Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks

Fritscher M, Knödtel J, Mallah M, Pechmann S, Quesada EPB, Rizzi T, Wenger C, Reichenbach M (2021)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

DOI: 10.1007/978-3-031-04580-6_27

Authors with CRIS profile

Johannes Knödtel Lehrstuhl für Informatik 3 (Rechnerarchitektur) Stefan Pechmann Lehrstuhl für Technische Elektronik Marc Reichenbach Lehrstuhl für Informatik 3 (Rechnerarchitektur)

Involved external institutions

Leibniz-Institut für innovative Mikroelektronik / IHP GmbH - Innovations for High Performance Microelectronics DE Germany (DE) Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Fritscher, M., Knödtel, J., Mallah, M., Pechmann, S., Quesada, E.P.-B., Rizzi, T.,... Reichenbach, M. (2021). Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks. In Proceedings of the 21th International Conference, SAMOS 2021.

MLA:

Fritscher, Markus, et al. "Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks." Proceedings of the 21th International Conference, SAMOS 2021 2021.

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