A relevant paper came out 3 days ago talking about how AlphaGo used Bayesian hyperparameter optimization and how that improved performance: https://arxiv.org/pdf/1812.06855v1.pdf
It's interesting to set the OpenAI compute article's graph to linear scale so you can see that the compute that went into AlphaGo utterly dwarfs everything else. It seems like DeepMind is definitely ahead of nearly everyone else on the engineering effort and money they've put into scaling.
I just checked and seems it was fp32. I agree this makes it less impressive, I forgot to check that originally. I still think this somewhat counts as a software win, because working fp16 training required a bunch of programmer effort to take advantage of the hardware, just like optimization to make better use of cache would.
However, there's also a different set of same-machine datapoints available in the benchmark, where training time on a single Cloud TPU v2 went down from 12 hours 30 minutes to 2 hours 44 minutes, which is a 4.5x speedup similar to the 5
...I think that fp32 -> fp16 should give a >5x boost on a V100, so this 5x improvement still probably hides some inefficiencies when running in fp16.
I suspect the initial 15 - > 6 hour improvement on TPUs was also mostly dealing with low hanging fruit and cleaning up various inefficiencies from porting older code to a TPU / larger batch size / etc.. It seems plausible the last factor of 2 is more of a steady state improvement, I don't know.
My take on this story would be: "Hardware has been changing rapidly, giving large speedups, and people...
I went and checked and as far as I can tell they used the same 1024 batch size for the 12 and 6 hour time. The changes I noticed were better normalization, label smoothing, a somewhat tweaked input pipeline (not sure if optimization or refactoring) and updating Tensorflow a few versions (plausibly includes a bunch of hardware optimizations like you're talking about).
The things they took from fast.ai for the 2x speedup were training on progressively larger image sizes, and the better triangular learning rate schedule. Separately for their later submissions,
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