MobileNet network optimization based on convolutional block attention module

Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and complex model structures require more calculating resources.Since people generally can only carry and use mobile and portable devices in application scenarios,neural networks have limitations in terms of calculating resources,size and power consumption.Therefore,the efficient lightweight model MobileNet is used as the basic network in this study for optimization.First,the accuracy of the MobileNet model is improved by adding methods such as the convolutional block attention module(CBAM)and expansion convolution.Then,the MobileNet model is compressed by using pruning and weight quantization algorithms based on weight size.Afterwards,methods such as Python crawlers and data augmentation are employed to create a garbage classification data set.Based on the above model optimization strategy,the garbage classification mobile terminal application is deployed on mobile phones and raspberry pies,realizing completing the garbage classification task more conveniently.

An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning

The 5G network connecting billions of Internet of things(IoT)devices will make it possible to harvest an enormous amount of real-time mobile data.Furthermore,the 5G virtualization architecture will enable cloud computing at the(network)edge.The availability of both rich data and computation power at the edge has motivated Internet companies to deploy artificial intelligence(AI)there,creating the hot area of edge-AI.Edge learning,the theme of this project,concerns training edge-AI models,which endow on IoT devices intelligence for responding to real-time events.However,the transmission of high-dimensional data from many edge devices to servers can result in excessive communication latency,creating a bottleneck for edge learning.Traditional wireless techniques deigned for only radio access are ineffective in tackling the challenge.Attempts to overcome the communication bottleneck has led to the development of a new class of techniques for intelligent radio resource management(RRM),called data-importance aware RRM.Their designs feature the interplay of active machine learning and wireless communication.Specifically,the metrics that measure data importance in active learning(e.g.,classification uncertainty and data diversity)are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.This article aims at providing an introduction to the emerging area of importance-aware RRM.To this end,we will introduce the design principles,survey recent advancements in the area,discuss some design examples,and suggest some promising research opportunities.