In a digital–analog mixed neuromorphic system, various complex peripheral circuits may offset the integration and energy efficiency advantages of the dense crossbar. To simplify the peripheral circuits, a self-activation neural network (SANN) is proposed based on a passive crossbar array formed by the rectified memristive (ReMem) cell. The ReMem cell is a dual-mode resistive switching device with a VO2/TaOx bilayer structure. It shows a hybrid switching behavior including volatile threshold switching and multilevel nonvolatile resistive switching. This unique feature enables not only simultaneously self-selection and distributed activation, but also weight storage. The concept of SANN is theoretically examined, and a neural network is constructed and trained with SANN based on the proposed devices to perform recognition on handwritten digits in the MNIST database. Compared with neuromorphic computing systems using the CMOS-based activation module and additional selective element, the results show comparable recognition accuracy with reduced circuitry complexity. The proposed SANN can be a promising alternative to realize neural network computing systems with simplified peripheral circuits.

中文翻译：

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基于多级状态自选择记忆装置的自激活神经网​​络

在数模混合神经形态系统中，各种复杂的外围电路可能会抵消密集交叉开关的集成和能效优势。为了简化外围电路，提出了一种基于由整流忆阻 (ReMem) 单元形成的无源交叉阵列的自激活神经网​​络 (SANN)。ReMem 电池是具有 VO2/TaOx 双层结构的双模电阻开关器件。它显示了一种混合开关行为，包括易失性阈值开关和多级非易失性电阻开关。这种独特的功能不仅可以同时进行自选择和分布式激活，还可以实现权重存储。SANN的概念在理论上被检验，并且基于所提出的设备构建神经网络并使用 SANN 进行训练，以对 MNIST 数据库中的手写数字进行识别。与使用基于 CMOS 的激活模块和附加选择元件的神经形态计算系统相比，结果显示出可比的识别精度和降低的电路复杂性。所提出的 SANN 可以成为实现具有简化外围电路的神经网络计算系统的有前途的替代方案。