In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

Generative large language model (LLM) applications are growing rapidly, leading to large-
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …

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 …

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 …

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

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

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 …

Near-memory processing (NMP) is a prospective paradigm enabling memory-centric
computing. By moving the compute capability next to the main memory (DRAM modules), it …