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References
Wulf W A, McKee S A. Hitting the memory wall: implications of the obvious. ACM SIGARCH Comput Archit News, 1995, 23: 20–24
Schneider D. Deeper and cheaper machine learning. IEEE Spectr, 2017, 54: 42–43
Alibart F, Gao L, Hoskins B D, et al. High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm. Nanotechnology, 2012, 23: 075201
Kim S, Du C, Sheridan P, et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett, 2015, 15: 2203–2211
Chi P, Li S C, Xu C, et al. Prime: a novel processingin- memory architecture for neural network computation in ReRAM-based main memory. In: Proceedings of the 43rd Annual International Symposium on Computer Architecture, Seoul, 2016. 27–39
Ma W, Cai F, Du C, et al. Device nonideality effects on image reconstruction using memristor arrays. In: Proceedings of International Electron Devices Meeting, San Francisco, 2016
Shafiee A, Nag A, Muralimanohar N, et al. ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars. In: Proceedings of the 43rd Annual International Symposium on Computer Architecture, Seoul, 2016. 14–26
Liu C C, Yan B N, Yang C F, et al. A spiking neuromorphic design with resistive crossbar. In: Proceedings of the 52nd ACM/EDAC/IEEE Design Automation Conference, San Francisco, 2015
Indiveri G. A low-power adaptive integrate-and-fire neuron circuit. In: Proceedings of International Symposium on Circuits and Systems, Bangkok, 2003
Yan B N, Yang J H, Wu Q, et al. A Closed-loop design to enhance weight stability of memristor based neural network chips. In: Proceedings of International Conference on Computer-Aided Design, Irvine, 2017. 541–548
Liu C C, Yang Q, Yan B N, et al. A memristor crossbar based computing engine optimized for high speed and accuracy. In: Proceedings of IEEE Computer Society Annual Symposium on VLSI, Pittsburgh, 2016. 110–115
Yan B, Monmouth A M, Yang J, et al. A neuromorphic ASIC design using one-selector-one-memristor crossbar. In: Proceedings of International Symposium on Circuits and Systems, Montreal, 2016. 1390–1393
Liu Q, Long S B, Lv H B, et al. Controllable growth of nanoscale conductive filaments in solid-electrolytebased ReRAM by using a metal nanocrystal covered bottom electrode. ACS Nano, 2010, 4: 6162–6168
Chua L O. Local activity is the origin of complexity. Int J Bifurcat Chaos, 2005, 15: 3435–3456
Liu B Y, Li H, Chen Y R, et al. Reduction and IRdrop compensations techniques for reliable neuromorphic computing systems. In: Proceedings of International Conference on Computer-Aided Design, San Jose, 2014. 63–70
Wang Y D, Wen W, Liu B Y, et al. Group scissor: scaling neuromorphic computing design to big neural networks. In: Proceedings of the 54th Annual Design Automation Conference, Austin, 2017
Liu C, Hu M, Strachan J P, et al. Rescuing memristor-based neuromorphic design with high defects. In: Proceedings of the 54th Annual Design Automation Conference, Austin, 2017
Acknowledgements
This work was supported by National Science Foundation (NSF) (Grant No. CSR-1717885), and Air Force Research Laboratory (AFRL) (Grant No. FA8750-15-2-0048). Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of AFRL or its contractors.
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Yan, B., Chen, Y. & Li, H. Challenges of memristor based neuromorphic computing system. Sci. China Inf. Sci. 61, 060425 (2018). https://doi.org/10.1007/s11432-017-9378-3
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DOI: https://doi.org/10.1007/s11432-017-9378-3