Hardware efficient architectures for Eigenvalue computation

Yang Liu, Christos-Savvas Bouganis, Peter Y. K. Cheung, Philip Heng Wai Leong, Stephen J. Motley

Abstract: Eigenvalue computation is essential in many fields of science and engineering. For high performance and real-time applications, this may need to be done in hardware. This paper focuses on the exploration of hardware architectures which compute eigenvalues of symmetric matrices. We propose to use the approximate Jacobi method for general case symmetric matrix eigenvalue problem. The paper illustrates that the proposed architecture is more efficient than previous architectures reported in the literature. Moreover, for the special case of 3times3 symmetric matrices, we propose to use an algebraic method. It is shown that the pipelined architecture based on the algebraic method has a significant advantage in terms of area