By combining several probabilistic AI algorithms, a recent study demonstrates experimentally that the inherent noise and variation in memristor nanodevices can be exploited as features for energy-efficient on-chip learning.
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References
Ambrogio, S. et al. Nature 620, 768–775 (2023).
Lin, Y. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00744-y (2024).
Lin, Y. et al. Nat. Mach. Intell. 5, 714–723 (2023).
Bonnet, D. et al. Nat. Commun. 14, 7530 (2023).
Liu, S. et al. Front. Nanotechnol. 4, 1021943 (2022).
Sebastian, A. et al. Nat. Commun. 13, 6139 (2022).
Welling, M. & Teh, Y. W. Bayesian learning via stochastic gradient Langevin dynamics. In Proc. 28th Int. Conf. Machine Learning (eds Getoor, L. & Scheffer, T.) 681–688 (ICML, 2011).
Dalgaty, T. et al. Nat. Electron. 4, 151–161 (2021).
Harabi, K.-E. et al. Nat. Electron. 6, 52–63 (2023).
Knill, D. C. & Alexandre, P. Trends Neurosci. 27, 712–719 (2004).
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Querlioz, D. Memristors enabling probabilistic AI at the edge. Nat Comput Sci 5, 7–8 (2025). https://doi.org/10.1038/s43588-024-00761-x
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DOI: https://doi.org/10.1038/s43588-024-00761-x
