Design and implementation of a digital neural processor for detection applications

Balasubramanian, Balamurugan (1999) Design and implementation of a digital neural processor for detection applications. Masters thesis, Memorial University of Newfoundland.

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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.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/9042
Item ID: 9042
Additional Information: Bibliography: pages 129-138.
Department(s): Engineering and Applied Science, Faculty of
Date: 1999
Date Type: Submission
Library of Congress Subject Heading: Neural networks (Computer science)--Design; Demodulation (Electronics)

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