394 citations · 478 across the 4 of their papers we have counts for
4 papers
Supporting Massive DLRM Inference Through Software Defined Memory
Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee +17
Deep Learning Recommendation Models (DLRM) are widespread, account for a considerable data center footprint, and grow by more than 1.5x per year. With model size soon to be in tera…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…
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…
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Jongsoo Park, Maxim Naumov, Protonu Basu +25
The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models…