Scalpel: Customizing DNN pruning to the underlying hardware parallelism

As the size of Deep Neural Networks (DNNs) continues to grow to increase accuracy and solve more complex problems, their energy footprint also scales. Weight pruning reduces DNN model size and the computation by removing redundant weights. However, we implemented weight pruning for several popular n...

Celý popis

Podrobná bibliografia

Vydané v:	2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) s. 548 - 560
Hlavní autori:	Jiecao Yu, Lukefahr, Andrew, Palframan, David, Dasika, Ganesh, Das, Reetuparna, Mahlke, Scott
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 01.06.2017
Predmet:	Computational modeling Graphics processing units Hardware hardware parallelism Matrix converters Microcontrollers multiple data neural network pruning Parallel processing single instruction Sparse matrices
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!