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    Compiling models for dedicated hardware
    2.
    发明授权
    Compiling models for dedicated hardware 有权

    公开(公告)号：US11468338B2

    公开(公告)日：2022-10-11

    申请号：US16262809

    申请日：2019-01-30

    Applicant: Apple Inc.

    Inventor： Francesco Rossi ,  Cecile M. Foret ,  Gaurav Kapoor ,  Kit-Man Wan ,  Umesh S. Vaishampayan ,  Etienne Belanger ,  Albert Antony ,  Alexey Marinichev ,  Marco Zuliani ,  Xiaojin Shi

    IPC: G06N3/10 ,  G06N3/08 ,  G06N3/04 ,  G06F8/41 ,  G06N3/063 ,  G06F9/50

    Abstract: The subject technology provides receiving a neural network (NN) model to be executed on a target platform, the NN model including multiple layers that include operations and some of the operations being executable on multiple processors of the target platform. The subject technology further sorts the operations from the multiple layers in a particular order based at least in part on grouping the operations that are executable by a particular processor of the multiple processors. The subject technology determines, based at least in part on a cost of transferring the operations between the multiple processors, an assignment of one of the multiple processors for each of the sorted operations of each of the layers in a manner that minimizes a total cost of executing the operations. Further, for each layer of the NN model, the subject technology includes an annotation to indicate the processor assigned for each of the operations.

    Compiling models for dedicated hardware
    3.
    发明授权
    Compiling models for dedicated hardware 有权

    公开(公告)号：US12175375B2

    公开(公告)日：2024-12-24

    申请号：US17903991

    申请日：2022-09-06

    Applicant: Apple Inc.

    Inventor： Gaurav Kapoor ,  Cecile M. Foret ,  Francesco Rossi ,  Kit-Man Wan ,  Umesh S. Vaishampayan ,  Etienne Belanger ,  Albert Antony ,  Alexey Marinichev ,  Marco Zuliani ,  Xiaojin Shi

    IPC: G06N3/10 ,  G06F8/41 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08

    Abstract: The subject technology provides receiving a neural network (NN) model to be executed on a target platform, the NN model including multiple layers that include operations and some of the operations being executable on multiple processors of the target platform. The subject technology further sorts the operations from the multiple layers in a particular order based at least in part on grouping the operations that are executable by a particular processor of the multiple processors. The subject technology determines, based at least in part on a cost of transferring the operations between the multiple processors, an assignment of one of the multiple processors for each of the sorted operations of each of the layers in a manner that minimizes a total cost of executing the operations. Further, for each layer of the NN model, the subject technology includes an annotation to indicate the processor assigned for each of the operations.

    Enhanced image processing techniques for deep neural networks
    5.
    发明授权
    Enhanced image processing techniques for deep neural networks 有权

    公开(公告)号：US11367163B2

    公开(公告)日：2022-06-21

    申请号：US16794824

    申请日：2020-02-19

    Applicant: Apple Inc.

    Inventor： Francesco Rossi ,  Marco Zuliani ,  Bartlomiej W. Rymkowski ,  Albert Antony ,  Brian P. Keene ,  Xiaojin Shi

    IPC: G06T3/40 ,  G06T11/60 ,  G06N3/04

    Abstract: Artistic styles extracted from source images may be applied to target images to generate stylized images and/or video sequences. The extracted artistic styles may be stored as a plurality of layers in one or more neural networks, which neural networks may be further optimized, e.g., via the fusion of various elements of the networks' architectures. The artistic style may be applied to the target images and/or video sequences using various optimization methods, such as the use of a first version of the neural network by a first processing device at a first resolution to generate one or more sets of parameters (e.g., scaling and/or biasing parameters), which parameters may then be mapped for use by a second version of the neural network by a second processing device at a second resolution. Analogous multi-processing device and/or multi-network solutions may also be applied to other complex image processing tasks for increased efficiency.

    Enhanced Image Processing Techniques for Deep Neural Networks
    6.
    发明申请
    Enhanced Image Processing Techniques for Deep Neural Networks 审中-公开

    公开(公告)号：US20200380639A1

    公开(公告)日：2020-12-03

    申请号：US16794824

    申请日：2020-02-19

    Applicant: Apple Inc.

    Inventor： Francesco Rossi ,  Marco Zuliani ,  Bartlomiej W. Rymkowski ,  Albert Antony ,  Brian P. Keene ,  Xiaojin Shi

    IPC: G06T3/40 ,  G06T11/60 ,  G06N3/04

    Abstract: Artistic styles extracted from source images may be applied to target images to generate stylized images and/or video sequences. The extracted artistic styles may be stored as a plurality of layers in one or more neural networks, which neural networks may be further optimized, e.g., via the fusion of various elements of the networks' architectures. The artistic style may be applied to the target images and/or video sequences using various optimization methods, such as the use of a first version of the neural network by a first processing device at a first resolution to generate one or more sets of parameters (e.g., scaling and/or biasing parameters), which parameters may then be mapped for use by a second version of the neural network by a second processing device at a second resolution. Analogous multi-processing device and/or multi-network solutions may also be applied to other complex image processing tasks for increased efficiency.

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