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 …

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 …

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 …

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

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

In recent years, artificial intelligence (AI) and machine learning (ML) are resha** 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 …