文献
- US20210034978A1 Distributed Deep Learning System 公开/授权日:2021-02-04
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