Hardware implementation of memristor-based artificial neural networks

Mar 4, 2024 by F. Aguirre, A. Sebastian, M. Le Gallo, Wenhao Song, Tong Wang, J. J. Yang, Wei D. Lu, Meng-Fan Chang, D. Ielmini, Yuch-Chi Yang, Adnan Mehonic, Anthony J. Kenyon, M. A. Villena, J. Roldán, Yuting Wu, Hung-Hsi Hsu, N. Raghavan, J. Suñé, Enrique Miranda, A. Eltawil, G. Setti, Kamilya Smagulova, K. Salama, O. Krestinskaya, Xiaobing Yan, K. Ang, Samarth Jain, Sifan Li, O. Alharbi, S. Pazos, M. Lanza (Nature Communications)

DOI 10.1038/s41467-024-45670-9

We built a practical roadmap for turning memristors into real neural hardware, laying out the working principles, design tradeoffs, and tooling you actually need to prototype memristive ANNs. If you want a one-stop, nitty-gritty protocol to go from materials to system-level performance estimates and avoid the usual blind spots, this is it.

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