Biomedical image processing and reconstruction with dataflow computing on FPGAs

Abstract

Increasing chip sizes and better programming tools have made it possible to increase the boundaries of application acceleration with FPGAs. Two applications, localization microscopy and electron tomography, are presented in the author's PhD thesis and summarized in this paper. Both have been ported from imperative languages to the dataflow paradigm that maps well onto long processing pipelines in custom hardware. The results show that an acceleration of 200 compared to an Intel i5 450 CPU for localization microscopy, and an acceleration of 5 over an Nvidia Tesla C1060 for electron tomography while maintaining full accuracy. The main challenge arose from the need to fully understand and re-write most of the imperative source in a form suitable for dataflow computing.