Reservoir computing implemented in memristive hardware can process temporal data with greater energy efficiency than reservoir computers based on CMOS.
References
Koomey, J., Berard, S., Sanchez, M. & Wong, H. IEEE Ann. Hist. Comp. 33, 46–54 (2011).
Hasler, J. & Marr, H. B. Front. Neurosci. 7, 118 (2013).
Moon, J. et al. Nat. Electron. https://doi.org/10.1038/s41928-019-0313-3 (2019).
Jouppi, N. P. et al. In Proc. 44th Annu. Int. Symp. Computer Architecture https://go.nature.com/2MdbUER (ACM, 2017).
Alomar, M. L. et al. IEEE Trans. Circuits Syst. II Express Briefs 62, 977–981 (2015).
Acknowledgements
Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the US Department of Energy or the United States Government.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Marinella, M.J., Agarwal, S. Efficient reservoir computing with memristors. Nat Electron 2, 437–438 (2019). https://doi.org/10.1038/s41928-019-0318-y
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41928-019-0318-y
- Springer Nature Limited
This article is cited by
-
Emerging opportunities and challenges for the future of reservoir computing
Nature Communications (2024)
-
Brownian reservoir computing realized using geometrically confined skyrmion dynamics
Nature Communications (2022)
-
Neuromorphic sensory computing
Science China Information Sciences (2022)
-
Novel nondelay-based reservoir computing with a single micromechanical nonlinear resonator for high-efficiency information processing
Microsystems & Nanoengineering (2021)
-
On Improving The Computing Capacity of Dynamical Systems
Scientific Reports (2020)