Abstract Deep Neural Networks (DNNs) have gained widespread popularity in different
domain applications due to their dominant performance. Despite the prevalence of …

Recent advances in AI have motivated hardware manufacturers to design deep learning
friendly accelerators to keep with the ever-growing increases in model sizes and compu …

In this paper, we demonstrate the performance benefits of offloading deep learning
workloads to specialized hardware accelerators using an experimental setup consisting of a …

In mobile edge computing (MEC) systems, enhancing the learning capabilities of edge
nodes through Artificial Intelligence (AI) can improve the efficiency of dynamically allocating …

ARM TrustZone is a system-on-chip security solution that provides hardware guarantees to
isolate the untrusted applications running in the normal world from sensitive computation …

In today's era, many types of Artificial Intelligence (AI)-based applications are being
developed to fulfill a variety of needs, for example, counting objects recorded using a …

There is an increasing demand for integrating edge-based Artificial Intelligence (AI) into
complex large-scale network infrastructures. This need is driven by the requirement for faster …

The commercial industry has been working to develop ever-larger and more capable
machine learning (ML) models (such as recent models from OpenAI, Microsoft and Google …

Incorporating Machine Learning (ML) inference models into edge computing devices has
presented some performance and reliability enhancement challenges. Multi-threaded ML …

The emerging edge computing infrastructure provides distributed and heterogeneous
resources closer to where data is generated and where end-users are located, thereby …