Cargando…

Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses

Neuromorphic computing has shown great advantages towards cognitive tasks with high speed and remarkable energy efficiency. Memristor is considered as one of the most promising candidates for the electronic synapse of the neuromorphic computing system due to its scalability, power efficiency and cap...

Descripción completa

Detalles Bibliográficos
Autores principales:	Li, Ruiyi, Huang, Peng, Feng, Yulin, Zhou, Zheng, Zhang, Yizhou, Ding, Xiangxiang, Liu, Lifeng, Kang, Jinfeng
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951175/ https://www.ncbi.nlm.nih.gov/pubmed/35334725 http://dx.doi.org/10.3390/mi13030433

Ejemplares similares

Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing
por: Wang, Rui, et al.
Publicado: (2018)

Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
por: Camuñas-Mesa, Luis A., et al.
Publicado: (2019)

Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing
por: Park, See-On, et al.
Publicado: (2022)

Editorial: Memristor Computing for Neuromorphic Systems
por: Min, Kyeong-Sik, et al.
Publicado: (2021)

Robust Memristor Networks for Neuromorphic Computation Applications
por: Hajtó, Dániel, et al.
Publicado: (2019)

CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing
por: Wu, Facai, et al.
Publicado: (2022)

Beyond Memristors: Neuromorphic Computing Using Meminductors
por: Wang, Frank Zhigang
Publicado: (2023)

Investigation and Manipulation of Different Analog Behaviors of Memristor as Electronic Synapse for Neuromorphic Applications
por: Wang, Changhong, et al.
Publicado: (2016)

Superlow Power Consumption Artificial Synapses Based on WSe(2) Quantum Dots Memristor for Neuromorphic Computing
por: Wang, Zhongrong, et al.
Publicado: (2022)

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing
por: John, Rohit Abraham, et al.
Publicado: (2022)

Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications
por: Zeng, Jianmin, et al.
Publicado: (2023)

Efficient Synapse Memory Structure for Reconfigurable Digital Neuromorphic Hardware
por: Kim, Jinseok, et al.
Publicado: (2018)

Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware
por: Kim, Geunyoung, et al.
Publicado: (2022)

Recent Advances in Cerium Oxide-Based Memristors for Neuromorphic Computing
por: Ali, Sarfraz, et al.
Publicado: (2023)

Artificial Synapse Consisted of TiSbTe/SiC(x):H Memristor with Ultra-high Uniformity for Neuromorphic Computing
por: Chen, Liangliang, et al.
Publicado: (2022)

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
por: Miranda, Enrique, et al.
Publicado: (2020)

Fluidic memristor: Bringing chemistry to neuromorphic devices
por: Xiong, Tianyi, et al.
Publicado: (2023)

Neuromorphic computing with multi-memristive synapses
por: Boybat, Irem, et al.
Publicado: (2018)

Memristive Artificial Synapses for Neuromorphic Computing
por: Huang, Wen, et al.
Publicado: (2021)

An ultrasmall organic synapse for neuromorphic computing
por: Liu, Shuzhi, et al.
Publicado: (2023)

Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
por: Han, Geonhui, et al.
Publicado: (2021)

Spin‐Torque Memristors Based on Perpendicular Magnetic Tunnel Junctions for Neuromorphic Computing
por: Zhang, Xueying, et al.
Publicado: (2021)

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection
por: Yu, Zheqi, et al.
Publicado: (2020)

Multi-state MRAM cells for hardware neuromorphic computing
por: Rzeszut, Piotr, et al.
Publicado: (2022)

An unsupervised neuromorphic clustering algorithm
por: Diamond, Alan, et al.
Publicado: (2019)

Neuromorphic Hardware Learns to Learn
por: Bohnstingl, Thomas, et al.
Publicado: (2019)

A brain-plausible neuromorphic on-the-fly learning system implemented with magnetic domain wall analog memristors
por: Yue, Kun, et al.
Publicado: (2019)

HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses
por: Gopalakrishnan, Roshan, et al.
Publicado: (2020)

Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware
por: Han, Joon-Kyu, et al.
Publicado: (2021)

Sputtering-deposited amorphous SrVO(x)-based memristor for use in neuromorphic computing
por: Lee, Tae-Ju, et al.
Publicado: (2020)

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing
por: Kang, Jaehyun, et al.
Publicado: (2022)

Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing
por: Zhang, Runqing, et al.
Publicado: (2023)

STDP and STDP variations with memristors for spiking neuromorphic learning systems
por: Serrano-Gotarredona, T., et al.
Publicado: (2013)

Memristor Neural Network Training with Clock Synchronous Neuromorphic System
por: Jo, Sumin, et al.
Publicado: (2019)

On the Application of a Diffusive Memristor Compact Model to Neuromorphic Circuits
por: Cisternas Ferri, Agustín, et al.
Publicado: (2019)

Low‐Voltage Oscillatory Neurons for Memristor‐Based Neuromorphic Systems
por: Hua, Qilin, et al.
Publicado: (2019)

Emergent solution based IGZO memristor towards neuromorphic applications
por: Martins, Raquel Azevedo, et al.
Publicado: (2022)

Foreword to the focus issue: materials and technologies for memristors and neuromorphic devices
por: Zhou, Ye, et al.
Publicado: (2023)

Graphene memristive synapses for high precision neuromorphic computing
por: Schranghamer, Thomas F., et al.
Publicado: (2020)

Parallelization of Neural Processing on Neuromorphic Hardware
por: Peres, Luca, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_qjbh3eei9ocrp29fnmvmc758i6