With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

Recurrent neural networks are effective models to process sequences. However, they are
unable to learn long-term dependencies because of their inherent sequential nature. As a …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Artificial Intelligence (AI) technology is a huge opportunity for the Industrial Internet of Things
(IIoT) in the fourth industrial revolution (Industry 4.0). However, most AI-driven applications …

GPUs are critical for maximizing the throughput-per-Watt of deep neural network (DNN)
applications. However, DNN applications often underutilize GPUs, even when using large …

In recent years, artificial intelligence (AI) and machine learning (ML) are reshaping society's
production methods and productivity, and also changing the paradigm of scientific research …

Benchmarking deep learning algorithms before deploying them in hardware-constrained
execution environments, such as imaging satellites, is pivotal in real-life applications …

Graphics processing units (GPUs) are continually evolving to cater to the computational
demands of contemporary general-purpose workloads, particularly those driven by artificial …

Large Language Models (LLMs) have seen great advance in both academia and industry,
and their popularity results in numerous open-source frameworks and techniques in …

Recently, the interest in spiking neural networks (SNNs) remarkably increased, as up to now
some key advances of biological neural networks are still out of reach. Thus, the energy …