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Consortium

Team

AI Flanders

Contact

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.

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Management team, <a href="https://www.imec-int.com/en" target="_blank" rel="noopener">imec</a>

Steven Latré

Management team &amp; WP1 Lead: Use Cases, <a href="https://www.imec-int.com/en" target="_blank" rel="noopener">imec</a>

Tanguy Coenen

WP2 Lead: Inter-device Algorithms, <a href="https://idlab.ugent.be/" target="_blank" rel="noopener">UGent - IDLab</a>

Bart Dhoedt

WP3 Lead: Intra-Device Algorithms, <a href="https://ipi.ugent.be/" target="_blank" rel="noopener">UGent - IPI</a>

Wilfried Philips

WP4 Lead: Software Tool Suites, Software to Hardware Mapping, <a href="https://www.imec-int.com/en" target="_blank" rel="noopener">imec</a>

Bart Goossens

WP5 Lead: Extreme Edge Hardware, <a href="https://www.esat.kuleuven.be/micas/" target="_blank" rel="noopener">KU Leuven - MICAS</a>

Marian Verhelst

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

Contacts

Real-time and power-efficient 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.