An FPGA-Based Deep Learning Accelerator for Remote Sensing Applications

Abstract

This thesis designs and implements an FPGA-based hardware accelerator for deep learning inference targeting remote sensing image classification tasks. The work covers architecture exploration, fixed-point quantization, HLS synthesis, and FPGA deployment evaluation.

Citation

@mastersthesis{mogaka2024thesis,
  author = {Mogaka, Obed M.},
  title = {An {FPGA}-Based Deep Learning Accelerator for Remote Sensing Applications},
  school = {Egypt-Japan University of Science and Technology (E-JUST)},
  year = {2024},
  type = {M.Sc.\ Thesis},
  keywords = {FPGA, deep learning accelerator, remote sensing, HLS, quantization}
}