Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs.
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| Title: | Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs. |
|---|---|
| Authors: | Sarangi, Satyabrata1 (AUTHOR) ssarangi@ucdavis.edu, Baas, Bevan1 (AUTHOR) bbaas@ucdavis.edu |
| Source: | Integration: The VLSI Journal. Jan2023, Vol. 88, p156-165. 10p. |
| Subjects: | Array processors, Intel Corp., Huffman codes, Data compression, Energy consumption, Graphics processing units |
| Geographic Terms: | Canterbury (England), Calgary (Alta.) |
| Abstract: | Data compression is essential to reduce high storage and communication costs for a wide range of systems and applications. Canonical Huffman coding plays a pivotal role for several compression standards. This paper presents bit-parallel static and dynamic canonical Huffman decoder implementations using an optimized lookup table approach on a fine-grain many-core processor array and an Intel FPGA. The decoder implementation results are compared with an Intel i7-4850HQ and a massively parallel Nvidia GT 750M GPU executing the corpus benchmarks: Calgary, Canterbury, Artificial, and Large. The many-core implementations achieve a scaled throughput per chip area that is 891× and 7× greater on average than the i7 and GT 750M respectively. Moreover, the many-core implementations result in a scaled energy efficiency (compressed bits decoded per energy) that is 149.5×, 3.9×, and 2.5× greater on average than the i7, GT 750M, and Intel FPGA respectively. In addition, the optimized lookup-table-based static canonical Huffman decoder on the Intel FPGA yields performance and energy efficiency improvements of 2.1× and 3.68× respectively on average compared to a dynamic canonical Huffman decoder at a 17% cost in compression ratio. • The optimized look-up table approach speeds up the canonical Huffman decoding. • Static decoder executes faster than the dynamic decoder at the cost of compression ratio. • Many-core array implementation outperforms GPU, CPU, and FPGA in terms of area and energy efficiency. [ABSTRACT FROM AUTHOR] |
| Copyright of Integration: The VLSI Journal is the property of Elsevier B.V. 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: 160331725 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sarangi%2C+Satyabrata%22">Sarangi, Satyabrata</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ssarangi@ucdavis.edu</i><br /><searchLink fieldCode="AR" term="%22Baas%2C+Bevan%22">Baas, Bevan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bbaas@ucdavis.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Integration%3A+The+VLSI+Journal%22">Integration: The VLSI Journal</searchLink>. Jan2023, Vol. 88, p156-165. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Array+processors%22">Array processors</searchLink><br /><searchLink fieldCode="DE" term="%22Intel+Corp%2E%22">Intel Corp.</searchLink><br /><searchLink fieldCode="DE" term="%22Huffman+codes%22">Huffman codes</searchLink><br /><searchLink fieldCode="DE" term="%22Data+compression%22">Data compression</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Canterbury+%28England%29%22">Canterbury (England)</searchLink><br /><searchLink fieldCode="DE" term="%22Calgary+%28Alta%2E%29%22">Calgary (Alta.)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Data compression is essential to reduce high storage and communication costs for a wide range of systems and applications. Canonical Huffman coding plays a pivotal role for several compression standards. This paper presents bit-parallel static and dynamic canonical Huffman decoder implementations using an optimized lookup table approach on a fine-grain many-core processor array and an Intel FPGA. The decoder implementation results are compared with an Intel i7-4850HQ and a massively parallel Nvidia GT 750M GPU executing the corpus benchmarks: Calgary, Canterbury, Artificial, and Large. The many-core implementations achieve a scaled throughput per chip area that is 891× and 7× greater on average than the i7 and GT 750M respectively. Moreover, the many-core implementations result in a scaled energy efficiency (compressed bits decoded per energy) that is 149.5×, 3.9×, and 2.5× greater on average than the i7, GT 750M, and Intel FPGA respectively. In addition, the optimized lookup-table-based static canonical Huffman decoder on the Intel FPGA yields performance and energy efficiency improvements of 2.1× and 3.68× respectively on average compared to a dynamic canonical Huffman decoder at a 17% cost in compression ratio. • The optimized look-up table approach speeds up the canonical Huffman decoding. • Static decoder executes faster than the dynamic decoder at the cost of compression ratio. • Many-core array implementation outperforms GPU, CPU, and FPGA in terms of area and energy efficiency. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Integration: The VLSI Journal is the property of Elsevier B.V. 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: 10.1016/j.vlsi.2022.09.015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 156 Subjects: – SubjectFull: Array processors Type: general – SubjectFull: Intel Corp. Type: general – SubjectFull: Huffman codes Type: general – SubjectFull: Data compression Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Graphics processing units Type: general – SubjectFull: Canterbury (England) Type: general – SubjectFull: Calgary (Alta.) Type: general Titles: – TitleFull: Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sarangi, Satyabrata – PersonEntity: Name: NameFull: Baas, Bevan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 01679260 Numbering: – Type: volume Value: 88 Titles: – TitleFull: Integration: The VLSI Journal Type: main |
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