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We present a novel cryptography architecture based on memristor crossbar array, binary hypervectors, and neural network.
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2009
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C. Yang, Q. Hu, Y. Yu, R. Zhang, Y. Yao, and J. Cai, “Memristor-based chaotic circuit for text/image encryption and decryption,” in 2015 8th International Symposium on Computational Intelligence and Design (ISCID) , vol. 1, 2015, pp. 447–450
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W. Yi, S. E. Savel’ev, G. Medeiros-Ribeiro, F. Miao, M.-X. Zhang, J. J. Yang, A. M. Bratkovsky, and R. S. Williams, “Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors,” Nature Communications , vol. 7, no. 1, p. 11142, Apr 2016. [Online]. Available: https://doi.org/10.1038/ncomms11142
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J. Wu, L. Wang, G. Chen, and S. Duan, “A memristive chaotic system with heart-shaped attractors and its implementation,” Chaos, Solitons & Fractals , vol. 92, pp. 20–29, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0960077916302648
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C. Du, F. Cai, M. A. Zidan, W. Ma, S. H. Lee, and W. D. Lu, “Reservoir computing using dynamic memristors for temporal information processing,” Nature Communications , vol. 8, no. 1, p. 2204, Dec 2017. [Online]. Available: https://doi.org/10.1038/s41467-017-02337-y
2017
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D. Ielmini and H.-S. P. Wong, “In-memory computing with resistive switching devices,” Nature Electronics , vol. 1, no. 6, pp. 333–343, Jun 2018. [Online]. Available: https://doi.org/10.1038/s41928-018-0092-2
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M. Le Gallo, A. Sebastian, R. Mathis, M. Manica, H. Giefers, T. Tuma, C. Bekas, A. Curioni, and E. Eleftheriou, “Mixed-precision in-memory computing,” Nature Electronics , vol. 1, no. 4, pp. 246–253, Apr 2018. [Online]. Available: https://doi.org/10.1038/s41928-018-0054-8
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S. Ambrogio, P. Narayanan, H. Tsai, R. M. Shelby, I. Boybat, C. di Nolfo, S. Sidler, M. Giordano, M. Bodini, N. C. P. Farinha, B. Killeen, C. Cheng, Y. Jaoudi, and G. W. Burr, “Equivalent-accuracy accelerated neural-network training using analogue memory,” Nature , vol. 558, no. 7708, pp. 60–67, Jun 2018. [Online]. Available: https://doi.org/10.1038/s41586-018-0180-5
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C. Li, Z. Wang, M. Rao, D. Belkin, W. Song, H. Jiang, P. Yan, Y. Li, P. Lin, M. Hu, N. Ge, J. P. Strachan, M. Barnell, Q. Wu, R. S. Williams, J. J. Yang, and Q. Xia, “Long short-term memory networks in memristor crossbar arrays,” Nature Machine Intelligence , vol. 1, no. 1, pp. 49–57, Jan 2019. [Online]. Available: https://doi.org/10.1038/s42256-018-0001-4
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P.-Y. Chen and S. Yu, “Technological benchmark of analog synaptic devices for neuroinspired architectures,” IEEE Design Test , vol. 36, no. 3, pp. 31–38, 2019
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A. Amirsoleimani, F. Alibart, V. Yon, J. Xu, M. R. Pazhouhandeh, S. Ecoffey, Y. Beilliard, R. Genov, and D. Drouin, “In-memory vector-matrix multiplication in monolithic complementary metal–oxide–semiconductor-memristor integrated circuits: Design choices, challenges, and perspectives,” Advanced Intelligent Systems , vol. 2, no. 11, p. 2000115, 2020. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202000115
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R. Zhang, H. Jiang, Z. R. Wang, P. Lin, Y. Zhuo, D. Holcomb, D. H. Zhang, J. J. Yang, and Q. Xia, “Nanoscale diffusive memristor crossbars as physical unclonable functions,” Nanoscale , vol. 10, pp. 2721–2726, 2018. [Online]. Available: http://dx.doi.org/10.1039/C7NR06561B
2018
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H. Jiang, C. Li, R. Zhang, P. Yan, P. Lin, Y. Li, J. J. Yang, D. Holcomb, and Q. Xia, “A provable key destruction scheme based on memristive crossbar arrays,” Nature Electronics , vol. 1, no. 10, pp. 548–554, Oct 2018. [Online]. Available: https://doi.org/10.1038/s41928-018-0146-5
2018
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H. Nili, G. C. Adam, B. Hoskins, M. Prezioso, J. Kim, M. R. Mahmoodi, F. M. Bayat, O. Kavehei, and D. B. Strukov, “Hardware-intrinsic security primitives enabled by analogue state and nonlinear conductance variations in integrated memristors,” Nature Electronics , vol. 1, no. 3, pp. 197–202, Mar 2018. [Online]. Available: https://doi.org/10.1038/s41928-018-0039-7
2018
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P.-Y. Chen, X. Peng, and S. Yu, “Neurosim: A circuit-level macro model for benchmarking neuro-inspired architectures in online learning,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , vol. 37, no. 12, pp. 3067–3080, 2018
2018
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M. Rahimi Azghadi, Y.-C. Chen, J. K. Eshraghian, J. Chen, C.-Y. Lin, A. Amirsoleimani, A. Mehonic, A. J. Kenyon, B. Fowler, J. C. Lee, and Y.-F. Chang, “Complementary metal-oxide semiconductor and memristive hardware for neuromorphic computing,” Advanced Intelligent Systems , vol. 2, no. 5, p. 1900189, 2020. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.201900189
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A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, and E. Eleftheriou, “Memory devices and applications for in-memory computing,” Nature Nanotechnology , vol. 15, no. 7, pp. 529–544, Jul 2020. [Online]. Available: https://doi.org/10.1038/s41565-020-0655-z
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G. Karunaratne, M. Le Gallo, G. Cherubini, L. Benini, A. Rahimi, and A. Sebastian, “In-memory hyperdimensional computing,” Nature Electronics , vol. 3, no. 6, pp. 327–337, Jun 2020. [Online]. Available: https://doi.org/10.1038/s41928-020-0410-3
2020
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S. Lv, J. Liu, and Z. Geng, “Application of memristors in hardware security: A current state-of-the-art technology,” Advanced Intelligent Systems , vol. 3, no. 1, p. 2000127, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202000127
2021
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