A Deep Dive into the Computational Fidelity of High Variability Low Energy Barrier Magnet Technology for Accelerating Optimization and Bayesian Problems
arXiv:2302.08074 · doi:10.1109/LMAG.2023.3274051
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 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.
arXiv comment5 pages, 8 figures
References in corpus (10)
- Stochastic p-bits for Invertible Logic
- p-Bits for Probabilistic Spin Logic
- Intrinsic optimization using stochastic nanomagnets
- Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing
- Low Barrier Nanomagnets as p-bits for Spin Logic
- Low Barrier Nanomagnet Design for Binary Stochastic Neurons: Design Challenges for Real Nanomagnets with Fabrication Defects
- Voltage-driven Building Block for Hardware Belief Networks
- Robustness and scalability of p-bits implemented with low energy barrier nanomagnets
- Analog Signal Processing Using Stochastic Magnets
- Sensitivity of the Power Spectra of Magnetization Fluctuations in Low Barrier Nanomagnets to Barrier Height Modulation and Defects