Advanced science (Weinheim, Baden-Wurttemberg, Germany). 2022 Aug;9(22):e2201117. doi: 10.1002/advs.202201117 Q114.12025

A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing

一种可调学习率且可靠的TiOx忆阻器阵列用于鲁棒、快速和准确的神经形态计算翻译改进

Jingon Jang¹, Sanggyun Gi², Injune Yeo², Sanghyeon Choi¹, Seonghoon Jang¹, Seonggil Ham¹, Byunggeun Lee², Gunuk Wang^{1 3 4}

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¹ KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

² School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123, Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea, Buk-gu, 61005, Republic of Korea.

³ Department of Integrative Energy Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

⁴ Center for Neuromorphic Engineering, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea.

DOI: 10.1002/advs.202201117 PMID: 35666073

摘要 Ai翻译

Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiOx ) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector-matrix multiplication process between multisig... ...点击完成人机验证后继续浏览

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期刊名：Advanced science

缩写：ADV SCI

ISSN：N/A

e-ISSN：2198-3844

IF/分区：14.1/Q1

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A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing

一种可调学习率且可靠的TiOx忆阻器阵列用于鲁棒、快速和准确的神经形态计算翻译改进

Self-rectifying-based memristors for neuromorphic computing

基于自整流的神经形态计算忆阻器

Electret-Based Organic Synaptic Transistor for Neuromorphic Computing

基于驻极体的有机突触晶体管用于神经形态计算

Investigation of the Versatile Utilization of Three-Dimensional Vertical Resistive Random-Access Memory in Neuromorphic Computing

三维竖直电阻随机存取存储器在神经形态计算中通用性的研究

Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing

基于剥离(In,Ga)N薄膜的柔性光刺激人工突触用于神经形态计算

Neuromorphic Computing via Fission-based Broadband Frequency Generation

基于裂变的宽带频率生成的神经形态计算

Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing

用于大规模神经形态计算的硅基磷化铟突触器件模拟生物突触功能

Adaptive Neuromorphic Architecture (ANA)

自适应神经形态架构(ANA)

Conductive-bridging random-access memories for emerging neuromorphic computing

新型类脑计算应用的导电细丝型ReRAM研究进展与发展挑战

A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing

一种可调学习率且可靠的TiOx忆阻器阵列用于鲁棒、快速和准确的神经形态计算 翻译改进

Self-rectifying-based memristors for neuromorphic computing

基于自整流的神经形态计算忆阻器

Electret-Based Organic Synaptic Transistor for Neuromorphic Computing

基于驻极体的有机突触晶体管用于神经形态计算

Investigation of the Versatile Utilization of Three-Dimensional Vertical Resistive Random-Access Memory in Neuromorphic Computing

三维竖直电阻随机存取存储器在神经形态计算中通用性的研究

Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing

基于剥离(In,Ga)N薄膜的柔性光刺激人工突触用于神经形态计算

Neuromorphic Computing via Fission-based Broadband Frequency Generation

基于裂变的宽带频率生成的神经形态计算

Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing

用于大规模神经形态计算的硅基磷化铟突触器件模拟生物突触功能

Adaptive Neuromorphic Architecture (ANA)

自适应神经形态架构(ANA)

Conductive-bridging random-access memories for emerging neuromorphic computing

新型类脑计算应用的导电细丝型ReRAM研究进展与发展挑战

一种可调学习率且可靠的TiOx忆阻器阵列用于鲁棒、快速和准确的神经形态计算翻译改进