Fast Kriging-based Error Evaluation for Approximate Computing Systems

Approximate computing techniques trade-off the performance of an application for its accuracy. The challenge when implementing approximate computing in an application is to efficiently evaluate the quality at the output of the application to optimize the noise budgeting of the different approximation sources. It is commonly achieved with an optimization algorithm to minimize the implementation cost of the application subject to a quality constraint. During the optimization process, numerous approximation configurations are tested, and the quality at the output of the application is measured for each configuration with simulations. The optimization process is a time-consuming task. We propose a new method for infering the accuracy or quality metric at the output of an application using kriging, a geostatistical method.