High Dimensional Convolution Acceleration via Tensor Decomposition.
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| Title: | High Dimensional Convolution Acceleration via Tensor Decomposition. |
|---|---|
| Authors: | Du, Xinyu1 (AUTHOR) |
| Source: | Journal of Circuits, Systems & Computers. 2022, Vol. 31 Issue 10, p1-21. 21p. |
| Subjects: | Computer vision, Computational complexity, Problem solving |
| Abstract: | The high-dimensional convolution, in either linear or nonlinear form, has been employed in a wide range of computer vision solutions due to its beneficial smoothing property. However, its full-kernel implementation is extremely slow. We do need a fast algorithm for this important operation. To solve this problem, we propose an acceleration pipeline assembled by three steps: d -D nonlinear convolution ⇒ 1 d -D linear convolution ⇒ 2 1-D dimensional convolution ⇒ 3 1-D recursive box filter. Thanks to the low computational complexity of box filtering, we speed up the computation significantly. Roughly speaking, our contribution is two-fold: (1) establishing the connection between the high-dimensional convolution acceleration algorithm and tensor decomposition; (2) propose total four acceleration technologies including demultiplexing–blurring–multiplexing framework, convolution decomposition, periodic tensorization and recursively box filtering to compose our acceleration pipeline under the line of the above connection. The effectiveness of these techniques is demonstrated in various comparisons and experiments. The running times of various applications are largely shortened from several minutes to fewer seconds or less. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 157709104 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: High Dimensional Convolution Acceleration via Tensor Decomposition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Du%2C+Xinyu%22">Du, Xinyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> <email>jinhuitang@njust.edu.cn</email></i><br /><searchLink fieldCode="AR" term="%22Gu%2C+Zichen%22">Gu, Zichen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> guzichen2018@rails.cn</i><br /><searchLink fieldCode="AR" term="%22Dai%2C+Longquan%22">Dai, Longquan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> dailongquan@njust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tang%2C+Jinhui%22">Tang, Jinhui</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jinhuitang@njust.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Circuits%2C+Systems+%26+Computers%22">Journal of Circuits, Systems & Computers</searchLink>. 2022, Vol. 31 Issue 10, p1-21. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The high-dimensional convolution, in either linear or nonlinear form, has been employed in a wide range of computer vision solutions due to its beneficial smoothing property. However, its full-kernel implementation is extremely slow. We do need a fast algorithm for this important operation. To solve this problem, we propose an acceleration pipeline assembled by three steps: d -D nonlinear convolution ⇒ 1 d -D linear convolution ⇒ 2 1-D dimensional convolution ⇒ 3 1-D recursive box filter. Thanks to the low computational complexity of box filtering, we speed up the computation significantly. Roughly speaking, our contribution is two-fold: (1) establishing the connection between the high-dimensional convolution acceleration algorithm and tensor decomposition; (2) propose total four acceleration technologies including demultiplexing–blurring–multiplexing framework, convolution decomposition, periodic tensorization and recursively box filtering to compose our acceleration pipeline under the line of the above connection. The effectiveness of these techniques is demonstrated in various comparisons and experiments. The running times of various applications are largely shortened from several minutes to fewer seconds or less. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218126622501870 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1 Subjects: – SubjectFull: Computer vision Type: general – SubjectFull: Computational complexity Type: general – SubjectFull: Problem solving Type: general Titles: – TitleFull: High Dimensional Convolution Acceleration via Tensor Decomposition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Du, Xinyu – PersonEntity: Name: NameFull: Gu, Zichen – PersonEntity: Name: NameFull: Dai, Longquan – PersonEntity: Name: NameFull: Tang, Jinhui IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: 2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 02181266 Numbering: – Type: volume Value: 31 – Type: issue Value: 10 Titles: – TitleFull: Journal of Circuits, Systems & Computers Type: main |
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