The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

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 …

Abstract “Unmanned aerial vehicles”(UAVs) are now being used for a wide range of
surveillance applications. Specifically, the detection of on-ground vehicles from UAV images …

As “deep neural networks”(DNNs) achieve increasing accuracy, they are getting employed
in increasingly diverse applications, including security-critical applications such as medical …

There are many deep learning (eg DNN) powered mobile and wearable applications today
continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of …

Deep neural networks (DNNs) have been successfully utilized in many scientific problems
for their high prediction accuracy, but their application to genetic studies remains …

In recent years, researchers have focused on reducing the model size and number of
computations (measured as “multiply-accumulate” or MAC operations) of DNNs. The energy …

Real-world Facial Expression Recognition (FER) systems require models to constantly learn
and adapt with novel data. Traditional Machine Learning (ML) approaches struggle to adapt …

This work presents a structured view of the state-of-the-art research on Artificial Intelligence
(AI), from the point of view of efficiency and reduction of the energy consumption of AI …

As convolutional neural networks (CNNs) improve in accuracy, their model size and
computational overheads have also increased. These overheads make it challenging to …