Design of Stochastic Nanomagnets for Probabilistic Spin Logic

Abstract

Probabilistic spin logic (PSL) is a new paradigm of computing that relies on probabilistic bits (p-bits) that fluctuate randomly between metastable states. PSL may be more efficient than conventional CMOS-based logic in terms of intrinsic optimization, Bayesian inference, invertible Boolean logic, and hardware machine learning. Effectively tunable random number generators, p-bits, can be realized as stochastic nanomagnets that can be made to prefer one state over others by an external input, such as voltage or current. This letter looks at the design of stochastic nanomagnets that is most suitable as p-bits for PSL. Experimental evidence, supported by theory and numerical simulation, shows that the scaling of magnetic anisotropy is more effective than the scaling of the net magnetic moment for voltage-driven PSL applications. A novel system that can be used as a tunable random number generator is demonstrated experimentally and analyzed theoretically: a magnet with perpendicular magnetic anisotropy that is initialized to its hard axis by giant spin Hall effect torque. With zero external input, this system provides a potentially better alternative to other nanomagnet-based random number generators. By tuning the randomness through an external input, this system is suitable for probabilistic networks for Bayesian inference.