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Deep Neural Network Feasibility Using Analog Spiking Neurons

Thomas Soupizet Zalfa Jouni Joao Frischenbruder Sulzbach A. Benlarbi-Delai 1 Pietro Maris Ferreira 1 
1 GeePs - Laboratoire Génie électrique et électronique de Paris
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Conference papers
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https://hal-centralesupelec.archives-ouvertes.fr/hal-03689837
Contributor : Pietro Maris Ferreira Connect in order to contact the contributor
Submitted on : Tuesday, June 7, 2022 - 3:56:14 PM
Last modification on : Thursday, June 9, 2022 - 3:37:24 AM

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  • HAL Id : hal-03689837, version 1

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CNRS | CENTRALESUPELEC | GEEPS | UNIV-PARIS-SACLAY | SORBONNE-UNIVERSITE | SU-SCIENCES | GS-ENGINEERING | INSTITUT-SCIENCES-LUMIERE

Citation

Thomas Soupizet, Zalfa Jouni, Joao Frischenbruder Sulzbach, A. Benlarbi-Delai, Pietro Maris Ferreira. Deep Neural Network Feasibility Using Analog Spiking Neurons. ACM IEEE Symp. Integr. Circuits Syst. Design, Aug 2022, Porto Alegre, France. ⟨hal-03689837⟩

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