With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

As an innovative strategy, edge computing has been considered a viable option to address
the limitations of cloud computing in supporting the Internet‐of‐Things applications …

The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

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 …

Edge computing is a new paradigm enabling intelligent applications for the Internet of
Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the …

The development of electric power systems has become more complex. Consequently,
electric power systems are operating closer to their limits and are more susceptible to …

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …

For model inference of convolutional neural networks (CNNs), we nowadays witness a shift
from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute-and …

Prevalent large transformer models present significant computational challenges for
resource-constrained devices at the Edge. While distributing the workload of deep learning …