基于字级统计的功耗宏模型
Power Macromodel Based on Word-Level Statistics
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摘要: 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,具有更好的健壮性,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,给出低功耗编码的优化效果.Abstract: 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.
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