While the recent advances in deep neural networks (DNN) bring remarkable success, the computational cost also increases considerably. In this paper, we introduce Greenformer, a toolkit to accelerate the computation of neural networks through matrix factorization while maintaining performance. Greenformer can be easily applied with a single line of code to any DNN model. Our experimental results show that Greenformer is effective for a wide range of scenarios. We provide the showcase of Greenformer at https://samuelcahyawijaya.github.io/greenformer-demo/.
@article{arxiv.2109.06762,
title = {Greenformer: Factorization Toolkit for Efficient Deep Neural Networks},
author = {Samuel Cahyawijaya and Genta Indra Winata and Holy Lovenia and Bryan Wilie and Wenliang Dai and Etsuko Ishii and Pascale Fung},
journal= {arXiv preprint arXiv:2109.06762},
year = {2021}
}