Switching activity analysis considering spatiotemporal correlations
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Estimation of maximum transition counts at internal nodes in CMOS VLSI circuits
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Hierarchical sequence compaction for power estimation
DAC '97 Proceedings of the 34th annual Design Automation Conference
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Maximum power estimation using the limiting distributions of extreme order statistics
DAC '98 Proceedings of the 35th annual Design Automation Conference
Computer-Aided Design Techniques for Low Power Sequential Logic Circuits
Computer-Aided Design Techniques for Low Power Sequential Logic Circuits
Characterizing Individual Gate Power Sensitivity in Low Power Design
VLSID '99 Proceedings of the 12th International Conference on VLSI Design - 'VLSI for the Information Appliance'
Maximum power estimation for CMOS circuits using deterministic and statistical approaches
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Power-Aware Design via Micro-architectural Link to Implementation
Integrated Circuit and System Design. Power and Timing Modeling, Optimization and Simulation
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In this paper, we study a new problem of statically estimating the power sensitivity of a given logic circuit with respect to the primary inputs. The power sensitivity defines the characteristics of power dissipation due to changes in state of primary inputs. Consequently, estimating the power sensitivity among the inputs is essential not only to measure the power consumption of the circuit efficiently but also to provide potential opportunities of redesigning the circuit for low power. In this context, we propose a fast and reliable static estimation technique for power sensitivity based on a new concept calledpower equations, which are then collectively transformed into a table calledpower table. Experimental data on MCNC benchmark examples show that the proposed technique is useful and effective in estimating power consumption. In summary, the relative error for the estimation of maximum power consumption is 9.4\% with a huge speed-up in simulation.