Apple Inc.

Andrew Luke Chronister

Mohammad Rastegari

Peter Zatloukal

Ali Farhadi

Hessam Bagherinezhad

David S Bakin

Anish Jnyaneshwar Prabhu

Saman Naderiparizi

Sayyed Karen Khatamifard

Sophie Lebrecht

Matthew S Weaver

Dmitry Belenko

Alexander Kirchhoff

Keith Patrick Wyss

James C Gabriel

Jonathan Gelsey

Andrew Z Luo

Chenfan Sun

In one embodiment, a method includes receiving a user request from a client device associated with a user, accessing an instructional file comprising one or more binary inference engines and one or more encrypted model data corresponding to the one or more binary inference engines, respectively, selecting a binary inference engine from the one or more binary inference engines in the accessed instructional file based on the user request, sending a validation request for a permission to execute the binary inference engine to a licensing server, receiving the permission from the licensing server, decrypting the encrypted model data corresponding to the binary inference engine by a decryption key, executing the binary inference engine based on the user request and the decrypted model data, and sending one or more execution results responsive to the execution of the binary inference engine to the client device.

Integrating binary inference engines and model data for efficiency of inference tasks

Systems and processes for training and compressing a convolutional neural network model include the use of quantization and layer fusion. Quantized training data is passed through a convolutional layer of a neural network model to generate convolutional results during a first iteration of training the neural network model. The convolutional results are passed through a batch normalization layer of the neural network model to update normalization parameters of the batch normalization layer. The convolutional layer is fused with the batch normalization layer to generate a first fused layer and the fused parameters of the fused layer are quantized. The quantized training data is passed through the fused layer using the quantized fused parameters to generate output data, which may be quantized for a subsequent layer in the training iteration.

Compressed convolutional neural network models

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Seattle, WA, United States of America

Andrew Luke Chronister