Développer des solutions d'intelligence artificielle embarquée performantes, bas coût et basse consommation, nécessite la mise en commun de compétences variées, du développement de mémoires non-volatile, de capteurs, de circuits, sans oublier le développement d'algorithmes avancés, tels que l'apprentissage incrémental.

​​Until recently, everything you needed to know about AI was software-related. Edge AI has slightly changed the tune, with the need for on-device algorithm processing. As algorithms are becoming more and more complex to address most advanced AI, hardware is requiring new microelectronic solutions to meet the evolving demands of IoT packed with AI. Transfer cloud-based software solutions within a highly miniaturized chip is no small task. Of central importance is the need to achieve unprecedented efficiency and speed in the collection and analysis of data, while also managing power consumption and form factor. In the hardware domain, this will require innovative thinking and new paradigms in sensors, processors, memory, interconnection, and packaging.

Because conventional chips can't keep up with the upcoming of most sophisticated AI supported within devices, a growing need for semiconductor technologies has emerged. Yesterday's Cloud-based Web giant are now heavily investing into the semiconductor industry, and actively looking for new R&D solutions to migrate most AI to the edge. To help industry keep up the race and integrate AI into already ultra-miniaturized chips, CEA-Leti's launched a specific Edge AI program to pioneer quick and reliable semiconductor solutions, from computing, sensing to data storage.