A novel approach for high-level power modeling of sequential circuits using recurrent neural networks

Wen Tsan Hsieh, Chih Chieh Shiue, Chien-Nan Liu

研究成果: Conference article同行評審

9 引文 斯高帕斯(Scopus)

摘要

In this work, we propose a novel power model for CMOS sequential circuits by using recurrent neural networks (RNN) to learn the relationship between input/output signal statistics and the corresponding power dissipation. The complexity of our neural power model has almost no relationship with circuit size and the numbers of inputs, outputs and flip-flops 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 nonlinear characteristic of power distributions and the temporal correlation of the input sequences. The experimental results have shown that the estimations are still accurate with smaller variation even for short sequences. It implies that our power model can be used in various applications.

原文English
文章編號1465406
頁(從 - 到)3591-3594
頁數4
期刊Proceedings - IEEE International Symposium on Circuits and Systems
DOIs
出版狀態Published - 1 12月 2005
事件IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan
持續時間: 23 5月 200526 5月 2005

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