4 ms·

A better comparison would be the f1.16xlarge[1] instance @ ~$4/hr. It comes with 8 FPGAs (12 Gbps link) and 64 vCPUs. [1]: https://aws.amazon.com/ec2/instance-

• dharma1 9y ago
  Do much deep learning training with the FPGAs?
  • Cyph0n 9y ago
    Nope, but I'm sure others do.
    • jongomez 9y ago
      Microsoft uses FPGAs for Deep Learning. Source: https://techcrunch.com/2017/08/22/microsoft-brainwave-aims-to-accelerate-deep-learning-with-fpgas/ https://techcrunch.com/2017/08/22/microsoft-brainwave-aims-t...
      • twtw 9y ago
        ... 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/$.
        • p1esk 9y ago
          Training on FPGAs starts to make a lot of sense when you consider low bit precision computation (e.g. DoReFa-Net).
• boulos 9y ago
  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.
  • Cyph0n 9y ago
    Fair enough. I wonder if there are any model compilers targeting FPGA training backends...