Cellular neural networks enable real-time, parallel computation for tasks such as high-speed image processing and solving partial differential equations. We found that low-power memristors are well suited for synaptic weights in such networks, even at extreme temperatures, allowing a single system-on-a-chip to be reconfigured for different tasks.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to the full article PDF.
USD 39.95
Prices may be subject to local taxes which are calculated during checkout
References
von Neumann, J. First draft of a report on the EDVAC. IEEE Ann. Hist. Comput. 15, 27–75 (1993). This paper reports on the fundamentals of the von Neumann architecture.
Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990). A survey of the use and advantages of biologically inspired processing systems.
Chua, L. O. & Yang, L. Cellular neural networks: theory. IEEE Trans. Circuits Syst. 35, 1257–1272 (1988). This paper reports on the foundational theory and use of the CeNN.
Zarandy, A., Horvath, A. & Szolgay, P. CNN technology-tools and applications. IEEE Circuits Syst. Mag. 18, 77–89 (2018). A review article that presents previous implementations of the CeNN.
Jiang, H. et al. Sub-10 nm Ta channel responsible for superior performance of a HfO2 memristor. Sci. Rep. 6, 28525 (2016). This paper reports on the memristor device used within the synapses of the CeNN and its properties.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Ravichandran, V. et al. Memristive cellular neural networks for fast in-pixel computing. Nat. Electron. https://doi.org/10.1038/s41928-025-01555-3 (2026).
Rights and permissions
About this article
Cite this article
Low-power memristor-based cellular neural networks with reduced overhead. Nat Electron (2026). https://doi.org/10.1038/s41928-025-01559-z
Published:
Version of record:
DOI: https://doi.org/10.1038/s41928-025-01559-z
