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

Vu et al., 2024 - Google Patents

Spiking Neural Networks with Nonidealities from Memristive Silicon Oxide Devices

Vu et al., 2024

View PDF
Document ID
4452415117485456809
Author
Vu V
Kenyon A
Joksas D
Mehonic A
Mannion D
Ng W
Publication year
Publication venue
2024 IEEE 24th International Conference on Nanotechnology (NANO)

External Links

Snippet

Recent years have seen a rapid surge in the application of artificial neural networks in diverse cognitive settings. The augmented computational demands of these structures have led to an interest in new technologies and paradigms. Of all the artificial neural networks, the …
Continue reading at discovery.ucl.ac.uk (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/049Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0472Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps

Similar Documents

Publication Publication Date Title
Demin et al. Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network
US8965821B2 (en) Learning method of neural network circuit
Liu et al. Implementation of memristive neural network with full-function Pavlov associative memory
Huang et al. Artificial neural networks based on memristive devices: From device to system
JP6501146B2 (en) Neural network circuit and learning method thereof
US8775346B2 (en) Learning method of neural network circuit
Mikhaylov et al. One-board design and simulation of double-layer perceptron based on metal-oxide memristive nanostructures
Hansen et al. Double-barrier memristive devices for unsupervised learning and pattern recognition
JP2021507349A (en) A method for storing weights in a crosspoint device of a resistance processing unit array, its crosspoint device, a crosspoint array for performing a neural network, its system, and a method for performing a neural network. Method
CN111275177B (en) Full memristor neural network and preparation method and application thereof
Prezioso et al. Spiking neuromorphic networks with metal-oxide memristors
CN111967589A (en) Neuron analog circuit, driving method thereof and neural network device
Boybat et al. Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks
US20210012974A1 (en) Fully-printed all-solid-state organic flexible artificial synapse for neuromorphic computing
US11488001B2 (en) Neuromorphic devices using layers of ion reservoirs and ion conductivity electrolyte
Yang et al. On learning with nonlinear memristor-based neural network and its replication
Milo et al. Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks
Milo et al. Brain-inspired recurrent neural network with plastic RRAM synapses
Peng et al. Memristor based Spiking Neural Networks: Cooperative Development of Neural Network Architecture/Algorithms and Memristors
US20200387779A1 (en) Neuromorphic device with oxygen scavenging gate
WO2022118340A1 (en) Novel activation function with hardware realization for recurrent neuromorphic networks
Huang et al. Adaptive SRM neuron based on NbOx memristive device for neuromorphic computing
Vu et al. Spiking Neural Networks with Nonidealities from Memristive Silicon Oxide Devices
Siegel et al. System model of neuromorphic sequence learning on a memristive crossbar array
CN109978019B (en) Image mode recognition analog and digital mixed memristor equipment and preparation thereof, and STDP learning rule and image mode recognition method are realized