Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization

Rui Wang, Tuo Shi (), Xumeng Zhang, Jinsong Wei, Jian Lu, Jiaxue Zhu, Zuheng Wu, Qi Liu () and Ming Liu
Additional contact information
Rui Wang: Institute of Microelectronics Chinese Academy of Sciences
Tuo Shi: Institute of Microelectronics Chinese Academy of Sciences
Xumeng Zhang: Fudan University
Jinsong Wei: Institute of Microelectronics Chinese Academy of Sciences
Jian Lu: Institute of Microelectronics Chinese Academy of Sciences
Jiaxue Zhu: Institute of Microelectronics Chinese Academy of Sciences
Zuheng Wu: Institute of Microelectronics Chinese Academy of Sciences
Qi Liu: Institute of Microelectronics Chinese Academy of Sciences
Ming Liu: Institute of Microelectronics Chinese Academy of Sciences

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract A self-organizing map (SOM) is a powerful unsupervised learning neural network for analyzing high-dimensional data in various applications. However, hardware implementation of SOM is challenging because of the complexity in calculating the similarities and determining neighborhoods. We experimentally demonstrated a memristor-based SOM based on Ta/TaOx/Pt 1T1R chips for the first time, which has advantages in computing speed, throughput, and energy efficiency compared with the CMOS digital counterpart, by utilizing the topological structure of the array and physical laws for computing without complicated circuits. We employed additional rows in the crossbar arrays and identified the best matching units by directly calculating the similarities between the input vectors and the weight matrix in the hardware. Using the memristor-based SOM, we demonstrated data clustering, image processing and solved the traveling salesman problem with much-improved energy efficiency and computing throughput. The physical implementation of SOM in memristor crossbar arrays extends the capability of memristor-based neuromorphic computing systems in machine learning and artificial intelligence.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-022-29411-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29411-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-29411-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().