Jongsoo Park | DeepAI

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∙ 04/19/2023

MTrainS: Improving DLRM training efficiency using heterogeneous memories

Recommendation models are very large, requiring terabytes (TB) of memory...

0 Hiwot Tadese Kassa, et al. ∙

∙ 02/16/2023

With Shared Microexponents, A Little Shifting Goes a Long Way

This paper introduces Block Data Representations (BDR), a framework for ...

0 Bita Rouhani, et al. ∙

∙ 11/09/2022

RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure

We present RecD (Recommendation Deduplication), a suite of end-to-end in...

0 Mark Zhao, et al. ∙

∙ 07/08/2021

First-Generation Inference Accelerator Deployment at Facebook

In this paper, we provide a deep dive into the deployment of inference a...

0 Michael Anderson, et al. ∙

∙ 05/26/2021

Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale

Tremendous success of machine learning (ML) and the unabated growth in M...

0 Zhaoxia, et al. ∙

∙ 05/04/2021

Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems

Deep learning recommendation systems at scale have provided remarkable g...

15 Xiaocong Du, et al. ∙

∙ 04/12/2021

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many busines...

32 Dheevatsa Mudigere, et al. ∙

∙ 02/27/2021

Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models

Soft error, namely silent corruption of signal or datum in a computer sy...

0 Sihuan Li, et al. ∙

∙ 01/13/2021

FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference

Deep learning models typically use single-precision (FP32) floating poin...

0 Daya Khudia, et al. ∙

∙ 10/21/2020

Mixed-Precision Embedding Using a Cache

In recommendation systems, practitioners observed that increase in the n...

0 Jie Amy Yang, et al. ∙

∙ 10/16/2020

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data

Large scale deep learning provides a tremendous opportunity to improve t...

16 Mao Ye, et al. ∙

∙ 11/05/2019

Post-Training 4-bit Quantization on Embedding Tables

Continuous representations have been widely adopted in recommender syste...

0 Hui Guan, et al. ∙

∙ 05/31/2019

Deep Learning Recommendation Model for Personalization and Recommendation Systems

With the advent of deep learning, neural network-based recommendation mo...

0 Maxim Naumov, et al. ∙

∙ 05/29/2019

A Study of BFLOAT16 for Deep Learning Training

This paper presents the first comprehensive empirical study demonstratin...

0 Dhiraj Kalamkar, et al. ∙

∙ 01/08/2019

Spatial-Winograd Pruning Enabling Sparse Winograd Convolution

Deep convolutional neural networks (CNNs) are deployed in various applic...

0 Jiecao Yu, et al. ∙

∙ 11/24/2018

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

The application of deep learning techniques resulted in remarkable impro...

0 Jongsoo Park, et al. ∙

∙ 11/24/2018

On Periodic Functions as Regularizers for Quantization of Neural Networks

Deep learning models have been successfully used in computer vision and ...

0 Maxim Naumov, et al. ∙

∙ 05/02/2018

Glow: Graph Lowering Compiler Techniques for Neural Networks

This paper presents the design of Glow, a machine learning compiler for ...

0 Nadav Rotem, et al. ∙

∙ 02/28/2017

Enabling Sparse Winograd Convolution by Native Pruning

Sparse methods and the use of Winograd convolutions are two orthogonal a...

0 Sheng Li, et al. ∙

∙ 08/04/2016

Faster CNNs with Direct Sparse Convolutions and Guided Pruning

Phenomenally successful in practical inference problems, convolutional n...

0 Jongsoo Park, et al. ∙

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