A survey of power estimation techniques in VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISLPED '96 Proceedings of the 1996 international symposium on Low power electronics and design
Power modeling for high-level power estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Dependency preserving probabilistic modeling of switching activity using bayesian networks
Proceedings of the 38th annual Design Automation Conference
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Application-Based, Transistor-Level Full-Chip Power Analysis for 700 MHz PowerPC(tm) Microprocessor
ICCD '00 Proceedings of the 2000 IEEE International Conference on Computer Design: VLSI in Computers & Processors
Probabilistic modeling of dependencies during switching activity analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Stratified random sampling for power estimation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This paper proposes a circuit power estimation method using Bayesian inference and neural networks. Based on statistical distribution of circuit leakage power and switching energy, the entire state and transition space of a circuit are classified using neural networks into a limited few classes that represent different power consumption average values. This technique enables efficient table-lookup of circuit power of the entire state and transition space. Theoretical basis of Bayesian inference, feature extraction for neural networks of circuit leakage power and switching energy are discussed. Experiments on a wide range of circuit topologies demonstrated the robustness of the proposed method for estimating circuit leakage power of all possible states and switching energy of all possible transitions.