Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Neural network quantification method and device and computer readable storage medium

A technology of neural network and quantization method, applied in computer-readable storage media, neural network quantization method, and device fields, can solve problems such as loss of precision, and achieve high quantization accuracy and good quantization effect

Pending Publication Date: 2020-07-10
CANAAN BRIGHT SIGHT CO LTD
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the quantization parameters used in the quantization process of the neural network in the above-mentioned prior art lead to unnecessary loss of precision

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Neural network quantification method and device and computer readable storage medium
  • Neural network quantification method and device and computer readable storage medium
  • Neural network quantification method and device and computer readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0033]In the present invention, it should be understood that terms such as "comprising" or "having" are intended to indicate the presence of features, numbers, steps, acts, components, parts or combinations thereof disclosed in the specification, and are not intended to exclude one or multiple other features, numbers, steps, acts, parts, parts or combinations thereof.

[0034] In addition, it should be noted that, in the case of no...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a neural network quantification method and device, and a computer readable storage medium. The method comprises the steps of determining distribution data of activation output of a target network layer of a neural network according to a correction data set; determining the target quantization range of the target network layer according to the distribution data; and performing fixed-point quantization on the target network layer according to the target quantization range and the target quantization bit width. By utilizing the method, the precision loss in neural network quantization can be reduced, and a better quantization effect is achieved.

Description

technical field [0001] The invention belongs to the field of neural network calculation, and in particular relates to a neural network quantization method, device and computer-readable storage medium. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] In recent years, with the rapid development of deep learning, deep learning has been proved to have good results in tasks including image classification (Image Classification), object detection (Object Detection), natural language processing (Natural Language Processing) and so on. Deep learning uses a large amount of data to train a neural network model with functions such as analysis and prediction. However, as the scale of the neural network model increases, more storage resources, bandwidth resources, and computing resourc...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/063G06N3/08
CPCG06N3/063G06N3/08G06N3/045
Inventor 霍成海张楠赓
Owner CANAAN BRIGHT SIGHT CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com