Researchers from the Univ. of South Florida College of Engineering have proposed a new form of computing that uses circular nanomagnets to solve quadratic optimization problems orders of magnitude faster than that of a conventional computer.
A wide range of application domains can be potentially accelerated through this research such as finding patterns in social media, error-correcting codes to big data and biosciences.
In an article published in Nature Nanotechnology, authors Sanjukta Bhanja, D.K. Karunaratne, Ravi Panchumarthy, Srinath Rajaram and Sudeep Sarkar discuss how their work harnessed the energy-minimization nature of nanomagnetic systems to solve the quadratic optimization problems that arise in computer vision applications, which are computationally expensive.
According to the authors, magnets have been used as computer memory/data storage since as early as 1920; they even made an entry into common hardware terminology like multi-"core." The field of nanomagnetism has recently attracted tremendous attention as it can potentially deliver low-power, high speed and dense non-volatile memories. It is now possible to engineer the size, shape, spacing, orientation and composition of sub-100-nm magnetic structures. This has spurred the exploration of nanomagnets for unconventional computing paradigms.