Pushing artificial intelligence (AI) from central cloud to network edge has reached board
consensus in both industry and academia for materializing the vision of artificial intelligence …

Deep Neural Networks (DNNs) have drawn attention because of their outstanding
performance on various tasks. However, deploying full-fledged DNNs in resource …

As the key technology of artificial intelligence (AI), Deep Neural Networks (DNNs) have been
widely used in mobile applications, such as video analytics in autonomous driving …

Inference carried out on pretrained deep neural networks (DNNs) is particularly effective as
it does not require retraining and entails no loss in accuracy. Unfortunately, resource …

Deep learning inference is increasingly run at the edge. As the programming and system
stack support becomes mature, it enables acceleration opportunities within a mobile system …

5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous
capabilities of the end devices, edge servers, and the cloud and thus has the potential to …

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

Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

In a level-5 autonomous driving system, the autonomous driving vehicles (AVs) are
expected to sense the surroundings via analyzing a large amount of data captured by a …

Recent advancements in deep neural networks (DNNs) have enabled us to solve
traditionally challenging problems. To deploy a service based on DNNs, since DNNs are …