Customized deep neural network model compression method and system based on cloud edge cooperation

Abstract

The invention discloses a customized deep neural network model compression method and system based on cloud-edge cooperation, and the method comprises the steps of carrying out the classification of concerned classification and unconcerned classification of a network model based on a high-precision network model, trained for a long time, of a cloud end and the personalized demands of a user; and then, in combination with the concerned classification, the data set of the user and the model compression ratio, through a neural network model compression method based on knowledge arrangement, lightening the model, so that the scene requirement that the user edge node resources are limited is met, the model reasoning speed is increased, and meanwhile, certain model accuracy is ensured. Accordingto the model training method based on user attention classification requirements and the knowledge distillation technology, verification is carried out based on a public data set in a picture classification scene.

Description

technical field [0001] The invention belongs to the field of edge computing and deep learning, and in particular relates to a customized deep neural network model compression method and system based on cloud-edge collaboration. Background technique [0002] Deep learning is an important branch of artificial intelligence. Deep learning, through deep neural networks, is having an increasingly important influence in fields such as machine vision, NLP, and speech recognition. With the development of edge computing, deep learning begins to play a more important role in a wider range of hardware devices - that is, "edge empowerment", which enables many hardware in the Internet of Things to have more powerful identification, detection, and intelligent processing. ability. However, many hardware devices in edge computing scenarios have relatively weak computing power, and the inference speed of deep neural networks is relatively slow. How to optimize and accelerate deep neural net...

AI Technical Summary

Problems solved by technology

The present invention can solve the problem that edge scenarios are limited by hardware constraints, such as insufficient CPU / GPU / NPU / memory size and computing power, resulting in the inability to deploy on the edge side or perform real-time reasoning; in fragmented edge scenarios, user-based Customized requirements for model compression and acceleration

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