  Compute, data, and algorithmic advances are the three fundamental factors
that guide the progress of modern Machine Learning (ML). In this paper we study
trends in the most readily quantified factor - compute. We show that before
2010 training compute grew in line with Moore's law, doubling roughly every 20
months. Since the advent of Deep Learning in the early 2010s, the scaling of
training compute has accelerated, doubling approximately every 6 months. In
late 2015, a new trend emerged as firms developed large-scale ML models with 10
to 100-fold larger requirements in training compute. Based on these
observations we split the history of compute in ML into three eras: the Pre
Deep Learning Era, the Deep Learning Era and the Large-Scale Era. Overall, our
work highlights the fast-growing compute requirements for training advanced ML
systems.
