Deploying deep learning (DL) models for visual recognition on embedded systems is often
constrained by their limited compute power and storage capacity and has stringent latency …

Running deep visual analytics models for real-time applications is challenging for mobile
devices. Offloading the computation to edge server can mitigate computation bottleneck at …

Recent years have witnessed sensors becoming an indispensable part of our life with the
camera being one of the most popular and widely deployed sensors. The camera gives rise …

The rapid progress in edge computing (EC) and 5G wireless communication technology has
opened up novel opportunities for intelligent applications driven by Deep Neural Networks …

Wearable devices with built-in cameras present interesting opportunities for users to capture
various aspects of their daily life and are potentially also useful in supporting users with low …

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

Offloading tasks to the edge or the Cloud has the potential to improve accuracy of
classification and detection tasks as more powerful hardware and machine learning models …

As the complexity of Deep Neural Network (DNN) models increases, their deployment on
mobile devices becomes increasingly challenging, especially in complex vision tasks such …

Efforts to leverage the benefits of Deep Learning (DL) models for performing inference in
resource constrained embedded devices is very popular nowadays. Researchers worldwide …

To enable computation-intensive video analytics, streaming video data and offloading
computation from the source to the inference server running deep neural networks has now …