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Probabilistic computing with binary stochastic neurons (BSN) implemented withlow- or zero-energy barrier nanoscale ferromagnets (LBMs) possessing in-planemagnetic anisotropy has emerged as an efficient paradigm for solvingcomputationally hard problems. The fluctuating magnetization of an LBM at roomtemperature encodes a p-bit which is the building block of a BSN. Its onlydrawback is that the dynamics of common (transition metal) ferromagnets arerelatively slow and hence the number of uncorrelated p-bits that can begenerated per second - the so-called "flips per second" (fps) - isinsufficient, leading to slow computational speed in autonomous co-processingwith p-computers. Here, we show that a simple way to increase fps is to replacecommonly used ferromagnets (e.g. Co, Fe, Ni), which have large saturationmagnetization Ms, with a dilute magnetic semiconductor like GaMnAs with muchsmaller saturation magnetization. The smaller Ms reduces the energy barrierwithin the LBM and increases the fps significantly. It also offers otherbenefits such as increased packing density for increased parallelization andreduced device to device variation. This provides a way to realize the hardwareacceleration and energy efficiency promise of p-computers.

Increasing Flips per Second and Speed of p-Computers by Using Dilute Magnetic Semiconductors to Implement Binary Stochastic Neurons