Self-Activation Neural Network Based on Self-Selective Memory Device With Rectified Multilevel States
Zongwei Wang, Qilin Zheng, Jian Kang, Zhizhen Yu, Guofang Zhong, Yaotian Ling, Lin Bao, Shengyu Bao, Guandong Bai, Shan Zheng, Yimao Cai, John Robertson, Ru Huang
Abstract
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 VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /TaO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> 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.