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    GENERATING INTEGRATED CIRCUIT FLOORPLANS USING NEURAL NETWORKS
    31.
    发明申请
    GENERATING INTEGRATED CIRCUIT FLOORPLANS USING NEURAL NETWORKS 有权

    公开(公告)号：US20250053714A1

    公开(公告)日：2025-02-13

    申请号：US18805439

    申请日：2024-08-14

    Applicant: Google LLC

    Inventor： Chian-min Richard Ho ,  William Hang ,  Mustafa Nazim Yazgan ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Azalia Mirhoseini ,  Emre Tuncer ,  Ya Wang ,  Anand Babu

    IPC: G06F30/27 ,  G06F30/392

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

    CLUSTERING DATA USING NEURAL NETWORKS BASED ON NORMALIZED CUTS
    36.
    发明申请
    CLUSTERING DATA USING NEURAL NETWORKS BASED ON NORMALIZED CUTS 有权

    公开(公告)号：US20220383036A1

    公开(公告)日：2022-12-01

    申请号：US17764015

    申请日：2020-09-25

    Applicant: Google LLC

    Inventor： Azade Nazi ,  Azalia Mirhoseini ,  Anna Darling Goldie ,  Sujith Ravi ,  William Hang

    IPC: G06K9/62 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a clustering neural network. One of the methods includes obtaining unlabeled training data; and training the clustering neural network on the unlabeled training data to determine trained values of the clustering parameters by minimizing a normalized cuts loss function that includes a first term that measures an expected normalized cuts of clustering nodes in a graph representing the data set into the plurality of clusters according to clustering outputs generated by the clustering neural network.

    Hierarchical device placement with reinforcement learning
    37.
    发明授权
    Hierarchical device placement with reinforcement learning 有权

    公开(公告)号：US11455514B2

    公开(公告)日：2022-09-27

    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 ,  G06F9/50 ,  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.

    MIXTURE OF EXPERTS NEURAL NETWORKS
    39.
    发明申请
    MIXTURE OF EXPERTS NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200279150A1

    公开(公告)日：2020-09-03

    申请号：US16879187

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Noam M. Shazeer ,  Azalia Mirhoseini ,  Krzysztof Stanislaw Maziarz

    IPC: G06N3/04 ,  G06N3/08

    Abstract: A system includes a neural network that includes a Mixture of Experts (MoE) subnetwork between a first neural network layer and a second neural network layer. The MoE subnetwork includes multiple expert neural networks. Each expert neural network is configured to process a first layer output generated by the first neural network layer to generate a respective expert output. The MoE subnetwork further includes a gating subsystem that selects, based on the first layer output, one or more of the expert neural networks and determine a respective weight for each selected expert neural network, provides the first layer output as input to each of the selected expert neural networks, combines the expert outputs generated by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate an MoE output, and provides the MoE output as input to the second neural network layer.

    Mixture of experts neural networks
    40.
    发明授权
    Mixture of experts neural networks 有权

    公开(公告)号：US10719761B2

    公开(公告)日：2020-07-21

    申请号：US16393063

    申请日：2019-04-24

    Applicant: Google LLC

    Inventor： Noam M. Shazeer ,  Azalia Mirhoseini ,  Krzysztof Stanislaw Maziarz

    IPC: G06N3/04 ,  G06N3/08

    Abstract: A system includes a neural network that includes a Mixture of Experts (MoE) subnetwork between a first neural network layer and a second neural network layer. The MoE subnetwork includes multiple expert neural networks. Each expert neural network is configured to process a first layer output generated by the first neural network layer to generate a respective expert output. The MoE subnetwork further includes a gating subsystem that selects, based on the first layer output, one or more of the expert neural networks and determine a respective weight for each selected expert neural network, provides the first layer output as input to each of the selected expert neural networks, combines the expert outputs generated by the selected expert neural networks in accordance with the weights for the selected expert neural networks to generate an MoE output, and provides the MoE output as input to the second neural network layer.

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