Instruction Set Extensions for Matrix Decompositions on Software Defined Radio Architectures
Emerging wireless applications consistently demand higher data rates. Unfortunately, it is challenging to achieve high data rates within the limited amount of available frequency spectrum. Hence, enhanced spectral efficiency and link reliability within the available frequency spectrum are of the utm...
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Veröffentlicht in: | Journal of signal processing systems 2013-03, Vol.70 (3), p.289-303 |
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description | Emerging wireless applications consistently demand higher data rates. Unfortunately, it is challenging to achieve high data rates within the limited amount of available frequency spectrum. Hence, enhanced spectral efficiency and link reliability within the available frequency spectrum are of the utmost importance in current and next generation wireless protocols. To attain high spectral efficiency and link reliability, wireless protocols employ increasingly complex 2-dimensional techniques that involve computationally-intensive matrix operations. Multiple-Input Multiple-Output (MIMO) communication is an example of a promising technique employed by wireless protocols to deliver higher data rates at the cost of increased algorithmic complexity. Application Specific Integrated Circuits (ASICs) have traditionally been used to implement compute-intensive wireless protocols. The wireless industry has been gradually moving towards an alternative programmable platform called Software Defined Radio (SDR) due to its significant benefits, such as reduced development costs, and accelerated time-to-market. The computationally-intensive matrix operations used in current and next generation wireless protocols are extremely expensive to implement in SDR platforms with conventional Digital Signal Processor (DSP) instruction sets. Hence there is a need for novel instructions, hardware designs and algorithm enhancements to enable higher spectral efficiency on SDR platforms. In this paper, we propose Single Instruction Multiple Data (SIMD) CoOrdinate Rotation DIgital Computer (CORDIC) instruction set extensions with CORDIC hardware support to speedup computationally-intensive matrix decomposition algorithms. The CORDIC instruction set extensions have been implemented on the Sandbridge Sandblaster SB3000 SDR platform and evaluated on conventional algorithms used for decomposing a closed loop 4-by-4 Worldwide Interoperability for Microwave Access (WiMAX) MIMO channel into independent Single-Input Single-Output (SISO) channels. Our experimental results on the closed-loop MIMO channel decomposition using CORDIC instructions demonstrate more than 6
x
speedup over a Sandblaster baseline implementation that uses state-of-the-art SIMD DSP instructions. The CORDIC instructions also provide similar numerical accuracy when compared to the baseline implementation. The techniques we propose in this paper are also applicable to other SDR and embedded processor architectures. |
doi_str_mv | 10.1007/s11265-012-0665-7 |
format | Article |
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x
speedup over a Sandblaster baseline implementation that uses state-of-the-art SIMD DSP instructions. The CORDIC instructions also provide similar numerical accuracy when compared to the baseline implementation. The techniques we propose in this paper are also applicable to other SDR and embedded processor architectures.</description><identifier>ISSN: 1939-8018</identifier><identifier>EISSN: 1939-8115</identifier><identifier>DOI: 10.1007/s11265-012-0665-7</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Algorithms ; Channels ; Circuits and Systems ; Computer Imaging ; Decomposition ; Electrical Engineering ; Engineering ; Image Processing and Computer Vision ; Pattern Recognition ; Pattern Recognition and Graphics ; Platforms ; Protocol (computers) ; Signal processing ; Signal,Image and Speech Processing ; Spectra ; Vision ; Wireless communication</subject><ispartof>Journal of signal processing systems, 2013-03, Vol.70 (3), p.289-303</ispartof><rights>Springer Science+Business Media, LLC 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c321t-eec8f9c8466b72e9726c858fe28f6750de89ad94aab1574d21bcddcdf3f63633</citedby><cites>FETCH-LOGICAL-c321t-eec8f9c8466b72e9726c858fe28f6750de89ad94aab1574d21bcddcdf3f63633</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11265-012-0665-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11265-012-0665-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Senthilvelan, Murugappan</creatorcontrib><creatorcontrib>Sima, Mihai</creatorcontrib><creatorcontrib>Iancu, Daniel</creatorcontrib><creatorcontrib>Schulte, Michael</creatorcontrib><creatorcontrib>Glossner, John</creatorcontrib><title>Instruction Set Extensions for Matrix Decompositions on Software Defined Radio Architectures</title><title>Journal of signal processing systems</title><addtitle>J Sign Process Syst</addtitle><description>Emerging wireless applications consistently demand higher data rates. Unfortunately, it is challenging to achieve high data rates within the limited amount of available frequency spectrum. Hence, enhanced spectral efficiency and link reliability within the available frequency spectrum are of the utmost importance in current and next generation wireless protocols. To attain high spectral efficiency and link reliability, wireless protocols employ increasingly complex 2-dimensional techniques that involve computationally-intensive matrix operations. Multiple-Input Multiple-Output (MIMO) communication is an example of a promising technique employed by wireless protocols to deliver higher data rates at the cost of increased algorithmic complexity. Application Specific Integrated Circuits (ASICs) have traditionally been used to implement compute-intensive wireless protocols. The wireless industry has been gradually moving towards an alternative programmable platform called Software Defined Radio (SDR) due to its significant benefits, such as reduced development costs, and accelerated time-to-market. The computationally-intensive matrix operations used in current and next generation wireless protocols are extremely expensive to implement in SDR platforms with conventional Digital Signal Processor (DSP) instruction sets. Hence there is a need for novel instructions, hardware designs and algorithm enhancements to enable higher spectral efficiency on SDR platforms. In this paper, we propose Single Instruction Multiple Data (SIMD) CoOrdinate Rotation DIgital Computer (CORDIC) instruction set extensions with CORDIC hardware support to speedup computationally-intensive matrix decomposition algorithms. The CORDIC instruction set extensions have been implemented on the Sandbridge Sandblaster SB3000 SDR platform and evaluated on conventional algorithms used for decomposing a closed loop 4-by-4 Worldwide Interoperability for Microwave Access (WiMAX) MIMO channel into independent Single-Input Single-Output (SISO) channels. Our experimental results on the closed-loop MIMO channel decomposition using CORDIC instructions demonstrate more than 6
x
speedup over a Sandblaster baseline implementation that uses state-of-the-art SIMD DSP instructions. The CORDIC instructions also provide similar numerical accuracy when compared to the baseline implementation. The techniques we propose in this paper are also applicable to other SDR and embedded processor architectures.</description><subject>Algorithms</subject><subject>Channels</subject><subject>Circuits and Systems</subject><subject>Computer Imaging</subject><subject>Decomposition</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Image Processing and Computer Vision</subject><subject>Pattern Recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Platforms</subject><subject>Protocol (computers)</subject><subject>Signal processing</subject><subject>Signal,Image and Speech Processing</subject><subject>Spectra</subject><subject>Vision</subject><subject>Wireless communication</subject><issn>1939-8018</issn><issn>1939-8115</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kMFOwzAQRC0EEqXwAdxy5BLwOontHKtSoFIREvSIZLn2GlK1cbEdUf6ehMKV085qZlbaR8gl0GugVNxEAMarnALLKe-FOCIjqIs6lwDV8Z-mIE_JWYxrSjkVFYzI67yNKXQmNb7NXjBls33CNvZbzJwP2aNOodlnt2j8dudjk36cIetd-tQBe8s1LdrsWdvGZ5Ng3puEJnUB4zk5cXoT8eJ3jsnybracPuSLp_v5dLLITcEg5YhGutrIkvOVYFgLxo2spEMmHRcVtShrbetS6xVUorQMVsZaY13heMGLYkyuDmd3wX90GJPaNtHgZqNb9F1UULJaClaUQxQOURN8jAGd2oVmq8OXAqoGkOoAUvUg1QBSib7DDp3YZ9s3DGrtu9D2D_1T-gaaO3fa</recordid><startdate>20130301</startdate><enddate>20130301</enddate><creator>Senthilvelan, Murugappan</creator><creator>Sima, Mihai</creator><creator>Iancu, Daniel</creator><creator>Schulte, Michael</creator><creator>Glossner, John</creator><general>Springer US</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20130301</creationdate><title>Instruction Set Extensions for Matrix Decompositions on Software Defined Radio Architectures</title><author>Senthilvelan, Murugappan ; Sima, Mihai ; Iancu, Daniel ; Schulte, Michael ; Glossner, John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c321t-eec8f9c8466b72e9726c858fe28f6750de89ad94aab1574d21bcddcdf3f63633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Channels</topic><topic>Circuits and Systems</topic><topic>Computer Imaging</topic><topic>Decomposition</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>Image Processing and Computer Vision</topic><topic>Pattern Recognition</topic><topic>Pattern Recognition and Graphics</topic><topic>Platforms</topic><topic>Protocol (computers)</topic><topic>Signal processing</topic><topic>Signal,Image and Speech Processing</topic><topic>Spectra</topic><topic>Vision</topic><topic>Wireless communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Senthilvelan, Murugappan</creatorcontrib><creatorcontrib>Sima, Mihai</creatorcontrib><creatorcontrib>Iancu, Daniel</creatorcontrib><creatorcontrib>Schulte, Michael</creatorcontrib><creatorcontrib>Glossner, John</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of signal processing systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Senthilvelan, Murugappan</au><au>Sima, Mihai</au><au>Iancu, Daniel</au><au>Schulte, Michael</au><au>Glossner, John</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Instruction Set Extensions for Matrix Decompositions on Software Defined Radio Architectures</atitle><jtitle>Journal of signal processing systems</jtitle><stitle>J Sign Process Syst</stitle><date>2013-03-01</date><risdate>2013</risdate><volume>70</volume><issue>3</issue><spage>289</spage><epage>303</epage><pages>289-303</pages><issn>1939-8018</issn><eissn>1939-8115</eissn><abstract>Emerging wireless applications consistently demand higher data rates. Unfortunately, it is challenging to achieve high data rates within the limited amount of available frequency spectrum. Hence, enhanced spectral efficiency and link reliability within the available frequency spectrum are of the utmost importance in current and next generation wireless protocols. To attain high spectral efficiency and link reliability, wireless protocols employ increasingly complex 2-dimensional techniques that involve computationally-intensive matrix operations. Multiple-Input Multiple-Output (MIMO) communication is an example of a promising technique employed by wireless protocols to deliver higher data rates at the cost of increased algorithmic complexity. Application Specific Integrated Circuits (ASICs) have traditionally been used to implement compute-intensive wireless protocols. The wireless industry has been gradually moving towards an alternative programmable platform called Software Defined Radio (SDR) due to its significant benefits, such as reduced development costs, and accelerated time-to-market. The computationally-intensive matrix operations used in current and next generation wireless protocols are extremely expensive to implement in SDR platforms with conventional Digital Signal Processor (DSP) instruction sets. Hence there is a need for novel instructions, hardware designs and algorithm enhancements to enable higher spectral efficiency on SDR platforms. In this paper, we propose Single Instruction Multiple Data (SIMD) CoOrdinate Rotation DIgital Computer (CORDIC) instruction set extensions with CORDIC hardware support to speedup computationally-intensive matrix decomposition algorithms. The CORDIC instruction set extensions have been implemented on the Sandbridge Sandblaster SB3000 SDR platform and evaluated on conventional algorithms used for decomposing a closed loop 4-by-4 Worldwide Interoperability for Microwave Access (WiMAX) MIMO channel into independent Single-Input Single-Output (SISO) channels. Our experimental results on the closed-loop MIMO channel decomposition using CORDIC instructions demonstrate more than 6
x
speedup over a Sandblaster baseline implementation that uses state-of-the-art SIMD DSP instructions. The CORDIC instructions also provide similar numerical accuracy when compared to the baseline implementation. The techniques we propose in this paper are also applicable to other SDR and embedded processor architectures.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s11265-012-0665-7</doi><tpages>15</tpages></addata></record> |
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subjects | Algorithms Channels Circuits and Systems Computer Imaging Decomposition Electrical Engineering Engineering Image Processing and Computer Vision Pattern Recognition Pattern Recognition and Graphics Platforms Protocol (computers) Signal processing Signal,Image and Speech Processing Spectra Vision Wireless communication |
title | Instruction Set Extensions for Matrix Decompositions on Software Defined Radio Architectures |
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