Disclosure: I work on Google Cloud.

I didn’t downvote you, but presumably people disagree with “Here’s an FPGA” as comparable to being given a working piece of hardware. That is, would you have said that the best comparison to a V100 is this same FPGA box?

I (and others) get what you were trying to say: TPUs are ASICs that aren’t general purpose at all, so an FPGA is a better comparison than a more general purpose GPU. As an end user, that just isn’t true though. If someone hands you an f1.16xlarge, you have to build your own psuedo-chip for machine learning. While with this offering, TensorFlow handles the acceleration / offload for you.

Fair enough. I wonder if there are any model compilers targeting FPGA training backends...

Do much deep learning training with the FPGAs?

Nope, but I'm sure others do.

Microsoft uses FPGAs for Deep Learning. Source:

https://techcrunch.com/2017/08/22/microsoft-brainwave-aims-t...

... for inference. I don't know of anyone who takes training on FPGAs seriously. They tend to get crushed by GPU/TPU/other ASIC in throughput, perf/watt, and perf/$.

Training on FPGAs starts to make a lot of sense when you consider low bit precision computation (e.g. DoReFa-Net).