Better Performance of Memristive Convolutional Neural Network Due to Stochastic Memristors

  • Kechuan Wu
  • Xiaoping WangEmail author
  • Mian Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)


Convolutional Neural Network (CNN) has gotten admirable performance in the domain of image recognition. Nevertheless, the training of CNN on CPU or GPU is energy-intensive and time-consuming. Memristor crossbar is an alternative of the specific chip for CNN application. But it is hard to tune the memristor to certain conductance precisely. This work simulates the performance change of memristor-based CNN when memristor is with stochasticity. The simulation results demonstrate that stochastic memristor-based CNN performs better on CIFAR-10 dataset when memristive stochasticity is low. This is an encouragement for the engineer of memristor crossbar chip and edge computing application.


Stochastic memristor Convolutional neural network Dataset noise 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhanPeople’s Republic of China

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