Stochastic Switching of SHE-MTJ as a Natural Annealer for Efficient Combinatorial Optimization

Abstract

Efficient computing models for combinatorial optimization problems, like the Ising spin model, have been researched intensively as an alternative to the von-Neumann based general-purpose computing. The bottleneck mainly stems from the fact that the computing complexity of such optimization problems increases exponentially with the size of the problem. Although efficient heuristic algorithms have been designed for such combinatorial problems, yet hardware implementations of such top-down approaches suffer from complex control requirements and frequent memory accesses. Interestingly, unique device characteristics of the recent emerging devices, such as stochastic spintronic devices, can potentially pave the way for efficient hardware implementation of such combinatorial optimization problems. In this work, we leverage stochastic switching of nano-magnets in presence of thermal noise to implement an efficient combinatorial optimization solver and demonstrate its feasibility by solving realistic NP-complete problems.