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dc.contributor.advisorCardinal Warde.en_US
dc.contributor.authorRuedlinger, Benjamin Franklin, 1976-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2006-03-24T16:08:41Z
dc.date.available2006-03-24T16:08:41Z
dc.date.copyright2003en_US
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/29621
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.en_US
dc.descriptionIncludes bibliographical references (leaves 153-156).en_US
dc.description.abstractThe focus of this thesis is interconnects within the Compact Optoelectronic Neural Network Processor. The goal of the Compact Optoelectronic Neural Network Processor Project (CONNPP) is to build a small, rugged neural network co-processing unit. This processor will be optimized for solving various signal processing problems such as image segmentation or facial recognition. These represent a class of problems for which the traditional logic-based architectures are not optimized. The CONNPP utilizes the processing power of traditional electronic integrated circuits along with the communications benefits of optoelectronic interconnects to provide a three-dimensionally scalable architecture. The topic of interconnects includes both optoelectronic interconnects between processing planes as well as wire based interconnects within the processing planes. The optoelectronic inter-plane interconnects allow the CONNPP to achieve 3-dimensional scalablility. These interconnects employ an array of holograms designed and fabricated using an analytic model that has been developed. This analytic model takes into account reading and writing the hologram at different wavelengths as well as non-idealities of the optoelectronic devices being used. The analytic model was tested and showed agreement with experiment to within 10% of the calculated values. The photodetectors used for the testing of this system have been designed within standard process technologies using a lateral PiN structure and a novel lateral BJT structure. In addition, highly-linear transimpedance amplifiers were designed to condition the signal from the photodetector for further processing.en_US
dc.description.abstract(cont.) Finally, a new chip-level interconnect architecture has been proposed for the CONNPP that utilizes a system bus to provide global connectivity between the neurons within the plane of the chip. Software models were built to simulate various chip-level connectivity schemes. These simulations show the significant potential benefits of the global bus-based chip-level architecture that has been proposed.en_US
dc.description.statementofresponsibilityy Benjamin Franklin Ruedlinger.en_US
dc.format.extent156 leavesen_US
dc.format.extent11223882 bytes
dc.format.extent11223690 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleFundamental building blocks for a compact optoelectronic neural network processoren_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc53248163en_US


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