Artificial intelligence (AI) on an edge device has enormous potential, including advanced
signal filtering, event detection, optimization in communications and data compression …

Modern machine learning (ML) applications are often deployed in the cloud environment to
exploit the computational power of clusters. However, this in-cloud computing scheme …

With the rapid growth of the number of devices connected to the Internet, there is a trend to
move intelligent processing of the generated data with deep neural networks (DNNs) from …

There is increasing demand for specialized hardware for training deep neural networks
(DNNs), both in edge/IoT environments and in high-performance computing systems. The …

The modern trend of moving artificial intelligence computation near to the origin of data
sources has increased the demand for new hardware and software suitable for such …

Artificial Intelligence (AI) applications have become ubiquitous across the computing world,
spanning from large-scale data centers to mobile and Internet of Things (IoT) devices. 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 …

Recent years have witnessed the proliferation of mobile computing and Internet-of-Things
(IoT), where billions of mobile and IoT devices are connected to the Internet, generating …

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and
Architecture focuses on hardware architecture and embedded deep learning, including …

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 …