Borders, W.
, Prasad, N.
, Hoskins, B.
, Madhavan, A.
, Daniels, M.
, Gerogiou, V.
, Santos, T.
, Braganca, P.
, Stiles, M.
and McClelland, J.
(2023),
Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities, Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities, Sendai, JP, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936143
(Accessed October 28, 2025)
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Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities
Published
Author(s)
, , , , , Vasileia Gerogiou, Tiffany Santos, Patrick Braganca, ,
Abstract
Increasingly higher demand in chip area and power consumption for more sophisticated artificial neural networks has catalyzed efforts to develop architectures, circuits, and devices that perform like the human brain. However, many novel device technologies suffer from non-idealities such as device variation, or circuit sneak paths that reduce network accuracy. Here, we report that an array of magnetic tunnel junction devices integrated with complementary metal oxide semiconductors (CMOS) greatly reduces the impact of non-idealities in the circuit and performs inference with accuracies nearly identical to software.Proceedings Title
Magnetic tunnel junction-based crossbars: improving neural network performance by reducing the impact of non-idealities
Conference Dates
May 15-19, 2023
Conference Location
Sendai, JP
Pub Type
Conferences
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Citation
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Created July 13, 2023, Updated September 22, 2023