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    Method for visualizing neural network models
    1.
    发明授权
    Method for visualizing neural network models 有权

    公开(公告)号：US10936938B2

    公开(公告)日：2021-03-02

    申请号：US15857587

    申请日：2017-12-28

    申请人： International Business Machines Corporation

    发明人： Minwei Feng ,  Yufei Ren ,  Yandong Wang ,  Li Zhang ,  Wei Zhang

    IPC分类号： G06F3/048 ,  G06F3/0482 ,  G06F3/0484 ,  G06N3/04 ,  G06F16/904 ,  G06N3/10

    摘要： A method for providing a graphical visualization of a neural network to a user is provided. The method includes generating the graphical visualization of the neural network at least in part by: representing layers of the neural network as respective three-dimensional blocks, wherein at least a first dimension of a given block is proportional to a computational complexity of a layer of the neural network represented by the given block; and representing data flows between the layers of the neural network as respective three-dimensional structures connecting blocks representing the layers of the neural network, wherein a first dimension of a given structure is proportional to each of a first dimension and a second dimension of a data flow represented by the given structure. The method also includes displaying the graphical visualization of the neural network to the user.

    Computational storage for distributed computing
    2.
    发明授权
    Computational storage for distributed computing 有权

    公开(公告)号：US10423575B2

    公开(公告)日：2019-09-24

    申请号：US15447262

    申请日：2017-03-02

    申请人： International Business Machines Corporation

    发明人： Michel H. T. Hack ,  Yufei Ren ,  Wei Tan ,  Yandong Wang ,  Xingbo Wu ,  Li Zhang ,  Wei Zhang

    IPC分类号： G06F16/182 ,  G06F16/14

    摘要： Computational storage techniques for distribute computing are disclosed. The computational storage server receives input from multiple clients, which is used by the server when executing one or more computation functions. The computational storage server can aggregate multiple client inputs before applying one or more computation functions. The computational storage server sets up: a first memory area for storing input received from multiple clients; a second memory area designated for storing the computation functions to be executed by the computational storage server using the input data received from the multiple clients; a client specific memory management area for storing metadata related to computations performed by the computational storage server for specific clients; and a persistent storage area for storing checkpoints associated with aggregating computations performed by the computation functions.

    Hybrid aggregation for deep learning neural networks
    5.
    发明授权
    Hybrid aggregation for deep learning neural networks 有权

    公开(公告)号：US10783437B2

    公开(公告)日：2020-09-22

    申请号：US15450010

    申请日：2017-03-05

    申请人： International Business Machines Corporation

    发明人： Minwei Feng ,  Yufei Ren ,  Yandong Wang ,  Li Zhang ,  Wei Zhang

    IPC分类号： G06E1/00 ,  G06F12/00 ,  A61K35/74 ,  G06Q30/06 ,  G06F12/02 ,  G06N99/00 ,  G06N3/08 ,  G06N3/04

    摘要： A processing unit topology of a neural network including a plurality of processing units is determined. The neural network includes at least one machine in which each machine includes a plurality of nodes, and wherein each node includes at least one of the plurality of processing units. One or more of the processing units are grouped into a first group according to a first affinity. The first group is configured, using a processor and a memory, to use a first aggregation procedure for exchanging model parameters of a model of the neural network between the processing units of the first group. One or more of the processing units are grouped into a second group according to a second affinity. The second group is configured to use a second aggregation procedure for exchanging the model parameters between the processing units of the second group.