logo

SCIENCE CHINA Information Sciences, Volume 61, Issue 6: 060425(2018) https://doi.org/10.1007/s11432-017-9378-3

Challenges of memristor based neuromorphic computing system

More info
  • ReceivedOct 6, 2017
  • AcceptedJan 23, 2018
  • PublishedMay 15, 2018

Abstract

There is no abstract available for this article.


Acknowledgment

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.


References

[1] Wulf W A, McKee S A. Hitting the memory wall: implications of the obvious. ACM SIGARCH Comput Archit News, 1995, 23: 20--24. Google Scholar

[2] Schneider D. Deeper and cheaper machine learning. IEEE Spectr, 2017, 54: 42-43 CrossRef Google Scholar

[3] Alibart F, Gao L, Hoskins B D. High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm. Nanotechnology, 2012, 23: 075201 CrossRef PubMed ADS arXiv Google Scholar

[4] Kim S, Du C, Sheridan P. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett, 2015, 15: 2203-2211 CrossRef PubMed ADS Google Scholar

[5] Chi P, Li S C, Xu C, et al. Prime: a novel processing-in-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. Google Scholar

[6] 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. Google Scholar

[7] 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. Google Scholar

[8] 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. Google Scholar

[9] Indiveri G. A low-power adaptive integrate-and-fire neuron circuit. In: Proceedings of International Symposium on Circuits and Systems, Bangkok, 2003. Google Scholar

[10] 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. Google Scholar

[11] 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. Google Scholar

[12] 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. Google Scholar

[13] Liu Q, Long S B, Lv H B. Controllable growth of nanoscale conductive filaments in solid-electrolyte-based ReRAM by using a metal nanocrystal covered bottom electrode.. ACS Nano, 2010, 4: 6162-6168 CrossRef PubMed Google Scholar

[14] Chua L O. Local activity is the origin of complexity. Int J Bifurcat Chaos, 2005, 15: 3435-3456 CrossRef ADS Google Scholar

[15] Liu B Y, Li H, Chen Y R, et al. Reduction and IR-drop compensations techniques for reliable neuromorphic computing systems. In: Proceedings of International Conference on Computer-Aided Design, San Jose, 2014. 63--70. Google Scholar

[16] 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. Google Scholar

[17] 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. Google Scholar

  • Figure 2

    Caption 1.

  • Table 1   Tabel caption
    Title a Title b Title c Title d
    Aaa Bbb Ccc Ddd
    Aaa Bbb Ccc Ddd
    Aaa Bbb Ccc Ddd
  • Table 2   Tabel caption
    Title a Title b Title c Title d
    Aaa Bbb Ccc Ddd ddd ddd ddd. Ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd ddd.
    Aaa Bbb Ccc Ddd ddd ddd ddd.
    Aaa Bbb Ccc Ddd ddd ddd ddd.

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1       京公网安备11010102003388号