The amount of computation done per unit energy, isn't really the issue. Instead the problem is the amount of _USEFUL_ computation done per unit energy.
The majority of power in a modern system goes into moving data around, and other tasks which are not the actual desired computation. Examples of this are incrementing the program counter, figuring out instruction dependancies, and moving data between levels of caches. The actual computation of the data is tiny in comparison.
Why do we do this then? Most of the power goes to what is informally called the "Turing Tax" - the extra things required to allow a given processor to be general purpose - ie. to compute anything. A single purpose piece of hardware can only do one thing, but is vastly more efficient, because all the power used figuring out which bits of data need to go where can all be left out. Consider it like the difference between a road network that lets you go anywhere and a road with no junctions in a straight line between your house and your work. One is general purpose (you can go anywhere), the other is only good for one thing, but much quicker and more efficient.
To get nearer our goal, computers are getting components that are less flexible. Less flexibility means less Turing Tax. For example video encoder cores can do massive amounts of computation, yet they can only encode video - nothing else. For comparison, an HD video camera can record 1080p video in real time with only a couple of Watts. A PC (without hardware encoder) would take 15 mins or so to encode each minute of HD video, using far more power along the way.
The future of low power computing is to find clever ways of making special purpose hardware to do the most computationally heavy stuff such that the power hungry general purpose processors have less stuff left to do.
pay it forward haunted houses favicon.ico favicon.ico footloose best iphone 4 case best iphone 4 case
No comments:
Post a Comment
Note: Only a member of this blog may post a comment.