A team of researchers with the Universities of California and Sydney has sought to sidestep the enormous power consumption of artificial neural networks through the creation of a new, silver nanowire-based approach. Thanks to the properties of silver nanowire - nanostructures around one-thousandth the width of a human hair - and the similarity of its networks with those present in biological CPUs (brains), the research team was able to build a neuromorphic accelerator that results in much lower energy consumption in AI processing tasks. The work has been published in the journal Nature Communications.
Nanowire Networks (NWNs) explore the emergent properties of nanostructured materials – think graphene, XMenes, and other, mostly still under-development technologies – due to the way their atomic shapes naturally possess a neural network-like physical structure that’s significantly interconnected and possesses memristive elements. Memristive in the sense that it possesses structures that can both change their pattern in response to a stimulus (in this case, electricity) and maintain that pattern when that stimulus is gone (such as when you press the Off button).
