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    Machine learning inference calls for database query processing
    17.
    发明授权
    Machine learning inference calls for database query processing 有权

    公开(公告)号：US11449796B2

    公开(公告)日：2022-09-20

    申请号：US16578060

    申请日：2019-09-20

    Applicant: Amazon Technologies, Inc.

    Inventor： Sangil Song ,  Yongsik Yoon ,  Kamal Kant Gupta ,  Saileshwar Krishnamurthy ,  Stefano Stefani ,  Sudipta Sengupta ,  Jaeyun Noh

    IPC: G06F7/00 ,  G06N20/00 ,  G06F16/242 ,  G06F16/2453 ,  G06N5/04

    Abstract: Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

    Neural network training under memory restraint
    19.
    发明授权
    Neural network training under memory restraint 有权

    公开(公告)号：US12106222B2

    公开(公告)日：2024-10-01

    申请号：US18112036

    申请日：2023-02-21

    Applicant: Amazon Technologies, Inc.

    Inventor： Sudipta Sengupta ,  Randy Renfu Huang ,  Ron Diamant ,  Vignesh Vivekraja

    IPC: G06N3/084 ,  G06N3/04

    CPC classification number: G06N3/084 ,  G06N3/04

    Abstract: Methods and systems for training a neural network are provided. In one example, an apparatus comprises a memory that stores instructions; and a hardware processor configured to execute the instructions to: control a neural network processor to perform a loss gradient operation to generate data gradients; after the loss gradient operation completes, control the neural network processor to perform a forward propagation operation to generate intermediate outputs; control the neural network processor to perform a backward propagation operation based on the data gradients and the intermediate outputs to generate weight gradients; receive the weight gradients from the neural network processor; and update weights of a neural network based on the weight gradients.

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