Deep neural networks are the cornerstone of many mobile intelligent systems, and their
inference processes bring about computation-intensive tasks. Device-edge cooperative …

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

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

Deep Neural Network (DNN) models have achieved excellent performance in many
inference tasks and have been widely used in many intelligent applications. However, DNN …

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

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

The traditional approach to distributed deep neural network (DNN) inference in edge
computing systems is data-distributed inference. In this paradigm, each worker has a pre …

Various intelligent applications based on non-chain DNN models are widely used in Internet
of Things (IoT) scenarios. However, resource-constrained IoT devices usually cannot afford …

Owing to remarkable improvements in deep neural networks (DNNs), various computation-
intensive and delay-sensitive DNN services have been developed for smart IoT devices …

Deep Neural Networks (DNNs) have achieved excellent performance in intelligent
applications. Nevertheless, it is elusive for devices with limited resources to support …