05/21/2026
Brain-Inspired Memristors Could Slash AI Energy Use by 70 Percent
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.
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 percent 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.
“Energy consumption is one of the key challenges in current AI hardware,” said Lead Author Dr. Babak Bakhit, from Cambridge’s Department of Materials Science and Metallurgy. “To address that, you need devices with extremely low currents, excellent stability, outstanding uniformity across switching cycles and devices, and the ability to switch between many distinct states.”
Learn more and read an exclusive Tech Briefs interview with Bakhit: https://ow.ly/Vwze50Z2E9k