571 citations · 1.7k across the 33 of their papers we have counts for
6 papers · 1 filter
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Carole-Jean Wu +18
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 embed…
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere +2
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigab…
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov +1
Modern deep learning-based recommendation systems exploit hundreds to thousands of different categorical features, each with millions of different categories ranging from clicks to…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
Udit Gupta, Carole-Jean Wu, Xiaodong Wang +12
The widespread application of deep learning has changed the landscape of computation in the data center. In particular, personalized recommendation for content ranking is now large…
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi +21
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks…