[go: up one dir, main page]

Kim et al., 2017 - Google Patents

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

Kim et al., 2017

Document ID
5723569567719818117
Author
Kim H
Hwang S
Park J
Park B
Publication year
Publication venue
Nanotechnology

External Links

Snippet

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system …
Continue reading at iopscience.iop.org (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
    • G06N3/04Architectures, e.g. interconnection topology
    • 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
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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

Similar Documents

Publication Publication Date Title
Kim et al. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system
Ielmini et al. Emerging neuromorphic devices
Kim et al. Emerging memory technologies for neuromorphic computing
Jang et al. Polymer analog memristive synapse with atomic-scale conductive filament for flexible neuromorphic computing system
Cao et al. Emerging dynamic memristors for neuromorphic reservoir computing
Covi et al. Ferroelectric-based synapses and neurons for neuromorphic computing
Zhang et al. Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
Mullani et al. Surface modification of a titanium carbide mxene memristor to enhance memory window and low‐power operation
Saïghi et al. Plasticity in memristive devices for spiking neural networks
Park et al. 3-D stacked synapse array based on charge-trap flash memory for implementation of deep neural networks
Garbin et al. Variability-tolerant convolutional neural network for pattern recognition applications based on OxRAM synapses
US11514303B2 (en) Synaptic resistors for concurrent parallel signal processing, memory and learning with high speed and energy efficiency
Tian et al. Recent advances, perspectives, and challenges in ferroelectric synapses
Kim et al. Analog reservoir computing via ferroelectric mixed phase boundary transistors
Park et al. Intrinsic variation effect in memristive neural network with weight quantization
US20200320374A1 (en) Memristive Multi-terminal Spiking Neuron
Chen et al. A spiking neuron circuit based on a carbon nanotube transistor
Rajakumari et al. Demonstration of an ultralow energy PD-SOI FINFET based LIF neuron for SNN
Oh et al. Unsupervised online learning of temporal information in spiking neural network using thin-film transistor-type NOR flash memory devices
Zhang et al. Tolerance of intrinsic device variation in fuzzy restricted Boltzmann machine network based on memristive nano-synapses
Afshari et al. Unsupervised learning in hexagonal boron nitride memristor-based spiking neural networks
Suresh et al. Realizing spike-timing dependent plasticity learning rule in Pt/Cu: ZnO/Nb: STO memristors for implementing single spike based denoising autoencoder
Benatti et al. Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses
Tong et al. Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing
Walters et al. Unsupervised character recognition with graphene memristive synapses