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Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Ghouthi Boukli Hacene 1 Vincent Gripon 1 Matthieu Arzel 1, 2 Nicolas Farrugia 1 Yoshua Bengio
2 Lab-STICC_IMTA_CACS_IAS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-02934543
Contributor : Matthieu Arzel <>
Submitted on : Wednesday, September 9, 2020 - 2:23:36 PM
Last modification on : Friday, September 11, 2020 - 3:27:37 AM

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Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel, Nicolas Farrugia, Yoshua Bengio. Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks. NEWCAS 2020 : 18th IEEE International New Circuits and Systems Conference, Jun 2020, Montréal, Canada. pp.206-209, ⟨10.1109/NEWCAS49341.2020.9159769⟩. ⟨hal-02934543⟩

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