As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart
devices act in concert, rarely requiring human intervention, posing significant challenges in …

In recent years, affective computing has become a topic of considerable interest, driven by
its ability to enhance several domains, such as mental health monitoring, human–computer …

A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate
computation such as image processing and numerical processing applications. However, in …

Privacy and security challenges in Machine Learning (ML) have become increasingly
severe, along with ML's pervasive development and the recent demonstration of large attack …

Graphics Processing Units (GPU) are increasingly deployed on Cyber-physical Systems
(CPSs), frequently used to perform real-time safety-critical functions, such as object …

Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user
distributes the desired computation between the IoT and cloud nodes. While achieving …

Many mobile applications have resorted to deep neural networks (DNNs) because of their
strong inference capabilities. Since both input data and DNN architectures could be …

As an essential technology underpinning trusted computing, the trusted execution
environment (TEE) allows one to launch computation tasks on both on-and off-premises …

Abstract Deep Neural Networks (DNNs) are becoming common in" learning-enabled" time-
critical applications such as autonomous driving and robotics. One approach to protect DNN …

For mobile devices, it is compelling to run sensitive GPU computation within a TrustZone
trusted execution environment (TEE). To minimize GPU software deployed in TEE, the …