Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

Deep learning is currently widely used in a variety of applications, including computer vision
and natural language processing. End devices, such as smartphones and Internet-of-Things …

The Internet of Things (IoT) has created a ubiquitously connected world powered by a
multitude of wired and wireless sensors generating a variety of heterogeneous data over …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …

Most existing Augmented Reality (AR) and Mixed Reality (MR) systems are able to
understand the 3D geometry of the surroundings but lack the ability to detect and classify …

Applying deep convolutional neural networks (NN) to video data at scale poses a substantial
systems challenge, as improving inference accuracy often requires a prohibitive cost in …

The computation for today's intelligent personal assistants such as Apple Siri, Google Now,
and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires …

Recent advances in artificial intelligence have driven increasing intelligent applications at
the network edge, such as smart home, smart factory, and smart city. To deploy …