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...

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Bibliographic Details

Published in:	2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) pp. 548 - 560
Main Authors:	Jiecao Yu, Lukefahr, Andrew, Palframan, David, Dasika, Ganesh, Das, Reetuparna, Mahlke, Scott
Format:	Conference Proceeding
Language:	English
Published:	ACM 01.06.2017
Subjects:	Computational modeling Graphics processing units Hardware hardware parallelism Matrix converters Microcontrollers multiple data neural network pruning Parallel processing single instruction Sparse matrices
Online Access:	Get full text
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