A Flexible Memristor-Based Neural Network
Many memristor-based neural network arrays that have been proposed in recent years are simultaneously dealt with all of their signal inputs in signal reception status. Therefore, when a relatively small-scale neural network is implemented with this memristive array, some of the inputs which are not used may cause errors in the result due to the impact of an unexpected signal. In this paper, a flexible memristor-based neural network is proposed. Based on this network, the number of synapses used at work can be flexibly configured according to the required size, thereby improving system performance. The memristor-based neural network is simulated in Pspice to implement two different scales, which proves the feasibility and effectiveness of a flexible memristive neural network.
KeywordsMemristor Neural network Circuit Flexible memristor array
The work is supported by the State Key Program of National Natural Science of China (Grant No. 61632002), the National Key R&D Program of China for International S&T Cooperation Projects (No. 2017YFE010 3900), the National Natural Science of China (Grant Nos. 61603348, 61775198, 61603347, 61572446, 61472372), Science and Technology Innovation Talents Henan Province (Grant No. 174200510012), Research Program of Henan Province (Grant Nos. 172102210066, 17A120005, 182102210160), Youth Talent Lifting Project of Henan Province and the Science Foundation of for Doctorate Research of Zhengzhou University of Light Industry (Grant No. 2014BSJJ044).
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