基于字级统计的功耗宏模型

文中提出的功耗宏模型建立在字级信号统计的基础上，可用于各种音／视频信号处理芯片的功耗估计，与比特级统计的拟合模型相比，具有更好的健装性，能在很大的信号变化范围内保持令人满意的计算精度；与门级模拟结果相比较，本模型的计算时间下降了2－3个数量级，而相对误差不超过20％，此外，这个模型可以区别不同的数字表示，给出低功耗的编码的优化效果。

Power Macromodel Based on Word-Level Statistics

The power macromodel presented in this paper is built on the word level statistics of input stream, which can be applied to the power estimation of various Audio/Video DSP chips. In comparison with the fitting macromodel based on bit level statistics, it achieves better robustness and satisfactorily remains accurate when the signal pattern changes vastly. This macromodel only consumes 1%~0.1% run time of the gate level simulation and its relative error is no more than 20%. On the other hand, it can evaluate the low power encoding method by distinguishing the different digital presentations.