A hardware-adaptive learning algorithm for superlinear-capacity associative memory on memristor crossbars

Nature Communications 2026 Open access

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Authors: Chengping He, Mingrui Jiang, Keyi Shan, Shu Yang, Zefan Li, Shengbo Wang, Giacomo Pedretti, Jim Ignowski, Can Li
DOI: 10.1038/s41467-026-69958-0
Cited by: 1
References: 47

Abstract

, respectively). Leveraging crossbar parallelism with synchronous updates, the implementation reduces energy by 8.8× and latency by 99.7% for 64-dimensional patterns versus asynchronous schemes. These results provide a practical algorithm-hardware co-design for robust, efficient Hopfield-style associative recall.

Fields of Study & Subjects

Advanced Memory and Neural Computing Neural Networks Stability and Synchronization Ferroelectric and Negative Capacitance Devices

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
23 February 2026
Language
EN