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 …

In a few years, the world will be populated by billions of connected devices that will be
placed in our homes, cities, vehicles, and industries. Devices with limited resources will …

We are living in a world where massive end devices perform computing everywhere and
everyday. However, these devices are constrained by the battery and computational …

Abstract In the Internet of Things enabled intelligent transportation systems, a huge amount
of vehicle video data has been generated and real-time and accurate video analysis are …

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 immense increase in multimedia-on-demand traffic that refers to audio, video, and
images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to …

The implementation of smart technology in cities is often hailed as the solution to many
urban challenges such as transportation, waste management, and environmental protection …

Video streaming is crucial for AI applications that gather videos from sources to servers for
inference by deep neural nets (DNNs). Unlike traditional video streaming that optimizes …

Video analytics will drive a wide range of applications with great potential to impact society.
A geographically distributed architecture of public clouds and edges that extend down to the …

To cope with the high resource (network and compute) demands of real-time video analytics
pipelines, recent systems have relied on frame filtering. However, filtering has typically been …