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A Method for Estimating the Mean-Squared Error of Distributed Arithmetic
Jun TAKEDA Shin-ichi URAMOTO Masahiko YOSHIMOTO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1994/01/25
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Digital Signal Processing
error analysis, mean-squared error, distributed arithmetic, DCT/IDCT,
Full Text: PDF(660.5KB)>>
It is important for LSI system designers to estimate computational errors when designing LSI's for numeric computations. Both for the prediction of the errors at an early stage of designing and for the choice of a proper hardware configuration to achieve a target performance, it is desirable that the errors can be estimated in terms of a minimum of parameters. This paper presents a theoretical error analysis of multiply-accumulation implemented by distributed arithmetic(DA) and proposes a new method for estimating the mean-squared error. DA is a method of implementing the multiply-accumulation that is defined as an inner product of an input vector and a fixed coefficient vector. Using a ROM which stores partial products. DA calculates the output by accumulating the partial products bitserially. As DA uses no parallel multipliers, it needs a smaller chip area than methods using parallel multipliers. Thus DA is effectively utilitzed for the LSI implementation of a digital signal processing system which requires the multiply-accumulation. It has been known that, if the input data are uniformly distributed, the mean-squared error of the multiply-accumulation implemented by DA is a function of only the word lengths of the input, the output, and the ROM. The proposed method for the error estimation can calculate the mean-squared error by using the same parameters even when the input data are not uniformly distributed. The basic idea of the method is to regard the input data as a combination of uniformly distributed partial data with a different word length. Then the mean-squared error can be predicted as a weighted sum of the contribution of each partial data, where the weight is the ratio of the partial data to the total input data. Finally, the method is applied to a two-dimensional inverse discrete cosine transform (IDCT) and the practicability of the method is confirmed by computer simulations of the IDCT implemented by DA.