# A Survey of Machine Learning Applied to Computer Architecture Design

arXiv: https://arxiv.org/abs/1909.12373 · HTML: https://mlsanity.com/paper/1909.12373/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning](https://mlsanity.com/paper/2105.05821/) (2021)
- [NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction](https://mlsanity.com/paper/2203.08339/) (2022)
- [Benchmarking Processor Performance by Multi-Threaded Machine Learning Algorithms](https://mlsanity.com/paper/2109.05276/) (2021)
- [Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression](https://mlsanity.com/paper/2204.04831/) (2022)
- [SCOPE: Safe Exploration for Dynamic Computer Systems Optimization](https://mlsanity.com/paper/2204.10451/) (2022)
- [Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learning](https://mlsanity.com/paper/2305.00510/) (2023)

## Abstract (arXiv)

Machine learning has enabled significant benefits in diverse fields, but, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has explored broader applicability for design, optimization, and simulation. Notably, machine learning based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This paper reviews machine learning applied system-wide to simulation and run-time optimization, and in many individual components, including memory systems, branch predictors, networks-on-chip, and GPUs. The paper further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated architectural design.