CAPTION: The
artist's portrayal is an illustration of a nanomagnetic coprocessor solving
complex
optimization problems and highlights the shape-engineered nanomagnet's two
unique energy
minimum states -- vortex and single domain. CREDIT: Illustration by Ryan
Wakefield
(October 28, 2015) Researchers
from College of Engineering at University of South Florida 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.
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.
In this work “Non Boolean computing with nanomagnets for
computer vision applications” as published in Nature Nanotechnology, the USF research team has
harnessed the energy-minimization nature of nanomagnetic systems to solve the quadratic optimization
problems that arise in computer vision applications, which are computationally
expensive. By exploiting the magnetization states of nanomagnetic disks as
state representations of a vortex and single domain, the team has created a modeling
framework to address the vortex and in-plane single domain in a unified
framework and developed a magnetic Hamiltonian which is quadratic in nature.
The implemented magnetic system can identify the salient features of a given
image with more than 85% true positive rate. This form of computing, on average, is 1,528 times faster than IBM ILOG
CPLEX (an industry standard software optimizer) with sparse affinity matrices (four neighbor),
and 468 times faster with denser (eight neighbor) affinity matrices. These
results show the potential of this alternative computing method to develop a magnetic
coprocessor that might solve complex problems in fewer clock cycles than
traditional processors.
journal reference >>