Online learning system based on memristor cross array

Through an online learning system based on memristor cross-array, combined with the forward computing subsystem, memristor value online adjustment subsystem and visual subsystem, dynamic update and optimization of memristor resistance values ​​are achieved, solving the problem of low learning efficiency and accuracy in neural network training calculations, and improving the efficiency and performance of online learning.

CN120542498APending Publication Date: 2025-08-26SHANGHAI DIANJI UNIV
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Patent Information

Application Number
CN202510662901.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-22
Publication Date
2025-08-26

AI Technical Summary

Technical Problem

Currently, neural network training calculations based on memristor cross-arrays have problems with low learning efficiency and low learning accuracy, which cannot meet the online learning needs.

Method used

The online learning system based on memristor cross-array is adopted, combined with the forward computing subsystem, the memristor value online adjustment subsystem and the visual subsystem, the derivative is calculated through differential method and dynamic update and optimization of the memristor resistance value, including the resistance value update mechanism combining coarse and fine-tuning.

Benefits of technology

It significantly improves the learning efficiency and performance of neural networks, solves the problems of low learning efficiency and accuracy, and realizes efficient online learning and independent optimization.

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Abstract

The invention relates to an online learning system based on a memristor cross array, and belongs to the technical field of machine learning. The system comprises a forward calculation subsystem, a memristor resistance value online adjustment subsystem and a visualization subsystem, the forward calculation subsystem comprises a memristor cross array simulation device and a computer module, and derivative calculation in neural network training and memristor resistance value dynamic adjustment are combined through a simulation platform. A memory resistor is used for achieving storage and calculation integration, a computer module completes derivative calculation in a differential mode, a differential derivative calculation mechanism of the computer module directly obtains a derivative through actual response of a physical device, the defect that a storage and calculation integration framework cannot store intermediate variables is overcome, and calculation autonomy and efficiency are remarkably improved. Meanwhile, an introduced resistance updating mechanism combining coarse adjustment and fine adjustment can realize efficient and accurate control of the resistance of the memristor. Finally, the neural network learning efficiency and performance based on the memristor cross array are improved.
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