Using magnetic tunnel junctions as unconventional computing devices

Hardware for specialized computing tasks is becoming more and more relevant in computational research due to the nearing intrinsic physical limitations of Von Neumann architecture. Most of the industrially relevant problems belong to the combinatorial optimization class and require a nondeterministic approach to be solved efficiently. To this end, magnetic tunnel junctions (MTJs) are one of the best candidate devices as building blocks to implement unconventional computation paradigms thanks to their tunable stochasticity, their size, and their compatibility with current CMOS technologies. Here, we present an MTJ-based implementation of two state-of-the-art paradigms, evaluate them and compare them over relevant computational problems.