The wide adoption of smart devices and Internet-of-Things (IoT) sensors has led to massive
growth in data generation at the edge of the Internet over the past decade. Intelligent real …

Due to limited resources on edge and different characteristics of deep neural network (DNN)
models, it is a big challenge to optimize DNN inference performance in terms of energy …

Mobile-edge computing (MEC) has emerged as a promising supporting architecture
providing a variety of resources to the network edge, thus acting as an enabler for edge …

Edge intelligence emerges as a novel paradigm facilitating real-time training and inference
at the wireless edge, empowering critical applications. This necessitates the dense …

In recent developments in machine learning, a trend has emerged where larger models
achieve better performance. At the same time, deploying these models in real-life scenarios …

With the proliferation of edge computing and Internet of Things (IoT), billions of edge devices
(eg, smartphone, AR/VR headset, autonomous car, etc) are deployed in our daily life and …

In order to support last-mile wireless connectivity of computation-intensive applications,
edge systems can benefit from secondary (ie, opportunistic) utilization of licensed spectrum …

Deep learning has achieved remarkable successes in various areas such as computer
vision and natural language processing. Many sophisticated models have been proposed to …

Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-
capable devices at the network edge, that is, edge servers, can significantly reduce capture …

With the advent of emerging IoT applications, such as autonomous driving, digital-twin,
metaverse, etc., featuring massive data sensing, analyzing, inference, and critical latency in …