There is great interest in how the growth of artificial intelligence and machine learning may
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …

Given recent algorithm, software, and hardware innovation, computing has enabled a
plethora of new applications. As computing becomes increasingly ubiquitous, however, so …

The growth of artificial intelligence applications demands ever larger and more complex
deep learning models, dominating today's—and tomorrow's—data center and high …

In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of
datatypes developed for production cloud-scale inferencing on custom hardware. Through …

Neural personalized recommendation models are used across a wide variety of datacenter
applications including search, social media, and entertainment. State-of-the-art models …

Deep learning recommendation models (DLRMs) have been used across many business-
critical services at Meta and are the single largest AI application in terms of infrastructure …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

The use of GPUs has proliferated for machine learning workflows and is now considered
mainstream for many deep learning models. Meanwhile, when training state-of-the-art …

We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …

Embedding learning is an important technique in deep recommendation models to map
categorical features to dense vectors. However, the embedding tables often demand an …