Like electronics or photonics, magnonics is an engineering subfield that aims to advance information technologies when it comes to speed, device architecture, and energy consumption. A magnon corresponds to the specific amount of energy required to change the magnetization of a material via a collective excitation called a spin wave.

Because they interact with magnetic fields, magnons can be used to encode and transport data without electron flows, which involve through heating (known as Joule heating) of the conductor used. As Dirk Grundler, head of the Lab of Nanoscale Magnetic Materials and Magnonics (LMGN) in the School of Engineering explains, energy losses are an increasingly serious barrier to electronics as data speeds and storage demands soar.

"With the advent of AI, the use of computing technology has increased so much that threatens its development," Grundler says. "A major issue is traditional computing architecture, which separates processors and memory. The signal conversions involved in moving data between different components slow down computation and waste energy."

This inefficiency, known as the memory wall or Von Neumann bottleneck, has had researchers searching for new computing architectures that can better support the demands of big data. And now, Grundler believes his lab might have stumbled on such a "holy grail".

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