Memristor network-based residual neural network model and application method thereof

Abstract

The invention provides a memristor network-based residual neural network model and an application method thereof. A memristor network constructed based on a memristor can perform large-scale parallelprocessing, and meanwhile, has huge memory space. The resistance state of the memristor can be flexibly changed by adjusting applied voltage at the two ends of the memristor, and therefore, synaptic plasticity is achieved. The memristor-based memristor network has the advantages of low power consumption, high speed, modularization and the like, and can be built into various neural networks according to the requirements of developers. The memristor network built by the memristor is used as the hardware support of a residual error neural network; on the basis of the memristor network, a residualneural network model is constructed; the functions of training, testing and the like of the residual neural network model are further achieved; and the resistance value of the memristor is controlledby applying voltages to the two sides of the memristor, and therefore, various needed arithmetic operations in the residual neural network model are achieved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a residual neural network model based on a memristive network and its application method in speech recognition. Background technique [0002] Memristor is a new type of nonlinear two-terminal circuit element. Its natural memory function, continuous input and output characteristics and non-volatility make it widely used in artificial neural networks, pattern recognition and image processing. potential. Memristors not only have good compatibility, but also have the advantages of low power consumption, high reliability and scalability. Building neurons with memristors makes neural network circuit design superior and reliable. [0003] The calculation of a large-scale deep convolutional neural network requires a large amount of computing resources, and the current general-purpose computing platform is difficult to meet the needs of neural network computing for compu...

AI Technical Summary

Problems solved by technology

[0004] In the field of speech recognition, the traditional algorithm GMM-UBM acoustic model has always occupied a dominant position, but due to the characteristics of the GMM-UBM acoustic model itself, it requires a high amount of data, and it has a large amount of calculation and slow running speed during operation. , resulting in a longer training time of the model, so there are certain defects in the actual application process

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