A parallel design and implementation for backpropagation neural network using MIMD architecture
The paper illustrates a parallel backpropagation algorithm that depends upon a transputer network. Since the backpropagation algorithm is regarded as being communication intensive, a mechanism is developed to decrease the communication time overhead between different related processor elements. The...
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| container_end_page | 1475 vol.3 |
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| container_volume | 3 |
| creator | Fathy, S.K. Syiam, M.M. |
| description | The paper illustrates a parallel backpropagation algorithm that depends upon a transputer network. Since the backpropagation algorithm is regarded as being communication intensive, a mechanism is developed to decrease the communication time overhead between different related processor elements. The developed parallel backpropagation algorithm is applied to the problem of printed Arabic character recognition. The experimental results revealed that the speed up approaches the maximum value of 15.2 when a network of 16-transputers is utilized. |
| doi_str_mv | 10.1109/MELCON.1996.551228 |
| format | Conference Proceeding |
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Since the backpropagation algorithm is regarded as being communication intensive, a mechanism is developed to decrease the communication time overhead between different related processor elements. The developed parallel backpropagation algorithm is applied to the problem of printed Arabic character recognition. The experimental results revealed that the speed up approaches the maximum value of 15.2 when a network of 16-transputers is utilized.</description><identifier>ISBN: 0780331095</identifier><identifier>ISBN: 9780780331099</identifier><identifier>DOI: 10.1109/MELCON.1996.551228</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Backpropagation algorithms ; Character recognition ; Computer architecture ; Computer networks ; Convergence ; Neural networks ; Neurons ; Parallel algorithms ; Training data</subject><ispartof>Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96), 1996, Vol.3, p.1472-1475 vol.3</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/551228$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,782,786,791,792,2062,4054,4055,27934,54929</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/551228$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Fathy, S.K.</creatorcontrib><creatorcontrib>Syiam, M.M.</creatorcontrib><title>A parallel design and implementation for backpropagation neural network using MIMD architecture</title><title>Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96)</title><addtitle>MELCON</addtitle><description>The paper illustrates a parallel backpropagation algorithm that depends upon a transputer network. Since the backpropagation algorithm is regarded as being communication intensive, a mechanism is developed to decrease the communication time overhead between different related processor elements. The developed parallel backpropagation algorithm is applied to the problem of printed Arabic character recognition. The experimental results revealed that the speed up approaches the maximum value of 15.2 when a network of 16-transputers is utilized.</description><subject>Algorithm design and analysis</subject><subject>Backpropagation algorithms</subject><subject>Character recognition</subject><subject>Computer architecture</subject><subject>Computer networks</subject><subject>Convergence</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Parallel algorithms</subject><subject>Training data</subject><isbn>0780331095</isbn><isbn>9780780331099</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9jsFqwzAQRAWlkKb1D-S0PxBXsqI2PgbHJYU4veRuNvbGVSPLQpIp_fsY0nPm8mAeA8PYQvBUCJ6_VuW--DqkIs_fUqVElq0f2Jy_r7mUk1YzloTww6eslMqkfGL1Bhx6NIYMtBR0ZwFtC7p3hnqyEaMeLJwHDydsLs4PDrtbZ2mcdhPi7-AvMAZtO6g-qy2gb751pCaOnl7Y4xlNoOSfz2zxUR6L3VITUe287tH_1ber8q68AhuNRLk</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Fathy, S.K.</creator><creator>Syiam, M.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1996</creationdate><title>A parallel design and implementation for backpropagation neural network using MIMD architecture</title><author>Fathy, S.K. ; Syiam, M.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_5512283</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Algorithm design and analysis</topic><topic>Backpropagation algorithms</topic><topic>Character recognition</topic><topic>Computer architecture</topic><topic>Computer networks</topic><topic>Convergence</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Parallel algorithms</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Fathy, S.K.</creatorcontrib><creatorcontrib>Syiam, M.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fathy, S.K.</au><au>Syiam, M.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A parallel design and implementation for backpropagation neural network using MIMD architecture</atitle><btitle>Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96)</btitle><stitle>MELCON</stitle><date>1996</date><risdate>1996</risdate><volume>3</volume><spage>1472</spage><epage>1475 vol.3</epage><pages>1472-1475 vol.3</pages><isbn>0780331095</isbn><isbn>9780780331099</isbn><abstract>The paper illustrates a parallel backpropagation algorithm that depends upon a transputer network. Since the backpropagation algorithm is regarded as being communication intensive, a mechanism is developed to decrease the communication time overhead between different related processor elements. The developed parallel backpropagation algorithm is applied to the problem of printed Arabic character recognition. The experimental results revealed that the speed up approaches the maximum value of 15.2 when a network of 16-transputers is utilized.</abstract><pub>IEEE</pub><doi>10.1109/MELCON.1996.551228</doi></addata></record> |
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| identifier | ISBN: 0780331095 |
| ispartof | Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96), 1996, Vol.3, p.1472-1475 vol.3 |
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| language | eng |
| recordid | cdi_ieee_primary_551228 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Backpropagation algorithms Character recognition Computer architecture Computer networks Convergence Neural networks Neurons Parallel algorithms Training data |
| title | A parallel design and implementation for backpropagation neural network using MIMD architecture |
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