Empirical Method Based on Neural Networks for Analog Power Modeling

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 o...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2010-05, Vol.29 (5), p.839-844
Main Authors: Suissa, A, Romain, O, Denoulet, J, Hachicha, K, Garda, P
Format: Article
Language:English
Subjects:
Analog circuit
Batteries
Circuit simulation
Computer Science
Computer simulation
Embedded computing
Embedded system
Empirical analysis
Energy consumption
Error analysis
Error detection
High performance computing
Mathematical analysis
Mathematical models
Neural networks
Power consumption
Power measurement
power modeling
Power system modeling
system level
Wireless sensor networks
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Summary: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.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2010.2043759