Field coupled computing is a radically different paradigm where electrical, magnetic or spin coupling among nano-devices are utilized for computation. By far, most advances has been in the nano-fabrication of nanomagnets, mostly driven by the need for denser memory and patterned magnetic storage media. This research will open up unconventional front in computing. Unlike other current works in nanocomputing that seeks to replicate traditional computing involving Boolean logic, this research is using magnetic field-based computing (MFC) to solve optimization problems in computer vision. In the long run, this research will help design computers that will be able to solve hard problems in automated object recognition in a computationally efficient manner.
The ground state of a nanomagnet collection minimizes the Hamiltonian that is governed by the pairwise dipolar interactions between the nanomagnets. The specific focus of this research is to harnesses this energy minimization aspect to solve quadratic energy minimization problems in computer vision. The investigators are developing computational method, based on multi-dimensional scaling, to decide upon the spatial arrangement of nanomagnets that matches a particular quadratic minimization problem. Each variable is represented by a nanomagnet and the distances between them are such that the dipolar interactions match the corresponding pair wise energy term in the optimization problem. The nanomagnets that participate in a specific computation are to be selected from a field of regularly placed nanomagnets. Some of the scientific questions being considered are. What would be the optimal geometry and appropriate material for these nanomagnets? How would one select the nanomagnets from an array of uniformly spaced nanomagnets to involve in computation? Can we demonstrate these functions by fabricating circuits?
