Data processing method, system and device and readable storage medium

A data processing and parameter technology, applied in the field of data processing, can solve the problems of less consideration, low data efficiency, and inability to optimally deploy, and achieve the effect of optimal deployment and improved efficiency

Active Publication Date: 2020-11-06
SUZHOU LANGCHAO INTELLIGENT TECH CO LTD
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Regardless of the quantization model, researchers pre-set the quantization bit width based on prior knowledge before performing quantization processing, and seldom consider the actual model structure of the network and the hardware environment that needs to be deployed, resulting in the preset quantization bit width. It is suitable for the quantification of the network model structure, and cannot be optimally deployed in the corresponding hardware environment, resulting in low efficiency when using the network model to process data

Method used

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  • Data processing method, system and device and readable storage medium
  • Data processing method, system and device and readable storage medium
  • Data processing method, system and device and readable storage medium

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Embodiment Construction

[0053] The core of the present application is to provide a data processing method, system, device and readable storage medium for improving the efficiency of data processing.

[0054] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this 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.

[0055] In the existing technology, researchers pre-set the quantization bit width based on prior knowledge and then perform quantization processing, while seldom considering th...

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Abstract

The invention discloses a data processing method. The method comprises the steps of marking each layer of a network model as a key layer or a non-key layer according to acquired structure informationof the network model; respectively determining a quantization bit width range of the key layer and a quantization bit width range of the non-key layer according to hardware resource information needing to be deployed; determining the optimal quantization bit width of each layer of the network model in the quantization bit width range; and training the network model based on the optimal quantization bit width of each layer of the network model to obtain an optimal network model, and performing data processing by using the optimal network model. According to the optimal network model obtained based on optimal quantization bit width training, the model structure is compressed to the maximum extent under the condition that the optimal precision of the network model is guaranteed, optimal deployment of a hardware end is achieved, and then the efficiency of processing data through the optimal network model is improved. The invention also provides a data processing system and device, and a readable storage medium, which have the above beneficial effects.

Description

technical field [0001] The present application relates to the field of data processing, in particular to a data processing method, system, device and readable storage medium. Background technique [0002] With the continuous development of artificial intelligence technology, artificial intelligence technology has gradually been applied in daily life. In the field of artificial intelligence technology, deep learning is one of the more typical technologies. Although the capabilities of deep neural networks in image classification and detection are close to or surpass those of humans, in actual deployment, there are still problems such as large models and high computational complexity, which require high hardware costs. In practical applications, in order to reduce hardware costs, many neural networks are deployed on some terminal devices or edge devices. These devices generally have low computing power, and memory and power consumption are also limited. [0003] Therefore, i...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N3/04
CPCG06N3/082G06N3/045G06N3/063G06N3/0464G06N3/0495G06N3/09G06N3/091
Inventor 梁玲燕董刚赵雅倩
Owner SUZHOU LANGCHAO INTELLIGENT TECH CO LTD
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