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The most accurate power analysis tools are available for the circuit level but unfortunately, even with switch- rather than device-level modelling, tools at the circuit level have disadvantages like they are either too slow or require too much memory thus inhibiting large chip handling.
The majority of these are simulators like SPICE and have been used by the designers for many years as performance analysis tools.
Due to these disadvantages, gate-level power estimation tools have begun to gain some acceptance where faster, probabilistic techniques have begun to gain a foothold.
But it also has its trade off as speedup is achieved on the cost of accuracy, especially in the presence of correlated signals. Over the years it has been realized that biggest wins in low power design cannot come from circuit- and gate-level optimizations whereas architecture, system, and algorithm optimizations tend to have the largest impact on power consumption.
Therefore, there has been a shift in the incline of the tool developers towards high-level analysis and optimization tools for power. It is well known that more significant power reductions are possible if optimizations are made on levels of abstraction, like the architectural and algorithmic level, which are higher than the circuit or gate level [3] This provides the required motivation for the developers to focus on the development of new architectural level power analysis tools.
This in no way implies that lower level tools are unimportant. Instead, each layer of tools provides a foundation upon which the next level can be built.
The abstractions of the estimation techniques at a lower level can be used on a higher level with slight modifications. It is a technique based on the concept of gate equivalents.
The complexity of a chip architecture can be described approximately in terms of gate equivalents where gate equivalent count specifies the average number of reference gates that are required to implement the particular function.
The total power required for the particular function is estimated by multiplying the approximated number of gate equivalents with the average power consumed per gate.
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