Abstract
Artificial neural networks(ANN)have been extensively researched due to their significant energy-saving benefits.Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem.This letter reports a dropout neuronal unit(1R1T-DNU)based on one memristor-one electrolyte-gated transistor with an ultralow energy consumption of 25 pJ/spike.A dropout neural network is constructed based on such a device and has been verified by MNIST dataset,demonstrating high recognition accuracies(≥ 90％)within a large range of dropout probabilities up to 40％.The running time can be reduced by increasing dropout probability without a significant loss in accuracy.Our results indicate the great potential of introducing such IRIT-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.
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