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    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING
    11.
    发明申请
    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20200279163A1

    公开(公告)日：2020-09-03

    申请号：US16878720

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

    IPC: G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06N3/10

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

    NEURAL ARCHITECTURE SEARCH
    12.
    发明申请
    NEURAL ARCHITECTURE SEARCH 审中-公开

    公开(公告)号：US20200265315A1

    公开(公告)日：2020-08-20

    申请号：US16859781

    申请日：2020-04-27

    Applicant: Google LLC

    Inventor： Barret Zoph ,  Yun Jia Guan ,  Hieu Hy Pham ,  Quoc V. Le

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network, a batch of output sequences, each output sequence in the batch specifying a respective subset of a plurality of components of a large neural network that should be active during the processing of inputs by the large neural network; for each output sequence in the batch: determining a performance metric of the large neural network on the particular neural network task (i) in accordance with current values of the large network parameters and (ii) with only the subset of components specified by the output sequences active; and using the performance metrics for the output sequences in the batch to adjust the current values of the controller parameters of the controller neural network.

    HIERARCHICAL DEVICE PLACEMENT WITH REINFORCEMENT LEARNING
    13.
    发明申请
    HIERARCHICAL DEVICE PLACEMENT WITH REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20190392294A1

    公开(公告)日：2019-12-26

    申请号：US16554217

    申请日：2019-08-28

    Applicant: Google LLC

    Inventor： Benoit Steiner ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le

    IPC: G06N3/04 ,  G06N20/00 ,  G06F16/901 ,  G06N3/063 ,  G06N3/08 ,  G06N5/04

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices. Determining the placement includes: generating, from the data, a respective operation embedding for each of the operations; grouping the operations into multiple operation groups, comprising processing each of the respective operation embeddings using a grouper neural network having multiple grouper parameters, in which the grouper neural network is configured to, for each of the operations, process the operation embedding for the operation in accordance with first values of the grouper parameters to generate a grouper output that assigns the operation to an operation group from the multiple operation groups; and assigning each of the operation groups to a respective device from the multiple hardware devices.

    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING
    14.
    发明申请
    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20190303761A1

    公开(公告)日：2019-10-03

    申请号：US16445330

    申请日：2019-06-19

    Applicant: Google LLC

    Inventor： Samy Bengio ,  Mohammad Edward Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

    IPC: G06N3/08 ,  G06N5/04

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

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