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We consider the problem of enabling robust range estimation of eigenvaluedecomposition (EVD) algorithm for a reliable fixed-point design. The simplicityof fixed-point circuitry has always been so tempting to implement EVD algo-rithms in fixed-point arithmetic. Working towards an effective fixed-pointdesign, integer bit-width allocation is a significant step which has a crucialimpact on accuracy and hardware efficiency. This paper investigates theshortcomings of the existing range estimation methods while deriving bounds forthe variables of the EVD algorithm. In light of the circumstances, we introducea range estimation approach based on vector and matrix norm properties togetherwith a scaling procedure that maintains all the assets of an analytical method.The method could derive robust and tight bounds for the variables of EVDalgorithm. The bounds derived using the proposed approach remain same for anyinput matrix and are also independent of the number of iterations or size ofthe problem. Some benchmark hyperspectral data sets have been used to evaluatethe efficiency of the proposed technique. It was found that by the proposedrange estimation approach, all the variables generated during the computationof Jacobi EVD is bounded within $\pm1$.

An Overflow Free Fixed-point Eigenvalue Decomposition Algorithm: Case Study of Dimensionality Reduction in Hyperspectral Images