Pub Date : 2023-09-27DOI: 10.1109/LMAG.2023.3319992
Rahnuma Rahman;Supriyo Bandyopadhyay
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