# RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

arXiv: https://arxiv.org/abs/1912.12953 · HTML: https://mlsanity.com/paper/1912.12953/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 30 of 49)

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- [RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference](https://mlsanity.com/paper/2102.00075/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation](https://mlsanity.com/paper/2201.10095/) (2022)
- [Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization](https://mlsanity.com/paper/2406.12930/) (2024)
- [Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks](https://mlsanity.com/paper/2109.14320/) (2021)
- [Accelerating Recommendation System Training by Leveraging Popular Choices](https://mlsanity.com/paper/2103.00686/) (2021)
- [Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterization](https://mlsanity.com/paper/2109.06133/) (2021)
- [PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model Inference](https://mlsanity.com/paper/2502.07578/) (2025)
- [MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems](https://mlsanity.com/paper/2310.02784/) (2023)
- [Heterogeneous Acceleration Pipeline for Recommendation System Training](https://mlsanity.com/paper/2204.05436/) (2022)
- [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)
- [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)
- [Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models](https://mlsanity.com/paper/2103.00768/) (2021)
- [GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing](https://mlsanity.com/paper/2111.00680/) (2021)
- [PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System](https://mlsanity.com/paper/2404.07164/) (2024)
- [CHIME: A Case for Efficient Long-Context Attention-FC Disaggregated Inference with DIMM-PIM](https://mlsanity.com/paper/2504.17584/) (2025)
- [FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models](https://mlsanity.com/paper/2301.02959/) (2023)
- [UpDLRM: Accelerating Personalized Recommendation using Real-World PIM Architecture](https://mlsanity.com/paper/2406.13941/) (2024)
- [Mem-Rec: Memory Efficient Recommendation System using Alternative Representation](https://mlsanity.com/paper/2305.07205/) (2023)
- [PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures](https://mlsanity.com/paper/2402.16731/) (2024)
- [Toward Robust and Efficient ML-Based GPU Caching for Modern Inference](https://mlsanity.com/paper/2509.20979/) (2025)
- [An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2207.07886/) (2022)
- [PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models](https://mlsanity.com/paper/2406.14571/) (2024)
- [Debunking the CUDA Myth Towards GPU-based AI Systems](https://mlsanity.com/paper/2501.00210/) (2024)
- [Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases](https://mlsanity.com/paper/2205.14664/) (2022)

## Abstract (arXiv)

Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose a fundamental challenge to accelerate. This paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference. The in-depth characterization of production-grade recommendation models shows that embedding operations with high model-, operator- and data-level parallelism lead to memory bandwidth saturation, limiting recommendation inference performance. We propose RecNMP which provides a scalable solution to improve system throughput, supporting a broad range of sparse embedding models. RecNMP is specifically tailored to production environments with heavy co-location of operators on a single server. Several hardware/software co-optimization techniques such as memory-side caching, table-aware packet scheduling, and hot entry profiling are studied, resulting in up to 9.8x memory latency speedup over a highly-optimized baseline. Overall, RecNMP offers 4.2x throughput improvement and 45.8% memory energy savings.