# Learning Memory Access Patterns

arXiv: https://arxiv.org/abs/1803.02329 · HTML: https://mlsanity.com/paper/1803.02329/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 28 of 28)

- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [OmniPred: Language Models as Universal Regressors](https://mlsanity.com/paper/2402.14547/) (2024)
- [Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning](https://mlsanity.com/paper/2109.12021/) (2021)
- [Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics](https://mlsanity.com/paper/2402.12535/) (2024)
- [The fine line between dead neurons and sparsity in binarized spiking neural networks](https://mlsanity.com/paper/2201.11915/) (2022)
- [CXL-SpecKV: A Disaggregated FPGA Speculative KV-Cache for Datacenter LLM Serving](https://mlsanity.com/paper/2512.11920/) (2025)
- [SGDP: A Stream-Graph Neural Network Based Data Prefetcher](https://mlsanity.com/paper/2304.03864/) (2023)
- [ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design](https://mlsanity.com/paper/2306.08888/) (2023)
- [Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching](https://mlsanity.com/paper/2205.02269/) (2022)
- [Puppeteer: A Random Forest-based Manager for Hardware Prefetchers across the Memory Hierarchy](https://mlsanity.com/paper/2201.12027/) (2022)
- [PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models](https://mlsanity.com/paper/2402.13441/) (2024)
- [Canopy: Property-Driven Learning for Congestion Control](https://mlsanity.com/paper/2412.10915/) (2024)
- [A Pluggable Learned Index Method via Sampling and Gap Insertion](https://mlsanity.com/paper/2101.00808/) (2021)
- [Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics](https://mlsanity.com/paper/2212.05250/) (2022)
- [Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching](https://mlsanity.com/paper/2401.06362/) (2023)
- [A Bring-Your-Own-Model Approach for ML-Driven Storage Placement in Warehouse-Scale Computers](https://mlsanity.com/paper/2501.05651/) (2025)
- [Coordinated Reinforcement Learning Prefetching Architecture for Multicore Systems](https://mlsanity.com/paper/2509.10719/) (2025)
- [A Joint Learning Approach to Hardware Caching and Prefetching](https://mlsanity.com/paper/2510.10862/) (2025)
- [Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning](https://mlsanity.com/paper/2601.17615/) (2026)
- [Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents](https://mlsanity.com/paper/2602.23556/) (2026)
- [Artificial Intelligence in the Low-Level Realm -- A Survey](https://mlsanity.com/paper/2111.00881/) (2021)
- [Analyzing a Caching Model](https://mlsanity.com/paper/2112.06989/) (2021)
- [TransforMAP: Transformer for Memory Access Prediction](https://mlsanity.com/paper/2205.14778/) (2022)
- [MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction](https://mlsanity.com/paper/2211.02177/) (2022)
- [NPS: A Framework for Accurate Program Sampling Using Graph Neural Network](https://mlsanity.com/paper/2304.08880/) (2023)
- [Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks](https://mlsanity.com/paper/2305.17244/) (2023)
- [Learning Semantics, Not Addresses: Runtime Neural Prefetching for Far Memory](https://mlsanity.com/paper/2506.00384/) (2025)
- [AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies](https://mlsanity.com/paper/2608.02569/) (2026)

## Abstract (arXiv)

The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the space of machine learning for computer hardware architecture is only lightly explored. In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance. We focus on the critical problem of learning memory access patterns, with the goal of constructing accurate and efficient memory prefetchers. We relate contemporary prefetching strategies to n-gram models in natural language processing, and show how recurrent neural networks can serve as a drop-in replacement. On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall. This work represents the first step towards practical neural-network based prefetching, and opens a wide range of exciting directions for machine learning in computer architecture research.