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dc.contributor.advisorSchaefer, Benjamin Carrion
dc.creatorKrishnani, Akshay Raju
dc.date.accessioned2020-10-15T19:52:45Z
dc.date.available2020-10-15T19:52:45Z
dc.date.created2020-08
dc.date.issued2020-08
dc.date.submittedAugust 2020
dc.identifier.urihttps://hdl.handle.net/10735.1/9026
dc.description.abstractThe main objective of this thesis is to investigate the efficiency of in-situ trainable Convolutional Neural Networks (CNNs) on modern programmable System-on-Chip (SoC) Field Programmable Gate Arrays (FPGAs) composed of embedded processors and reconfigurable fabric and to study the robustness of the system when faults happen. One particular characteristic of this work is that CNN is developed exclusively using High-Level Synthesis (HLS), particularly in SystemC, generating Verilog code. In this thesis, the feature maps are also being trained on the FPGA, which is traditionally done offline. The CNN architecture is instantiated on the FPGA and weights are trained through the software model on the ARM processor embedded into the FPGA and updated in the architecture through the AXI bus interface. Moreover, since CNN is implemented in hardware the resource used need to be minimized. This allows to choose a smaller, and cheaper FPGA, as well as reducing the total power consumption. To address this, the effect of bitwidth reduction of the CNN is investigated with respect to the accuracy of handwritten characters recognitions. Finally, the robustness of the CNN is analyzed by breaking internal connection of different neurons studying how the accuracy drops when the fault happens at different layers If the accuracy is reduced, then the CNN is re-trained in-situ to increase the accuracy of the CNN.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rights©2020 Akshay Raju Krishnani. All rights reserved.
dc.subjectNeural networks (Computer science)
dc.subjectSystems on a chip
dc.subjectField programmable gate arrays
dc.titleIn-Situ Implementation and Training of Convolutional Neural Network on FPGAs
dc.typeThesis
dc.date.updated2020-10-15T19:52:45Z
dc.type.materialtext
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.departmentElectrical Engineering
thesis.degree.levelMasters
thesis.degree.nameMS


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