Soft competitive learning using stochastic arithmetic

dc.contributor.authorBrown, Bradley D.en_US
dc.date.accessioned2007-05-17T12:39:02Z
dc.date.available2007-05-17T12:39:02Z
dc.date.issued1998-05-01T00:00:00Zen_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractArtificial Neural Networks (ANNs) are massively parallel systems which can benefit from technology which allows implementation of an unusually large number of simple computational elements on a single integrated circuit. Unlike traditional computation devices such as microprocessors, neural networks are also characterized by a tolerance for much less accurate computation. Stochastic arithmetic may be performed by computational elements which are both very small and compatible with modern VLSI design and manufacturing technology. This thesis presents a number of stochastic computational elements, several of which are introduced for the first time in this thesis, and an analysis of their operation. The applicability of stochastic arithmetic to neural networks is demonstrated through the successful implementation of a sample problem, optical character recognition, using stochastic computation. While the accuracy, power and speed characteristics of stochastic computation may not compare favorably with more conventional binary radix based computation, the low circuit area requirements make them attractive for VLSI implementation of ANNs. Results are presented for an example ANN application. Optical character recognition is performed on the characters in the E-13B MICR (Magnetic Ink Character Recognition) font. (Abstract shortened by UMI.)en_US
dc.format.extent3197770 bytes
dc.format.extent184 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.identifier.urihttp://hdl.handle.net/1993/1513
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.titleSoft competitive learning using stochastic arithmeticen_US
dc.typemaster thesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
MQ32908.pdf
Size:
3.05 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
184 B
Format:
Plain Text
Description: