Apple Inc.

Alexey Marinichev

Xiaojin Shi

Marco Zuliani

Francesco Rossi

Gaurav Kapoor

Umesh Suresh Vaishampayan

Kit-Man Wan

Xiaohuan Corina Wang

Cecile Marie Foret

Bartlomiej W Rymkowski

Etienne Belanger

Brian Edward Walsh

Omid Khalili

Benjamin Poulain

Albert Antony

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

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.

Flexible resolution support for image and video style transfer

Real-time selection of DNN style transfer networks from DNN sets

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Mountain View, CA, United States of America

Alexey Marinichev