A tableless approach for high-level power modeling using neural networks

作者：Hsu Chih Yang^*; Hsieh Wen Tsan; Liu Chien Nan Jimmy; Jou Jing Yang

来源：Journal of Information Science and Engineering, 2007, 23(1): 71-90.

• power macromodel
• power estimation
• neural network
• low power design
• RTL

For complex digital circuits, building their power models is a popular approach to estimate their power consumption without detailed circuit information. In the literature, most of power models have to increase their complexity in order to meet the accuracy requirement. In this paper, we propose a tableless power model for complex circuits that uses neural networks to learn the relationship between power dissipation and input/output signal statistics. The complexity of our neural power model has almost no relationship with circuit size and number of inputs and outputs such that this power model can be kept very small even for complex circuits. Using such a simple structure, the neural power models can still have high accuracy because they can automatically consider the non-linear characteristic of power distributions and the effects of both state-dependent leakage power and transition-dependent switching power. The experimental results have shown the accuracy and efficiency of our approach on benchmark circuits and one practical design for different test sequences with wide range of input distributions.

• 出版日期2007-1
• 单位中央民族大学