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-Automatic word length determination of hardware data paths may require considering several error models, user specifications and hardware costs. A new automatic method for determining the word length of hardware data paths that consider these requirements is proposed and analyzed. The search-based method uses a C/C++ fixed-point simulation tool to model the impact of finite word lengths on overall accuracy. By computing dissimilarities between fixed-point and floating-point simulation results, a procedure searches for a combination of word lengths that meets accuracy criteria specified by the designer. This method is presented with four novel maximization procedures. These four automatic procedures are compared with other procedures given in the literature and adapted into the method. The comparison helps to select a procedure that finds a combination of word lengths that meet user specifications, in a small number of iterations. The procedures are characterized and compared using a dozen DSP algorithms.

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A reformulated Adaptive Resonance Theory (ART) neural network algorithm has recently been implemented in digital hardware. Naturally, the fixed point, fixed word length data format used causes some output differences with respect to floating point computer simulation. These differences are observed when using realistic input data. The effects of input quantization and the accumulation of round off errors in the arithmetic operations making up the algorithm are analyzed. Even a small quantization or round off error can trigger a change in the clustering produced. This does not mean that the clustering is not valid. Indeed, the validity of the clustering can be comparable to that obtained by floating point computer simulation, provided the word length is sufficient. This is verified on realistic input data consisting of radar pulses received from a number of emitters.