mlsanityData to 7 Oct 2026

RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference

arXiv 2102.00075 ↗First posted 29 Jan 2021cs.AR cs.LGcited by 35

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Abstract (arXiv)

Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment. State-of-the-art models comprise large embedding tables that have billions of parameters requiring large memory capacities. Unfortunately, large and fast DRAM-based memories levy high infrastructure costs. Conventional SSD-based storage solutions offer an order of magnitude larger capacity, but have worse read latency and bandwidth, degrading inference performance. RecSSD is a near data processing based SSD memory system customized for neural recommendation inference that reduces end-to-end model inference latency by 2X compared to using COTS SSDs across eight industry-representative models.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 29 Jan 2021.

Builds on

14 of 44 references.

References · 14

  1. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
  2. 2018Deep Interest Evolution Network for Click-Through Rate Predictioncited by 336
  3. 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
  4. 2019The Architectural Implications of Facebook's DNN-based Personalized Recommendationcited by 83
  5. 2020Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systemscited by 59
  6. 2020DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inferencecited by 54
  7. 2019RecNMP: Accelerating Personalized Recommendation with Near-Memory Processingcited by 49
  8. 2019TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learningcited by 42
  9. 2020Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systemscited by 37
  10. 2018Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implicationscited by 35
  11. 2020Understanding Capacity-Driven Scale-Out Neural Recommendation Inferencecited by 29
  12. 2018Bandana: Using Non-volatile Memory for Storing Deep Learning Modelscited by 14
2 more
  1. 2019Basic Performance Measurements of the Intel Optane DC Persistent Memory Modulecited by 9
  2. 2019NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Unitscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 10; 2023: 8; 2024: 5; 2025: 4; 2026: 1.

35 papers.

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  1. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  2. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  3. 2021Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Trainingcited by 28
  4. 2022RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendationcited by 28
  5. 2023Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Modelscited by 23
  6. 2021Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameterscited by 17
  7. 2022STI: Turbocharge NLP Inference at the Edge via Elastic Pipeliningcited by 14
  8. 2023MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systemscited by 14
  9. 2024Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real Systemcited by 14
  10. 2022SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architecturescited by 12
  11. 2022Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwardscited by 11
  12. 2022BagPipe: Accelerating Deep Recommendation Model Trainingcited by 10
23 more
  1. 2022A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Modelscited by 9
  2. 2023MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendationcited by 9
  3. 2021RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performancecited by 8
  4. 2023ESPN: Memory-Efficient Multi-Vector Information Retrievalcited by 7
  5. 2025Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Trainingcited by 6
  6. 2021Supporting Massive DLRM Inference Through Software Defined Memorycited by 5
  7. 2021A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunitiescited by 5
  8. 2025Request-Only Optimization for Recommendation Systemscited by 5
  9. 2022FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learningcited by 4
  10. 2023FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Modelscited by 4
  11. 2024PreSto: An In-Storage Data Preprocessing System for Training Recommendation Modelscited by 2
  12. 2026TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimizationcited by 2
  13. 2022Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphscited by 1
  14. 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databasescited by 1
  15. 2023Failure Tolerant Training with Persistent Memory Disaggregation over CXLcited by 1
  16. 2024PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferencescited by 1
  17. 2024Disaggregating Embedding Recommendation Systems with FlexEMRcited by 1
  18. 2025Cocoon: A System Architecture for Differentially Private Training with Correlated Noisescited by 1
  19. 2021ECRM: Efficient Fault Tolerance for Recommendation Model Training via Erasure Codingcited by 0
  20. 2023Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendationscited by 0
  21. 2023MTrainS: Improving DLRM training efficiency using heterogeneous memoriescited by 0
  22. 2024ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Modelscited by 0
  23. 2025SCRec: A Scalable Computational Storage System with Statistical Sharding and Tensor-train Decomposition for Recommendation Modelscited by 0