OpenCL for GPU vs. FPGA

Question

I read recently about OpenCL/CUDA for FPGA vs. GPU As I understood FPGA wins in power criteria. The explanation for that ,I`ve found in some article:

Reconfigurable devices can have much lower power consumption from peak values since only configured portions of the chip are active

Based on said above I have a question - does it mean that ,if some CU [Compute Unit] doen`t execute any work-item,it still consumes power? (and if yes - what for it consumes power?)

Answer 1

Yes, idle circuitry still consumes power. It doesn't consume as much, but it still consumes some. The reason for this is down to how transistors work, and how CMOS logic gates consume power.

Classically, CMOS logic (the type on all modern chips) only consumes power when it switches state. This made is very low power when compared to the technologies that came before it which consumed power all the time. Even so, every time a clock edge occurs, some logic changes state even if there's no work to do. The higher the clock rate, the more power used. GPUs tend to have high clock rates so they can do lots of work; FPGAs tend to have low clock rates. That's the first effect, but it can be mitigated by not clocking circuits that have no work to do (called 'clock gating')

As the size of transistors became smaller and smaller, the amount of power used when switching became smaller, but other effects (known as leakage) became more significant. Now we're at a point where the leakage power is very significant, and it's multiplied up by the number of gates you have in a design. Complex designs have high leakage power; Simple designs have low leakage power (in very basic terms). This is a second effect.

Hence, for a simple task it may be more power efficient to have a small dedicated low speed FPGA rather than a large complex, but high speed / general purpose CPU/GPU.

Answer 2

As always, it depends on the workload. For workloads that are well-supported by native GPU hardware (e.g. floating point, texture filtering), I doubt an FPGA can compete. Anecdotally, I've heard about image processing workloads where FPGAs are competitive or better. That makes sense, since GPUs are not optimized to operate on small integers. (For that reason, GPUs often are uncompetitive with CPUs running SSE2-optimized image processing code.)

As for power consumption, for GPUs, suitable workloads generally keep all the execution units busy, so it's a bit of an all-or-nothing proposition.