Large-scale training is important to ensure high performance and accuracy of machine-
learning models. At Facebook we use many different models, including computer vision …

The widespread application of deep learning has changed the landscape of computation in
data centers. In particular, personalized recommendation for content ranking is now largely …

The application of deep learning techniques resulted in remarkable improvement of
machine learning models. In this paper provides detailed characterizations of deep learning …

At Facebook, machine learning provides a wide range of capabilities that drive many
aspects of user experience including ranking posts, content understanding, object detection …

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

Deep learning recommendation models have grown to the terabyte scale. Traditional
serving schemes-that load entire models to a single server-are unable to support this scale …

Personalized recommendation systems leverage deep learning models and account for the
majority of data center AI cycles. Their performance is dominated by memory-bound sparse …

Machine learning sits at the core of many essential products and services at Facebook. This
paper describes the hardware and software infrastructure that supports machine learning at …

During the last two years, the goal of many researchers has been to squeeze the last bit of
performance out of HPC system for AI tasks. Often this discussion is held in the context of …

Neural personalized recommendation is the cornerstone of a wide collection of cloud
services and products, constituting significant compute demand of cloud infrastructure. Thus …