Award Date


Degree Type


Degree Name

Master of Science in Electrical Engineering (MSEE)


Electrical and Computer Engineering

First Committee Member

Venkatesan Muthukumar

Second Committee Member

Emma Regentova

Third Committee Member

Sahjendra Singh

Fourth Committee Member

Ajoy K. Datta

Number of Pages



Differential equations play a significant role in many disciplines of science and engineering. Solving and implementing Ordinary Differential Equations (ODEs) and partial Differential Equations (PDEs) effectively are very essential as most complex dynamic systems are modeled based on these equations. High Performance Computing (HPC) methodologies are required to compute and implement complex and data intensive applications modeled by differential equations at higher speed. There are, however, some challenges and limitations in implementing dynamic system, modeled by non-linear ordinary differential equations, on digital hardware. Modeling an integrator involves data approximation which results in accuracy error if data values are not considered properly. Accuracy and precision are dependent on the data types defined for each block of a system and subsystems. Also, digital hardware mostly works on fixed point data which leads to some data approximations. Using Field Programmable Gate Array (FPGA), it is possible to solve ordinary differential equations (ODE) at high speed. FPGA also provides scalable, flexible and reconfigurable features.

The goal of this thesis is to explore and compare implementation of control algorithms on reconfigurable logic. This thesis focuses on implementing control algorithms modeled by second and fourth order PDEs and ODEs using Xilinx System Generator (XSG) and LabVIEW FPGA module synthesis tools. Xilinx System Generator for DSP allows integration of legacy HDL code, embedded IP cores, MATLAB functions, and hardware components targeted for Xilinx FPGAs to create complete system models that can be simulated and synthesized within the Simulink environment. The National Instruments (NI) LabVIEW FPGA Module extends LabVIEW graphical development to Field-Programmable Gate Arrays (FPGAs) on NI Reconfigurable I/O hardware. This thesis also focuses on efficient implementation and performance comparison of these implementations. Optimization of area, latency and power has also been explored during implementation and comparison results are discussed.


Differential equations; Field programmable gate arrays; FPGA; Generators (Computer programs); Hardware-software co-design; High performance computing; Inferior olive neurons; LabVIEW FPGA; Reconfigurable device; Xilinx system generator


Computer Engineering | Hardware Systems | Software Engineering | Theory and Algorithms

File Format


Degree Grantor

University of Nevada, Las Vegas




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