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 …

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 based recommendation systems form the backbone of most personalized
cloud services. Though the computer architecture community has recently started to take …

Personalized recommendation is an important class of deep-learning applications that
powers a large collection of internet services and consumes a considerable amount of …

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

Recent years have witnessed an exponential growth of model scale in deep learning-based
recommender systems---from Google's 2016 model with 1 billion parameters to the latest …

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 …

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 Recommender Systems (DLRSs) need to update models at low latency, thus
promptly serving new users and content. Existing DLRSs, however, fail to do so. They …

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 …