Details

Autor(en) / Beteiligte

• Bhattacharya, Soham

Titel

Hardware Acceleration of Numerical Methods for Solving Ordinary Differential Equations

Ort / Verlag

ProQuest Dissertations & Theses

Erscheinungsjahr

2024

Link zum Volltext

• ProQuest

Quelle

ProQuest Dissertations & Theses A&I

Beschreibungen/Notizen

• Along with the advancement in technology, the role of hardware accelerators is increasing consistently, delivering advancements in scientific simulations and data analysis in scientific computing, signal processing tasks in communication systems, matrix operations, and neural network computations in artificial intelligence and machine learning models. On the other hand, several high-speed computer applications in this era of high-performance computing often depend on ordinary differential equations (ODEs); however, their nonlinear nature can present a challenge to obtaining analytic solutions. Consequently, numerical approaches prove effective in delivering only approximate solutions to these equations. This research discusses the implementation of a customized hardware accelerator for solving an ordinary differential equation (ODE) by utilizing numerical approaches while evaluating several performance metrics, including on-chip power consumption, FPGA hardware resources, and timing summary. The third-party vendor AXI4 stream Xilinx single-precision floating-point IP support has been used to develop the accelerator for solving the ordinary differential equation using those methods. The accelerator will determine the iteration approximation result of the ODE using those methods. The entire work uses VHDL hardware description language and the Xilinx Vivado Design Suite and has been deployed on the Zynq-ZC702 FPGA Evaluation Board, along with a design space exploration.