A suitable FPGA implementation of floating-point matrix inversion based on Gauss-Jordan elimination

Abstract

This work presents an architecture to compute matrix inversions in a hardware reconfigurable FPGA with single-precision floating-point representation, whose main unit is the processing component for Gauss-Jordan elimination. This component consists of other smaller arithmetic units, organized to maintain the accuracy of the results without the need to internally normalize and de-normalize the floating-point data. The implementation of the operations and the whole unit take advantage of the resources available in the Virtex-5 FPGA. The performance and resource consumption of the implementation are improvements in comparison with different more elaborated architectures whose implementations are more complex for low cost applications. Benchmarks are done with solutions implemented previously in FPGA and software, such as Matlab.