Architectural power analysis: the dual bit type method
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Energy characterization based on clustering
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Register-transfer level estimation techniques for switching activity and power consumption
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
ISLPED '96 Proceedings of the 1996 international symposium on Low power electronics and design
Cycle-accurate macro-models for RT-level power analysis
ISLPED '97 Proceedings of the 1997 international symposium on Low power electronics and design
Power macromodeling for high level power estimation
DAC '97 Proceedings of the 34th annual Design Automation Conference
A new parameterizable power macro-model for datapath components
DATE '99 Proceedings of the conference on Design, automation and test in Europe
Adaptive least mean square behavioral power modeling
EDTC '97 Proceedings of the 1997 European conference on Design and Test
Safe integration of parameterized IP
Integration, the VLSI Journal - Special issue: IP and design reuse
Power-optimal RTL arithmetic unit soft-macro selection strategy for leakage-sensitive technologies
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
An integrated thermal estimation framework for industrial embedded platforms
Proceedings of the 20th symposium on Great lakes symposium on VLSI
Efficient PVT independent abstraction of large IP blocks for hierarchical power analysis
Proceedings of the International Conference on Computer-Aided Design
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We propose a new power macromodel for usage in the context of register-transfer level (RTL) power estimation. The model is suitable for reconfigurable, synthesizable, soft macros because it is parameterized with respect to the input data size (i.e., bit width) and can also be automatically scaled with respect to different technology libraries and/or synthesis options. The power model is precharacterized once and for all for each soft macro and then adapted to each specific instance by means of a single additional experiment to be performed by the end user. No intellectual-property disclosure is required for model scaling. The proposed model is derived from empirical analysis of the sensitivity of power consumption on input statistics, input data size, and technology. The experiments prove that with limited approximation, it is possible to decouple the effects on power of these three factors. The proposed solution is innovative since no previous macromodel supports automatic technology scaling and yields average estimation errors around 10%.