Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Optimization of FPGA Based Neural Network Processor

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 (Unpublished)

[img] PDF
Download (2161Kb)

Abstract

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

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...