Joint Optimization of Quantization and Structured Sparsity for Compressed Deep Neural Networks
Gaurav Srivastava, Deepak Kadetotad, Shihui Yin, Visar Berisha, Chaitali Chakrabarti, Jae-sun Seo
Abstract
The usage of Deep Neural Networks (DNN) on resource-constrained edge devices has been limited due to their high computation and large memory requirement. In this work, we propose an algorithm to compress DNNs by jointly optimizing structured sparsity and quantization constraints in a single DNN training framework. The proposed algorithm has been extensively validated on high/low capacity DNNs and wide/deep sparse DNNs. Further, we perform Pareto-optimal analysis to extract optimal DNN models from a large set of trained DNN models. The optimal structurally-compressed DNN model achieves ~50X weight memory reduction without test accuracy degradation, compared to floating-point uncompressed DNN.
BibTeX
@inproceedings{icassp2019_jointoptimizatio,
title = {Joint Optimization of Quantization and Structured Sparsity for Compressed Deep Neural Networks},
author = {Gaurav Srivastava and Deepak Kadetotad and Shihui Yin and Visar Berisha and Chaitali Chakrabarti and Jae-sun Seo},
booktitle = {ICASSP 2019},
year = {2019}
}