Recently, deep neural networks (DNNs) have been applied to most intelligent applications
and deployed on different kinds of devices. However, DNN inference is resource-intensive …

Deep Neural Networks (DNN) have been widely used in a large number of application
scenarios. However, DNN models are generally both computation-intensive and memory …

In mobile edge computing systems, a task offloading approach should balance efficiency,
adaptability, trust management, and reliability. This approach aims to maximize resource …

With deep neural networks (DNNs) gaining popularity for tasks like object detection and
image segmentation in domains like autonomous driving and smart agriculture, DNN …

Deep neural network (DNN) inference poses unique challenges in serving computational
requests due to high request intensity, concurrent multi-user scenarios, and diverse …

In recent years, deep neural networks (DNNs) have witnessed a booming of artificial
intelligence Internet of Things applications with stringent demands across high accuracy and …

Deep neural network (DNN)-enabled edge intelligence has been widely adopted to support
a variety of smart applications because of its ability to preserve privacy and conserve …

With the rapid development of deep learning, recent research on intelligent and interactive
mobile applications (eg, health monitoring, speech recognition) has attracted extensive …

The increasing expansion of Internet-of-Things (IoT) in the world requires Big Data analytic
infrastructures to produce valuable knowledge in IoT applications. IoT includes devices with …

Deep neural networks (DNN) are the de-facto solution behind many intelligent applications
of today, ranging from machine translation to autonomous driving. DNNs are accurate but …