Memorial University DAI: Design and implementation of a digital neural processor for detection applications

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Title	Design and implementation of a digital neural processor for detection applications
Author	Balasubramanian, Balamurugan, 1975-
Description	Thesis (M. Eng.), Memorial University of Newfoundland, 1999. Engineering and Applied Science
Date	1999.
Pagination	138 leaves : ill.
Subject	Neural networks (Computer science)--Design; Demodulation (Electronics)
Degree	M. Eng.
Degree Grantor	Memorial University of Newfoundland. Faculty of Engineering and Applied Science
Discipline	Engineering and Applied Science
Language	Eng
Notes	Bibliography: p. 129-138
Abstract	The main focus of this research is to develop a digital neural network (processor) and hardware (VLSI) implementation of the same for detection applications, for example in the distance protection of power transmission lines. Using a hardware neural processor will improve the protection system performance over software implementations in terms of speed of operation, response time for faults etc. The main aspects of this research are software design, performance analysis, hardware design and hardware implementation of the digital neural processor. The software design is carried out by developing an object oriented neural network simulator with backpropagation training using C++ language. A preliminary analysis shows that the inputs to the neural network need to be preprocessed. Two filters have been developed for this purpose, based on the analysis of the training data available. The performance analysis involves studying quantization effects (determination of precision requirements) in the network. -- The hardware design involves design of the neural network and the preprocessors. The neural processor consists of three types of processing elements (neurons): input, hidden and output neurons. The input neurons form the input layer of the processor which receive input from the preprocessors. The input layer can be configured to directly receive external input by changing the mode of operation. The output layer gives the signal to the relay for tripping the line under fault. Each neuron consists of datapath and local control unit. Datapath consists of the components for forward and backward passes of the processor and the register file. The local control unit controls the flow of data within a neuron and co-ordinates with the global control unit which controls the flow of data between layers. The neurons and the layers are pipelined for improving the throughput of the processor. The neural processor and the filters are implemented in VLSI using hardware description language (VHDL) and Synopsys / Cadence CAD tools. All the components are individually verified and tested for their functionality and implemented using 0.5 μ CMOS technology.
Type	Text
Format	Image/jpeg; Application/pdf
Source	Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifier	a1355210
Rights	The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
Collection	Electronic Theses and Dissertations
Scanning Status	Completed
PDF File	(16.11 MB) -- http://collections.mun.ca/PDFs/theses/Balasubramanian_Balamurugan.pdf
CONTENTdm file name	101697.cpd