  Approximate computing is a nascent energy-efficient computing paradigm
suitable for error-tolerant applications. However, the value of approximation
error depends on the applied inputs where individual output error may reach
intolerable level while the average output error is acceptable. Thus, it is
critical to show the response of approximate design for various applied inputs
where understanding the interdependence between the inputs and the
approximation error could be utilized to control the output quality. In this
report, we exhaustively analyze the accuracy of 20 different designs of 8-bit
approximate array multipliers. We designed these multipliers based on various
configurations including the type of approximable component and how much of the
result to approximate, i.e., approximation degree. Accuracy analysis shows a
strong correlation between the applied inputs and the magnitude of the observed
error, i.e., error distance, normalized error distance and peak-to-signal-noise
ratio. We may utilize this input-dependency of approximation error, in
controlling the quality of approximate computing by eliminating large magnitude
errors and improving the quality of the final results.
