Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

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Weighted Synapses Without Carry Operations for RRAM-Based Neuromorphic Systems.Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.Training LSTM Networks With Resistive Cross-Point Devices

P2860

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P2860

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

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http://www.wikidata.org/entity/Q42700543

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2017 nî lūn-bûn

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2017年の論文

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2017年論文

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2017年論文

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2017年論文

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2017年論文

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2017年論文

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2017年论文

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2017年论文

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2017年论文

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name

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

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Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

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type

Item

label

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

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Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

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prefLabel

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

@en

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

@nl

P1476

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

@en

P2093

Tayfun Gokmen

http://www.w3.org/2001/XMLSchema#string

Wilfried Haensch

http://www.w3.org/2001/XMLSchema#string

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Deep learning

Learning representations by back-propagating errors

Gradient-based learning applied to document recognition

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding

Synaptic electronics: materials, devices and applications.

Li-Ion Synaptic Transistor for Low Power Analog Computing.

Repeatable, accurate, and high speed multi-level programming of memristor 1T1R arrays for power efficient analog computing applications.

A generic systolic array building block for neural networks with on-chip learning.

Training and operation of an integrated neuromorphic network based on metal-oxide memristors.

P304

538

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scholarly article

P356

10.3389/FNINS.2017.00538

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

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Murat Onen

P577

2017-10-10T00:00:00Z

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29066942

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1705.08014

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P921

convolutional neural network

deep learning

deep neural network

P932

5641314

http://www.w3.org/2001/XMLSchema#string

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

about

P2860

P2860

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

description

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type

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P1433

P1476

P2093

P2860

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P921

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P356

P1433

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P50

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P818

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P932