  Hyperspectral imaging is gathering significant attention due to its potential
in various domains such as geology, agriculture, ecology, and surveillance.
However, the associated processing algorithms, which are essential for
enhancing output quality and extracting relevant information, are often
computationally intensive and have to deal with substantial data volumes. Our
focus lies on reconfigurable hardware, particularly recent FPGAs. While FPGA
design can be complex, High Level Synthesis (HLS) workflows have emerged as a
solution, abstracting low-level design intricacies and enhancing productivity.
Despite successful prior efforts using HLS for hyperspectral imaging
acceleration, we lack a comprehensive research to benchmark various algorithms
and architectures within a unified framework. This study aims to quantitatively
evaluate performance across different inversion algorithms and design
architectures, providing insights for optimal trade-offs for specific
applications. We apply this analysis to the case study of spectrum
reconstruction processed from interferometric acquisitions taken by Fourier
transform spectrometers.
