SCIENCE CHINA Information Sciences, Volume 61 , Issue 6 : 060423(2018) https://doi.org/10.1007/s11432-018-9425-1

Artificial neural networks based on memristive devices

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  • ReceivedFeb 22, 2018
  • AcceptedMar 27, 2018
  • PublishedMay 15, 2018



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  • Figure 1

    (Color online) Neuron and synapse structure. (a) Simplified biological structure of pre-synaptic and post-synaptic neurons showing the axon, synapses, and dendrites [6]@Copyright 2017 Scientific Reports; (b) biological synaptic structure and function [7]@Copyright 2011 Frontiers in Neuroscience.

  • Figure 2

    (Color online) (a) Memristor crossbar for vector matrix multiplication (VMM); (b) current (mA) output was experimentally measured for a 6464 crossbar for VMM and the resulting current from column 1 and column 64 were compared with the expected values using software VMM. Non-idealities in the devices and circuitry result in a drastic difference between the ideal output and the actual output [9]@Copyright 2018 Nature Electronics.

  • Figure 3

    (a) ISAAC architecture using tiles [13]. (b) Simplified design of PRIME [14]. External I/O data is fed into RRAM crossbar based banks. Memory subarrays only store data. Full function subarrays can store data or perform computation, using a CMOS controller to enable configuration. The buffer subarrays serve as data buffers for the full function subarrays. (c) AEPE Implementation with efficient tile architecture [15]. The register buffer feeds into M rows of crossbar based PE arrays through an assigned DAC for each row.

  • Figure 4

    (Color online) (a) A single-layer is mapped onto the one transistor one memristor (1T1R) array; (b) micrograph of the 1024 cell 1T1R array fabricated using CMOS compatible processes [16]@Copyright 2017 Nature Communications.

  • Figure 5

    (Color online) (a) Photograph of fabricated 1T1R crossbars. Two dies are shown, each one containing various array sizes from 4$\times$4 to 128$\times$64 cells. (b) Micrograph of four cells in a 1T1R array (scale bar, 10 mm). (c) Relatively linear I-V curves for all the devices over the chosen conductance range. (d) Device state retention and read disturbance (1000 cycles of 0.2 V read pulses) at room temperature show no discernible drift [9]@Copyright 2017 Nature Electronics.

  • Figure 6

    (Color online) (a) Synapse array with peripheral circuitry; (b) a pre-synaptic device controls the FET gate of the 1T1R structure, while the post-synaptic device receives the input current and controls the synapse top electrode to induce synaptic current and stimulate synaptic potentiation or depression during the fire [6]@Copyright 2017 Scientific Reports.

  • Figure 7

    (Color online) Diffusive memristor neuron schematic. The diffusive memristor behaves in a fundamentally similar manner as a biological ion channel on the soma membrane of a neuron [17]@Copyright 2018 Nature Electronics.

  • Figure 8

    (Color online) (a) All memristor network fabricated using (b) and (c) drift memristors for synapses and (d) and (e) diffusive memristors for neurons [17]@Copyright 2018 Nature Electronics.

  • Table 1   Summary of experimental memristor based network implementations presented in literature$^{\rm~a)}$
    ImplementationComputing efficiencyAccuracyPower consumptionMemristor used/Dielectric thicknessCrossbar cell countReference
    1T1R face classifying perceptron networkConverges within 10 iterations88% recognition accuracy within 10 iterations$\sim$30 nJ per epoch for classification taskMemristor-8 nm HfAl$_{y}$O$_{x}$128$\times$8 cells[16]
    1T1R Ta/HfO$_{2}$ memristor network119.7 trillion operations per second per watt91.71% recognition accuracy on MNIST data set13.7 mW for image compressionMemristor-5 nm HfO$_{2}$128$\times$64 cells[9,19,20]
    1T1R STDP networkMemristor-10 nm HfO$_{2}$4$\times$4 pre-cells and 1 post-cell[6]
    Diffusive memristor artificial neuron networkDiffusive memristor-10 nm Ag/SiO$_{2}$ par Drift par Memristor-5 nm HfO$_{2}$8$\times$8 synaptic crossbar with 8 neurons[17]