Evaluation of AutoML Methods for Performance Estimation
Bachelor’s Thesis / Student Research Project
Abstract
This student project’s goal is to evaluate different AutoML methods for Performance Estimation based on benchmark data. The methods should be compared to each other as well as to state-of-the-art statistical performance modeling methods like ANNETTE or the Performance Representatives (PR).
References
- A. L.-F. Jung, J. Steinmetz, J. Gietz, K. Lübeck, und O. Bringmann, “It’s all about PR – Smart Benchmarking AI Accelerators using Performance Representatives”. arXiv, 12. Juni 2024.
- M. Wess, M. Ivanov, C. Unger, A. Nookala, A. Wendt, und A. Jantsch, “ANNETTE: Accurate Neural Network Execution Time Estimation With Stacked Models”
- AutoML for Tabular Data
Requirements
- Python
- Machine Learning
- Linux
- Successfully atteded the lecture “Grundlagen der Rechnerarchitektur”