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Computer Science > Machine Learning

arXiv:2109.06762 (cs)
[Submitted on 14 Sep 2021 (v1), last revised 9 Oct 2021 (this version, v3)]

Title:Greenformer: Factorization Toolkit for Efficient Deep Neural Networks

Authors:Samuel Cahyawijaya, Genta Indra Winata, Holy Lovenia, Bryan Wilie, Wenliang Dai, Etsuko Ishii, Pascale Fung
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Abstract: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 this https URL.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2109.06762 [cs.LG]
  (or arXiv:2109.06762v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.06762

Submission history

From: Samuel Cahyawijaya [view email]
[v1] Tue, 14 Sep 2021 15:27:05 UTC (1,719 KB)
[v2] Sun, 3 Oct 2021 02:43:21 UTC (1,719 KB)
[v3] Sat, 9 Oct 2021 17:13:03 UTC (1,719 KB)
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