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 …

Deep learning plays an increasingly important role in human life. However, resource-
constrained IoT devices are still inefficient in performing deep neural network (DNN) …

Deep learning technologies are empowering IoT devices with an increasing number of
intelligent services. However, the contradiction between resource-constrained IoT devices …

Deep neural networks (DNN) have become indispensable tools for intelligent applications
today. The demand for deploying DNN on the edge devices increases dramatically …

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 …

As the number of edge devices with computing resources (eg, embedded GPUs, mobile
phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col …

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 …

This paper investigates the problem of performing distributed deep learning (DDL) to train
machine learning (ML) models at the edge with resource-constrained embedded devices …

Industrial Internet of Things (IIoT) applications can benefit from leveraging edge computing.
For example, applications underpinned by deep neural networks (DNN) models can be …

Recent advances in artificial intelligence have driven increasing intelligent applications at
the network edge, such as smart home, smart factory, and smart city. To deploy …