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 …

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 …

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 …

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

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 …

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

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

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 …

Personalized recommendations are one of the most widely deployed machine learning (ML)
workload serviced from cloud datacenters. As such, architectural solutions for high …