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.

公开(公告)号：US10909657B1

公开(公告)日：2021-02-02

申请号：US16032844

申请日：2018-07-11

Applicant: Apple Inc.

Inventor： Francesco Rossi ,  Xiaohuan C. Wang ,  Brian E. Walsh ,  Bartlomiej W. Rymkowski ,  Xiaojin Shi ,  Marco Zuliani ,  Alexey Marinichev ,  Benjamin Poulain ,  Omid Khalili

IPC: G06T3/40 ,  G06T11/00 ,  G06N5/04

Abstract: Artistic styles extracted from one or more source images may be applied to one or more target images, e.g., in the form of stylized images and/or stylized video sequences. The extracted artistic style may be stored as a plurality of layers in a neural network, which neural network may be further optimized, e.g., via the fusion of various elements of the network's architectures. An optimized network architecture may be determined for each processing environment in which the network will be applied. The artistic style may be applied to the obtained images and/or video sequence of images using various optimization methods, such as the use of scalars to control the resolution of the unstylized and stylized images, temporal consistency constraints, as well as the use of dynamically adjustable or selectable versions of Deep Neural Networks (DNN) that are responsive to system performance parameters, such as available processing resources and thermal capacity.

公开(公告)号：US10664963B1

公开(公告)日：2020-05-26

申请号：US16032879

申请日：2018-07-11

Applicant: Apple Inc.

Inventor： Francesco Rossi ,  Xiaohuan C. Wang ,  Bartlomiej W. Rymkowski ,  Xiaojin Shi ,  Marco Zuliani ,  Alexey Marinichev

IPC: G06T5/50 ,  G06N3/08 ,  G06N3/04

Abstract: Artistic styles extracted from one or more source images may be applied to one or more target images, e.g., in the form of stylized images and/or stylized video sequences. The extracted artistic style may be stored as a plurality of layers in a neural network, which neural network may be further optimized, e.g., via the fusion of various elements of the network's architectures. An optimized network architecture may be determined for each processing environment in which the network will be applied. The artistic style may be applied to the obtained images and/or video sequence of images using various optimization methods, such as the use of scalars to control the resolution of the unstylized and stylized images, temporal consistency constraints, as well as the use of dynamically adjustable or selectable versions of Deep Neural Networks (DNN) that are responsive to system performance parameters, such as available processing resources and thermal capacity.

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.

公开(公告)号：US12051006B2

公开(公告)日：2024-07-30

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

CPC classification number: G06N3/10 ,  G06F8/41 ,  G06F8/443 ,  G06F8/4441 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F9/50 ,  G06N3/08 ,  G06N3/063 ,  G06N3/04 ,  G06N3/10

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.