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    TRAINING METHOD, APPARATUS, CHIP, AND SYSTEM FOR NEURAL NETWORK MODEL
    2.
    发明申请
    TRAINING METHOD, APPARATUS, CHIP, AND SYSTEM FOR NEURAL NETWORK MODEL 审中-公开

    公开(公告)号：US20190279088A1

    公开(公告)日：2019-09-12

    申请号：US16424760

    申请日：2019-05-29

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Changzheng ZHANG ,  Xiaolong BAI ,  Dandan TU

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method for training a neural network model are disclosed. Each training period includes K iterations, and for an ith iteration of one of N worker modules within each training period, each worker module performs in parallel the following steps: calculating a model parameter of an (i+1)th iteration based on a local gradient of the ith iteration and a model parameter of the ith iteration, and if i is less than K, calculating a local gradient of the (i+1)th iteration based on the model parameter of the (i+1)th iteration and sample data of the (i+1)th iteration; and pulling, by the worker module, a global gradient of an rth iteration from a server module and/or pushing, by the worker module, a local gradient of an fth iteration to the server module. In this way, time windows of a calculation process and a communication process overlap, thereby reducing time delay.

    DATA PROCESSING SYSTEM AND METHOD
    3.
    发明申请
    DATA PROCESSING SYSTEM AND METHOD 审中-公开

    公开(公告)号：US20190287022A1

    公开(公告)日：2019-09-19

    申请号：US16432617

    申请日：2019-06-05

    Applicant: HUAWEI TECHNOLOGIES CO., LTD.

    Inventor： Changzheng ZHANG ,  Xiaolong BAI ,  Dandan TU

    IPC: G06N20/00 ,  G06F17/16

    Abstract: Embodiments of the present invention disclose a data processing apparatus. The apparatus is configured to: after calculating a set of gradient information of each parameter by using a sample data subset, delete the sample data subset, read a next sample data subset, calculate another set of gradient information of each parameter by using the next sample data subset, and accumulate a plurality of sets of calculated gradient information of each parameter, to obtain an update gradient of each parameter.

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