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    Device placement optimization with reinforcement learning
    2.
    发明授权
    Device placement optimization with reinforcement learning 有权

    公开(公告)号：US10692003B2

    公开(公告)日：2020-06-23

    申请号：US16445330

    申请日：2019-06-19

    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.

    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING
    3.
    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.

    DEVICE PLACEMENT OPTIMIZATION WITH REINFORCEMENT LEARNING
    5.
    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.

    Methods and Systems for Automatically Equalizing Audio Output based on Room Characteristics
    9.
    发明申请
    Methods and Systems for Automatically Equalizing Audio Output based on Room Characteristics 审中-公开

    公开(公告)号：US20190103848A1

    公开(公告)日：2019-04-04

    申请号：US16058820

    申请日：2018-08-08

    Applicant: GOOGLE LLC

    Inventor： Benjamin Louis Shaya ,  Rasmus Munk Larsen ,  Richard F. Lyon ,  Michael Smedegaard

    IPC: H03G5/16 ,  H04R1/40 ,  H04R3/12 ,  H04R3/04 ,  H04R3/00 ,  G06F3/16

    Abstract: The various implementations described herein include methods, devices, and systems for automatic audio equalization. In one aspect, a method is performed at a computing system that includes speaker(s), microphones, processors and memory. The computing system outputs audio user content and automatically equalizes the audio output of the computing system. The equalizing includes: (1) receiving the outputted audio content at each microphone of the plurality of microphones; (2) based on the received audio content, determining an acoustic transfer function for the room; (3) based on the determined acoustic transfer function, obtaining a frequency response for the room; and (4) adjusting one or more properties of the speakers based on the determined frequency response.

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