Global-Gate Controlled One-Transistor One-Digital-Memristor Structure for Low-Bit Neural Network
Mingqiang Huang, Guangchao Zhao, Xingli Wang, Wei Zhang, Philippe Coquet, Beng Kang Tay, Gaokuo Zhong, Jiangyu Li
Abstract
Memristor based neuromorphic computing system has recently attracted enormous attention due to its fast and energy-efficient matrix vector multiplication, thus providing a novel approach to implement neural networks for artificial intelligence. However, the widely studied analogue memristors exhibit major flaws in terms of high conductance variation and nonlinear/asymmetric characteristics. In this work, we develop global gate controlled one transistor one digital memristor (1T1DM) architecture as the basic binary electronic synapse. Inspired by the current research highlights about low-bit networks, we further implement low-bit neuromorphic computing onto our 1T1DM systems by simulation. Compared to the classical analogue type of memristor with one transistor one analogue memristor (1T1R) structure, our 1T1DM network is light-weighted, highly robust and can work well on challenging visual tasks. Besides, benefiting from the global gated device structure, the on-state conductance of the digital memristors in the network can be simultaneously modulated by the controlling gate, offering possibility to tune the power consumption and operation speed while will not increase the circuit complexity.