  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 work, 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 compute 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.
