With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …

Object detection is one of the most challenging tasks in computer vision. With the advances
in semiconductor devices and chip technology, hardware accelerators have been widely …

In recent years, convolutional neural network (CNN)-based methods have been widely used
for optical remote sensing object detection and have shown excellent performance. Some …

This review article systematically summarizes the existing literature on utilizing machine
learning (ML) techniques for the control and monitoring of electric machine drives. It is …

Executing deep neural networks (DNNs) on resource-constraint edge devices, such as
drones, offers low inference latency, high data privacy, and reduced network traffic …

Abstract General-Purpose Processors (GPP)-based computers and Application Specific
Integrated Circuits (ASICs) are the typical computing platforms used to develop the back …

With the rise of various smart electronics and mobile/edge devices, many existing high-
accuracy convolutional neural network (CNN) models are difficult to be applied in practice …

The burgeoning complexity of contemporary deep learning models, while achieving
unparalleled accuracy, has inadvertently introduced deployment challenges in resource …

Object detection via deep neural networks has undergone considerable advancements in
recent years. Yet, the detection of smaller objects, specifically those with a few pixels (ie …

Conventionally, DNN models are trained once in the cloud and deployed in edge devices
such as cars, robots, or unmanned aerial vehicles (UAVs) for real-time inference. However …