3 ms·

>> That study's way out of date I don't know about 'way' out of date, it was first published just a few months ago (November) and the authors pushed a revised

• vrv 10y ago
  And even those numbers on the front page are out of date :) (we're even faster now: https://github.com/soumith/convnet-benchmarks/pull/96 https://github.com/soumith/convnet-benchmarks/pull/96, which is from a few weeks ago.) The field is moving quickly enough that many published benchmarks are stale within 3 months, and it's a lot of hard work to maintain up to date benchmarks, given how many frameworks there are. Also there are performance/memory/scalability/flexibility tradeoffs everywhere, so it's hard to capture everything in one number without a tremendous number of caveats.
• dgacmu 10y ago
  vrv addressed why I called it "way" out of date - in the time since the study was done with CuDNNv2, we've moved TensorFlow to CuDNN v4, and NVidia released the CuDNNv5 release candidate a week ago. Each of those releases provides a pretty big speed bump for specific types of DNNs, and we've been pushing out some very significant speed bumps for TensorFlow at the same time. My conclusion from this is that Soumith's approach to having a living repository is the way to go. It's harder to call it a "publication", but it's providing something of more lasting value than a static performance snapshot in a field where the engineering is moving so quickly.