AI in the edge


"Improving edge device environments through the co-optimization 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."

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.

Rudy Lauwereins imec Management team
Axel Nackaerts imec Management team and WP1 Lead: Use Cases
Bart Dhoedt UGent - IDLab WP2 Lead: Inter-device Algorithms
Wilfried Philips UGent - IPI WP3 Lead: Intra-Device Algorithms
Peter Debacker imec WP4 Lead: Software Tool Suites, Software to Hardware Mapping
Marian Verhelst KULeuven - MICAS WP5 Lead: Extreme Edge Hardware

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

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