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Optimization of FPGA Based Neural Network Processor

Sun, Ivan Teh Fu (2004) Optimization of FPGA Based Neural Network Processor. Universiti Teknologi Petronas. (Unpublished)

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Neural information processing is an emerging new field, providing an alternative form of computation for demanding tasks such as pattern recognition problems which are usually reserved for human attention. Neural network computation i s sought after where classification of input data is difficult to be worked out using equations or sets of rules. Technological advances in integrated circuits such as Field Programmable Gate Array (FPGA) systems have made it easier to develop and implement hardware devices based on these neural network architectures. The motivation in hardware implementation of neural networks is its fast processing speed and suitability in parallel and pipelined processing. The project revolves around the design of an optimized neural network processor. The processor design is based on the feedforward network architecture type with BackPropagation trained weights for the Exclusive-OR non-linear problem. Among the highlights of the project is the improvement in neural network architecture through reconfigurable and recursive computation of a single hidden layer for multiple layer applications. Improvements in processor organization were also made which enables the design to parallel process with similar processors. Other improvements include design considerations to reduce the amount of logic required for implementation without much sacrifice of processing speed.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Prototyping
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 30 Sep 2013 16:55
Last Modified: 25 Jan 2017 09:47
URI: http://utpedia.utp.edu.my/id/eprint/7947

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