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 metadatas

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

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Identifiers

Collections

Citation

Export

Metrics

16

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Identifiers

Collections

Citation

Export

Share

Metrics

16