Energy Efficiency of Computing: What’s Next?
Electronic Design, November 28, 2016
For a long time, processor performance and peak output energy efficiency moved forward together at a rapid clip. The gains started well before 1965, the year Gordon Moore published his first projection. Peak output efficiency doubled about every 1.6 years from the dawn of the computer age. As a result, the energy efficiency of computation at peak computing output improved by ten orders of magnitude from the mid-1940s to the year 2000—a factor of more than ten billion over more than five decades.
By the turn of the millennium, weighed down by the physics of CMOS circuit scaling, the growth in peak output energy efficiency started to slow, so that it was only doubling about every 2.6 years or so (Fig. 1). Chipmakers turned to architectural changes to compensate for the slowdown, such as multiple cores for CPUs, but they weren’t able to maintain historical growth rates of performance and efficiency.
The difference between these two growth rates is substantial. A doubling every year and a half results in a 100-fold increase in efficiency every decade. A doubling every two and a half years yields just a 16-fold increase.
Efficiency Trends in Typical Use
Fortunately, the efficiency gains we’ve already obtained over the last half-century or so were more than sufficient to launch us into entirely new design space [5]. To a large extent, the frontiers of engineering have shifted from creating the fastest CPUs to building ultra-low-power processors. The latter can be used not only inside battery-powered smartphones, tablets, and wearable devices, but also in large-scale computing settings such as data centers.
As a result, maximizing peak output efficiency is no longer the central goal for many chip designers. Instead, the engineering focus has shifted more to minimizing average electricity use, often to extend battery life.
Few computing devices are computing at full output all of the time (with some exceptions, such as servers in specialized scientific computing applications [6]). Instead most computers are used intermittently; typically operating at their computational peak for 1-2% of the time.1 Fully optimized systems reduce power use for the 98%+ of the time a device is idle or off. The best power-management designs make a computer “energy proportional,” in that electricity use and computational output go up proportionally with utilization, and electricity use goes to zero (or nearly so) when the device is idle [7].
Peak output efficiency, the metric for many earlier studies, is an indicator of how efficiently computers run when at full speed. Koomey et al. [3] calculate that metric using Equation 1:
