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Development and Design Space Exploration of Deep Convolution Neural Network for Image Recognition

dc.contributor.authorAboki, Nasiru Aliu
dc.date.accessioned2019-06-03T15:04:58Z
dc.date.available2019-06-03T15:04:58Z
dc.date.issued2017-11-22
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/123456789/4881
dc.description.abstractDeep Neural Networks are now deployed for many modern artificial Intelligence applications including computer vision, speech recognition, self-driving cars, cancer detection, gaming and robotics. Inspired by the mammalian visual cortex, Convolutional Neural Networks (CNNs) have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions. Software simulations of CNNs are an efficient way to evaluate and explore the performance of the system. In this thesis, I present a software implementation and study of a Deep CNN for image recognition. The parameterization of our design offers the flexibility to adjust the design in order to balance performance and flexibility, particularly for resource-constrained systems. I also present a design space exploration for obtaining the implementation with the highest performance on a given platform.en_US
dc.description.sponsorshipAUST, AfDBen_US
dc.language.isoenen_US
dc.subjectAboki Nasiru Aliuen_US
dc.subjectProf Ben Abdallahen_US
dc.subject2017 Computer Science and Engineering Thesesen_US
dc.subjectConvolution Neural Networken_US
dc.subjectImage recognitionen_US
dc.titleDevelopment and Design Space Exploration of Deep Convolution Neural Network for Image Recognitionen_US
dc.typeThesisen_US


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    This collection contains Computer Science Student's Theses from 2009-2022

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