Stability based filter pruning for accelerating deep CNNs

Pravendra Singh, Vinay Sameer Raja Kadi, Nikhil Verma, Vinay P. Namboodiri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)
4 Downloads (Pure)

Abstract

Convolutional neural networks (CNN) have achieved impressive performance on the wide variety of tasks (classification, detection, etc.) across multiple domains at the cost of high computational and memory requirements. Thus, leveraging CNNs for real-time applications necessitates model compression approaches that not only reduce the total number of parameters but reduce the overall computation as well. In this work, we present a stability-based approach for filter-level pruning of CNNs. We evaluate our proposed approach on different architectures (LeNet, VGG-16, ResNet, and Faster RCNN) and datasets and demonstrate its generalizability through extensive experiments. Moreover, our compressed models can be used at run-time without requiring any special libraries or hardware. Our model compression method reduces the number of FLOPS by an impressive factor of 6.03X and GPU memory footprint by more than 17X, significantly outperforming other state-of-the-art filter pruning methods.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherIEEE
Pages1166-1174
Number of pages9
ISBN (Electronic)9781728119755
DOIs
Publication statusPublished - 7 Mar 2019
Event19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 - Waikoloa Village, USA United States
Duration: 7 Jan 201911 Jan 2019

Publication series

NameProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
ISSN (Print)1550-5790

Conference

Conference19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
CountryUSA United States
CityWaikoloa Village
Period7/01/1911/01/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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