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    LARGE MODEL SUPPORT IN DEEP LEARNING
    1.
    发明申请
    LARGE MODEL SUPPORT IN DEEP LEARNING 有权

    公开(公告)号：US20230064057A1

    公开(公告)日：2023-03-02

    申请号：US18048203

    申请日：2022-10-20

    申请人： International Business Machines Corporation

    发明人： Minsik Cho ,  Ulrich Alfons Finkler ,  Vladimir Zolotov ,  David S. Kung

    IPC分类号： G06N3/08 ,  G06F13/42 ,  G06N3/04

    摘要： Techniques that facilitate model support in deep learning are provided. In one example, a system includes a graphics processing unit and a central processing unit memory. The graphics processing unit processes data to train a deep neural network. The central processing unit memory stores a portion of the data to train the deep neural network. The graphics processing unit provides, during a forward pass process of the deep neural network that traverses through a set of layers for the deep neural network from a first layer of the set of layers to a last layer of the set of layers that provides a set of outputs for the deep neural network, input data for a layer from the set of layers for the deep neural network to the central processing unit memory.

    Large model support in deep learning
    3.
    发明授权
    Large model support in deep learning 有权

    公开(公告)号：US11526759B2

    公开(公告)日：2022-12-13

    申请号：US16180864

    申请日：2018-11-05

    申请人： International Business Machines Corporation

    发明人： Minsik Cho ,  Ulrich Alfons Finkler ,  Vladimir Zolotov ,  David S. Kung

    IPC分类号： G06F15/82 ,  G06N3/08 ,  G06F13/42 ,  G06N3/04

    摘要： Techniques that facilitate model support in deep learning are provided. In one example, a system includes a graphics processing unit and a central processing unit memory. The graphics processing unit processes data to train a deep neural network. The central processing unit memory stores a portion of the data to train the deep neural network. The graphics processing unit provides, during a forward pass process of the deep neural network that traverses through a set of layers for the deep neural network from a first layer of the set of layers to a last layer of the set of layers that provides a set of outputs for the deep neural network, input data for a layer from the set of layers for the deep neural network to the central processing unit memory.

    Large model support in deep learning
    4.
    发明授权
    Large model support in deep learning 有权

    公开(公告)号：US11915147B2

    公开(公告)日：2024-02-27

    申请号：US18048203

    申请日：2022-10-20

    申请人： International Business Machines Corporation

    发明人： Minsik Cho ,  Ulrich Alfons Finkler ,  Vladimir Zolotov ,  David S. Kung

    IPC分类号： G06F15/82 ,  G06N3/084 ,  G06F13/42 ,  G06N3/04

    CPC分类号： G06N3/084 ,  G06F13/4282 ,  G06N3/04

    摘要： Techniques that facilitate model support in deep learning are provided. In one example, a system includes a graphics processing unit and a central processing unit memory. The graphics processing unit processes data to train a deep neural network. The central processing unit memory stores a portion of the data to train the deep neural network. The graphics processing unit provides, during a forward pass process of the deep neural network that traverses through a set of layers for the deep neural network from a first layer of the set of layers to a last layer of the set of layers that provides a set of outputs for the deep neural network, input data for a layer from the set of layers for the deep neural network to the central processing unit memory.

    CONFIGURING A NEURAL NETWORK USING SMOOTHING SPLINES
    5.
    发明申请
    CONFIGURING A NEURAL NETWORK USING SMOOTHING SPLINES 有权

    公开(公告)号：US20220121924A1

    公开(公告)日：2022-04-21

    申请号：US17075963

    申请日：2020-10-21

    申请人： International Business Machines Corporation

    发明人： Ulrich Alfons Finkler ,  Michele Merler ,  Mayoore Selvarasa Jaiswal ,  Hui Wu ,  Rameswar Panda ,  Wei Zhang

    IPC分类号： G06N3/08 ,  G06N7/00 ,  G06K9/62

    摘要： An embodiment includes identifying an initial plurality of sets of hyperparameter values at which to evaluate an objective function that relates hyperparameter values to performance values of a neural network. The embodiment also executes training processes on the neural network with the hyperparameters set to the each of the initial sets of hyperparameter values such that the training process provides an initial set of the performance values for the objective function. The embodiment also generates an approximation of the objective function using splines at selected performance values. The embodiment approximates a point at which the approximation of the objective function reaches a maximum value, then determines an updated set of hyperparameter values associated with the maximum value. The embodiment then executes a runtime process using the neural network with the hyperparameters set to the updated set of hyperparameter values.