With the increasing popularity of the Internet of Things and massive Machine Type
Communication technologies, the number of connected devices is rising. However, although …

Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications
depends on their smoothness and immersiveness. Modern AR applications applying …

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …

Wearable, embedded, and IoT devices are a centrepiece of many ubiquitous computing
applications, such as fitness tracking, health monitoring, home security and voice assistants …

The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …

Edge-Cloud collaboration for deep learning inference has been actively studied, to enhance
the inference performance by leveraging both Edge and Cloud resources. However …

Empowered by machine learning, edge devices including smartphones, wearable, and IoT
devices have become growingly intelligent, raising conflicts with the limited resource. On …

Embedded distributed inference of Neural Networks has emerged as a promising approach
for deploying machine-learning models on resource-constrained devices in an efficient and …

Radical progress in the field of deep learning (DL) has led to unprecedented accuracy in
diverse inference tasks. As such, deploying DL models across mobile platforms is vital to …

Facing enormous data generated at the network edge, Edge Intelligence (EI) emerges as
the fusion of Edge Computing and Artificial Intelligence, revolutionizing edge data …