The results show that compared with the existing incentive mechanisms, up to 20.50% of the Data center colocation Energy-saving cost can be reduced in the coarse-grained mechanism and 28.34% of the cost reduction can be achieved in the fine-grained mechanism.

Approximate computing has seen significant interest as a design philosophy oriented to performance and energy efficiency . Precision tuning is an approximate computing technique that trades off the accuracy of operations for performance and energy by employing less precise data types, such as fixed point instead of floating point. However, the current state-of-the-art does not consider the possibility of optimizing mathematical functions whose computation is usually off-loaded to a library.

In this work, we extend a precision-tuning framework to perform tuning of trigonometric functions as well. We developed a new mathematical function library, which is parameterizable at compile-time depending on the data type and works natively in the fixed point numeric representation. Through modification of a compiler pass, the parameterized implementations of these trigonometric functions are inserted into the program seamlessly during the precision tuning process.