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 …

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 …

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

With the breakthroughs in deep learning, the recent years have witnessed a booming of
artificial intelligence (AI) applications and services, spanning from personal assistant to …

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 …

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 …

Recent advances in deep neural networks (DNNs) have substantially improved the accuracy
and speed of a variety of intelligent applications. Nevertheless, one obstacle is that DNN …

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 …

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 …

Deep learning shows great promise in providing more intelligence to augmented reality (AR)
devices, but few AR apps use deep learning due to lack of infrastructure support. Deep …