Investigation of neural networks for the scheduling and allocation problem in high-level synthesis

Persistent Link:
http://hdl.handle.net/10150/278303
Title:
Investigation of neural networks for the scheduling and allocation problem in high-level synthesis
Author:
Glassen, David Wayne
Issue Date:
1993
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
In recent years neural network have been shown to be quite effective in solving difficult combinatorial optimization problems. In this work a Hopfield neural network is used to schedule operations in a dataflow graph. This is an important step in behavioral synthesis systems. These operations must be assigned to a limited number of control steps, functional units, and busses. Also, there is an objective to minimize the lengths of data paths. Current methods which do this type of scheduling typically rely on heuristic algorithms. The neural network devised to solve this problem is one of the most complex to date. A special mechanism, "flag" neurons, was developed to enable the neural network to encode a bussing constraint. The neural network has been tested with problems from literature and problems randomly generated. The results have been consistently superior to those produced by a heuristic algorithm called ALAP.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Artificial Intelligence.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Carothers, Jo Dale

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleInvestigation of neural networks for the scheduling and allocation problem in high-level synthesisen_US
dc.creatorGlassen, David Wayneen_US
dc.contributor.authorGlassen, David Wayneen_US
dc.date.issued1993en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractIn recent years neural network have been shown to be quite effective in solving difficult combinatorial optimization problems. In this work a Hopfield neural network is used to schedule operations in a dataflow graph. This is an important step in behavioral synthesis systems. These operations must be assigned to a limited number of control steps, functional units, and busses. Also, there is an objective to minimize the lengths of data paths. Current methods which do this type of scheduling typically rely on heuristic algorithms. The neural network devised to solve this problem is one of the most complex to date. A special mechanism, "flag" neurons, was developed to enable the neural network to encode a bussing constraint. The neural network has been tested with problems from literature and problems randomly generated. The results have been consistently superior to those produced by a heuristic algorithm called ALAP.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectArtificial Intelligence.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorCarothers, Jo Daleen_US
dc.identifier.proquest1352364en_US
dc.identifier.bibrecord.b27051791en_US
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