论文标题
基于BDD的近似误差指标的优化计算
Optimization of BDD-based Approximation Error Metrics Calculations
论文作者
论文摘要
引入的软件方法用于自动设计算术电路的近似实现，这取决于对近似候选人实现的快速准确评估。为了加速电路误差的评估，我们提出了四种基于二进制决策图的确切最坏情况和平均绝对误差分析的新算法。由于这些算法在特征函数中没有计算任何绝对值，该算法基本上将候选近似电路与黄金电路进行比较，因此误差评估的速度明显快于基于标准BDD的错误分析。平均而言，提出的算法的速度比8到32位近似添加剂的基线快三倍（在某些情况下，比基线快30倍）。这些结果是从超过4.9万次运行中获得的，该运行具有不同的方法。提出的错误评估算法可作为开源软件https://github.com/ehw-fit/bdd-evaluation获得。
Software methods introduced for automated design of approximate implementations of arithmetic circuits rely on fast and accurate evaluation of approximate candidate implementations. To accelerate the evaluation of circuit error, we propose four novel algorithms for the exact worst-case and mean absolute error analysis based on Binary Decision Diagrams. As these algorithms do not compute any absolute values in the characteristic function, which basically compares a candidate approximate circuit with a golden circuit, the error evaluation is significantly faster than the standard BDD-based error analysis. On average, the proposed algorithms are three times faster (in some cases, 30 times faster) than the baseline for 8- to 32-bit approximate adders. These results were obtained from more than 49 thousand runs with different configurations of the method. The proposed error evaluation algorithms are available as an open-source software https://github.com/ehw-fit/bdd-evaluation.
