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

arXiv: https://arxiv.org/abs/2102.00075 · HTML: https://mlsanity.com/paper/2102.00075/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [The Architectural Implications of Facebook's DNN-based Personalized Recommendation](https://mlsanity.com/paper/1906.03109/) (2019)
- [Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems](https://mlsanity.com/paper/2003.05622/) (2020)
- [DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference](https://mlsanity.com/paper/2001.02772/) (2020)
- [RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing](https://mlsanity.com/paper/1912.12953/) (2019)
- [TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning](https://mlsanity.com/paper/1908.03072/) (2019)
- [Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems](https://mlsanity.com/paper/2003.09518/) (2020)
- [Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications](https://mlsanity.com/paper/1811.09886/) (2018)
- [Understanding Capacity-Driven Scale-Out Neural Recommendation Inference](https://mlsanity.com/paper/2011.02084/) (2020)
- [Bandana: Using Non-volatile Memory for Storing Deep Learning Models](https://mlsanity.com/paper/1811.05922/) (2018)
- [Basic Performance Measurements of the Intel Optane DC Persistent Memory Module](https://mlsanity.com/paper/1903.05714/) (2019)
- [NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units](https://mlsanity.com/paper/1911.06859/) (2019)

## Built on by (most cited first, 30 of 35)

- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training](https://mlsanity.com/paper/2108.09373/) (2021)
- [RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation](https://mlsanity.com/paper/2201.10095/) (2022)
- [Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models](https://mlsanity.com/paper/2310.09949/) (2023)
- [Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://mlsanity.com/paper/2111.05897/) (2021)
- [Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System](https://mlsanity.com/paper/2403.06664/) (2024)
- [STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining](https://mlsanity.com/paper/2207.05022/) (2022)
- [MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems](https://mlsanity.com/paper/2310.02784/) (2023)
- [SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures](https://mlsanity.com/paper/2205.04711/) (2022)
- [Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards](https://mlsanity.com/paper/2205.04702/) (2022)
- [BagPipe: Accelerating Deep Recommendation Model Training](https://mlsanity.com/paper/2202.12429/) (2022)
- [A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models](https://mlsanity.com/paper/2210.08804/) (2022)
- [MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation](https://mlsanity.com/paper/2302.10872/) (2023)
- [RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance](https://mlsanity.com/paper/2105.08820/) (2021)
- [ESPN: Memory-Efficient Multi-Vector Information Retrieval](https://mlsanity.com/paper/2312.05417/) (2023)
- [Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training](https://mlsanity.com/paper/2504.09307/) (2025)
- [Supporting Massive DLRM Inference Through Software Defined Memory](https://mlsanity.com/paper/2110.11489/) (2021)
- [A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities](https://mlsanity.com/paper/2111.14247/) (2021)
- [Request-Only Optimization for Recommendation Systems](https://mlsanity.com/paper/2508.05640/) (2025)
- [FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning](https://mlsanity.com/paper/2206.03852/) (2022)
- [FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models](https://mlsanity.com/paper/2301.02959/) (2023)
- [PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models](https://mlsanity.com/paper/2406.14571/) (2024)
- [TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization](https://mlsanity.com/paper/2605.20150/) (2026)
- [Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs](https://mlsanity.com/paper/2201.09189/) (2022)
- [Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases](https://mlsanity.com/paper/2205.14664/) (2022)
- [Failure Tolerant Training with Persistent Memory Disaggregation over CXL](https://mlsanity.com/paper/2301.07492/) (2023)
- [PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences](https://mlsanity.com/paper/2409.16633/) (2024)
- [Disaggregating Embedding Recommendation Systems with FlexEMR](https://mlsanity.com/paper/2410.12794/) (2024)
- [Cocoon: A System Architecture for Differentially Private Training with Correlated Noises](https://mlsanity.com/paper/2510.07304/) (2025)

## 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.