The evolution of the Internet of Things (IoT) has been driving the explosive growth of deep
neural network (DNN)-based applications and processing demands. Hence, edge …

The key impediments to deploying deep neural networks (DNNs) in Internet of Things (IoT)
edge environments lie in the gap between the expensive DNN computation and the limited …

With the rapid development of computing power and artificial intelligence, IoT devices
equipped with ubiquitous sensors are gradually installed with intelligence. People can enjoy …

Multiaccess edge computing (MEC) is a promising approach to enhancing IoT devices
running AI-based services. Especially, the edge–cloud architecture acts as a strong …

New applications such as intelligent manufacturing, autonomous vehicles and smart cities
drive large-scale deep learning models deployed in the Internet of Things (IoT) edge …

Deep Neural Networks (DNNs) have become an essential and important supporting
technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of …

Collaborative deep neural network (DNN) inference over edge and cloud is emerging as an
effective approach for enabling several Internet of Things (IoT) applications. Edge devices …

Deep Neural Networks (DNNs) are widely used to analyze the abundance of data collected
by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data …

In the era of beyond 5G technology, it is expected that more and more applications can use
deep neural network (DNN) models for different purposes with minimum inference time …

Modern Internet of Things (IoT) systems are increasingly leveraging deep neural networks
(DNNs) with the goal of enabling intelligence at the edge of the network. While applying …