Deep neural networks offer considerable potential across a range of applications, from
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …

Deep learning has recently become immensely popular for image recognition, as well as for
other recognition and pattern matching tasks in, eg, speech processing, natural language …

The success of deep learning comes at the cost of very high computational complexity.
Consequently, Internet of Things (IoT) edge nodes typically offload deep learning tasks to …

The future will be populated with intelligent devices that require inexpensive, low-power
hardware platforms. Deep neural networks have evolved to be the state-of-the-art technique …

TinyML is a fast-growing multidisciplinary field at the intersection of machine learning,
hardware, and software, that focuses on enabling deep learning algorithms on embedded …

Edge computing and artificial intelligence (AI), especially deep learning algorithms, are
gradually intersecting to build the novel system, namely edge intelligence. However, the …

Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …

Deep learning is now present in a wide range of services and applications, replacing and
complementing other machine learning algorithms. Performing training and inference of …

While machine learning is traditionally a resource intensive task, embedded systems,
autonomous navigation, and the vision of the Internet of Things fuel the interest in resource …

The growing number of low-power smart devices in the Internet of Things is coupled with the
concept of “edge computing” that is moving some of the intelligence, especially machine …