India, Sept. 26 -- Breaking through a technological roadblock that has long limited efficient edge-AI learning, a team of French scientists developed the first hybrid memory technology to support adaptive local training and inference of artificial neural networks.

In a paper titled "A Ferroelectric-Memristor Memory for Both Training and Inference" published in Nature Electronics, the team presents a new hybrid memory system that combines the best traits of two previously incompatible technologies-ferroelectric capacitors and memristors-into a single, CMOS-compatible memory stack.

This novel architecture delivers a long-sought solution to one of edge AI's most vexing challenges: how to perform both learning and inference on a chip, witho...