US 2019/0378013 A1 · SELF-TUNING MODEL COMPRESSION METHODOLOGY FOR RECONFIGURING DEEP NEURAL NETWORK AND ELECTRONIC DEVICE

Abstract

A self-tuning model compression methodology for reconfiguring a Deep Neural Network includes: receiving a DNN model and a data set, wherein the DNN includes an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the DNN model includes a plurality of neurons; compressing the DNN model into a reconfigured model according to the data set, wherein the reconfigured model includes an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the reconfigured model includes a plurality of neurons, and a size of the reconfigured model is smaller than a size of the DNN model; and executing the reconfigured model on a user terminal for an end-user application.

Claims17

Dependent claims

A self-tuning model compression methodology for reconfiguring a Deep Neural Network (DNN), comprising:

receiving a DNN model and a data set, wherein the DNN model comprises an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the DNN model comprise a plurality of neurons;
compressing the DNN model into a reconfigured model according to the data set, wherein the reconfigured model comprises an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the reconfigured model comprise a plurality of neurons, and a size of the reconfigured model is smaller than a size of the DNN model;
and
executing the reconfigured model on a user terminal for an end-user application.

1 more element

An electronic device, comprising:

a storage device, arranged to store a program code;
and
a processor, arranged to execute the program code;
wherein when loaded and executed by the processor, the program code instructs the processor to execute the following steps:
receiving a DNN model and a data set, wherein the DNN comprises an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the DNN model comprise a plurality of neurons;
and
compressing the DNN model into a reconfigured model according to the data set, wherein the reconfigured model comprises an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the reconfigured model comprise a plurality of neurons, and a size of the reconfigured model is smaller than a size of the DNN model.

4 more elements

Dependencies are inferred from the claim text.

Self-tuning model compression methodology for reconfiguring deep neural network and electronic device

Life of this patent

Self-tuning model compression methodology for reconfiguring deep neural network and electronic device

Life of this patent

Abstract

Claims17