As an aside, Google used CPU-based machine learning (using enormous numbers of CPUs) for a long time before custom ASICS or tensorflow even existed.

The big ones were SmartASS (ads serving) and Sibyl (everything else serving). There was an internal debate over the value of GPUs with a prominent engineer writing an influential doc that caused Google continue with fat CPU nodes when it was clear that accelerators were a good alternative. This was around the time ImageNet blew up, and some eng were stuffing multiple GPUs in their dev boxes to demonstrate training speeds on tasks like voice recognition.

Sibyl was a heavy user of embeddings before there was any real custom ASIC support for that and there was an add-on for TPUs called barnacore to give limited embedding support (embeddings are very useful for maximizing profit through ranking).