Deep neural networks are known to be vulnerable to adversarial examples, ie, images that
are maliciously perturbed to fool the model. Generating adversarial examples has been …

Most of the research on deep neural networks so far has been focused on obtaining higher
accuracy levels by building increasingly large and deep architectures. Training and …

Ultradense Internet of Things (IoT) mesh networks and machine-to-machine
communications herald an enormous opportunity for new computing paradigms and are …

Nowadays deep neural networks are widely used to accurately classify input data. An
interesting application area is the Internet of Things (IoT), where a massive amount of sensor …

In recent years there has been a notable increase in the size of commonly used image
classification models. This growth has empowered models to recognize thousands of …

Nowadays artificial neural networks are widely used to accurately classify and recognize
patterns. An interesting application area is the Internet of Things (IoT), where physical things …

The omnipresence of real-time embedded systems enabled the smart paradigm, with smart
devices being adopted by a larger portion of people worldwide. Such devices are capable of …

While hand pose estimation is a critical component of most interactive extended reality and
gesture recognition systems, contemporary approaches are not optimized for computational …

It is desirable to train convolutional networks (CNNs) to run more efficiently during inference.
In many cases however, the computational budget that the system has for inference cannot …

We consider a distributed system, consisting of a heterogeneous set of devices, ranging
from low-end to high-end. These devices have different profiles, eg, different energy …