A systolic architecture for the symmetric tridiagonal eigenvalue problem

Abstract

The first step in the development of a chip set to support eigenvalue-eigenvector-based estimation algorithms is presented. It is based on the assumption that an averaging technique will produce a symmetric covariance matrix. Such a matrix can be reduced to a symmetric tridiagonal matrix, and hence the eigenvalues and eigenvectors can be found by successive iterations involving QR decomposition. The architecture is unique in that other architectures either solve only for the eigenvalues or use methods other than QR iteration. It has potential for use in a systolic computer for computer intensive digital signal processing based on modern spectral-analysis techniques.<>