Increasing Flips per Second and Speed of p-Computers by Using Dilute Magnetic Semiconductors

Abstract

Probabilistic computing with binary stochastic neurons (BSNs) implemented with low-barrier magnets (LBMs) or zero-energy barrier nanoscale ferromagnets possessing in-plane magnetic anisotropy has emerged as an efficient paradigm for solving computationally hard problems. The fluctuating magnetization of an LBM at room temperature encodes a p-bit, which is the building block of a BSN. Its drawback, however, is that the dynamics of common (transition metal) ferromagnets are relatively slow, and, hence, the number of uncorrelated p-bits that can be generated per second—the so-called “flips per second” ( fps )—is insufficient, leading to slow computational speed in autonomous coprocessing with p-computers. Here, we show that a simple way to increase fps in LBMs is to replace commonly used ferromagnets (e.g., Co, Fe, and Ni), which have large saturation magnetization M_s , with a dilute magnetic semiconductor, such as GaMnAs with much smaller saturation magnetization. The smaller M_s reduces any residual energy barrier within an LBM and increases the fps significantly. It also offers other benefits, such as reduced dipole coupling between neighbors, resulting in larger density of uncorrelated p-bits for more processing power, and reduced device-to-device variation. All this provides a way to realize the hardware acceleration and energy efficiency promise of p-computers.