[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Kirilov et al., 2021 - Google Patents

A Neural Network with HfO 2 Memristors

Kirilov et al., 2021

View PDF
Document ID
2513347683046869260
Author
Kirilov S
Zaykov I
Publication year
Publication venue
Proc. Tech. Univ. of Sofia

External Links

Snippet

In the last twenty years, the neural networks are under intensive analyses. One of the main ideas of the scientists is to partially replace some of their CMOS-based elements by memristors. Memristors are preferred for application due to their memory effect, low power …
Continue reading at e-university.tu-sofia.bg (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Jang et al. Polymer analog memristive synapse with atomic-scale conductive filament for flexible neuromorphic computing system
Demin et al. Hardware elementary perceptron based on polyaniline memristive devices
Jang et al. ReRAM-based synaptic device for neuromorphic computing
Guo et al. An associative memory circuit based on physical memristors
Li et al. In-memory computing with memristor arrays
Ma et al. Neuromorphic computing with memristive devices
Mikhaylov et al. One-board design and simulation of double-layer perceptron based on metal-oxide memristive nanostructures
Lin et al. Analysis and simulation of capacitor-less ReRAM-based stochastic neurons for the in-memory spiking neural network
Saxena A compact CMOS memristor emulator circuit and its applications
CN107122828B (en) Circuit structure, driving method thereof and neural network
Ji et al. A flexible memristor model with electronic resistive switching memory behavior and its application in spiking neural network
García et al. Current pulses to control the conductance in RRAM devices
Payvand et al. Analog weight updates with compliance current modulation of binary rerams for on-chip learning
Zhang et al. An electronic synapse device based on solid electrolyte resistive random access memory
KR102002212B1 (en) Potentiation operation method of the synapse device for application on neuromorphic system
Fouda et al. Independent component analysis using RRAMs
Mahalanabis et al. Demonstration of spike timing dependent plasticity in CBRAM devices with silicon neurons
Mladenov A modified tantalum oxide memristor model for neural networks with memristor-based synapses
KR102105936B1 (en) Weight matrix input circuit and weight matrix circuit
Soni et al. A review on different memristor modeling and applications
Mladenov A new simplified model for HfO 2-based memristor
Donahue et al. Design and analysis of neuromemristive echo state networks with limited-precision synapses
Afshari et al. Unsupervised learning in hexagonal boron nitride memristor-based spiking neural networks
Kirilov et al. A Neural Network with HfO 2 Memristors
Rafiq et al. Investigation of ReRAM Variability on Flow-Based Edge Detection Computing Using HfO 2-Based ReRAM Arrays