Google never stated they use those to train models as far as I know. It seems that they are primarily used to spare energy when deploying trained models at scale.
Theres no reason they couldn't use them to train, as long as they can account for the lower precision operations. I think it would be much cheaper to train on them, at that scale anyway.
Afaik the Google TPU does inference only, at 8 bits. I don't think it's possible to train a neural network at 8 bit precision at this point in time. FP16 works for training though, and is twice as fast as FP32 on certain nvidia chips
Backpropagation can work with any precision, as long as you use stochastic rounding (so that the rounding errors are not correlated.) Without stochastic rounding even 16 bits will have rounding error bias.
I haven't seen 8bit training implemented in any (public) frameworks yet - that's not to say it's not possible. If it works then that's great, especially for specialised hardware.