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
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.
Multiple research groups collaborate on this research domain. This table mentions the contact person and his/her affiliation.
Management team, imec
Management team & WP1 Lead: Use Cases, imec
WP2 Lead: Inter-device Algorithms, UGent - IDLab
WP3 Lead: Intra-Device Algorithms, UGent - IPI
WP4 Lead: Software Tool Suites, Software to Hardware Mapping, imec
WP5 Lead: Extreme Edge Hardware, KU Leuven - MICAS