Approximate computing has seen significant interest as a design philosophy oriented to performance and energy efficiency [1]. 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.

Our approach, which we test on two microcontrollers with different architectures, achieves a speedup of up to $180\%$, and energy savings up to $60\%$, with a negligible cost in terms of error in the results.

近似计算作为面向性能和能源效率的设计理念已引起人们极大的兴趣[1]。精度调整是一种近似的计算技术，它通过采用精度较低的数据类型（例如定点而不是浮点）来权衡性能和能量的运算精度。但是，当前的最新技术没有考虑优化数学函数的可能性，这些数学函数的计算通常被卸载到库中。

在这项工作中，我们扩展了一个精密调整框架，以执行三角函数的调整。我们开发了一个新的数学函数库，该函数库可在编译时根据数据类型进行参数化，并以定点数字表示形式固有地工作。通过修改编译器遍历，可以在精度调整过程中将这些三角函数的参数化实现无缝地插入到程序中。

我们的方法在两个具有不同架构的微控制器上进行了测试，可将速度提高多达 $180％$，并且节能高达 $60％$，其结果误差可忽略不计。