# Accelerating Computer Architecture Simulation through Machine Learning

arXiv: https://arxiv.org/abs/2402.18746 · HTML: https://mlsanity.com/paper/2402.18746/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [The GAP Benchmark Suite](https://mlsanity.com/paper/1508.03619/) (2015)
- [The gem5 Simulator: Version 20.0+](https://mlsanity.com/paper/2007.03152/) (2020)
- [DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates](https://mlsanity.com/paper/2010.04017/) (2020)
- [The Tribes of Machine Learning and the Realm of Computer Architecture](https://mlsanity.com/paper/2012.04105/) (2020)

## Abstract (arXiv)

This paper presents our approach to accelerate computer architecture simulation by leveraging machine learning techniques. Traditional computer architecture simulations are time-consuming, making it challenging to explore different design choices efficiently. Our proposed model utilizes a combination of application features and micro-architectural features to predict the performance of an application. These features are derived from simulations of a small portion of the application. We demonstrate the effectiveness of our approach by building and evaluating a machine learning model that offers significant speedup in architectural exploration. This model demonstrates the ability to predict IPC values for the testing data with a root mean square error of less than 0.1.