Year: 2019
Type: article
Abstract: Binary stochastic neurons (BSNs) form an integral part of many machine learning algorithms, motivating the development of hardware accelerators for this complex function. It has been recognized that hardware BSNs can be implemented using low-barrier magnets (LBMs) by minimally modifying present-day magnetoresistive random-access memory (MRAM) devices. A crucial parameter that determines the response of these LBM-based BSN designs is thecorrelation timeof magnetization$\tau _c$. In this letter, we show that, for magnets with low-energy barriers ($\Delta \approx k_BT$and below), circular disk magnets with in-plane magnetic anisotropy (IMA) lead to$\tau _c$values that are two orders of magnitude smaller than$\tau _c$of magnets with perpendicular magnetic anisotropy (PMA). Analytical descriptions demonstrate that this striking difference in$\tau _c$is due to a precessionlike fluctuation mechanism that is enabled by the large demagnetization field in IMA magnets. We provide a detailed energy-delay performance evaluation of previously proposed BSN designs based on spin-orbit torque MRAM and spin-transfer torque MRAM employing low-barrier circular IMA magnets by SPICE simulations. The designs exhibit subnanosecond response times leading to energy requirements of approximately a few femtojoules to evaluate the BSN function, orders of magnitude lower than digital CMOS implementations with a much larger surface area. While modern MRAM technology is based on PMA magnets, results in this letter suggest that low-barrier circular IMA magnets may be more suitable for this application.
Source: IEEE Magnetics Letters
Authors: Orchi Hassan, Rafatul Faria, Kerem Yunus Camsari, Jonathan Z. Sun, Supriyo Datta
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G2954347219, G5943845530
OpenAlex ID: W2938733960