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Training andoperation of an integrated neuromorphic network based on metal-oxide memristors

机译：基于金属氧化物忆阻器的集成神经形态网络的训练和操作

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Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex(1), with its approximately 10(14) synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks(2) based on circuits(3,4) combining complementary metaloxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one(3) or several(4) crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits(5-12), including first demonstrations(5,6,12) of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks(13-18). Very recently, such experiments have been extended(19) to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors(11,20,21), whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm(22) to perform the perfect classification of 3 3 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.
机译：尽管半导体集成电路技术取得了长足的进步，但人类大脑皮层（1）的极高复杂性及其大约10（14）个突触使神经形态网络的硬件实现以及相当数量的设备异常具有挑战性。为了提供可比的复杂性，同时又能以更快的速度运行并具有可控制的功耗，已经开发了基于电路（3,4）的网络（2），该电路将互补金属氧化物半导体（CMOS）与可调式两端电阻器件（忆阻器）相结合。在这样的电路中，通常的CMOS堆栈增加了一个（3）或几个（4）交叉开关层，每个交叉点处都有忆阻器。最近，在这种忆阻交叉开关的制造及其与CMOS电路的集成中取得了显着的改进（5-12），包括其垂直集成的首次演示（5、6、12）。另外，离散忆阻器已被用作神经形态网络中的人工突触（13-18）。最近，这种实验已经扩展（19）到相变忆阻器件的交叉阵列。然而，对此类器件的调整需要在每个交叉点处增加一个晶体管，因此与金属氧化物忆阻器（11,20,21）相比，这些器件的缩放难度要大得多，金属氧化物忆阻器的非线性电流-电压曲线可实现无晶体管工作。在这里，我们报告了无晶体管金属氧化物忆阻器交叉开关的实验实现，其设备可变性足够低，以允许集成神经网络在一个简单的网络中运行：单层感知器（线性分类算法）。可以使用增量规则算法（22）的粗粒度变体来就地教授网络，以将3 3 3像素黑白图像完美地分类为三个类别（代表字母）。该演示是迈向更大，更复杂的忆阻神经形态网络的重要一步。

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  • 来源
    《Nature》 |2015年第7550期|61-64|共4页
  • 作者

    Prezioso M.; Merrikh-Bayat F.; Hoskins B. D.; Adam G. C.; Likharev K. K.; Strukov D. B.;

  • 作者单位

    Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA;

    Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA;

    Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA;

    Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA;

    SUNY Stony Brook, Dept Phys & Astron, Stony Brook, NY 11794 USA;

    Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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