The Internet revolution and Moore's Law drove the rapid expansion of connected consumer
electronics. As massive data is generated by Internet of Things (IoT) devices, edge …

Artificial intelligence (AI) systems are increasingly popular in many applications.
Nevertheless, AI technologies are still developing, and many issues need to be addressed …

Brain-inspired hyperdimensional computing (HDC) is continuously gaining remarkable
attention. It is a promising alternative to traditional machine-learning approaches due to its …

With significant potential improvement in device-to-device (D2D) communication due to
improved wireless link capacity (eg, 5G and NextG systems), a collaboration of multiple …

To maximize the performance and energy efficiency of Spiking Neural Network (SNN)
processing on resource-constrained embedded systems, specialized hardware …

Machine Learning (ML) has become indispensable for real-world complex systems, such as
perception systems of autonomous systems and vehicles. However, ML-based systems are …

Neural networks running on low-power edge devices can help in achieving ubiquitous
computing with limited infrastructure. When such edge devices are deployed in conventional …

Redundancy-reliability calculation for deep neural networks is of great importance for their
lightweight operation. This article proposes a method for calculating the redundancy …

The aim of survival analysis in healthcare is to estimate the probability of occurrence of an
event, such as a patient's death in an intensive care unit (ICU). Recent developments in …

In this paper, we investigate the robustness of the ML end application performance to
network Quality of Service (QoS) degradation, and the ways to improve it. We introduce a …