中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

9 records (took 0.012 seconds)

    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS
    3.
    发明申请
    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200167193A1

    公开(公告)日：2020-05-28

    申请号：US16776338

    申请日：2020-01-29

    Applicant: Apple Inc.

    Inventor： Francesco ROSSI ,  Gaurav KAPOOR ,  Michael R. SIRACUSA ,  William B. MARCH

    IPC: G06F9/50 ,  G06F8/41 ,  G06N3/02 ,  G06N3/063

    Abstract: The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.

    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS
    4.
    发明公开
    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230176907A1

    公开(公告)日：2023-06-08

    申请号：US18074440

    申请日：2022-12-02

    Applicant: Apple Inc.

    Inventor： Francesco ROSSI ,  Gaurav KAPOOR ,  Michael R. SIRACUSA ,  William B. MARCH

    IPC: G06F9/50 ,  G06N3/063 ,  G06F8/41 ,  G06F9/48 ,  G06N3/02

    CPC classification number: G06F9/50 ,  G06F9/5044 ,  G06N3/063 ,  G06F8/451 ,  G06F9/485 ,  G06F9/5038 ,  G06N3/02 ,  G06F2209/509

    Abstract: The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.

    INTEGRATION OF LEARNING MODELS INTO A SOFTWARE DEVELOPMENT SYSTEM
    5.
    发明申请
    INTEGRATION OF LEARNING MODELS INTO A SOFTWARE DEVELOPMENT SYSTEM 审中-公开

    公开(公告)号：US20200380415A1

    公开(公告)日：2020-12-03

    申请号：US16875565

    申请日：2020-05-15

    Applicant: Apple Inc.

    Inventor： Michael R. SIRACUSA ,  Anil Kumar KATTI ,  Mohammad Reza FARHADI ,  Aseem WADHWA ,  Michael Ryan BRENNAN ,  Andrew Joseph RACHWALSKI

    IPC: G06N20/00 ,  G06F8/33 ,  G06F8/10

    Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language), wherein the machine learning model includes a model parameter of the machine learning model. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model and the object includes an interface to update the model parameter. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

Patent Agency Ranking