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

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 …

Deep learning recommendation systems must provide high quality, personalized content
under strict tail-latency targets and high system loads. This paper presents RecPipe, a …

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

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 …

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 …

With the advent of deep learning, neural network-based recommendation models have
emerged as an important tool for tackling personalization and recommendation tasks. These …

Building a scalable and real-time recommendation system is vital for many businesses
driven by time-sensitive customer feedback, such as short-videos ranking or online ads …

Personalized recommendations are the backbone machine learning (ML) algorithm that
powers several important application domains (eg, ads, e-commerce, etc) serviced from …