In the evolving landscape of the digital era, edge computing emerges as an essential
paradigm, especially critical for low-latency, real-time applications and Internet of Things …

Edge computing is becoming a reality and attracts an increasing interest both from
academia and industry. This is driven by its promises of enabling/improving use cases …

Machine learning (ML) applications have experienced remarkable growth and integration
into various domains. However, challenges with cloud-based deployments, such as latency …

There are over 30 billion devices at the network edge. This is largely driven by the
unprecedented growth of the Internet-of-Things (IoT) and 5G technologies. These devices …

In this paper, we systematically evaluate the inference performance of the Edge TPU by
Google for neural networks with different characteristics. Specifically, we determine that …

With the tremendous growth of IoT, the role of IoT cloud gateways in facilitating
communication between IoT devices and the cloud has become more important than ever …

Deep Learning and Machine Learning algorithms achieved good results in various tasks,
from computer vision and speech recognition to condition monitoring and predictive …

Mobile and stationary mechatronic systems are often driven by hydraulic actuators which are
controlled using valves. Hydraulic systems have a high power density, are robust and cost …

Our growing reliance on edge-computing applications makes it crucial to improve the
reliability of edge-computing systems. With multiple classes of edge-computing applications …

Modern Cyber-Physical Production Systems get more and more intelligent by higher
capacities of the used resources and more resource-efficient AI-algorithms. However, a …