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5 October 2023 Tunable nanomagnetic synapses implement hardware variational inference engines
Christopher H. Bennett, Tianyao Patrick Xiao, Samuel Liu, Nicholas Zogbi, Andrew Maicke, Thomas Leonard, Jamin Pillars, Todd Monson, Sapan Agarwal, Jean Anne Incorvia
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Proceedings Volume PC12656, Spintronics XVI; PC1265614 (2023) https://doi.org/10.1117/12.2677849
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
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.
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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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