Machine learning-based cache miss prediction - Jelačić, Edin; Seceleanu, Cristina; Xiong, Ning; Backeman, Peter; Yaghoobi, Sharifeh; Seceleanu, Tiberiu (International journal on software tools for technology transfer, 2025)
Integrating machine learning into computer architecture simulation offers a new approach to performance analysis, moving away from traditional algorithmic methods. While existing simulators accurately replicate hardware, they often suffer from slow execution, complex documentation, and require deep CPU knowledge, limiting their usability for quick insights. This paper presents a deep learning-based approach for simulating a key CPU component, cache memory. Our model “learns” cache characteristics by observing cache miss distributions, without needing detailed manual modeling. This method accelerates simulations and adapts to different program needs, demonstrating accuracy comparable to traditional simulators. Tested on Sysbench and image processing algorithms, it shows promise for faster, scalable, and hardware-independent simulations.