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

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 …

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 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 …

Modern mobile applications are benefiting significantly from the advancement in deep
learning, eg, implementing real-time image recognition and conversational system. Given a …

Edge devices are seeing tremendous growth in sensing and computational capabilities.
Running state-of-the-art deep neural network (NN) based data processing on multi-core …

Emerging edge computing platforms often contain machine learning (ML) accelerators that
can accelerate inference for a wide range of neural network (NN) models. These models are …

Deep learning is currently widely used in a variety of applications, including computer vision
and natural language processing. End devices, such as smartphones and Internet-of-Things …

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 …