An Automated Design Framework for Floating Point Scientific Algorithms using Field Programmable Gate Arrays (FPGAs) (Abstract Only)

This paper presents a reconfigurable computing environment while addressing the problem of porting High Performance Computing (HPC) applications directly to Field Programmable Gate Arrays (FPGAs)-based architectures. The objectives of this research are developing a comprehensive floating point library of essential functions for scientific applications; demonstrate order of magnitude speedup of reconfigurable computing applications, demonstrating the effectiveness of automated design framework for both development and test of scientific algorithms. The developed framework can be reused in various scientific applications which shares kernel functions. The study of this research has identified an exponential function as a kernel for cellular ophthalmoscopy camera processing, traffic monitoring and light wave simulation. The paper demonstrates 30x speedup of these kernels in three algorithms using its novel architecture and its automated toolset. Exponential kernel generation case study and its flexible hardware implementation on an FPGA has been validated onto a Xilinx LX-100 device and the Nallatech H101-PCIXM FPGA board.