A significance-driven programming framework for energy-constrained approximate computing

Abstract

Approximate execution is a viable technique for energy-constrained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget. We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints. Evaluation on a dual-socket 8-core server shows that the proposed framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.