# SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning

arXiv: https://arxiv.org/abs/2105.05821 · HTML: https://mlsanity.com/paper/2105.05821/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks](https://mlsanity.com/paper/1808.07412/) (2018)
- [DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates](https://mlsanity.com/paper/2010.04017/) (2020)
- [A Survey of Machine Learning Applied to Computer Architecture Design](https://mlsanity.com/paper/1909.12373/) (2019)
- [Evaluation of the RIKEN Post-K Processor Simulator](https://mlsanity.com/paper/1904.06451/) (2019)

## Built on by (most cited first, 3 of 3)

- [Tao: Re-Thinking DL-based Microarchitecture Simulation](https://mlsanity.com/paper/2404.10921/) (2024)
- [Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion](https://mlsanity.com/paper/2503.23076/) (2025)
- [Learning Generalizable Program and Architecture Representations for Performance Modeling](https://mlsanity.com/paper/2310.16792/) (2023)

## Abstract (arXiv)

While discrete-event simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate discrete-event simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction properties and dynamic processor states is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional simulators significantly.