Machine learning-based cache miss prediction

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...

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Veröffentlicht in:International journal on software tools for technology transfer Jg. 27; H. 1; S. 53 - 80
Hauptverfasser: Jelačić, Edin, Seceleanu, Cristina, Xiong, Ning, Backeman, Peter, Yaghoobi, Sharifeh, Seceleanu, Tiberiu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025
Springer Nature B.V
Schlagworte:
Algorithms
Architecture
Business metrics
Cache
Computer Science
Deep learning
Design
Hardware
Image processing
Image processing systems
Machine learning
Neural networks
Simulation
Simulators
Software
Software Engineering
Software Engineering/Programming and Operating Systems
Theory of Computation
Simulation
Machine learning
Cache
ISSN:1433-2779, 1433-2787, 1433-2787
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1433-2779
1433-2787
1433-2787
DOI:10.1007/s10009-025-00800-6