U. Glaeser

FIGURE 20.1
from issues related to power dissipation are often critical. Thus, shrinking device geometries, higher
clocking speeds, and increased heat dissipation create circuit design challenges.
The Environmental Protection Agency’s (EPA) constant encouragement for green machines and its
Energy Star program are also pushing computer designers to consider power dissipation as one of the
major design constraints. Hence, there is an increasing need for accurate estimation of power consumption
of a system during the design phase so that the power consumption specifications can be met early
in the design cycle and expensive redesign process can be avoided.
Intuitively, a straightforward method to estimate the average power consumption is by simulating the
circuits with all possible combinations of valid inputs. Then, by monitoring power supply current
waveforms, the power consumption under each input combination can be computed. Eventually, the
results are averaged. The advantage of this method is its generality. This method can be applied to different
technologies, design styles, and architectures; however, the method requires not only a large number of
input waveforms combination, but also complete and specific knowledge of the input waveforms. Hence,
the simulation method is prohibitively expensive and impractical for large circuits.
In order to solve the problem of input pattern dependence, probabilistic techniques [21] are used to describe
the set of all possible input combinations.
Using the probabilistic measures, the signal activities can be
estimated. The calculated signal activities are then used to estimate the power consumption [1,3,6,12]. As
illustrated in Fig. 20.1 [2], probabilistic approaches average all the possible input combinations and then use
the probability values as inputs to the analysis tool to estimate power. Furthermore, the probabilistic approach
requires only one simulation run to estimate power, so it is much faster than the simulation-based approaches,
which require several simulation runs. In practice, some information about the typical input waveforms are
given by the user, which make the probabilistic approach a weakly pattern dependent approach.
Another alternative method to estimate power is the use of statistical techniques,
which tries to combine
the speed of the probabilistic techniques with the accuracy of the simulation-based techniques. Similar to
other simulation-based techniques, the statistical techniques are slower compared to the probabilistic
techniques, as it needs to run a certain number of samples before simulation converges to the userspecified
accuracy parameters.
This chapter is organized as follows. Section 20.2 describes how power is consumed in CMOS circuits.
Probabilistic and statistical techniques to estimate power are presented in sections 20.3 and 20.4, respectively.
Both techniques consider the temporal and spatial correlations of signals into account. Experimental results
for both techniques are presented in section 20.5. Section 20.6 summarizes and concludes this chapter.
20.2 Power Consumption
Power dissipation in a CMOS circuit consists of the following components: static, dynamic, and direct
path power. Static power component is due to the leakage current drawn continuously from the power
supply. The dynamic power component is dependent on the supply voltage, the load capacitances, and
the frequency of operation. The direct path power is due to the switching transient current that exists
© 2002 by CRC Press LLC
A large number
of input patterns
Many circuit
simulation runs
Circuit Simulator
A large number of
current waveforms
Average Probability Values
Probabilistic and simulation-based power estimation.
Average Power
A single analysis run
Analysis Tool Power