Empirical Method Based on Neural Networks for Analog Power Modeling

Abraham Suissa 1 Olivier Romain 1 Julien Denoulet 1 Khalil Hachicha 1 Patrick Garda 1
1 SYEL - Systèmes Electroniques
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We introduce an empirical method for power consumption modeling of analog components at system level. The principal step of this method uses neural networks to approximate the mathematical curve of the power consumption as a function of the inputs and parameters of the analog component. For a node of a wireless sensors network, we found an average error of 1.53% with a maximum error of 3.06% between our estimation and the measured power consumption. This novel method is suitable for Platform-Based Design and has three key features for architecture exploration purposes. Firstly, the method is generic as it can be applied to any analog component in any modeling and simulation environment. Secondly, the method is suitable for the total (analog and digital) power consumption estimation of a heterogeneous system. Thirdly, the method provides an online estimation of the instantaneous power consumption of analog blocks.
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Abraham Suissa, Olivier Romain, Julien Denoulet, Khalil Hachicha, Patrick Garda. Empirical Method Based on Neural Networks for Analog Power Modeling. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE, 2010, 29 (5), pp.839-844. ⟨10.1109/TCAD.2010.2043759⟩. ⟨hal-01198824⟩



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