Cambridge University researchers have developed a new kind of nanoelectronic device that could dramatically cut the energy consumed by AI hardware by mimicking the human brain.
Caption: Dr Babak Bakhit, University of Cambridge
The researchers 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.
You can find the results in the journal Science Advances.
Current AI systems consume large amounts of electricity, and global demand is rising quickly as AI adoption expands.
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.
“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.”
Most existing memristors rely on the formation of tiny conductive filaments inside metal oxide material. But these filaments behave unpredictably and typically require high forming and operating voltages, limiting their usefulness in large-scale data storage and computing systems.
The team instead created a new type of hafnium-based thin film that switches states in a completely different way. By adding strontium and titanium and growing the film using a two-step method, the researchers were able to form tiny electronic gates, or ‘p-n junctions’, inside the oxide where the layers meet. This allows the device to change its resistance smoothly by shifting the height of an energy barrier at the interface, rather than by growing or rupturing the filaments.
Bakhit said this mechanism overcomes one of the biggest challenges in developing memristor technology. “Filamentary devices suffer from random behaviour,” he said. “But because our devices switch at the interface, they show outstanding uniformity from cycle to cycle and from device to device.”
Using the hafnium-based devices, the researchers achieved switching currents about a million times lower than those of some conventional oxide-based devices. The memristors also produced hundreds of distinct, stable conductance levels, a key requirement for analogue ‘in-memory’ computing.
Image credit: Babak Bakhit