Tran et al. (2005) proposed a power estimation model for digital
CMOS circuits. The circuit was divided into five sections and the power
dissipation of each part has been estimated individually. Further the
implemented gates were also counted, the proposed power investigation
model suggest early guidelines for design of circuits. The leakage power
estimation is most important factor the study of design feasibility.
Derakhshandeh et al. (2005) identified the relationship between the leakage
power and threshold voltage, where initially the number of input gates were
identified, followed by identifying the number of inputs to the gates and their
corresponding states. The predicted results on benchmark ICs reported
improved accuracy than the conventional methods of estimation.
Ligang et al. (2006) introduced neural network models for power
estimation for VLSI circuits. In the proposed study the authors used ISCAS89
benchmark circuits and the corresponding experimental results were noted.
The neural network based power estimation techniques produced better
results as compared to the conventional methods such as Monte-Carlo and
other statistical techniques. A linear programming based leakage power
estimation technique has been proposed by Chen et al. (2006), where Genetic
Algorithm was implemented for Minimum Leakage Vector (MLV) searching,
the leakage power is estimated on gate level by linear programming method.
The study assists in reducing the leakage power of VLSI circuits with easy
implementations.
Chaudhry et al. (2006) presented accurate power estimation
strategy by extracting the switching and clock activity of macro power
models. Do et al. (2007) discussed a high power estimation models for
proposed 2-kB 6T - SRAM array, in the proposed study the authors computed
the threshold leakage by combining the probing methodology. The memory
