Recently, there has been impressive progress in the field of artificial intelligence. A striking example is Alphago, an algorithm developed by Google, that defeated the world champion Lee Sedol at the game of Go. However, in terms of power consumption, the brain remains the absolute winner, by four orders of magnitudes. Indeed, today, brain inspired algorithms are running on our current sequential computers, which have a very different architecture than the brain. If we want to build smart chips capable of cognitive tasks with a low power consumption, we need to fabricate on silicon huge parallel networks of artificial synapses and neurons, bringing memory close to processing. The aim of the presented work is to deliver a new breed of bio-inspired magnetic devices for pattern recognition. Their functionality is based on the magnetic reversal properties of an artificial spin ice in a Kagome geometry for which the magnetic switching occurs by avalanches.

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Michel Hehn, François Montaigne, Daniel Lacour, Yann Perrin, Benjamin Canals, Nicolas Rougemaille, Julie Grollier, Damien Querlioz, and Aurélien Masseboeuf, "Bio-Inspired computing based on artificial spin ices (Conference Presentation)," Proc. SPIE 10732, Spintronics XI, 107323D (Presented at SPIE Nanoscience + Engineering: August 23, 2018; Published: 18 September 2018); https://doi.org/10.1117/12.2325724.5836436193001.