This department provides an overview the progress the authors have made to the emerging
area of embedded and mobile forms of on-device deep learning. Their work addresses two …

Sensor-equipped smartphones and wearables are transforming a variety of mobile apps
ranging from health monitoring to digital assistants. However, reliably inferring user behavior …

Detecting and reacting to user behavior and ambient context are core elements of many
emerging mobile sensing and Internet-of-Things (IoT) applications. However, extracting …

How can the advantages of deep learning be brought to the emerging world of embedded
IoT devices? The authors discuss several core challenges in embedded and mobile deep …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

Recent years have witnessed an exponential increase in the use of mobile and embedded
devices. With the great success of deep learning in many fields, there is an emerging trend …

In the last few years, we have witnessed an exponential growth in research activity into the
advanced training of convolutional neural networks (CNNs), a field that has become known …

Modern mobile applications are benefiting significantly from the advancement in deep
learning, eg, implementing real-time image recognition and conversational system. Given a …

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 …

Edge accelerator is a class of brand-new purpose-built System On a Chip (SoC) for running
deep learning models efficiently on edge devices. These accelerators offer various benefits …