Offloading techniques are considered one of the key enablers of deep neural network (DNN)-
based artificial intelligence (AI) services on end devices with limited computing resources …

Implementing intelligent mobile applications on IoT devices with DNN technology has
become an inevitable trend. Due to the limitations of the size of DNN model deployed onto …

With the emergence of Industry 5.0, there has been a significant surge in the need for
intelligent services within the realm of smart devices. Currently, deep neural networks …

Task offloading is a widely used technology in edge computing (EC), which declines the
makespan of user task with the aid of resourceful edge servers. How to solve the competition …

Deep Neural Network (DNN) is a promising technology for Internet of Visual Things (IoVT)
devices to extract their visual information from unstructured data. However, it is hard to …

DNN offloading has become an important supporting technology for edge intelligence.
However, most of the existing works do not consider thread scheduling, which can achieve …

In recent years, deep learning algorithms are increasingly adopted by a wide range of data-
intensive and time-critical Internet of Things (IoT) applications. As a result, several new …

Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are widely used
in IoT related applications. However, inferencing pre-trained large DNNs and CNNs …

With the ever-increasing computational demand of DNN training workloads, distributed
training has been widely adopted. A combination of data, model and pipeline parallelism …

Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …