Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …

Machine learning is an expanding field with an ever-increasing role in everyday life, with its
utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this …

There has been much interest in deploying deep learning algorithms on low-powered
devices, including smartphones, drones, and medical sensors. However, full-scale deep …

Although mission-critical applications require the use of deep neural networks (DNNs), their
continuous execution at mobile devices results in a significant increase in energy …

Edge computing is a new paradigm enabling intelligent applications for the Internet of
Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the …

We design deep neural networks (DNNs) and corresponding networks' splittings to distribute
DNNs' workload to camera sensors and a centralized aggregator on head-mounted devices …

Machine learning requires a large volume of sample data, especially when it is used in high-
accuracy medical applications. However, patient records are one of the most sensitive …

This article considers a setting where embedded devices are used to acquire and classify
images. Because of limited computing capacity, embedded devices rely on a parsimonious …

Thanks to advances in computer vision and AI, there has been a large growth in the demand
for cloud-based visual analytics in which images captured by a low-powered edge device …

On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-
preserving machine learning at the edge. However, the intensive training workload and …