Researchers have developed a new kind of nanoelectronic device that could dramatically cut the energy consumed by artificial intelligence hardware by mimicking the human brain. The researchers, led by the University of Cambridge, developed a form of hafnium oxide that acts as a highly stable, low-energy “memristor”—a component designed to mimic the efficient way neurons are connected in the brain. The results are reported in the journal Science Advances.

Current AI systems rely on conventional computer chips that shuttle data back and forth between memory and processing units. This constant movement consumes large amounts of electricity, and global demand is exploding as AI adoption expands across industries.

Brain-inspired, or neuromorphic, computing is an alternative way to process information that could reduce energy use by as much as 70% by storing and processing information in the same place, and doing so with extremely low power. Such a system would also be far more adaptable, in the same way our own brains are able to learn and adapt.