Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Ghouthi Boukli Hacene; Vincent Gripon; Matthieu Arzel; Nicolas Farrugia; Yoshua Bengio

doi:10.1109/NEWCAS49341.2020.9159769

Conference papers

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

1 IMT Atlantique - ELEC - Département Electronique

2 Lab-STICC_IMTA_CACS_IAS

Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance

Document type :

Conference papers

Domain :

Computer Science [cs] / Hardware Architecture [cs.AR]

Computer Science [cs] / Embedded Systems

Computer Science [cs] / Signal and Image Processing

Engineering Sciences [physics] / Signal and Image processing

Engineering Sciences [physics] / Electronics

Complete list of metadata

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 : Wednesday, July 21, 2021 - 7:42:02 AM

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

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Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

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