Power macromodeling for high level power estimation
DAC '97 Proceedings of the 34th annual Design Automation Conference
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A new parameterizable power macro-model for datapath components
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Power modeling for high-level power estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Clustered Table-Based Macromodels for RTL Power Estimation
GLS '99 Proceedings of the Ninth Great Lakes Symposium on VLSI
Lookup Table Power Macro-Models for Behavioral Library Components
VOLTA '99 Proceedings of the IEEE Alessandro Volta Memorial Workshop on Low-Power Design
Power Monitors: A Framework for System-Level Power Estimation Using Heterogeneous Power Models
VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
Activity-sensitive architectural power analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Analytical models for RTL power estimation of combinational and sequential circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A Markov chain sequence generator for power macromodeling
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 46th Annual Design Automation Conference
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RTL power macromodeling is a mature research topic with a variety of equation and table-based approaches. Despite its maturity, macromodeling is not yet widely accepted as an industrial de facto standard for power estimation at the RT level. Each approach has many variants depending upon the parameters chosen to capture power variation. Every macromodeling technique has some intrinsic limitation affecting either its performance or its accuracy. Therefore, alternative macromodeling methods can be envisaged as part of a power modeling toolkit from which the most suitable method for a given component should be automatically selected. Thispaper describes a new multi-model power estimation engine that selects the macromodeling technique leading to the least estimation error for a given system component depending on the properties of its input-vector stream. A proper selection function is built after component characterization and used during estimation. Experimental results show that our multi-model engine improves the robustness of power analysis with negligible usage overhead. Accuracy becomes 3 times better on average, as compared to conventional single-model estimators, while the overall maximum estimation error is divided by 8.