Accuracy-aware optimization of approximate programs

Many modern applications (such as multimedia processing, machine learning, and big-data analytics) exhibit an inherent tradeoff between performance and the accuracy of the produced results. These applications allow us to investigate new, more aggressive program optimizations. We present a novel approximate optimization framework based on accuracy-aware program transformations. These transformations reduce accuracy of computation in return for improved performance, energy efficiency, and/or resilience. The optimization framework includes program analyses that characterize the accuracy of transformed programs, and search techniques that navigate the tradeoff space induced by transformations to find approximate programs with profitable tradeoffs. Our research shows that this approach can significantly improve program performance while providing acceptably accurate results.