A new highly stable and energy-efficient memristor based on a hafnium oxide material can emulate the behaviour of synapses in the brain. The neuromorphic device could help dramatically cut the energy consumed by artificial intelligence (AI) hardware, say its developers at the University of Cambridge in the UK.
Today’s AI systems rely on conventional digital computers. These have separate processing and storage units and consume huge amounts of energy when performing data-intensive tasks. As global AI use is exploding, this energy consumption has already become unsustainable, says materials scientist Babak Bakhit, who led this new study.
Neuromorphic computers could provide an alternative way to process information. As their name suggests, they are inspired by the architecture of the human brain. The circuits in these computers are made up of highly connected artificial neurons and artificial synapses that simulate the brain’s structure and functions. These machines have combined processing and memory units that allow them to process information at the same time as they store it, in the same way as a multi-tasking human brain. This means they could reduce energy consumption by as much as 70% compared with their digital counterparts.
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