Deep Neural Network Feasibility Using Analog Spiking Neurons Article Swipe

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Thomas Soupizet , Zalfa Jouni , Joao Frischenbruder Sulzbach , Aziz Benlarbi‐Delaï , Pietro M. Ferreira ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1109/sbcci55532.2022.9893216

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Neuromorphic engineering Spiking neural network Computer science Artificial neural network Artificial intelligence Deep learning Efficient energy use Von Neumann architecture Energy (signal processing) Deep neural networks Domain (mathematical analysis) Power (physics) Computer architecture Electronic engineering Electrical engineering Engineering Mathematics Physics Statistics Quantum mechanics Operating system Mathematical analysis

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1109/sbcci55532.2022.9893216
OA Status: green
Cited By: 6
References: 17
Related Works: 10
OpenAlex ID: https://openalex.org/W4281619927

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DOI: https://doi.org/10.1109/sbcci55532.2022.9893216

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Title: Deep Neural Network Feasibility Using Analog Spiking Neurons

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-08-22

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Authors: Thomas Soupizet, Zalfa Jouni, Joao Frischenbruder Sulzbach, Aziz Benlarbi‐Delaï, Pietro M. Ferreira

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Landing page: https://doi.org/10.1109/sbcci55532.2022.9893216

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Concepts: Neuromorphic engineering, Spiking neural network, Computer science, Artificial neural network, Artificial intelligence, Deep learning, Efficient energy use, Von Neumann architecture, Energy (signal processing), Deep neural networks, Domain (mathematical analysis), Power (physics), Computer architecture, Electronic engineering, Electrical engineering, Engineering, Mathematics, Physics, Statistics, Quantum mechanics, Operating system, Mathematical analysis

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Cited by: 6

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