We present a hardware implementation of the Jacobi algorithm to compute the eigenvalue decomposition (EVD). The computation of eigenvalues and eigenvectors has many applications where real time processing is required, and thus hardware implementations are often mandatory. Some of these implementations have been carried out with field programmable gate array (FPGA) devices using low level register transfer level (RTL) languages. In the present study, we used the Xilinx Vivado HLS tool to develop a high level synthesis (HLS) design and evaluated different hardware architectures. After analyzing the design for different input matrix sizes and various hardware configurations, we compared it with the results of other studies reported in the literature, concluding that although resource usage may be higher when HLS tools are used, the design performance is equal to or better than low level hardware designs.
Polynomial Matrix Eigenvalue Decomposition-Based Source Separation Using Informed Spherical Microphone Arrays
