US 2026/0037875 A1 · EDGE COMPUTING DEVICE AND METHOD OF OPTIMIZING DEEP LEARNING MODEL

Abstract

Proposed is a method of optimizing a deep learning model, which is performed by an edge computing device. The method may include measuring a similarity between data collected from an installation environment of the edge computing device and training data used for training a deep learning model installed in the edge computing device. The method may also include determining whether to perform optimization of the deep learning model based on a measurement result of the similarity. The method may further include generating training data for performing the optimization of the deep learning model based on reliability information, and training and generating a deep learning model based on the training data. The method may further include updating the existing deep learning model applied to the edge computing device with the generated deep learning model.

Claims11

Dependent claims

A method of optimizing a deep learning model, which is performed by an edge computing device, comprising:

measuring a similarity between data collected from an installation environment of the edge computing device and training data used for training a deep learning model installed in the edge computing device;
determining whether to perform optimization of the deep learning model based on a measurement result of the similarity;
generating training data for performing the optimization of the deep learning model based on reliability information;
training and generating a deep learning model based on the training data;
and
updating the existing deep learning model applied to the edge computing device with the generated deep learning model.

3 more elements

Dependencies are inferred from the claim text.

Edge computing device and method of optimizing deep learning model

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Edge computing device and method of optimizing deep learning model

Life of this patent

Abstract

Claims11