sumption in active mode as a tradeoff to increased performance, but any power consumed when the system is idle is a complete waste and ideally should be avoided by turning the system off. A typical current system is so complex that parts of it will likely be inactive even during active periods, and they can be turned off to reduce power with no impact on performance. The introduction of finer-grained power modes—for example, run at half speed and with lower power supply voltage—can further refine such simple strategies to reduce power in time (turn off the system during idle times) and space (turn off inactive elements), leading to more complex tradeoffs in terms of performance and verification costs. Although such power-aware strategies can be activated either in hardware or software, they usually are activated in software with hardware support. The techniques also can be static (compile-time) or online (runtime), with online methods being more flexible but generally having worse results than those achieved with a profile-guided static method. How to compare such different power-aware computing methods against each other poses an important question. Depending on the application—peak power, dynamic power, average power, energy, energy-delay product, energy-delaysquared product, power density—we can use several figures of merit for this purpose. The two metrics that have proved most useful until now are the energy-delay product (inversely