Author:
Huang Lixing,Diao Jietao,Teng Shuhua,Li Zhiwei,Wang Wei,Liu Sen,Li Minghou,Liu Haijun
Abstract
AbstractRecently, memristor based binarized convolutional neural network has been widely investigated owing to its strong processing capability, low power consumption and high computing efficiency.However, it has not been widely applied in the field of embedded neuromorphic computing for manufacturing technology of the memristor being not mature. With respect to this, we propose a method for obtaining highly robust memristor based binarized convolutional neural network. To demonstrate the performance of the method, a convolutional neural network architecture with two layers is used for simulation, and the simulation results show that binarized convolutional neural network can still achieve more than 96.75% recognition rate on MNIST dataset under the condition of 80% yield of the memristor array, and the recognition rate is 94.53% when the variation of memristance is 26%, and it is 94.66% when the variation of the neuron output is 0.8.
Publisher
Springer Nature Singapore
Reference15 articles.
1. Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 (2016)
2. Courbariaux, M., Bengio, Y., David, J.-P.: BinaryConnect: training deep neural networks with binary weights during propagations. Adv. Neural Inf. Process. Syst. 28 (2015)
3. Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: imagenet classification using binary convolutional neural networks. Computer Vision - Eccv 2016, Pt Iv (2016)
4. Qiao, G.C., Hu, S.G., Chen, T.P., et al.: STBNN: Hardware-friendly spatio-temporal binary neural network with high pattern recognition accuracy. Neurocomputing 409, 351–360 (2020)
5. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
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