mlsanityData to 7 Oct 2026

A Survey of Machine Learning for Computer Architecture and Systems

arXiv 2102.07952 ↗First posted 16 Feb 2021cs.LG cs.ARcited by 53

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Abstract (arXiv)

It has been a long time that computer architecture and systems are optimized for efficient execution of machine learning (ML) models. Now, it is time to reconsider the relationship between ML and systems, and let ML transform the way that computer architecture and systems are designed. This embraces a twofold meaning: improvement of designers' productivity, and completion of the virtuous cycle. In this paper, we present a comprehensive review of the work that applies ML for computer architecture and system design. First, we perform a high-level taxonomy by considering the typical role that ML techniques take in architecture/system design, i.e., either for fast predictive modeling or as the design methodology. Then, we summarize the common problems in computer architecture/system design that can be solved by ML techniques, and the typical ML techniques employed to resolve each of them. In addition to emphasis on computer architecture in a narrow sense, we adopt the concept that data centers can be recognized as warehouse-scale computers; sketchy discussions are provided in adjacent computer systems, such as code generation and compiler; we also give attention to how ML techniques can aid and transform design automation. We further provide a future vision of opportunities and potential directions, and envision that applying ML for computer architecture and systems would thrive in the community.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). Builds on 68 of its 346 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

68 of 346 references.

References · 68

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38 more
  1. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
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50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 12; 2024: 23; 2025: 10; 2026: 5.

53 papers.

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  1. 2023Ensuring User-side Fairness in Dynamic Recommender Systemscited by 27
  2. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendationcited by 26
  3. 2022High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancingcited by 25
  4. 2024EXGC: Bridging Efficiency and Explainability in Graph Condensationcited by 20
  5. 2024AlignRec: Aligning and Training in Multimodal Recommendationscited by 20
  6. 2025Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendationcited by 15
  7. 2024RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Searchcited by 12
  8. 2024Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendationcited by 11
  9. 2024Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classificationcited by 11
  10. 2023APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendationcited by 10
  11. 2024One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributescited by 9
  12. 2024View-based Explanations for Graph Neural Networkscited by 8
38 more
  1. 2024Mitigating Propensity Bias of Large Language Models for Recommender Systemscited by 8
  2. 2022LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Modelscited by 7
  3. 2023SGDP: A Stream-Graph Neural Network Based Data Prefetchercited by 7
  4. 2023Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systemscited by 7
  5. 2024Cooperative Classification and Rationalization for Graph Generalizationcited by 7
  6. 2024Symmetric Graph Contrastive Learning against Noisy Views for Recommendationcited by 7
  7. 2023GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hintcited by 6
  8. 2023Shape-aware Graph Spectral Learningcited by 6
  9. 2024The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphscited by 6
  10. 2024TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformationscited by 5
  11. 2023Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designscited by 4
  12. 2024Mixture of Experts for Node Classificationcited by 4
  13. 2025Simple yet Effective Graph Distillation via Clusteringcited by 4
  14. 2021SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learningcited by 3
  15. 2023LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendationcited by 3
  16. 2024Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendationcited by 3
  17. 2024You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learningcited by 3
  18. 2024Exploring Feature-based Knowledge Distillation for Recommender System: A Frequency Perspectivecited by 3
  19. 2025Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purificationcited by 3
  20. 2025Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusioncited by 3
  21. 2024BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulationcited by 2
  22. 2024Scalable Expressiveness through Preprocessed Graph Perturbationscited by 2
  23. 2024MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansioncited by 2
  24. 2025Rethinking Client-oriented Federated Graph Learningcited by 2
  25. 2025MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendationcited by 2
  26. 2023BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardwarecited by 1
  27. 2023Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketingcited by 1
  28. 2023Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Frameworkcited by 1
  29. 2023Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Surveycited by 1
  30. 2024DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedbackcited by 1
  31. 2024A Benchmark on Directed Graph Representation Learning in Hardware Designscited by 1
  32. 2025Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocolscited by 1
  33. 2025LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Trainingcited by 1
  34. 2024Implementation and Evaluation of a Gradient Descent-Trained Defensible Blackboard Architecture Systemcited by 0
  35. 2024Watermarking Recommender Systemscited by 0
  36. 2025How Feasible is Augmenting Fake Nodes with Learnable Features as a Counter-strategy against Link Stealing Attacks?cited by 0
  37. 2026Rudder: Steering Prefetching in Distributed GNN Training using LLM Agentscited by 0
  38. 2026RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendationcited by 0

50 most cited of 53 All 53 (JSON)