Hardware Efficient Architectures for Eigenvalue Computation

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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. The authors 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 3

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