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Making Big Data Processing more Energy Efficient using Magnetic Circuits

lateral inhibition magnetic processing
Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing

New research finds that magnetic wires, spaced a certain way, can lead to a 20-30x reduction in the amount of energy needed to run neural network training algorithms.

The rapid progression of technology has led to a huge increase in energy usage to process the massive troves of data generated by devices. But researchers in the Cockrell School of Engineering at The University of Texas at Austin have found a way to make the new generation of smart computers more energy efficient.

Traditionally, silicon chips have formed the building blocks of the infrastructure that powers computers...

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Physicists Catch a Magnetic Wave that offers Promise for more Energy-Efficient Computing

 

A team of physicists has taken pictures of a theorized but previously undetected magnetic wave, which offers the potential to be an energy-efficient means to transfer data in electronics. “This is an exciting discovery because it shows that small magnetic waves–known as spin-waves–can add up to a large one in a magnet, a wave that can maintain its shape as it moves,” explains NYU Prof Andrew Kent. “A specialized x-ray method that can focus on particular magnetic elements with very high spatial resolution enabled this discovery and should enable many more insights into this behavior.”

To push the limits of energy efficiency in the future we need to understand better how magnetic devices behave on fast timescales at the nanoscale, which is why we are using this dedicated ultrafast x-ra...

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