PROJECT TITLE :
Probabilistic Error Modeling for Approximate Adders - 2017
Approximate adders are widely being advocated as a means to attain performance gain in error resilient applications. In this paper, a generic methodology for analytical modeling of chance of prevalence of error and the Probability Mass Perform (PMF) of error worth during a selected category of approximate adders is presented, which will serve as performance metrics for the comparative analysis of numerous adders and their configurations. The proposed model is applicable to approximate adders that comprise of subadder units of uniform as well as non-uniform lengths. Employing a systematic methodology, we tend to derive closed form expressions for the chance of error for a number of state-of-the-art high-performance approximate adders. The probabilistic analysis is meted out for arbitrary input distributions. It can be used to study the dependence of error statistics in an adder's output on its configuration and input distribution. Moreover, it's shown that by building upon the proposed error model, we tend to will estimate the likelihood of error in circuits with multiple approximate adders. We have a tendency to also demonstrate that, using the proposed analysis, the comparative performance of different approximate adders will be properly predicted in practical applications of image processing.
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