Deep neural networks (DNNs) have been a pivotal technology in a myriad of fields, boasting
remarkable achievements. Nevertheless, their substantial workload and inherent …

In deep neural networks, the filters of convolutional layers play an important role in
extracting the features from the input. Redundant filters often extract similar features, leading …

Deep neural network models, especially CNNs, have a wide range of applications in many
fields, but their high computational power requirements limit the deployment applications in …

Training and inference of deep convolutional neural networks are usually slow due to the
depth of the network and the number of parameters in the network. Although high …

Although large‐scale pretrained convolutinal neural networks (CNN) models have shown
impressive transfer learning capabilities, they come with drawbacks such as high energy …

Deploying Deep Neural Networks (DNNs) on edge devices to handle artificial intelligence
(AI) tasks is increasingly important, but this is often limited by the computational and energy …

Recently, various normalization layers have been proposed to stabilize the training of deep
neural networks. Among them, group normalization is a generalization of layer normalization …

At present, edge computing has attracted widespread attention because of its potential to
overcome the problems of high latency and high network occupancy in cloud computing, but …