In early 2017, software engineers inside Google’s headquarters were stuck on a persistent efficiency problem. Google Translate relied on Recurrent Neural Networks, systems that processed text sequentially, one word at a time. To translate a sentence from German to English, the model read the first word, calculated its internal representation, and then moved to the second word.
This strict step-by-step sequence created a major computational bottleneck. Graphics processing units, built to execute thousands of mathematical calculations simultaneously, sat underutilized while waiting for sequential loops to finish. Longer sentences made the problem worse.
