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Deep learning model reasoning method, system and equipment and computer medium

A technology of deep learning and learning models, applied in the field of deep learning, can solve the problems of poor inference accuracy of deep learning models, and achieve the effect of improving inference accuracy

Pending Publication Date: 2022-05-06
INSPUR SUZHOU INTELLIGENT TECH CO LTD
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing quantization methods are all hierarchical quantization, which makes the inference accuracy of the deep learning model poor

Method used

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  • Deep learning model reasoning method, system and equipment and computer medium
  • Deep learning model reasoning method, system and equipment and computer medium
  • Deep learning model reasoning method, system and equipment and computer medium

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Embodiment Construction

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0041] see figure 1 , figure 1 It is a flow chart of a deep learning model reasoning method provided by the embodiment of this application.

[0042] A deep learning model reasoning method provided in an embodiment of the present application may include the following steps:

[0043] Step S101: Determine the channel quantization parameters of each channel in the target deep learning model.

[0044] In practical applications, the channel quantization parameters of each channel in th...

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Abstract

The invention discloses a deep learning model reasoning method, system and device, and a computer medium. The method comprises the following steps: determining channel quantization parameters of each channel in a target deep learning model; determining a branch quantization parameter of the model branch based on the channel quantization parameter according to a relationship between a channel in the target deep learning model and the model branch; quantizing the target deep learning model based on the branch quantization parameters to obtain a quantized deep learning model; and reasoning based on the quantized deep learning model to obtain a reasoning result. In the application, the channel quantization parameter of each channel in the target deep learning module is determined, fine-grained quantization parameter determination is realized, the branch quantization parameter of the model branch is determined based on the channel quantization parameter, reasoning is performed based on the quantized deep learning model, and the reasoning result is obtained. The deep learning model reasoning based on the fine-grained channel quantization parameters is realized, and the reasoning precision of the deep learning model is improved.

Description

technical field [0001] The present application relates to the technical field of deep learning, and more specifically, relates to a deep learning model reasoning method, system, device and computer medium. Background technique [0002] Deep learning (that is, deep neural network) is a branch of machine learning. It is an algorithm that uses artificial neural networks as a framework to realize pattern recognition (that is, reasoning) by learning (that is, training) information features. Deep learning is characterized by the use of unsupervised or semi-supervised feature learning and hierarchical feature extraction to replace manual feature extraction. The engineering implementation of deep learning training and inference process generally depends on the deep learning framework. The deep learning framework mainly focuses on the rich deep learning model structure and operator expression, but has no idea how to efficiently implement inference tasks on different hardware backends...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06N5/04
CPCG06N3/08G06N5/04G06N3/045
Inventor 徐天赐景璐
Owner INSPUR SUZHOU INTELLIGENT TECH CO LTD
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