Efficient Design and Optimization of Artificial Neural Networks: SW and HW

dc.contributor.advisorCarrion Schaefer, Benjamin
dc.contributor.committeeMemberBhatia, Dinesh
dc.contributor.committeeMemberNourani, Mehrdad
dc.creatorSenthil Kumar, Valliyappan 1999-
dc.creator.orcid0009-0004-1348-5702
dc.date.accessioned2023-10-16T18:20:15Z
dc.date.available2023-10-16T18:20:15Z
dc.date.created2023-05
dc.date.issuedMay 2023
dc.date.submittedMay 2023
dc.date.updated2023-10-16T18:20:15Z
dc.description.abstractArtificial Neural Networks (ANNs) have achieved significant advancements in machine intelligence due to their ability to learn complex tasks. However, their deployment on devices with limited resources or real-time applications requires consideration of the network's physical size or area. High-Level Synthesis (HLS) is a design methodology that simplifies the implementation of complex algorithms like ANN design by using high-level programming languages. This work investigates the construction of efficient ANNs by combining techniques such as architecture search, pruning, quantization, and compression to optimize the network's architecture, input/output data size, and computation precision. The optimization framework minimizes the area of the equivalent SystemC ANN model by reducing the bit width allocated for either the neurons or weights. The primary focus is on the application of handwriting recognition using the MNIST dataset, which serves as a prototype problem for understanding neural networks in general. The contributions of this thesis include an explorer to perform architecture search and an optimization framework to minimize the network's area, providing valuable insights into accuracy and hardware efficiency trade-offs.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/9938
dc.language.isoEnglish
dc.subjectEngineering, Electronics and Electrical
dc.titleEfficient Design and Optimization of Artificial Neural Networks: SW and HW
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeSchool of Engineering and Computer Science
thesis.degree.departmentElectrical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.nameMaster of Science

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