AI in the edge

Improving edge device environments through the co-optimisation between power efficient Al processors and advanced machine learning tasks with as purpose to increase the real-time performance, reliable low-latency communication, power-efficient processing and data security.

Real-time and power-efficient AI in the edge

Icon for AI in the edge

Powerful smartphones, cars and robots can take over tasks from the cloud (edge ​​computing), leading to faster decisions, lower energy consumption and better privacy protection. This opens new possibilities for AI applications based on intelligent systems and components with low power, often on batteries.
The research will lead to application-oriented cases far ahead of the state of the art for distributed and hierarchical AI systems, advanced signal processing, and learning algorithms for extracting actionable information from the edge.

Structure of the challenge

Visual of the structure


Multiple research groups collaborate on this research domain. This table mentions the contact person and his/her affiliation.

Rudy Lauwereins

Management team, imec

Axel Nackaerts

Management team & WP1 Lead: Use Cases, imec

Bart Dhoedt

WP2 Lead: Inter-device Algorithms, UGent - IDLab

Wilfried Philips

WP3 Lead: Intra-Device Algorithms, UGent - IPI

Peter Debacker

WP4 Lead: Software Tool Suites, Software to Hardware Mapping, imec

Marian Verhelst

WP5 Lead: Extreme Edge Hardware, KU Leuven - MICAS