The recent development of smart devices has lead to an explosion in data generation and
heterogeneity. Hence, current networks should evolve to become more intelligent, efficient …

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …

The number of devices, from smartphones to IoT hardware, interconnected via the Internet is
growing all the time. These devices produce a large amount of data that cannot be analyzed …

Recent trends in high-performance computing and deep learning have led to the
proliferation of studies on large-scale deep neural network training. However, the frequent …

In pursuit of higher inference accuracy, deep neural network (DNN) models have
significantly increased in complexity and size. To overcome the consequent computational …

Artificial intelligence has made remarkable progress in handling complex tasks, thanks to
advances in hardware acceleration and machine learning algorithms. However, to acquire …

Deep learning (DL) has gained great success in recent years, leading to state-of-the-art
performance in research community and industrial fields like computer vision and natural …

Modern deep learning models have been exploited in various domains, including computer
vision (CV), natural language processing (NLP), search and recommendation. In practical AI …

Federated Learning (FL) is an intriguing distributed machine learning approach due to its
privacy-preserving characteristics. To balance the trade-off between energy and execution …