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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Vydáno v:	International journal on software tools for technology transfer Ročník 27; číslo 1; s. 53 - 80
Hlavní autoři:	Jelačić, Edin, Seceleanu, Cristina, Xiong, Ning, Backeman, Peter, Yaghoobi, Sharifeh, Seceleanu, Tiberiu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025 Springer Nature B.V
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Jelačić, Edin Seceleanu, Cristina Backeman, Peter Xiong, Ning Yaghoobi, Sharifeh Seceleanu, Tiberiu
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SubjectTerms	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
Title	Machine learning-based cache miss prediction
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