Deep Edge Intelligence

The goal of this event is to bring researchers in machine learning, AI, distributed systems, and electrical and electronic engineering together from across the globe.

Aim and Scope

With the breakthroughs in Deep Learning (DL), recent years have witnessed the booming of Artificial Intelligence (AI) applications and services. Driven by the rapid advances in mobile computing and the Artificial Intelligence of Things (AIoT), billions of mobile and IoT devices are connected to the Internet, generating zillion bytes of data at the network edge.

The ability to add intelligence to interconnected devices is at the forefront of this technological revolution. In this regard, conventional machine learning techniques have rapidly adapted to various applications in multiple domains. However, DL techniques, though having demonstrated unparalleled performance primarily in Computer Vision and Natural Language Processing fields, are often subjected to significant computation and memory costs as well as massive data requirements. This poses a great challenge to empower devices at the network edge with DL capability. Accelerated by the remarkable success of DL and IoT technologies, there is an urgent need to push the DL frontier to the network edge to fully unleash the potential values of big data. The emerging Edge Computing (EC) paradigm provides a promising way to enable this, which leverages on distributed computing concepts to push computational loads from the network core to the network edge with the aim to provide faster responses to end users.

Deep Edge Intelligence (DEI) is a combination of DL, AI, EC and IoT. It enables the development and deployment of DL and AI techniques, based on EC, on edge devices, e.g., IoT devices, where the data are generated, aiming to enable diverse use of AI for every person and every organization at any place.

This special session seeks to bring together research that sheds light on the ways in which AI, Deep Learning, IoT, edge and fog computing will mutually shape the future of the next generation of information technology. Topics of interest include, but are not limited to:

• AI model compression, pruning and efficiency on edge devices

• Emerging nanotechnologies and circuit/architecture designs for deep edge intelligence

• Uncertainty-aware deep edge intelligence in dynamic environments

• Energy, computation, memory and network optimization for DEI applications

• Intelligent security and privacy protection for DEI applications

• Intelligent quality assurance for DEI applications

• AI enabled IoT and Social IoT

• Federated learning for IoT or edge devices

• Machine learning enabled edge intelligence

• Efficient edge/fog-cloud integration for federated learning

• Privacy-aware and energy-aware machine learning

• Advanced scheduling methods for efficient training, inference, and caching

• Computational intelligence for IoT/cloud/edge/fog service deployment, selection and orchestration

• Applications of DEI in Industry 4.0, autonomous driving, smart grids, networked robots, Internet of Energy (IoE), etc.