Accuracy Evaluation Based on Simulation for Finite Precision Systems Using Inferential Statistics
Justine Bonnot, Karol Desnos, Daniel Ménard · 2019
The conversion of an algorithm to fixed-point arithmetic is commonly achieved with a large and fixed-number of simulations. Nevertheless, when simulating a fixed and arbitrary large number of samples, no confidence information is given on the characterization, and this method is often time-inefficient. To overcome this limitation, we propose a new method for noise evaluation. The error induced by fixed-point coding is statistically characterized to compute the noise power with an adaptive and reduced number of simulations. From user-defined confidence requirements, the proposed method computes the minimal number of simulations to obtain a confidence interval of the noise power. Experiments on varied signal-processing elementary blocks show that the proposed method requires on average the simulation of only 0.04% of the simulation set required by State of the Art techniques to estimate the noise power of a 64thorder FIR filter with a relative error less than 0.01%.