A Deep Dive Into the Computational Fidelity of High-Variability Low Energy Barrier Magnet Technology for Accelerating Optimization and Bayesian Problems

Abstract

Low energy barrier magnet (LBM) technology has recently been proposed as a candidate for accelerating algorithms based on energy minimization and probabilistic graphs because their physical characteristics have a one-to-one mapping onto the primitives of these algorithms. Many of these algorithms have a much higher tolerance for error compared to high-accuracy numerical computation. LBM, however, is a nascent technology, and devices show high sample-to-sample variability. In this letter, we take a deep dive into the overall fidelity afforded by this technology in providing computational primitives for these algorithms. We show, that while the computed results show finite deviations from zero-variability devices, the margin of error is almost always certifiable to a certain percentage. This suggests that LBM technology could be a viable candidate as an accelerator for popular emerging paradigms of computing.