Optimization of DSP Applications Using Parameterized Error Models for Low Power Approximate Adders

Approximate circuit design has gained significance in recent years targeting error tolerant applications. In this paper, we first demonstrate that the commonly used assumption that the inputs to the adder are uniformly distributed results in an inaccurate prediction of error statistics for multi-level circuits. To overcome this problem, we derive parameterized error models that can be used within any optimization framework in order to optimize the number of approximate bits. We also show that in order to accurately compute the mean square error, the optimization framework needs to take into account not just the functionality of the adder, but also its position in the circuit, functionality of its parents and the number of approximate bits in the parent blocks. We demonstrate a significant improvement of accuracy in the prediction of the noise power of DSP systems containing approximate adders.

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