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기록

METHOD AND DEVICE FOR QUANTIZING DEEP LEARNING NEURAL NETWORK MODEL BY CONSIDERING CHANGE IN EXTERNAL ENVIRONMENT

발명심사 중
1조회수
14청구항 · 2 독립항
§ Ⅰ

개요

발명자

Won Jae LEE; Ji Eun LIM

IPC 분류

G6V 10/82

CPC 분류

G6V10/82

A quantization method of a deep learning neural network model is disclosed. An embodiment of the disclosure provides a quantization method comprising: detecting a feature change of input data caused by a change in an external environment, from input image data of a quantized deep learning neural network model based on a plurality of preset quantization parameters; performing quantization calibration for the deep learning neural network model to determine a new quantization parameter corresponding to the feature change of input data caused by the change in the external environment; and updating at least one of the plurality of preset quantization parameters based on the new quantization parameter.

원문 (중국어)

A quantization method of a deep learning neural network model is disclosed. An embodiment of the disclosure provides a quantization method comprising: detecting a feature change of input data caused by a change in an external environment, from input image data of a quantized deep learning neural network model based on a plurality of preset quantization parameters; performing quantization calibration for the deep learning neural network model to determine a new quantization parameter corresponding to the feature change of input data caused by the change in the external environment; and updating at least one of the plurality of preset quantization parameters based on the new quantization parameter.