  Approximate computing is being considered as a promising design paradigm to
overcome the energy and performance challenges in computationally demanding
applications. If the case where the accuracy can be configured, the quality
level versus energy efficiency or delay also may be traded-off. For this
technique to be used, one needs to make sure a satisfactory user experience.
This requires employing error predictors to detect unacceptable approximation
errors. In this work, we propose a scheduling-aware feature selection method
which leverages the intermediate results of the hardware accelerator to improve
the prediction accuracy. Additionally, it configures the error predictors
according to the energy consumption and latency of the system. The approach
enjoys the flexibility of the prediction time for a higher accuracy. The
results on various benchmarks demonstrate significant improvements in the
prediction accuracy compared to the prior works which used only the accelerator
inputs for the prediction.
