Sure, GPUs deliver impressive raw performance. To be useful, the task must benefit from massively parallel hardware. GPU hardware works fantastic for shading polygons, training neural networks, or raytracing. For compression and encryption algorithms however, GPUs aren’t terribly good.
Another reason is while a GPU delivers impressive bandwidth on parallel-friendly workloads, it’s usually possible to achieve lower latencies with FPGA. An FPGA doesn’t decode any instructions, and its computing modules exchange data directly.
Citation needed.
Maxwell Jetson TX1 is claimed to achieve 1TFlops FP16 at <10W, and soon to be released Pascal based replacement will probably be even more efficient.
The TX1 power consumption including DRAM and other subsystems peaks 20-30W. Typical usage is 10-15W if you're running anything useful.
That 1 TFLOP counts a FMA instruction as 2 flops - while accurate and useful for say dot products - for other workloads the throughput will be half of this number.
As an example of an FPGA performing significantly better than the TX1 is DeepPhi [0].
While not the TX1 vs FPGA result you want, this is very close. For example they aren't using the latest FPGA or GPU, and are not using TensorRT on the GPU and on the FPGA side they are using fatty 16-bit weights on an older FPGA rather than newer stuff you can do with lower precision (which improves the efficiency of the FPGA having more high speed RAM collocated with computation vs GPU which is primarily off-chip).
If you want to learn more about this stuff, I suggest a presentation by one of Bill Dally's students (chief scientist at NVIDIA): http://on-demand.gputechconf.com/gtc/2016/presentation/s6561...
I'm not saying you're wrong, just that to make a convincing claim that FPGAs are more power efficient than GPUs, one needs to do an apples to apples comparison.
And of course, let's not forget about price: Zynq ZC706 board is what, over $6k? And Jetson TK1 was what when released, $300? If you need to deploy a thousand of these chips in your datacenter, to save a million per year on power, you will need several years to break even, and by that time, you will probably need to upgrade.
It just seems that GPUs are a better deal currently, with or without looking at power efficiency.