394 citations · 895 across the 23 of their papers we have counts for
23 papers
With Shared Microexponents, A Little Shifting Goes a Long Way
Bita Rouhani, Ritchie Zhao, Venmugil Elango +19
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning. It enables compariso…
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions
Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli +4
A key characteristic of deep recommendation models is the immense memory requirements of their embedding tables. These embedding tables can often reach hundreds of gigabytes which…
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…
Differentiable NAS Framework and Application to Ads CTR Prediction
Ravi Krishna, Aravind Kalaiah, Bichen Wu +4
Neural architecture search (NAS) methods aim to automatically find the optimal deep neural network (DNN) architecture as measured by a given objective function, typically some comb…
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
Zhaoxia, Deng, Jongsoo Park +17
Tremendous success of machine learning (ML) and the unabated growth in ML model complexity motivated many ML-specific designs in both CPU and accelerator architectures to speed up…
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…