Which FPGAs in which workloads do they crush?
600 images/s inference (highest ever publically reported), projected to be as high as 900 images/s. ~$5000 per FPGA.
Compare and contrast with TitanX/GTX 980 TI now topping 5000 images/s. GTX 1080 will only be faster than this and $600.
https://github.com/soumith/convnet-benchmarks
And so far, no FPGA training numbers. But here are some distributed training numbers for $10Kish Xeon servers:
https://software.intel.com/en-us/articles/caffe-training-on-...
64 of them train Alexnet in ~5 hours. A DGX-1 does it in 2. Don't have/Can't get a $129K DGX-1? Fine, buy a Big Sur from one of several vendors and throw in 8 GTX 1080s as soon as they're out, implement "One Weird Trick", and you'll do it in <3. That ought to run you about $30-$35K versus >$600K for those 64 Xeon servers.
FPGAs OTOH are fantastic at low memory bandwidth embarrassingly parallel computation like bitcoin mining. Deep Learning is not such a domain.
Even your previous post had links to two page that were not measuring the same thing... and thus could not support your claims?
I think you need to clarify.