This study focuses on sensor classification using machine learning algorithms, to improve
data aggregation on the Edge. This aspect is particularly important in large-scale Internet of …

There is a growing demand to deploy computation-intensive deep learning (DL) models on
resource-constrained mobile devices for real-time intelligent applications. Equipped with a …

The explosive growth of various types of big data and advances in AI technologies have
catalyzed a new type of workloads called multi-modal DNNs. Multi-modal DNNs are capable …

End-to-end edge intelligence systems (EIS) at the Internet's edge have been receiving ever-
increasing attention in both academia and industry, driven by a myriad of factors, including …

Split Neural Network is a state-of-the-art distributed machine learning technique to enable
on-device deep learning applications without accessing to local data. Recently, Abuadbba …

Recent years witness a trend of applying large-scale distributed deep learning algorithms
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …

In the semiconductor industry, the utilization of advanced visual inspection technology
confronts challenges due to the scarcity of the defect data and the substantial sizes of the …

Neural networks (NNs) are becoming an increasingly important part of mobile robot control
systems. Compared with traditional methods, NNs (and other data-driven techniques) …

Atrial Fibrillation (AF) is the most common cardiac rhythm disorder. Advance knowledge of
an imminent switch from sinus rhythm (SR) to AF could prompt patients to take preventive …

Today, deep learning optimization is primarily driven by research focused on achieving high
inference accuracy and reducing latency. However, the energy efficiency aspect is often …