A Novel Time‐Division Multiplexing Architecture Revealed by Reconfigurable Synapse for Deep Neural Networks
Abstract
Abstract Deep learning's growing complexity demands advanced AI chips, increasing hardware costs. Time‐division multiplexing (TDM) neural networks offer a promising solution to simplify integration. However, it is difficult for current synapse transistors to physically implement TDM networks due to inherent device limitations, hindering their practical deployment in modern systems. Here, a novel graphene/2D perovskite/carbon nanotubes (CNTs) synapse transistor featuring a sandwich structure is presented. This transistor enables the realization of TDM neural networks at the hardware level. In this structure, the 2D perovskite layer, characterized by high ion concentration, serves as a neurotransmitter, thereby enhancing synaptic transmission efficiency. Additionally, the CNTs' field‐effect transistors, with their large on‐off ratio, demonstrate a wider range of synaptic current changes. The device mechanism is theoretically analyzed using molecular dynamics simulation. Furthermore, the impact of TDM on the scale, power, and latency of neural network hardware implementation is investigated. Qualitative analysis is performed to elucidate the advantages of TDM in the hardware implementation of larger deep learning models. This study offers a new approach to reducing the integration complexity of neural networks hardware implementation, holding significant promise for the development of intelligent nanoelectronic devices in the future.
Article Details
Authors (15)
Yu‐Tao Li
School of Laboratory Medicine Hubei University of Chinese Medicine Wuhan 430065 China
Kui Xu
Beijing Frontier Research Center for Biological Structures, State Key Laboratory of Membrane Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University
Yu‐Zhe Ma
Microelectronics Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou 511453 China
Jun‐Ze Li
School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China
Yang Luo
Xin‐Ru Li
School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing 100084 China
Peng‐Hui Shen
School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing 100084 China
Lu‐Yu Zhao
School of Integrated Circuits and Electronics MIIT Key Laboratory for Low‐Dimensional Quantum Structure and Devices Beijing Institute of Technology Beijing 100081 China
Hang Liu
Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay
Li Ren
De‐Hui Li
School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China
Lian‐Mao Peng
Key Laboratory for the Physics and Chemistry of Nanodevices and Center for Carbon‐based Electronics Department of Electronics Peking University Beijing 100871 China
Li Ding
Tian‐Ling Ren
School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing 100084 China
Yeliang Wang
School of Integrated Circuits and Electronics, MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices