
Meanderful: FPGAs and AI Processors: DNN and CNN for All - polskibus
https://meanderful.blogspot.com/2017/06/fpgas-and-ai-processors-dnn-and-cnn-for.html?m=1
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slivym
I find it interesting to see the external perspective of someone looking at
FPGAs for this application since I'm an FPGA guy myself and know quite a lot
about it.

The price is a fair complaint but it's a bit misleading - actually boards
aren't $50k for people who are serious about building an application with
them.

The bigger concern for me is that all the benchmarks I've seen show janky FPGA
solutions that cut a lot of corners barely compete with fairly mature GPU
solutions. So you're spending 20x the development time for 90% of the
performance with 95% of the algorithm. The big promise from Intel was that the
Stratix 10 chips would be 5x as big with 2x the Clock speed and your CNN
algorithm would just magically kick arse. That doesn't seem to have happened -
Stratix 10s are almost impossible to get hold off, the promises about
performance are getting watered down and of course, by the time you can
actually get an application running on it GPU technology has moved on and the
performance still isn't competitive. At this point I don't see FPGA being a
player in the AI market except for extremely embedded solutions.

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mhurd
Better than 50% discounts for reasonable FPGA volume, true. Still expensive
when compared to a V100.

Microsoft is seeing ~90Tops for their MS-FP8 datatypes:
[https://www.top500.org/news/microsoft-takes-fpga-powered-
dee...](https://www.top500.org/news/microsoft-takes-fpga-powered-deep-
learning-to-the-next-level/)

It is certainly hard to compete against an NVidia V100 with its tensor ops.

It can get interesting though. There are quite a few graphs that tensorflow
cannot place on a GPU, especially with RNNs. An FPGA does give you more
flexibility in architecture which was, and would remain one of the motivations
for MS to choose them as a platform.

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tehsauce
Been very curious to see a comparison between standard computing hardware and
neural specific hardware. Nice article!

