I've been reading ever more about Google's Tensor Processing Units (TPUs), as a majority of my ML is already done on Google Cloud GPUs, and paid public access to them aside from the Tensorflow Research Cloud doesn't seem all that far off. I would like to learn more about what to expect from working with a gen 2 TPU, or more specifically it's like working on the Tensorflow Research Cloud. I get the impression there's some sort of non-disclosure agreement with researchers, for I assume commercial/competitive reasons, as I see very little discussion about using them let alone editorials. I know there was the benchmark paper they released for the previous gen, but it would be refreshing to at least see some current practical benchmarks on MNIST or Imagenet/Inception considering they already have sample code available for lucky TPU Alpha customers. I'm also wondering why that magic 180 TFLOP/s number quoted by Google purposely omits which kind of floating point ops they're talking about

If artificial intelligence is rapidly eating software, then Google may have the biggest appetite around. At the company's annual developer conference today, CEO Sundar Pichai announced a new computer processor designed to perform the kind of machine learning that has taken the industry by storm in recent years (see "10 Breakthrough Technologies: Deep Learning"). The announcement reflects how rapidly artificial intelligence is transforming Google itself, and it is the surest sign yet that the company plans to lead the development of every relevant aspect of software and hardware. Perhaps most importantly, for those working in machine learning at least, the new processor not only executes at blistering speed, it can also be trained incredibly efficiently. Called the Cloud Tensor Processing Unit, the chip is named after Google's open-source TensorFlow machine-learning framework.