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A table-based method for high-speed function approximation in single-precision floating-point format is presented in this paper. Our focus is the approximation of reciprocal, square root, square root reciprocal, exponentials, logarithms, trigonometric functions, powering (with a fixed exponent p), or special functions. The algorithm presented here combines table look-up, an enhanced minimax quadratic approximation, and an efficient evaluation of the second-degree polynomial (using a specialized squaring unit, redundant arithmetic, and multioperand addition). The execution times and area costs of an architecture implementing our method are estimated, showing the achievement of the fast execution times of linear approximation methods and the reduced area requirements of other second-degree interpolation algorithms. Moreover, the use of an enhanced minimax approximation which, through an iterative process, takes into account the effect of rounding the polynomial coefficients to a finite size allows for a further reduction in the size of the look-up tables to be used, making our method very suitable for the implementation of an elementary function generator in state-ofthe-art DSPs or graphics processing units (GPUs).

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Floating-point division is generally regarded as a low frequency, high latency operation in typical floating-point applications. However, in the worst case, a high latency hardware floating-point divider can contribute an additional 0.50 CPI to a system executing SPECfp92 applications. This paper presents the system performance impact of floating-point division latency for varying instruction issue rates. It also examines the performance implications of shared multiplication hardware, shared square root, on-the-fly rounding and conversion, and fused functional units. Using a system level study as a basis, it is shown how typical floating-point applications can guide the designer in making implementation decisions and trade-offs.