Convolutional neural network acceleration with hardware/software co-design.
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| Title: | Convolutional neural network acceleration with hardware/software co-design. |
|---|---|
| Authors: | Chen, Andrew Tzer-Yeu1 andrew.chen@auckland.ac.nz, Biglari-Abhari, Morteza1 m.abhari@auckland.ac.nz, Wang, Kevin I-Kai1 kevin.wang@auckland.ac.nz, Bouzerdoum, Abdesselam2,3 bouzer@uow.edu.au, Tivive, Fok Hing Chi2 tivive@uow.edu.au |
| Source: | Applied Intelligence. May2018, Vol. 48 Issue 5, p1288-1301. 14p. |
| Subjects: | Embedded computer systems, Graphics processing units, Field programmable gate arrays, Machine learning, Computer vision |
| Abstract: | Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing and natural language processing. Inspired by the mammalian visual cortex, CNNs have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions. This paper presents a design methodology for accelerating CNNs using Hardware/Software Co-design techniques, in order to balance performance and flexibility, particularly for resource-constrained systems. The methodology is applied to a gender recognition case study, using an ARM processor and FPGA fabric to create an embedded system that can process facial images in real-time. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence is the property of Springer Nature 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: 128928714 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Convolutional neural network acceleration with hardware/software co-design. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Andrew+Tzer-Yeu%22">Chen, Andrew Tzer-Yeu</searchLink><relatesTo>1</relatesTo><i> andrew.chen@auckland.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Biglari-Abhari%2C+Morteza%22">Biglari-Abhari, Morteza</searchLink><relatesTo>1</relatesTo><i> m.abhari@auckland.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Kevin+I-Kai%22">Wang, Kevin I-Kai</searchLink><relatesTo>1</relatesTo><i> kevin.wang@auckland.ac.nz</i><br /><searchLink fieldCode="AR" term="%22Bouzerdoum%2C+Abdesselam%22">Bouzerdoum, Abdesselam</searchLink><relatesTo>2,3</relatesTo><i> bouzer@uow.edu.au</i><br /><searchLink fieldCode="AR" term="%22Tivive%2C+Fok+Hing+Chi%22">Tivive, Fok Hing Chi</searchLink><relatesTo>2</relatesTo><i> tivive@uow.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. May2018, Vol. 48 Issue 5, p1288-1301. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Embedded+computer+systems%22">Embedded computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Field+programmable+gate+arrays%22">Field programmable gate arrays</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing and natural language processing. Inspired by the mammalian visual cortex, CNNs have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions. This paper presents a design methodology for accelerating CNNs using Hardware/Software Co-design techniques, in order to balance performance and flexibility, particularly for resource-constrained systems. The methodology is applied to a gender recognition case study, using an ARM processor and FPGA fabric to create an embedded system that can process facial images in real-time. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Intelligence is the property of Springer Nature 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: 10.1007/s10489-017-1007-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1288 Subjects: – SubjectFull: Embedded computer systems Type: general – SubjectFull: Graphics processing units Type: general – SubjectFull: Field programmable gate arrays Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computer vision Type: general Titles: – TitleFull: Convolutional neural network acceleration with hardware/software co-design. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Andrew Tzer-Yeu – PersonEntity: Name: NameFull: Biglari-Abhari, Morteza – PersonEntity: Name: NameFull: Wang, Kevin I-Kai – PersonEntity: Name: NameFull: Bouzerdoum, Abdesselam – PersonEntity: Name: NameFull: Tivive, Fok Hing Chi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 48 – Type: issue Value: 5 Titles: – TitleFull: Applied Intelligence Type: main |
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