Optimize deep convolutional neural network with ternarized weights and high accuracy

Zhezhi He, Boqing Gong, Deliang Fan

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

9 Scopus citations

Abstract

Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource limited embedded systems. As the countermeasure to this problem, in this work, we propose statistical weight scaling and residual expansion methods to reduce the bit-width of the whole network weight parameters to ternary values (i.e. -1, 0, +1), with the objectives to greatly reduce model size, computation cost and accuracy degradation caused by the model compression. With about 16× model compression rate, our ternarized ResNet-32/44/56 could outperforms full-precision counterparts by 0.12%, 0.24% and 0.18% on CIFAR-10 dataset. We also test our ternarization method with AlexNet and ResNet-18 on ImageNet dataset, which both achieve the best top-1 accuracy compared to recent similar works, with the same 16× compression rate. If further incorporating our residual expansion method, compared to the full-precision counterpart, our ternarized ResNet-18 even improves the top-5 accuracy by 0.61% and merely degrades the top-1 accuracy only by 0.42% for ImageNet dataset, with 8× model compression rate. It outperforms the recent ABC-Net by 1.03% in top-1 accuracy and 1.78% in top-5 accuracy, with around 1.25× higher compression rate and more than 6× computation reduction due to the weight sparsity.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages913-921
Number of pages9
ISBN (Electronic)9781728119755
DOIs
StatePublished - Mar 4 2019
Externally publishedYes
Event19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 - Waikoloa Village, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

Conference

Conference19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Country/TerritoryUnited States
CityWaikoloa Village
Period1/7/191/11/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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