Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet
connectivity to meet the requirements of different applications, ranging from virtual reality to …

With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …

As mobile devices continuously generate streams of images and videos, a new class of
mobile deep vision applications are rapidly emerging, which usually involve running deep …

Video analytics applications use edge compute servers for processing videos. Compressed
models that are deployed on the edge servers for inference suffer from data drift where the …

Today's trend of building ever larger language models (LLMs), while pushing the
performance of natural language processing, adds significant latency to the inference stage …

The rapid development of deep learning algorithms, as well as innovative hardware
advancements, encourages multi-DNN workloads such as augmented reality applications …

Edge-cloud collaborative video analytics is transforming the way data is being handled,
processed, and transmitted from the ever-growing number of surveillance cameras around …

Accurate, real-time object detection on resource-constrained devices enables autonomous
mobile vision applications such as traffic surveillance, situational awareness, and safety …

Emerging deep learning-based video analytics tasks demand computation-intensive neural
networks and powerful computing resources on the cloud to achieve high accuracy. Due to …

Computer networks are dealing with growing complexity, given the ever-increasing volume
of data produced by all sorts of network nodes. Performance improvements are a non-stop …