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Mixed Dimension Embeddings with Application to Memory-Efficient
  Recommendation Systems
v1v2v3 (latest)

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

25 September 2019
Antonio A. Ginart
Maxim Naumov
Dheevatsa Mudigere
Jiyan Yang
James Zou
ArXiv (abs)PDFHTML

Papers citing "Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems"

41 / 41 papers shown
Title
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures
Marco Siracusa
Olivia Hsu
Victor Soria-Pardos
Joshua Randall
Arnaud Grasset
...
Doug Joseph
Randy Allen
Fredrik Kjolstad
Miquel Moretó Planas
Adrià Armejach
89
0
0
14 Apr 2025
DQRM: Deep Quantized Recommendation Models
DQRM: Deep Quantized Recommendation Models
Yang Zhou
Zhen Dong
Ellick Chan
Dhiraj Kalamkar
Diana Marculescu
Kurt Keutzer
MQ
104
1
0
26 Oct 2024
Unified Low-rank Compression Framework for Click-through Rate Prediction
Unified Low-rank Compression Framework for Click-through Rate Prediction
Hao Yu
Minghao Fu
Jiandong Ding
Yusheng Zhou
Jianxin Wu
55
0
0
28 May 2024
Fine-Grained Embedding Dimension Optimization During Training for
  Recommender Systems
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems
Qinyi Luo
Penghan Wang
Wei Zhang
Fan Lai
Jiachen Mao
...
Jun Song
Wei-Yu Tsai
Shuai Yang
Yuxi Hu
Xuehai Qian
79
0
0
09 Jan 2024
Proxy-based Item Representation for Attribute and Context-aware
  Recommendation
Proxy-based Item Representation for Attribute and Context-aware Recommendation
Jinseok Seol
Minseok Gang
Sang-goo Lee
Jaehui Park
85
5
0
11 Dec 2023
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale
  Recommendation Models
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models
Hailin Zhang
Zirui Liu
Boxuan Chen
Yikai Zhao
Tong Zhao
Tong Yang
Tengjiao Wang
68
11
0
06 Dec 2023
Enhancing Cross-Category Learning in Recommendation Systems with
  Multi-Layer Embedding Training
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training
Selim F. Yilmaz
Benjamin Ghaemmaghami
A. Singh
Benjamin Cho
Leo Orshansky
Lei Deng
Michael Orshansky
AI4TS
57
0
0
27 Sep 2023
Unleashing the Power of Graph Learning through LLM-based Autonomous
  Agents
Unleashing the Power of Graph Learning through LLM-based Autonomous Agents
Lanning Wei
Zhiqiang He
Huan Zhao
Quanming Yao
LLMAG
73
7
0
08 Sep 2023
Dynamic Embedding Size Search with Minimum Regret for Streaming
  Recommender System
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
Bowei He
Xu He
Renrui Zhang
Yingxue Zhang
Ruiming Tang
Chen Ma
AI4TS
84
12
0
15 Aug 2023
When Large Language Models Meet Personalization: Perspectives of
  Challenges and Opportunities
When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities
Jin Chen
Zheng Liu
Xunpeng Huang
Chenwang Wu
Qi Liu
...
Yuxuan Lei
Xiaolong Chen
Xingmei Wang
Defu Lian
Enhong Chen
ALM
92
132
0
31 Jul 2023
Mem-Rec: Memory Efficient Recommendation System using Alternative
  Representation
Mem-Rec: Memory Efficient Recommendation System using Alternative Representation
Gopu Krishna Jha
Anthony Thomas
Nilesh Jain
Sameh Gobriel
Tajana Rosing
Ravi Iyer
79
2
0
12 May 2023
MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation
MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation
Samuel Hsia
Udit Gupta
Bilge Acun
Newsha Ardalani
Pan Zhong
Gu-Yeon Wei
David Brooks
Carole-Jean Wu
113
17
0
21 Feb 2023
Clustered Embedding Learning for Recommender Systems
Clustered Embedding Learning for Recommender Systems
Yizhou Chen
Guangda Huzhang
Anxiang Zeng
Qingtao Yu
Hui Sun
Hengyi Li
Jingyi Li
Yabo Ni
Han Yu
Zhiming Zhou
70
11
0
03 Feb 2023
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation
  Models
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
Geet Sethi
Pallab Bhattacharya
Dhruv Choudhary
Carole-Jean Wu
Christos Kozyrakis
82
5
0
08 Jan 2023
A Comprehensive Survey on Trustworthy Recommender Systems
A Comprehensive Survey on Trustworthy Recommender Systems
Wenqi Fan
Xiangyu Zhao
Xiao Chen
Jingran Su
Jingtong Gao
...
Qidong Liu
Yiqi Wang
Hanfeng Xu
Lei Chen
Qing Li
FaML
114
48
0
21 Sep 2022
A Frequency-aware Software Cache for Large Recommendation System
  Embeddings
A Frequency-aware Software Cache for Large Recommendation System Embeddings
Jiarui Fang
Geng Zhang
Jiatong Han
Shenggui Li
Zhengda Bian
Yongbin Li
Jin Liu
Yang You
57
4
0
08 Aug 2022
The trade-offs of model size in large recommendation models : A 10000
  $\times$ compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)
The trade-offs of model size in large recommendation models : A 10000 ×\times× compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)
Aditya Desai
Anshumali Shrivastava
AI4CE
53
3
0
21 Jul 2022
Efficient Mixed Dimension Embeddings for Matrix Factorization
Efficient Mixed Dimension Embeddings for Matrix Factorization
D. Beloborodov
Andrei Zimovnov
Petr Molodyk
Dmitrii Kirillov
52
2
0
18 May 2022
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10
  minutes on 1 GPU
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Zangwei Zheng
Peng Xu
Xuan Zou
Da Tang
Zhen Li
...
Xiangzhuo Ding
Fuzhao Xue
Ziheng Qing
Youlong Cheng
Yang You
VLM
86
7
0
13 Apr 2022
Heterogeneous Acceleration Pipeline for Recommendation System Training
Heterogeneous Acceleration Pipeline for Recommendation System Training
Muhammad Adnan
Yassaman Ebrahimzadeh Maboud
Divyat Mahajan
Prashant J. Nair
84
19
0
11 Apr 2022
Learning to Collide: Recommendation System Model Compression with
  Learned Hash Functions
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions
Benjamin Ghaemmaghami
Mustafa Ozdal
Rakesh Komuravelli
D. Korchev
Dheevatsa Mudigere
Krishnakumar Nair
Maxim Naumov
67
6
0
28 Mar 2022
Learning Compressed Embeddings for On-Device Inference
Learning Compressed Embeddings for On-Device Inference
Niketan Pansare
J. Katukuri
Aditya Arora
F. Cipollone
R. Shaik
Noyan Tokgozoglu
Chandru Venkataraman
101
15
0
18 Mar 2022
RecShard: Statistical Feature-Based Memory Optimization for
  Industry-Scale Neural Recommendation
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
Geet Sethi
Bilge Acun
Niket Agarwal
Christos Kozyrakis
Caroline Trippel
Carole-Jean Wu
99
70
0
25 Jan 2022
Enhanced Exploration in Neural Feature Selection for Deep Click-Through
  Rate Prediction Models via Ensemble of Gating Layers
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers
L. Guan
Xia Xiao
Ming-yue Chen
Youlong Cheng
56
1
0
07 Dec 2021
Binary Code based Hash Embedding for Web-scale Applications
Binary Code based Hash Embedding for Web-scale Applications
Bencheng Yan
Pengjie Wang
Jinquan Liu
Wei-Chao Lin
Kuang-chih Lee
Jian Xu
Bo Zheng
67
20
0
24 Aug 2021
Learning Effective and Efficient Embedding via an Adaptively-Masked
  Twins-based Layer
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer
Bencheng Yan
Pengjie Wang
Kai Zhang
Wei Lin
Kuang-chih Lee
Jian Xu
Bo Zheng
75
28
0
24 Aug 2021
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf
  DLRM Model : 1000$\times$ Compression and 3.1$\times$ Faster Inference
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000×\times× Compression and 3.1×\times× Faster Inference
Aditya Desai
Li Chou
Anshumali Shrivastava
AI4CE
59
6
0
04 Aug 2021
FINT: Field-aware INTeraction Neural Network For CTR Prediction
FINT: Field-aware INTeraction Neural Network For CTR Prediction
Zhishan Zhao
Sen Yang
Guohui Liu
Dawei Feng
Ke Xu
56
9
0
05 Jul 2021
AutoLoss: Automated Loss Function Search in Recommendations
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao
Haochen Liu
Wenqi Fan
Hui Liu
Jiliang Tang
Chong Wang
83
60
0
12 Jun 2021
RecPipe: Co-designing Models and Hardware to Jointly Optimize
  Recommendation Quality and Performance
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
Udit Gupta
Samuel Hsia
J. Zhang
Mark Wilkening
Javin Pombra
Hsien-Hsin S. Lee
Gu-Yeon Wei
Carole-Jean Wu
David Brooks
67
33
0
18 May 2021
ECRM: Efficient Fault Tolerance for Recommendation Model Training via
  Erasure Coding
ECRM: Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding
Kaige Liu
J. Kosaian
K. V. Rashmi
62
4
0
05 Apr 2021
Accelerating Recommendation System Training by Leveraging Popular
  Choices
Accelerating Recommendation System Training by Leveraging Popular Choices
Muhammad Adnan
Yassaman Ebrahimzadeh Maboud
Divyat Mahajan
Prashant J. Nair
86
60
0
01 Mar 2021
Semantically Constrained Memory Allocation (SCMA) for Embedding in
  Efficient Recommendation Systems
Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems
Aditya Desai
Yanzhou Pan
K. Sun
Li Chou
Anshumali Shrivastava
57
10
0
24 Feb 2021
Learnable Embedding Sizes for Recommender Systems
Learnable Embedding Sizes for Recommender Systems
Siyi Liu
Chen Gao
Yihong Chen
Depeng Jin
Yong Li
138
86
0
19 Jan 2021
Understanding Training Efficiency of Deep Learning Recommendation Models
  at Scale
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
Bilge Acun
Matthew Murphy
Xiaodong Wang
Jade Nie
Carole-Jean Wu
K. Hazelwood
95
113
0
11 Nov 2020
Mixed-Precision Embedding Using a Cache
Mixed-Precision Embedding Using a Cache
J. Yang
Jianyu Huang
Jongsoo Park
P. T. P. Tang
Andrew Tulloch
120
37
0
21 Oct 2020
Cross-Stack Workload Characterization of Deep Recommendation Systems
Cross-Stack Workload Characterization of Deep Recommendation Systems
Samuel Hsia
Udit Gupta
Mark Wilkening
Carole-Jean Wu
Gu-Yeon Wei
David Brooks
BDLGNNHAI
137
32
0
10 Oct 2020
Training with Multi-Layer Embeddings for Model Reduction
Training with Multi-Layer Embeddings for Model Reduction
Benjamin Ghaemmaghami
Zihao Deng
B. Cho
Leo Orshansky
A. Singh
M. Erez
Michael Orshansky
3DV
52
10
0
10 Jun 2020
Differentiable Neural Input Search for Recommender Systems
Differentiable Neural Input Search for Recommender Systems
Weiyu Cheng
Yanyan Shen
Linpeng Huang
75
36
0
08 Jun 2020
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
Paul Pu Liang
Manzil Zaheer
Yuan Wang
Amr Ahmed
BDL
50
1
0
18 Mar 2020
DeepRecSys: A System for Optimizing End-To-End At-scale Neural
  Recommendation Inference
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Udit Gupta
Samuel Hsia
V. Saraph
Xiaodong Wang
Brandon Reagen
Gu-Yeon Wei
Hsien-Hsin S. Lee
David Brooks
Carole-Jean Wu
GNN
91
190
0
08 Jan 2020
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