Presentation
5 October 2023 Tunable nanomagnetic synapses implement hardware variational inference engines
Author Affiliations +
Abstract
Variational inference (VI) is an approximation of statistically valid Bayesian inference which is well-suited for analog accelerators, and stochastic nanomagnetic devices in particular are a strong candidate to implement this feature by exploiting tunable randomness in magnetic thin-films that can be run quickly and with a low power-draw. In this work, we a) discuss how VI can be reliably implemented with a combination of low-noise nanomagnetic synapses and tunable noise generating sources (magnetic tunnel junctions (MTJs) in a single analog design and b) summarize efforts to characterize the state-dependent noise profiles of various MTJ designs for various applications.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher H. Bennett, Tianyao Patrick Xiao, Samuel Liu, Nicholas Zogbi, Andrew Maicke, Thomas Leonard, Jamin Pillars, Todd Monson, Sapan Agarwal, and Jean Anne Incorvia "Tunable nanomagnetic synapses implement hardware variational inference engines", Proc. SPIE PC12656, Spintronics XVI, PC1265614 (5 October 2023); https://doi.org/10.1117/12.2677849
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KEYWORDS
Calibration

Design and modelling

Electrochemical etching

Magnetic tunnel junctions

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