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413
68. M. Courbariaux, Y. Bengio, BinaryNet: training deep neural networks with weights and
activations constrained to +1 or −1, CoRR, vol. abs/1602.02830 (2016)
69. M. Rastegari, V. Ordonez, J. Redmon, A. Farhadi, XNOR-Net: ImageNet classification using
binary convolutional neural networks, in Computer Vision—ECCV 2016, Cham, pp. 525–542:
Springer International Publishing (2016)
70. S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, Y. Zou, DoReFa-Net: training low bitwidth
convolutional neural networks with low bitwidth gradients, arXiv preprint arXiv:1606.06160
(2016)
71. L. Ni, Z. Liu, H. Yu, R.V. Joshi, An Energy-Efficient Digital ReRAM-Crossbar-Based CNN
With Bitwise Parallelism. IEEE Journal on Exploratory Solid-State Computational Devices
and Circuits 3, 37–46 (2017)
72. S. Ambrogio, S. Balatti, A. Cubeta, A. Calderoni, N. Ramaswamy, D. Ielmini, Statistical
fluctuations in HfO x resistive-switching memory: Part I—set/reset variability. IEEE Trans.
Electr. Dev. 61(8), 2912–2919 (2014)
73. S. Ambrogio, S. Balatti, A. Cubeta, A. Calderoni, N. Ramaswamy, D. Ielmini, Statistical
fluctuations in HfOx resistive-switching memory: part II—random telegraph noise. IEEE
Trans. Electr. Dev. 61(8), 2920–2927 (2014)
74. C. Yakopcic, M.Z. Alom, T.M. Taha, Memristor crossbar deep network implementation based
on a convolutional neural network, in 2016 International Joint Conference on Neural Networks
(IJCNN), pp. 963–970 (2016)
75. C. Yakopcic, M. Z. Alom, T.M. Taha, Extremely parallel memristor crossbar architecture
for convolutional neural network implementation, in 2017 International Joint Conference on
Neural Networks (IJCNN), pp. 1696–1703 (2017)
76. S.K. Esser et al., Convolutional networks for fast, energy-efficient neuromorphic computing.
Proc. Natl. Acad. Sci. 113(41), 11441 (2016)
77. P. Yao et al., Face classification using electronic synapses. Nat. Commun. 8(1), 15199,
2017/05/12 (2017)
78. C. Li et al., Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.
Nat. Commun. 9(1), 2385, 2018/06/19 (2018)
79. H. Jiang et al., Sub-10 nm Ta channel responsible for superior performance of a HfO2
memristor. Sci. Rep. 6(1), 28525, 2016/06/23 (2016)
80. Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document
recognition. Proc. IEEE 86(11), 2278–2324 (1998)
81. A. Krizhevsky, G. Hinton, Learning multiple layers of features from tiny images. Citeseer
(2009)
82. J. Deng, W. Dong, R. Socher, L. Li, L. Kai, F.-F. Li, ImageNet: a large-scale hierarchical
image database, in 2009 IEEE Conference on Computer Vision and Pattern Recognition,
pp. 248–255 (2009)
83. W. Maass, Networks of spiking neurons: the third generation of neural network models, Neural
Netw. 10(9), 1659–1671, 1997/12/01 (1997)
84. K. Fukushima, Neocognitron: a self-organizing neural network model for a mechanism of
pattern recognition unaffected by shift in position,. Biol. Cybern. 36(4), 193–202, 1980/04/01
(1980)
85. B.V. Benjamin et al., Neurogrid: a mixed-analog-digital multichip system for large-scale
neural simulations. Proc. IEEE 102(5), 699–716 (2014)
86. L.A. Plana et al., SpiNNaker: design and implementation of a GALS multicore system-onchip. J. Emerg. Technol. Comput. Syst. 7(4), 1–18 (2011)
87. F. Akopyan et al., TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable
neurosynaptic chip. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34(10), 1537–1557
(2015)
88. P.U. Diehl, D. Neil, J. Binas, M. Cook, S. Liu, M. Pfeiffer, Fast-classifying, high-accuracy
spiking deep networks through weight and threshold balancing, in 2015 International Joint
Conference on Neural Networks (IJCNN), pp. 1–8 (2015)