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 …

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 …

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 …

We present FireSim, an open-source simulation platform that enables cycle-exact
microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated …

Serverless or functions as a service runtimes have shown significant benefits to efficiency
and cost for event-driven cloud applications. Although serverless runtimes are limited to …

Machine Learning (ML) is an increasingly popular application in the cloud and data-center,
inspiring new algorithmic and systems techniques that leverage unique properties of ML …

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 …

Interactive massively parallel computations are critical for machine learning and data
analysis. These computations are a staple of the MIT Lincoln Laboratory Supercomputing …