Tensor Processing Units were purpose-built for machine learning: Pros, cons

Google said its own requirements drove the development of TPUs -- both the company's earlier first-generation TPU as well as the second-generation TPU that was announced in May 2017. "While our first TPU was designed to run machine learning models quickly and efficiently -- to translate a set of sentences or choose the next move in [the board game] Go -- those models still had to be trained separately. Training a machine learning model is even more difficult than running it, and days or weeks of computation on the best available CPUs and GPUs are commonly required to reach state-of-the-art levels of accuracy," Google stated in a May 17, 2017, blog. Although its research and engineering teams have made "great progress" in scaling the difficult task of training machine learning models using readily-available hardware, the blog post continued, the first-generation TPU "wasn't enough to meet our machine learning needs." Google's new machine learning system was built to eliminate bottlenecks and maximize overall performance, using second-generation TPUs to both train and run machine learning models, the company touted.