A neural networks algorithm for data path synthesis

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dc.contributor.author Harmanani, Haidar M.
dc.date.accessioned 2016-04-12T07:15:08Z
dc.date.available 2016-04-12T07:15:08Z
dc.date.copyright 2003
dc.date.issued 2016-04-12
dc.identifier.issn 1879-0755 en_US
dc.identifier.uri http://hdl.handle.net/10725/3535
dc.description.abstract This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. The algorithm is driven by a motion equation that determines the neurons firing conditions based on the modified Hopfield neural network model of computation. The method formulates the allocation problem using the clique partitioning problem, an NP-complete problem, and handles multicycle functional units as well as structural pipelining. The algorithm has a running time complexity of O(1) for a circuit with n operations and c shared resources. A sequential simulator was implemented on a Linux Pentium PC under X-Windows. Several benchmark examples have been implemented and favorable design comparisons to other synthesis systems are reported. en_US
dc.language.iso en en_US
dc.title A neural networks algorithm for data path synthesis en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SAS en_US
dc.author.idnumber 199490170 en_US
dc.author.woa N/A en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.relation.journal Computers & Electrical Engineering en_US
dc.journal.volume 29 en_US
dc.journal.issue 4 en_US
dc.article.pages 535-551 en_US
dc.keywords High-level synthesis en_US
dc.keywords Combinatorial optimization en_US
dc.keywords Hopfield neural networks en_US
dc.keywords Graph theory en_US
dc.identifier.doi http://dx.doi.org/10.1016/S0045-7906(01)00047-7 en_US
dc.identifier.ctation Harmanani, H. M. (2003). A neural networks algorithm for data path synthesis. Computers & Electrical Engineering, 29(4), 535-551. en_US
dc.author.email haidar.harmanani@lau.edu.lb
dc.identifier.url http://www.sciencedirect.com/science/article/pii/S0045790601000477

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