Deep learning inference is increasingly run at the edge. As the programming and system
stack support becomes mature, it enables acceleration opportunities in a mobile system …

Modern Deep Learning systems heavily rely on distributed training over high-performance
accelerator (eg, TPU, GPU)-based hardware platforms. Examples today include Google's …

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation
paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless …

With the proliferation of mobile computing and Internet of Things (IoT), massive mobile and
IoT devices are connected to the Internet. These devices are generating a huge amount of …

The rapidly expanding number of Internet of Things (IoT) devices is generating huge
quantities of data, but public concern over data privacy means users are apprehensive to …

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI
refers to the practice of doing AI computations near the users at the network's edge, instead …

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

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 …

As the number of edge devices with computing resources (eg, embedded GPUs, mobile
phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col …