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Usf Team Finds New Way of Computing With Interaction-Dependent State Change of Nanomagnets


Illustration of a nanomagnetic coprocessor solving complex optimization problems.

Researchers from the University of South Florida College of Engineering have proposed a new form of computing that uses circular nanomagnets to solve quadratic optimization problems.

Credit: Ryan Wakefield

University of South Florida researchers have proposed a new form of computing that uses circular nanomagnets to solve quadratic optimization problems orders of magnitude faster than a conventional computer.

The technology harnesses the energy-miniaturization nature of nanomagnetic systems to solve the quadratic optimization problems that arise in computer-vision applications. The field of nanomagentization can potentially deliver low-power, high-speed, and dense non-volatile memories, which has facilitated the exploration of nanomagnets for unconventional computing platforms. In addition, recent research has shown it is possible to engineer the size, shape, spacing, orientation, and composition of sub-100-nm magnetic structures.

The researchers created a modeling framework to address the vortex and in-plane single domain in a unified framework by exploiting the magnetization states of nanomagnetic disks as state representations of a vortex and single domain. The new magnetic system can identify the important features of a given image with more than 85 percent accuracy.

The researchers note this form of computing, on average, is 1,527 times faster than industry standard software optimizers with sparse affinity matrices, and 468 times faster with denser affinity matrices. They say their results show the potential for this new computing method to develop a magnetic coprocessor that could solve complex problems in fewer clock cycles than traditional processors.

From University of South Florida
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Abstracts Copyright © 2015 Information Inc., Bethesda, Maryland, USA


 

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