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This content will become publicly available on July 21, 2024

Title: Tensor Decomposition for Model Reduction in Neural Networks: A Review
Modern neural networks have revolutionized the fields of computer vision (CV) and Natural Language Processing (NLP). They are widely used for solving complex CV tasks and NLP tasks such as image classification, image generation, and machine translation. Most state-of-the-art neural networks are over-parameterized and require a high computational cost. One straightforward solution is to replace the layers of the networks with their low-rank tensor approximations using different tensor decomposition methods. This article reviews six tensor decomposition methods and illustrates their ability to compress model parameters of convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Transformers. The accuracy of some compressed models can be higher than the original versions. Evaluations indicate that tensor decompositions can achieve significant reductions in model size, run-time and energy consumption, and are well suited for implementing neural networks on edge devices.  more » « less
Award ID(s):
1954749
NSF-PAR ID:
10434926
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE circuits and systems magazine
Volume:
23
Issue:
2
ISSN:
1531-636X
Page Range / eLocation ID:
8-28
Subject(s) / Keyword(s):
["Tensor decomposition",",\n convolution neural network acceleration",",\n recurrent neural network acceleration",",\n transformer acceleration",",\n canonical polyadic decomposition",",\n Tucker decomposition",",\n tensor train decomposition",",\n tensor ring decomposition",",\n block-term decomposition",",\n hierarchical Tucker decomposition",",\n model compression"]
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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