Compute Trends Across Three Eras of Machine Learning

TL;DR

This paper analyzes the evolution of compute requirements in machine learning across three distinct eras, revealing accelerated growth especially with the advent of deep learning and large-scale models, emphasizing the rapid increase in computational demands.

Contribution

The paper introduces a historical framework dividing ML compute trends into three eras and quantifies the growth rates, highlighting the shift to larger-scale models since 2015.

Findings

Compute doubled every 20 months before 2010

Post-2010, compute doubled every 6 months

Large-scale models require 10 to 100 times more compute

Abstract

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.